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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/

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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
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<content url="file://$MODULE_DIR$" />
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<option name="PROJECT_TEST_RUNNER" value="Unittests" />
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<?xml version="1.0" encoding="UTF-8"?>
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<?xml version="1.0" encoding="UTF-8"?>
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<option name="LAST_RESOLUTION" value="IGNORE" />
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<option name="RECENT_TEMPLATES">
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<component name="PropertiesComponent">
<property name="last_opened_file_path" value="$PROJECT_DIR$" />
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<component name="RunDashboard">
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<component name="RunManager">
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<module name="LSR2" />
<option name="INTERPRETER_OPTIONS" value="" />
<option name="PARENT_ENVS" value="true" />
<envs>
<env name="PYTHONUNBUFFERED" value="1" />
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<option name="SDK_HOME" value="" />
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
<option name="IS_MODULE_SDK" value="true" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<option name="MODULE_MODE" value="false" />
<option name="REDIRECT_INPUT" value="false" />
<option name="INPUT_FILE" value="" />
<method v="2" />
</configuration>
<recent_temporary>
<list>
<item itemvalue="Python.main" />
</list>
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<component name="SvnConfiguration">
<configuration />
</component>
<component name="TaskManager">
<task active="true" id="Default" summary="Default task">
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Examples of FCL files (with data)
=======================================
This directory contains some examples of FCL files, along with data
that can be used to test them.
These files were gathered from two sources:
* [jFuzzyLogic](http://jfuzzylogic.sourceforge.net/)
GNU Lesser General Public License (GPL) 3.0
* [FuzzyLite](http://www.fuzzylite.com/)
GNU General Public License (GPL) 3.0
The files in this folder are a _subset_ of those provided by these
tools. I've removed any FCL files that used features not (yet)
supported by `scikit-fuzzy`. For `FuzzyLite` I've only used the
mamdani-style examples - this is a small subset of those supplied with
the tool. Also, I've tweaked some of the RANGEs for fuzzy variables
(or added them if not there), since this can have a dramatic effect on
the accuracy of the results.
Test Data
---------
Each of the FCL files in this folder is accompanied by an `.fld` file
which contains test data: basically a list of values for the input and
output variables. In the case of FuzzyLite these were supplied with
the tool. For jFuzzyLogic I ran the tool with some sample inputs;
since jFuzzyLogic also reports rule fire-strengths, I've included
these in the `.fld` file too as they helped with debugging.
The purpose of these files is to test the implementation: when we run
the same files through `scikit-fuzzy` we should get mostly the same
answers as the original tools.
The script [simulate.py](../simulate.py) can be used to run these -
note that running them _all_ make take a while.
Example run:
```
> python3 simulate.py Examples/jFuzzyLogic/tipper.fcl
======================================================================
= Examples/jFuzzyLogic/tipper.fcl on 08 Sep 2018 at 16:06
======================================================================
----------------------------------------------------------------------
Run 0: food=0.00 service=0.00
Output variables:
tip=5.00 CORRECT, ERROR=0.0%
Rule fire-strengths for test case 0:
RULE No1.1 = 1.00 (CORRECT)
RULE No1.2 = 0.00 (CORRECT)
RULE No1.3 = 0.00 (CORRECT)
----------------------------------------------------------------------
.....
```
[James Power](http://www.cs.nuim.ie/~jpower/),
8 Sept 2018.

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//Code automatically generated with fuzzylite 6.0.
FUNCTION_BLOCK AllTerms
VAR_INPUT
AllInputTerms: REAL;
END_VAR
VAR_OUTPUT
AllOutputTerms: REAL;
END_VAR
FUZZIFY AllInputTerms
RANGE := (0.000 .. 6.500);
TERM A := Sigmoid 0.500 -20.000;
TERM B := ZShape 0.000 1.000;
TERM C := Ramp 1.000 0.000;
TERM D := Triangle 0.500 1.000 1.500;
TERM E := Trapezoid 1.000 1.250 1.750 2.000;
TERM F := Concave 0.850 0.250;
TERM G := Rectangle 1.750 2.250;
TERM H := (2.000, 0.000) (2.250, 1.000) (2.500, 0.500) (2.750, 1.000) (3.000, 0.000);
TERM I := Gaussian 3.000 0.200;
TERM J := Cosine 3.250 0.650;
TERM K := GaussianProduct 3.500 0.100 3.300 0.300;
TERM L := Spike 3.640 1.040;
TERM M := Bell 4.000 0.250 3.000;
TERM N := PiShape 4.000 4.500 4.500 5.000;
TERM O := Concave 5.650 6.250;
TERM P := SigmoidDifference 4.750 10.000 30.000 5.250;
TERM Q := SigmoidProduct 5.250 20.000 -10.000 5.750;
TERM R := Ramp 5.500 6.500;
TERM S := SShape 5.500 6.500;
TERM T := Sigmoid 6.000 20.000;
END_FUZZIFY
DEFUZZIFY AllOutputTerms
RANGE := (0.000 .. 6.500);
TERM A := Sigmoid 0.500 -20.000;
TERM B := ZShape 0.000 1.000;
TERM C := Ramp 1.000 0.000;
TERM D := Triangle 0.500 1.000 1.500;
TERM E := Trapezoid 1.000 1.250 1.750 2.000;
TERM F := Concave 0.850 0.250;
TERM G := Rectangle 1.750 2.250;
TERM H := (2.000, 0.000) (2.250, 1.000) (2.500, 0.500) (2.750, 1.000) (3.000, 0.000);
TERM I := Gaussian 3.000 0.200;
TERM J := Cosine 3.250 0.650;
TERM K := GaussianProduct 3.500 0.100 3.300 0.300;
TERM L := Spike 3.640 1.040;
TERM M := Bell 4.000 0.250 3.000;
TERM N := PiShape 4.000 4.500 4.500 5.000;
TERM O := Concave 5.650 6.250;
TERM P := SigmoidDifference 4.750 10.000 30.000 5.250;
TERM Q := SigmoidProduct 5.250 20.000 -10.000 5.750;
TERM R := Ramp 5.500 6.500;
TERM S := SShape 5.500 6.500;
TERM T := Sigmoid 6.000 20.000;
METHOD : COG;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
RULEBLOCK
AND : MIN;
OR : MAX;
ACT : MIN;
RULE 1 : if AllInputTerms is A then AllOutputTerms is T
RULE 2 : if AllInputTerms is B then AllOutputTerms is S
RULE 3 : if AllInputTerms is C then AllOutputTerms is R
RULE 4 : if AllInputTerms is D then AllOutputTerms is Q
RULE 5 : if AllInputTerms is E then AllOutputTerms is P
RULE 6 : if AllInputTerms is F then AllOutputTerms is O
RULE 7 : if AllInputTerms is G then AllOutputTerms is N
RULE 8 : if AllInputTerms is H then AllOutputTerms is M
RULE 9 : if AllInputTerms is I then AllOutputTerms is L
RULE 10 : if AllInputTerms is J then AllOutputTerms is K
RULE 11 : if AllInputTerms is K then AllOutputTerms is J
RULE 12 : if AllInputTerms is L then AllOutputTerms is I
RULE 13 : if AllInputTerms is M then AllOutputTerms is H
RULE 14 : if AllInputTerms is N then AllOutputTerms is G
RULE 15 : if AllInputTerms is O then AllOutputTerms is F
RULE 16 : if AllInputTerms is P then AllOutputTerms is E
RULE 17 : if AllInputTerms is Q then AllOutputTerms is D
RULE 18 : if AllInputTerms is R then AllOutputTerms is C
RULE 19 : if AllInputTerms is S then AllOutputTerms is B
RULE 20 : if AllInputTerms is T then AllOutputTerms is A
END_RULEBLOCK
END_FUNCTION_BLOCK

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58
Examples/fuzzylite-mamdani/Laundry.fcl Normal file
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//Code automatically generated with fuzzylite 6.0.
FUNCTION_BLOCK Laundry
VAR_INPUT
Load: REAL;
Dirt: REAL;
END_VAR
VAR_OUTPUT
Detergent: REAL;
Cycle: REAL;
END_VAR
FUZZIFY Load
RANGE := (0.000 .. 6.000);
TERM small := (0.000, 1.000) (1.000, 1.000) (2.000, 0.800) (5.000, 0.000);
TERM normal := (3.000, 0.000) (4.000, 1.000) (6.000, 0.000);
END_FUZZIFY
FUZZIFY Dirt
RANGE := (0.000 .. 6.000);
TERM low := (0.000, 1.000) (2.000, 0.800) (5.000, 0.000);
TERM high := (1.000, 0.000) (2.000, 0.200) (4.000, 0.800) (6.000, 1.000);
END_FUZZIFY
DEFUZZIFY Detergent
RANGE := (0.000 .. 80.000);
TERM less_than_usual := (10.000, 0.000) (40.000, 1.000) (50.000, 0.000);
TERM usual := (40.000, 0.000) (50.000, 1.000) (60.000, 1.000) (80.000, 0.000);
TERM more_than_usual := (50.000, 0.000) (80.000, 1.000);
METHOD : MM;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
DEFUZZIFY Cycle
RANGE := (0.000 .. 20.000);
TERM short := (0.000, 1.000) (10.000, 1.000) (20.000, 0.000);
TERM long := (10.000, 0.000) (20.000, 1.000);
METHOD : MM;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
RULEBLOCK
AND : MIN;
OR : MAX;
ACT : MIN;
RULE 1 : if Load is small and Dirt is not high then Detergent is less_than_usual
RULE 2 : if Load is small and Dirt is high then Detergent is usual
RULE 3 : if Load is normal and Dirt is low then Detergent is less_than_usual
RULE 4 : if Load is normal and Dirt is high then Detergent is more_than_usual
RULE 5 : if Detergent is usual or Detergent is less_than_usual then Cycle is short
RULE 6 : if Detergent is more_than_usual then Cycle is long
END_RULEBLOCK
END_FUNCTION_BLOCK

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34
Examples/fuzzylite-mamdani/ObstacleAvoidance.fcl Normal file
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//Code automatically generated with fuzzylite 6.0.
FUNCTION_BLOCK ObstacleAvoidance
VAR_INPUT
obstacle: REAL;
END_VAR
VAR_OUTPUT
mSteer: REAL;
END_VAR
FUZZIFY obstacle
RANGE := (0.000 .. 1.000);
TERM left := Ramp 1.000 0.000;
TERM right := Ramp 0.000 1.000;
END_FUZZIFY
DEFUZZIFY mSteer
RANGE := (0.000 .. 1.000);
TERM left := Ramp 1.000 0.000;
TERM right := Ramp 0.000 1.000;
METHOD : COG;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
RULEBLOCK mamdani
ACT : PROD;
RULE 1 : if obstacle is left then mSteer is right
RULE 2 : if obstacle is right then mSteer is left
END_RULEBLOCK
END_FUNCTION_BLOCK

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Examples/fuzzylite-mamdani/SimpleDimmer.fcl Normal file
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//Code automatically generated with fuzzylite 6.0.
FUNCTION_BLOCK SimpleDimmer
VAR_INPUT
Ambient: REAL;
END_VAR
VAR_OUTPUT
Power: REAL;
END_VAR
FUZZIFY Ambient
RANGE := (0.000 .. 1.000) WITH .01;
TERM DARK := Triangle 0.000 0.250 0.500;
TERM MEDIUM := Triangle 0.250 0.500 0.750;
TERM BRIGHT := Triangle 0.500 0.750 1.000;
END_FUZZIFY
DEFUZZIFY Power
RANGE := (0.000 .. 1.000) WITH .01;
TERM LOW := Triangle 0.000 0.250 0.500;
TERM MEDIUM := Triangle 0.250 0.500 0.750;
TERM HIGH := Triangle 0.500 0.750 1.000;
METHOD : COG;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
RULEBLOCK
ACT : MIN;
RULE 1 : if Ambient is DARK then Power is HIGH
RULE 2 : if Ambient is MEDIUM then Power is MEDIUM
RULE 3 : if Ambient is BRIGHT then Power is LOW
END_RULEBLOCK
END_FUNCTION_BLOCK

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Examples/fuzzylite-mamdani/SimpleDimmerChained.fcl Normal file
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//Code automatically generated with fuzzylite 6.0.
FUNCTION_BLOCK SimpleDimmerChained
VAR_INPUT
Ambient: REAL;
END_VAR
VAR_OUTPUT
Power: REAL;
InversePower: REAL;
END_VAR
FUZZIFY Ambient
RANGE := (0.000 .. 1.000);
TERM DARK := Triangle 0.000 0.250 0.500;
TERM MEDIUM := Triangle 0.250 0.500 0.750;
TERM BRIGHT := Triangle 0.500 0.750 1.000;
END_FUZZIFY
DEFUZZIFY Power
RANGE := (0.000 .. 1.000);
TERM LOW := Triangle 0.000 0.250 0.500;
TERM MEDIUM := Triangle 0.250 0.500 0.750;
TERM HIGH := Triangle 0.500 0.750 1.000;
METHOD : COG;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
DEFUZZIFY InversePower
RANGE := (0.000 .. 1.000);
TERM LOW := Cosine 0.200 0.500;
TERM MEDIUM := Cosine 0.500 0.500;
TERM HIGH := Cosine 0.800 0.500;
METHOD : COG;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
RULEBLOCK
ACT : MIN;
RULE 1 : if Ambient is DARK then Power is HIGH
RULE 2 : if Ambient is MEDIUM then Power is MEDIUM
RULE 3 : if Ambient is BRIGHT then Power is LOW
RULE 4 : if Power is LOW then InversePower is HIGH
RULE 5 : if Power is MEDIUM then InversePower is MEDIUM
RULE 6 : if Power is HIGH then InversePower is LOW
END_RULEBLOCK
END_FUNCTION_BLOCK

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165
Examples/jFuzzyLogic/LICENSE Normal file
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GNU LESSER GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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55
Examples/jFuzzyLogic/QoS/LarsenQoSFewRules.fcl Normal file
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FUNCTION_BLOCK LarsenQoSFewRules
VAR_INPUT
commitment : REAL;
clarity : REAL;
influence : REAL;
END_VAR
VAR_OUTPUT
service_quality : REAL;
END_VAR
FUZZIFY commitment
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for commitment
END_FUZZIFY
FUZZIFY clarity
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for clarity
END_FUZZIFY
FUZZIFY influence
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for influence
END_FUZZIFY
DEFUZZIFY service_quality
TERM inadequate := GAUSS 0 1;
TERM sufficient := GAUSS 2.5 1;
TERM excellent := GAUSS 5 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-4.0 .. 9.0); // Added range for service_quality
END_DEFUZZIFY
RULEBLOCK No1
AND : PROD;
ACCU : MAX;
RULE 1 : IF commitment IS fully AND influence IS high THEN service_quality IS excellent;
RULE 2 : IF commitment IS partially AND clarity IS high AND influence IS low THEN service_quality IS sufficient;
RULE 3 : IF commitment IS nothing THEN service_quality IS inadequate;
END_RULEBLOCK
END_FUNCTION_BLOCK

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clarity commitment influence service_quality No1.1 No1.2 No1.3
-4.0 -4.0 -4.0 0.11814588732224174 6.639677199580735E-36 1.3164881474367886E-29 3.3546262790251185E-4
-4.0 -4.0 -1.4 0.11814588732224174 3.286415676451725E-27 1.4728693217741432E-26 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 0.11814588732224174 1.8856770853470695E-21 1.9102085371358956E-26 3.3546262790251185E-4
-4.0 -4.0 3.8 0.11814588732224174 1.2542423359420818E-18 2.8718823814904615E-29 3.3546262790251185E-4
-4.0 -4.0 6.4 0.11814588732224174 9.670855421101112E-19 5.005204418506582E-35 3.3546262790251185E-4
-4.0 -1.4 -4.0 2.1440130067640695E-4 3.286415676451725E-27 2.6699032394713253E-24 0.37531109885139957
-4.0 -1.4 -1.4 2.1440130067640695E-4 1.6266646214532314E-18 2.9870520149985134E-21 0.37531109885139957
-4.0 -1.4 1.2000000000000002 2.1440130820477154E-4 9.333463883457657E-13 3.873997628687189E-21 0.37531109885139957
-4.0 -1.4 3.8 2.144090802361429E-4 6.208075409403594E-10 5.8243198684705E-24 0.37531109885139957
-4.0 -1.4 6.4 2.1440721360819752E-4 4.786746389208764E-10 1.0150802737727316E-29 0.37531109885139957
-4.0 1.2000000000000002 -4.0 1.9531210161944423E-4 1.8856770853470695E-21 6.27687374904115E-22 0.48675225595997157
-4.0 1.2000000000000002 -1.4 1.9531210789947975E-4 9.333463883457657E-13 7.022482351711461E-19 0.48675225595997157
-4.0 1.2000000000000002 1.2000000000000002 2.0298466893760368E-4 5.35534780279311E-7 9.107668645013996E-19 0.48675225595997157
-4.0 1.2000000000000002 3.8 0.006619048840501731 3.562064783070342E-4 1.3692825997566989E-21 0.48675225595997157
-4.0 1.2000000000000002 6.4 0.005108750846418389 2.7465356997214227E-4 2.3864275788793028E-27 0.48675225595997157
-4.0 3.8 -4.0 0.05592821568344743 1.2542423359420818E-18 1.7106476574743098E-22 7.318024188804728E-4
-4.0 3.8 -1.4 0.05593013860181266 6.208075409403594E-10 1.9138497068616342E-19 7.318024188804728E-4
-4.0 3.8 1.2000000000000002 1.5301815892116188 3.562064783070342E-4 2.4821292661853207E-19 7.318024188804728E-4
-4.0 3.8 3.8 4.977155265586825 0.23692775868212168 3.731730420819674E-22 7.318024188804728E-4
-4.0 3.8 6.4 4.971945843841512 0.18268352405273452 6.503773870177941E-28 7.318024188804728E-4
-4.0 6.4 -4.0 1.274193733749591 9.670855421101112E-19 5.404394494224472E-26 1.2754076295260396E-9
-4.0 6.4 -1.4 1.7691439975272403 4.786746389208764E-10 6.046364237161134E-23 1.2754076295260396E-9
-4.0 6.4 1.2000000000000002 4.851873305784807 2.7465356997214227E-4 7.841711694114211E-23 1.2754076295260396E-9
-4.0 6.4 3.8 4.999596457247931 0.18268352405273452 1.1789536700961867E-25 1.2754076295260396E-9
-4.0 6.4 6.4 4.999475787552176 0.14085842092104486 2.0547165012090466E-31 1.2754076295260396E-9
-1.4 -4.0 -4.0 0.11814588732224174 6.639677199580735E-36 6.516171126319744E-21 3.3546262790251185E-4
-1.4 -4.0 -1.4 0.11814588732224271 3.286415676451725E-27 7.290205055072584E-18 3.3546262790251185E-4
-1.4 -4.0 1.2000000000000002 0.11814588732224349 1.8856770853470695E-21 9.454886273886531E-18 3.3546262790251185E-4
-1.4 -4.0 3.8 0.11814588732224174 1.2542423359420818E-18 1.42148465893067E-20 3.3546262790251185E-4
-1.4 -4.0 6.4 0.11814588732224174 9.670855421101112E-19 2.4774069236173352E-26 3.3546262790251185E-4
-1.4 -1.4 -4.0 2.144013006811132E-4 3.286415676451725E-27 1.3215118140625677E-15 0.37531109885139957
-1.4 -1.4 -1.4 2.1440131306941333E-4 1.6266646214532314E-18 1.4784897327670847E-12 0.37531109885139957
-1.4 -1.4 1.2000000000000002 2.1440131709138892E-4 9.333463883457657E-13 1.9174978172520684E-12 0.37531109885139957
-1.4 -1.4 3.8 2.144090802361429E-4 6.208075409403594E-10 2.882841370905708E-15 0.37531109885139957
-1.4 -1.4 6.4 2.1440721360819752E-4 4.786746389208764E-10 5.024304080316231E-21 0.37531109885139957
-1.4 1.2000000000000002 -4.0 1.9531210351410677E-4 1.8856770853470695E-21 3.106840237543436E-13 0.48675225595997157
-1.4 1.2000000000000002 -1.4 1.953156195923943E-4 9.333463883457657E-13 3.475891281239913E-10 0.48675225595997157
-1.4 1.2000000000000002 1.2000000000000002 2.0298466893760368E-4 5.35534780279311E-7 4.50798798061928E-10 0.48675225595997157
-1.4 1.2000000000000002 3.8 0.006619048840501731 3.562064783070342E-4 6.77748581153485E-13 0.48675225595997157
-1.4 1.2000000000000002 6.4 0.005108750846418389 2.7465356997214227E-4 1.1812009485101043E-18 0.48675225595997157
-1.4 3.8 -4.0 0.055928215817828315 1.2542423359420818E-18 8.467127406079305E-14 7.318024188804728E-4
-1.4 3.8 -1.4 0.05593013860181266 6.208075409403594E-10 9.472908832676052E-11 7.318024188804728E-4
-1.4 3.8 1.2000000000000002 1.5301815892116188 3.562064783070342E-4 1.2285700473339503E-10 7.318024188804728E-4
-1.4 3.8 3.8 4.977155265586825 0.23692775868212168 1.8470803604801732E-13 7.318024188804728E-4
-1.4 3.8 6.4 4.971945843841512 0.18268352405273452 3.219148124309352E-19 7.318024188804728E-4
-1.4 6.4 -4.0 1.2741937361045474 9.670855421101112E-19 2.6749925114838757E-17 1.2754076295260396E-9
-1.4 6.4 -1.4 1.7691439975272403 4.786746389208764E-10 2.9927458244202264E-14 1.2754076295260396E-9
-1.4 6.4 1.2000000000000002 4.851873305784807 2.7465356997214227E-4 3.88138210143395E-14 1.2754076295260396E-9
-1.4 6.4 3.8 4.999596457247931 0.18268352405273452 5.835421974217454E-17 1.2754076295260396E-9
-1.4 6.4 6.4 4.999475787552176 0.14085842092104486 1.017015182705546E-22 1.2754076295260396E-9
1.2000000000000002 -4.0 -4.0 0.11814588733053943 6.639677199580735E-36 3.738843709012425E-15 3.3546262790251185E-4
1.2000000000000002 -4.0 -1.4 0.11814590706366652 3.286415676451725E-27 4.182968307488742E-12 3.3546262790251185E-4
1.2000000000000002 -4.0 1.2000000000000002 0.11814591342684355 1.8856770853470695E-21 5.42501744955155E-12 3.3546262790251185E-4
1.2000000000000002 -4.0 3.8 0.11814588734260972 1.2542423359420818E-18 8.156183856243035E-15 3.3546262790251185E-4
1.2000000000000002 -4.0 6.4 0.11814588732224174 9.670855421101112E-19 1.42148465893067E-20 3.3546262790251185E-4
1.2000000000000002 -1.4 -4.0 2.1441090426171526E-4 3.286415676451725E-27 7.582560427911914E-10 0.37531109885139957
1.2000000000000002 -1.4 -1.4 2.2419211642465433E-4 1.6266646214532314E-18 8.483267135001927E-7 0.37531109885139957
1.2000000000000002 -1.4 1.2000000000000002 2.2700953918110934E-4 9.333463883457657E-13 1.1002204380606938E-6 0.37531109885139957
1.2000000000000002 -1.4 3.8 2.14422693302235E-4 6.208075409403594E-10 1.6541145274954016E-9 0.37531109885139957
1.2000000000000002 -1.4 6.4 2.1440721360819752E-4 4.786746389208764E-10 2.882841370905708E-15 0.37531109885139957
1.2000000000000002 1.2000000000000002 -4.0 1.97099029194876E-4 1.8856770853470695E-21 1.7826404266958715E-7 0.48675225595997157
1.2000000000000002 1.2000000000000002 -1.4 0.001797132556082525 9.333463883457657E-13 1.9943942536412287E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 1.2000000000000002 0.002252747097245062 5.35534780279311E-7 2.5865899122206357E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 3.8 0.006619048840501731 3.562064783070342E-4 3.888780650192924E-7 0.48675225595997157
1.2000000000000002 1.2000000000000002 6.4 0.005108750846418389 2.7465356997214227E-4 6.777485811534851E-13 0.48675225595997157
1.2000000000000002 3.8 -4.0 0.05607687723151827 1.2542423359420818E-18 4.8582619182234314E-8 7.318024188804728E-4
1.2000000000000002 3.8 -1.4 0.1885960877558892 6.208075409403594E-10 5.4353584196157534E-5 7.318024188804728E-4
1.2000000000000002 3.8 1.2000000000000002 1.5301815892116188 3.562064783070342E-4 7.04927986621179E-5 7.318024188804728E-4
1.2000000000000002 3.8 3.8 4.977155265586825 0.23692775868212168 1.0598163633130508E-7 7.318024188804728E-4
1.2000000000000002 3.8 6.4 4.971945843841512 0.18268352405273452 1.847080360480173E-13 7.318024188804728E-4
1.2000000000000002 6.4 -4.0 1.2881278403918364 9.670855421101112E-19 1.5348551671425328E-11 1.2754076295260396E-9
1.2000000000000002 6.4 -1.4 2.498580908582361 4.786746389208764E-10 1.7171754211781134E-8 1.2754076295260396E-9
1.2000000000000002 6.4 1.2000000000000002 4.851707658506448 2.7465356997214227E-4 2.227056467808863E-8 1.2754076295260396E-9
1.2000000000000002 6.4 3.8 4.999596457247931 0.18268352405273452 3.3482439786780416E-11 1.2754076295260396E-9
1.2000000000000002 6.4 6.4 4.999475787552176 0.14085842092104486 5.835421974217454E-17 1.2754076295260396E-9
3.8 -4.0 -4.0 0.11814589859782343 6.639677199580735E-36 2.486860610310162E-12 3.3546262790251185E-4
3.8 -4.0 -1.4 0.11816457197697199 3.286415676451725E-27 2.7822663710158708E-9 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 0.1181700887677895 1.8856770853470695E-21 3.608404965688875E-9 3.3546262790251185E-4
3.8 -4.0 3.8 0.11814591342684355 1.2542423359420818E-18 5.42501744955155E-12 3.3546262790251185E-4
3.8 -4.0 6.4 0.11814588732224349 9.670855421101112E-19 9.45488627388653E-18 3.3546262790251185E-4
3.8 -1.4 -4.0 2.203034376078097E-4 3.286415676451725E-27 5.043476625678883E-7 0.37531109885139957
3.8 -1.4 -1.4 0.005405046549942568 1.6266646214532314E-18 5.642574155726743E-4 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.006864070750542262 9.333463883457657E-13 7.318024188804728E-4 0.37531109885139957
3.8 -1.4 3.8 2.2700953918110934E-4 6.208075409403594E-10 1.100220438060694E-6 0.37531109885139957
3.8 -1.4 6.4 2.1440721360819752E-4 4.786746389208764E-10 1.917497817252069E-12 0.37531109885139957
3.8 1.2000000000000002 -4.0 0.001167805668872799 1.8856770853470695E-21 1.1857083645433894E-4 0.48675225595997157
3.8 1.2000000000000002 -1.4 0.5878199905727955 9.333463883457657E-13 0.13265546508012174 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 0.7056697584570883 5.35534780279311E-7 0.1720448638230505 0.48675225595997157
3.8 1.2000000000000002 3.8 0.006619048840501731 3.562064783070342E-4 2.586589912220635E-4 0.48675225595997157
3.8 1.2000000000000002 6.4 0.005108750846418389 2.7465356997214227E-4 4.5079879806192804E-10 0.48675225595997157
3.8 3.8 -4.0 0.13704384069610503 1.2542423359420818E-18 3.231432266044366E-5 7.318024188804728E-4
3.8 3.8 -1.4 2.4569501355751564 6.208075409403594E-10 0.036152831754046426 7.318024188804728E-4
3.8 3.8 1.2000000000000002 2.4819165096787366 3.562064783070342E-4 0.046887695219988486 7.318024188804728E-4
3.8 3.8 3.8 4.977155265586825 0.23692775868212168 7.04927986621179E-5 7.318024188804728E-4
3.8 3.8 6.4 4.971945843841512 0.18268352405273452 1.2285700473339503E-10 7.318024188804728E-4
3.8 6.4 -4.0 2.497644044698082 9.670855421101112E-19 1.0208960723597606E-8 1.2754076295260396E-9
3.8 6.4 -1.4 2.499997366179424 4.786746389208764E-10 1.1421648638660496E-5 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.755368368994469 2.7465356997214227E-4 1.4813079758803949E-5 1.2754076295260396E-9
3.8 6.4 3.8 4.999595998084372 0.18268352405273452 2.227056467808863E-8 1.2754076295260396E-9
3.8 6.4 6.4 4.999475787552176 0.14085842092104486 3.88138210143395E-14 1.2754076295260396E-9
6.4 -4.0 -4.0 0.1181458958393851 6.639677199580735E-36 1.9174978172520745E-12 3.3546262790251185E-4
6.4 -4.0 -1.4 0.1181602941754421 3.286415676451725E-27 2.14527089749972E-9 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.11816457197697199 1.8856770853470695E-21 2.782266371015869E-9 3.3546262790251185E-4
6.4 -4.0 3.8 0.11814590706366652 1.2542423359420818E-18 4.18296830748874E-12 3.3546262790251185E-4
6.4 -4.0 6.4 0.11814588732224271 9.670855421101112E-19 7.290205055072577E-18 3.3546262790251185E-4
6.4 -1.4 -4.0 2.1898334133570592E-4 3.286415676451725E-27 3.888780650192921E-7 0.37531109885139957
6.4 -1.4 -1.4 0.004264566189502784 1.6266646214532314E-18 4.3507157507873234E-4 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.005405046549942563 9.333463883457657E-13 5.64257415572674E-4 0.37531109885139957
6.4 -1.4 3.8 2.2419211642465433E-4 6.208075409403594E-10 8.483267135001922E-7 0.37531109885139957
6.4 -1.4 6.4 2.1440721360819752E-4 4.786746389208764E-10 1.478489732767084E-12 0.37531109885139957
6.4 1.2000000000000002 -4.0 9.52749510665797E-4 1.8856770853470695E-21 9.142423147817332E-5 0.48675225595997157
6.4 1.2000000000000002 -1.4 0.4841087069628467 9.333463883457657E-13 0.10228420671553745 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 0.5878219094240962 5.35534780279311E-7 0.13265546508012166 0.48675225595997157
6.4 1.2000000000000002 3.8 0.006619048840501731 3.562064783070342E-4 1.9943942536412276E-4 0.48675225595997157
6.4 1.2000000000000002 6.4 0.005108750846418389 2.7465356997214227E-4 3.4758912812399117E-10 0.48675225595997157
6.4 3.8 -4.0 0.11924101816258234 1.2542423359420818E-18 2.491600973150319E-5 7.318024188804728E-4
6.4 3.8 -1.4 2.4459234017320988 6.208075409403594E-10 0.027875698255247015 7.318024188804728E-4
6.4 3.8 1.2000000000000002 2.4773357168198733 3.562064783070342E-4 0.036152831754046405 7.318024188804728E-4
6.4 3.8 3.8 4.977155265586825 0.23692775868212168 5.4353584196157506E-5 7.318024188804728E-4
6.4 3.8 6.4 4.971945843841512 0.18268352405273452 9.472908832676046E-11 7.318024188804728E-4
6.4 6.4 -4.0 2.496963696852765 9.670855421101112E-19 7.871635355336256E-9 1.2754076295260396E-9
6.4 6.4 -1.4 2.4999966195044543 4.786746389208764E-10 8.806680295330318E-6 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.776536274876287 2.7465356997214227E-4 1.142164863866049E-5 1.2754076295260396E-9
6.4 6.4 3.8 4.999596107814058 0.18268352405273452 1.7171754211781124E-8 1.2754076295260396E-9
6.4 6.4 6.4 4.999475787552176 0.14085842092104486 2.9927458244202245E-14 1.2754076295260396E-9

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FUNCTION_BLOCK LarsenQoSFewRules
VAR_INPUT
commitment : REAL;
clarity : REAL;
influence : REAL;
END_VAR
VAR_OUTPUT
service_quality : REAL;
END_VAR
FUZZIFY commitment
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for commitment
END_FUZZIFY
FUZZIFY clarity
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for clarity
END_FUZZIFY
FUZZIFY influence
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for influence
END_FUZZIFY
DEFUZZIFY service_quality
TERM inadequate := GAUSS 0 1;
TERM sufficient := GAUSS 2.5 1;
TERM excellent := GAUSS 5 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-4.0 .. 9.0); // Added range for service_quality
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : PROD;
RULE 1 : IF commitment IS fully AND influence IS high THEN service_quality IS excellent;
RULE 2 : IF commitment IS fully AND influence IS medium THEN service_quality IS excellent WITH 0.8;
RULE 3 : IF commitment IS fully AND influence IS low THEN service_quality IS excellent WITH 0.6;
RULE 4 : IF commitment IS largely AND influence IS high AND clarity IS NOT high THEN service_quality IS excellent;
RULE 5 : IF commitment IS largely AND influence IS medium AND clarity IS NOT high THEN service_quality IS excellent WITH 0.66;
RULE 6 : IF commitment IS largely AND influence IS low AND clarity IS NOT high THEN service_quality IS excellent WITH 0.33;
RULE 7 : IF commitment IS largely AND influence IS high AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 8 : IF commitment IS largely AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 9 : IF commitment IS largely AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.1;
RULE 10 : IF commitment IS satISfactory AND influence IS high THEN service_quality IS sufficient;
RULE 11 : IF commitment IS satISfactory AND influence IS medium THEN service_quality IS sufficient WITH 0.66;
RULE 12 : IF commitment IS satISfactory AND influence IS low THEN service_quality IS sufficient WITH 0.33;
RULE 13 : IF commitment IS satISfactory AND influence IS high AND clarity IS high THEN service_quality IS sufficient;
RULE 14 : IF commitment IS satISfactory AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 15 : IF commitment IS satISfactory AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 16 : IF commitment IS satISfactory AND influence IS high AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.5;
RULE 17 : IF commitment IS satISfactory AND influence IS medium AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.3;
RULE 18 : IF commitment IS satISfactory AND influence IS low AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.1;
RULE 19 : IF commitment IS partially AND influence IS high AND clarity IS high THEN service_quality IS sufficient;
RULE 20 : IF commitment IS partially AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 21 : IF commitment IS partially AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 22 : IF commitment IS partially AND influence IS high AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.4;
RULE 23 : IF commitment IS partially AND influence IS medium AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.25;
RULE 24 : IF commitment IS partially AND influence IS low AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.1;
RULE 25 : IF commitment IS minimal AND influence IS high AND clarity IS high THEN service_quality IS inadequate WITH 0.5;
RULE 26 : IF commitment IS minimal AND influence IS medium AND clarity IS high THEN service_quality IS inadequate WITH 0.3;
RULE 27 : IF commitment IS minimal AND influence IS low AND clarity IS high THEN service_quality IS inadequate WITH 0.1;
RULE 28 : IF commitment IS minimal AND influence IS high AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.4;
RULE 29 : IF commitment IS minimal AND influence IS medium AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.2;
RULE 30 : IF commitment IS minimal AND influence IS low AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.05;
RULE 31 : IF commitment IS nothing THEN service_quality IS inadequate;
END_RULEBLOCK
END_FUNCTION_BLOCK

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clarity commitment influence service_quality No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7 No1.8 No1.9 No1.10 No1.11 No1.12 No1.13 No1.14 No1.15 No1.16 No1.17 No1.18 No1.19 No1.20 No1.21 No1.22 No1.23 No1.24 No1.25 No1.26 No1.27 No1.28 No1.29 No1.30 No1.31
-4.0 -4.0 -4.0 0.11814588935110436 6.639677199580735E-36 1.3794073625809013E-27 5.186434267821657E-22 3.263247861014401E-32 5.593061367045755E-24 1.4019569042462244E-18 5.549674102370404E-50 7.205980319737613E-42 1.0946977029531417E-36 5.900090541597062E-29 1.0112492178274336E-20 2.5347975461166915E-15 1.5203100247718292E-46 2.6057436111642533E-38 6.531557597224785E-33 2.950045270798531E-29 4.5965873537610616E-21 7.681204685202096E-16 1.0112214926104486E-43 1.7331885608247045E-35 4.344410886541402E-30 1.5697583431789854E-26 2.5478180157597454E-18 5.109089028063326E-13 1.2371887048697986E-41 1.927714808756546E-33 3.221340285992516E-28 3.8410720218034705E-24 4.987444106670752E-16 6.250764331933714E-11 3.3546262790251185E-4
-4.0 -4.0 -1.4 0.11814958430796335 3.286415676451725E-27 1.026488137920661E-21 5.802513252660671E-19 1.615197336289554E-23 4.1620853300310446E-18 1.5684906231279692E-15 2.7469010045217157E-41 5.362341481533575E-36 1.2247331405417942E-33 2.920345291727967E-20 7.525226809281491E-15 2.8358975732919886E-12 7.525020491647316E-38 1.9390681678819735E-32 7.30741923281549E-30 1.4601726458639834E-20 3.4205576405824954E-15 8.593629009975723E-13 5.005204418506583E-35 1.2897549677693547E-29 4.860468761854674E-27 7.769772945573413E-18 1.8959627458161753E-12 5.715980433472656E-10 6.123665702708946E-33 1.4345119782311848E-27 3.6039924031760686E-25 1.9012007553066216E-15 3.711414302197895E-10 6.993271485254499E-8 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 0.11815685039287001 1.8856770853470695E-21 8.85491981247484E-19 7.5254540156524905E-19 9.267666981929868E-18 3.590390428160152E-15 2.0342226797878067E-15 1.5761147675439327E-35 4.625782030201833E-33 1.5883932581053113E-33 1.6756334986383216E-14 6.4915781785706195E-12 3.67796088550483E-12 4.3177005299545283E-32 1.6727220221267184E-29 9.47721185891852E-30 8.378167493191608E-15 2.9507173538957358E-12 1.1145336016681302E-12 2.871882381490462E-29 1.1125970574266328E-26 6.303688172548456E-27 4.458134406672523E-12 1.6355374661854566E-9 7.413226998963544E-10 3.51363224581236E-27 1.2374705627873112E-24 4.674126179793921E-25 1.0908695222623033E-9 3.201622583131571E-7 9.069784193448386E-8 3.3546262790251185E-4
-4.0 -4.0 3.8 0.11816579723261839 1.2542423359420818E-18 8.85491981247484E-19 1.1314062512082418E-21 6.164311150872142E-15 3.590390428160152E-15 3.0583301040368564E-18 1.0483395503495055E-32 4.625782030201833E-33 2.3880526780968675E-36 1.1145336016681301E-11 6.4915781785706195E-12 5.529590545506461E-15 2.8718823814904604E-29 1.6727220221267184E-29 1.4248411748849945E-32 5.572668008340651E-12 2.9507173538957358E-12 1.6756334986383217E-15 1.9102085371358956E-26 1.1125970574266328E-26 9.477211858918525E-30 2.9652907995854175E-9 1.6355374661854566E-9 1.1145336016681308E-12 2.3370630898969603E-24 1.2374705627873112E-24 7.02726449162472E-28 7.255827354758709E-7 3.201622583131571E-7 1.3635869028278791E-10 3.3546262790251185E-4
-4.0 -4.0 6.4 0.11816124304793059 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 4.75300188826657E-15 4.162085330031037E-18 5.330151209755528E-24 8.083238727575837E-33 5.362341481533565E-36 4.161971218972297E-42 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 2.2143694644895412E-29 1.9390681678819702E-32 2.4832567622436145E-38 4.296814504987859E-12 3.4205576405824895E-15 2.920345291727967E-21 1.4728693217741423E-26 1.2897549677693525E-29 1.6517174581071724E-35 2.2863921733890607E-9 1.895962745816172E-12 1.9424432363933533E-18 1.801996201588033E-24 1.4345119782311823E-27 1.2247331405417894E-33 5.594617188203596E-7 3.7114143021978885E-10 2.376500944133277E-16 3.3546262790251185E-4
-4.0 -1.4 -4.0 2.1566273940289027E-4 3.286415676451725E-27 6.827599963572116E-19 2.567109570286311E-13 1.1996648568848618E-24 2.0561720868973193E-16 5.154001474985931E-11 2.040221666063764E-42 2.6491280212793504E-34 4.024427254928865E-29 1.6110272932682781E-22 2.7612289654375516E-14 6.9212972257169344E-9 4.1512260309717424E-40 7.115016366695864E-32 1.7834501830940759E-26 8.055136466341391E-23 1.255104075198887E-14 2.0973627956717985E-9 2.0508073272063395E-38 3.514992339407074E-30 8.810680690255373E-25 3.1835477545322526E-21 5.167101266429722E-13 1.0361485876900873E-7 1.8635835244683486E-37 2.90372644313013E-29 4.852320959649879E-24 5.785825968143316E-20 7.512611964378164E-12 9.415557528511377E-7 0.37531109885139957
-4.0 -1.4 -1.4 0.0013552945189541795 1.6266646214532314E-18 5.080769149993044E-13 2.87204783352526E-10 5.937935344814024E-16 1.5301036618904385E-10 5.766227878059916E-8 1.0098407294878434E-33 1.9713527442601284E-28 4.5024753569086456E-26 7.974040292384671E-14 2.0547728365038006E-8 7.743454713577994E-6 2.054716501209046E-31 5.294650514159714E-26 1.9953001982631742E-23 3.9870201461923353E-14 9.339876529562729E-9 2.346501428356968E-6 1.0150802737727316E-29 2.6156870255734497E-24 9.857271649495095E-22 1.5757484788399258E-12 3.845106457530496E-7 1.159229173904592E-4 9.224108228599837E-29 2.1608125565335446E-23 5.4287117546670765E-21 2.863788192011557E-11 5.590521897611475E-6 0.0010533999761520717 0.37531109885139957
-4.0 -1.4 1.2000000000000002 0.0033401946531637643 9.333463883457657E-13 4.3828858558482145E-10 3.7248452456421565E-10 3.4070640224298884E-10 1.3199319826783202E-7 7.478394421626833E-8 5.794256451153972E-28 1.7005720599836953E-25 5.83939575484603E-26 4.5753387694457995E-8 1.7725337515297175E-5 1.0042719392773184E-5 1.1789536700961867E-25 4.5673889454713485E-23 2.5877648590576898E-23 2.2876693847228997E-8 8.056971597862353E-6 3.043248300840359E-6 5.824319868470499E-24 2.2564019992380993E-21 1.2784192174667725E-21 9.041317627924226E-7 3.316951077567482E-4 1.5034391929775732E-4 5.2926017983859147E-23 1.8640080884570515E-20 7.040659606463864E-21 1.643182209012029E-5 0.004822620085363375 0.0013661861223646281 0.37531109885139957
-4.0 -1.4 3.8 0.005731124154253197 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 2.266180127765707E-7 1.3199319826783202E-7 1.1243311274018632E-10 3.854001198198379E-25 1.7005720599836953E-25 8.779176441142381E-29 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 7.841711694114211E-23 4.5673889454713485E-23 3.8905471113174165E-26 1.5216241504201794E-5 8.056971597862353E-6 4.5753387694457995E-9 3.873997628687189E-21 2.2564019992380993E-21 1.9220255565952648E-24 6.013756771910293E-4 3.316951077567482E-4 2.2603294069810565E-7 3.520329803231932E-20 1.8640080884570515E-20 1.058520359677183E-23 0.010929488978917025 0.004822620085363375 2.0539777612650365E-6 0.37531109885139957
-4.0 -1.4 6.4 0.004519283465705212 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 1.7473417812302764E-7 1.5301036618904356E-10 1.9595186637886281E-16 2.971633735559704E-25 1.9713527442601248E-28 1.5300617113452173E-34 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 6.04636423716113E-23 5.294650514159705E-26 6.780564453989853E-32 1.1732507141784832E-5 9.339876529562714E-9 7.974040292384671E-15 2.9870520149985116E-21 2.6156870255734456E-24 3.349764903450014E-30 4.6369166956183655E-4 3.8451064575304893E-7 3.9393711970998146E-13 2.714355877333536E-20 2.1608125565335405E-23 1.8448216457199674E-29 0.008427199809216569 5.590521897611466E-6 3.5797352400144464E-12 0.37531109885139957
-4.0 1.2000000000000002 -4.0 4.1968710934414503E-4 1.8856770853470695E-21 3.917535110203989E-13 1.4729541752583488E-7 5.1125681935973695E-20 8.76271397941174E-12 2.1964621085250285E-6 8.694738649668496E-38 1.1289692770970466E-29 1.7150755615549714E-24 5.099368797562266E-19 8.740090803000822E-11 2.190791382551568E-5 1.3139834801321656E-36 2.2521091111292437E-28 5.645137269665221E-23 2.549684398781133E-19 3.9727685468185554E-11 6.638761765307781E-6 4.821395204955692E-36 8.263656456581472E-28 2.0713683371835797E-22 7.484438774342709E-19 1.2147721991719894E-10 2.4359586422561893E-5 3.254101391107074E-36 5.070349750317637E-28 8.472893314185803E-23 1.010293559930985E-18 1.3118167618006242E-10 1.644100114070199E-5 0.48675225595997157
-4.0 1.2000000000000002 -1.4 0.18491722622280213 9.333463883457657E-13 2.9152398556059304E-7 1.6479214198328546E-4 2.5305483615118886E-11 6.520787259702742E-6 0.002457372413405034 4.303601597186924E-29 8.401242464363133E-24 1.9188035866917437E-21 2.524015106845201E-10 6.503952187730642E-5 0.02451028081073019 6.503773870177941E-28 1.6759105037339026E-22 6.315704032643393E-20 1.2620075534226006E-10 2.9563419035139285E-5 0.007427357821433391 2.3864275788793028E-27 6.149412826578897E-22 2.3174191760647823E-19 3.7045440882270904E-10 9.039746245755658E-5 0.027253179303401265 1.6106701429962517E-27 3.7731086660810757E-22 9.479359653504757E-20 5.00061146554695E-10 9.761904870468304E-5 0.018393972058572117 0.48675225595997157
-4.0 1.2000000000000002 1.2000000000000002 0.4404150022767337 5.35534780279311E-7 2.5148089102921975E-4 2.137238869842205E-4 1.451976112716682E-5 0.005625106239984455 0.003187040217087467 2.46931724870551E-23 7.247266246815995E-21 2.4885541019827166E-21 1.4482274668268805E-4 0.05610583596532173 0.031788120616062704 3.7317304208196737E-22 1.4457111168872598E-19 8.191026578411558E-20 7.241137334134403E-5 0.025502652711509876 0.009632763823049305 1.369282599756699E-21 5.304742983534518E-19 3.005530652854619E-19 2.1255904871930132E-4 0.07798066550816173 0.03534546819587802 9.241690886710733E-22 3.254842744659578E-19 1.229406673695594E-19 2.8692470404372553E-4 0.08421031052546425 0.023855695776051716 0.48675225595997157
-4.0 1.2000000000000002 3.8 0.6888340090725914 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.009657697627537778 0.005625106239984455 4.7915211719650505E-6 1.642445707308593E-20 7.247266246815995E-21 3.741389770765924E-24 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 2.4821292661853207E-19 1.4457111168872598E-19 1.2314710388704924E-22 0.04816381911524652 0.025502652711509876 1.4482274668268806E-5 9.107668645013996E-19 5.304742983534518E-19 4.518632579197107E-22 0.14138187278351208 0.07798066550816173 5.313976217982533E-5 6.14703336847797E-19 3.254842744659578E-19 1.8483381773421468E-22 0.19084556620841372 0.08421031052546425 3.586558800546569E-5 0.48675225595997157
-4.0 1.2000000000000002 6.4 0.5687815869853887 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.007446583070924341 6.52078725970273E-6 8.350809592989233E-12 1.2664103672165502E-20 8.401242464363119E-24 6.520608480586249E-30 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 1.913849706861633E-19 1.6759105037338995E-22 2.1462453771587204E-28 0.03713678910716693 2.956341903513923E-5 2.524015106845201E-11 7.022482351711458E-19 6.149412826578886E-22 7.875211010301699E-28 0.10901271721360502 9.039746245755642E-5 9.261360220567726E-11 4.739679826752375E-19 3.773108666081069E-22 3.2213402859925036E-28 0.14715177646857686 9.761904870468288E-5 6.250764331933688E-11 0.48675225595997157
-4.0 3.8 -4.0 0.5708220864589763 1.2542423359420818E-18 2.605715700709704E-10 9.797231455308489E-5 2.5257338998274624E-18 4.32899531394206E-10 1.0851060752863316E-4 4.295413836261338E-36 5.5773847253494E-28 8.472893314185803E-23 1.8711096935856773E-18 3.2069986058140523E-10 8.038663519445424E-5 4.821395204955692E-36 8.263656456581472E-28 2.0713683371835797E-22 9.355548467928386E-19 1.4577266390063872E-10 2.4359586422561893E-5 1.3139834801321656E-36 2.2521091111292437E-28 5.645137269665221E-23 2.0397475190249064E-19 3.3106404556821296E-11 6.638761765307781E-6 6.586923219445831E-38 1.0263357064518605E-29 1.7150755615549714E-24 2.045027277438948E-20 2.655367872549012E-12 3.3279728917045884E-7 7.318024188804728E-4
-4.0 3.8 -1.4 3.937862356477235 6.208075409403594E-10 1.9390473982737369E-4 0.10961011443164077 1.2501528663867376E-9 3.2214286072545405E-4 0.12140021558657597 2.126084588755052E-27 4.1504195326891836E-22 9.479359653504757E-20 9.261360220567725E-10 2.386493008879494E-4 0.08993549170122418 2.3864275788793028E-27 6.149412826578897E-22 2.3174191760647823E-19 4.6306801102838626E-10 1.084769549490679E-4 0.027253179303401265 6.503773870177941E-28 1.6759105037339026E-22 6.315704032643393E-20 1.0096060427380805E-10 2.4636182529282737E-5 0.007427357821433391 3.260304240293124E-29 7.637493149421029E-24 1.9188035866917437E-21 1.0122193446047554E-11 1.975996139303861E-6 3.7232915354621726E-4 7.318024188804728E-4
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1.2000000000000002 6.4 6.4 4.977397875614508 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.02105258191002947 1.843522144254115E-5 2.3608962835305205E-11 1.0175624587876668E-5 6.7504097883359245E-9 5.239317815188479E-15 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 8.483267135001907E-7 7.428585665090656E-10 9.513376523988819E-16 5.791904235955445E-4 4.61075111918881E-7 3.9364883557289013E-13 1.7171754211781124E-8 1.503687732003369E-11 1.92568925149176E-17 9.379137011743154E-6 7.777534654438611E-9 7.968204870410453E-15 6.393544710576151E-11 5.089698004133136E-14 4.345395448490757E-20 6.984252289152645E-8 4.6332846313015953E-11 2.966794974682767E-17 1.2754076295260396E-9
3.8 -4.0 -4.0 0.11814589071963325 6.639677199580735E-36 1.3794073625809013E-27 5.186434267821657E-22 1.6748546029090895E-32 2.8706261289136714E-24 7.19551218345717E-19 1.0483395503495055E-32 1.3612176190660418E-24 2.067896017880326E-19 5.900090541597062E-29 1.0112492178274336E-20 2.5347975461166915E-15 2.8718823814904604E-29 4.9222783811525995E-21 1.2338184239740997E-15 1.5141040800533008E-29 2.359188089600789E-21 3.942360976189673E-16 1.9102085371358956E-26 3.2740122807387982E-18 8.206640014023534E-13 8.05674928324627E-27 1.307661848813231E-18 2.6222284177531636E-13 2.3370630898969603E-24 3.64147450559438E-16 6.085147280085719E-11 1.9714215498859025E-24 2.559794436274499E-16 3.208190691890854E-11 3.3546262790251185E-4
3.8 -4.0 -1.4 0.11815196453926008 3.286415676451725E-27 1.026488137920661E-21 5.802513252660671E-19 8.289963890300767E-24 2.136180906140531E-18 8.050242738683686E-16 5.18892625191255E-24 1.0129522119452568E-18 2.313534391695759E-16 2.920345291727967E-20 7.525226809281491E-15 2.8358975732919886E-12 1.421484658930669E-20 3.662921126028225E-15 1.3803795414712844E-12 7.494303163986489E-21 1.7555934923878484E-15 4.4106607024869835E-13 9.454886273886531E-18 2.4363613394791054E-12 9.181479024352375E-10 3.9878184360188014E-18 9.7309860207409E-13 2.9337140624567847E-10 1.156767195847875E-15 2.7098089265953933E-10 6.807981343976337E-8 9.75786998628322E-16 1.904875017800966E-10 3.5892808132663314E-8 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 0.11815999963636283 1.8856770853470695E-21 8.85491981247484E-19 7.5254540156524905E-19 4.756609170989764E-18 1.8427597874761098E-15 1.0440602012761531E-15 2.977298155220469E-18 8.738153203420209E-16 3.000492359126224E-16 1.6756334986383216E-14 6.4915781785706195E-12 3.67796088550483E-12 8.15618385624303E-15 3.159790323159772E-12 1.7902557583520104E-12 4.300075565070094E-15 1.5144490251867489E-12 5.720318567129756E-13 5.42501744955155E-12 2.1017080956242625E-9 1.1907736386773288E-9 2.288127426851904E-12 8.3943591481263E-10 3.80482203327467E-10 6.637290011489407E-10 2.3375955226075254E-7 8.829475834462187E-8 5.598863213431508E-10 1.6432255680598873E-7 4.6550462762172937E-8 3.3546262790251185E-4
3.8 -4.0 3.8 0.1181700887677895 1.2542423359420818E-18 8.85491981247484E-19 1.1314062512082418E-21 3.1638187917459184E-15 1.8427597874761098E-15 1.5696810264266224E-18 1.9803249570233076E-15 8.738153203420209E-16 4.511057810940104E-19 1.1145336016681301E-11 6.4915781785706195E-12 5.529590545506461E-15 5.4250174495515465E-12 3.159790323159772E-12 2.6915406725602E-15 2.8601592835648775E-12 1.5144490251867489E-12 8.600151130140188E-16 3.608404965688875E-9 2.1017080956242625E-9 1.7902557583520114E-12 1.521928813309868E-9 8.3943591481263E-10 5.72031856712976E-13 4.414737917231093E-7 2.3375955226075254E-7 1.3274580022978815E-10 3.724037020973835E-7 1.6432255680598873E-7 6.998579016789385E-11 3.3546262790251185E-4
3.8 -4.0 6.4 0.11816457197697199 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 2.4394674965708124E-15 2.1361809061405274E-18 2.735688083799253E-24 1.5269326985192E-15 1.0129522119452551E-18 7.862009472594772E-25 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 4.182968307488738E-12 3.662921126028218E-15 4.6908993744712085E-21 2.2053303512434904E-12 1.7555934923878452E-15 1.4988606327972979E-21 2.782266371015869E-9 2.436361339479102E-12 3.1201124703825552E-18 1.173485624982713E-9 9.730986020740883E-13 9.969546090047003E-19 3.4039906719881664E-7 2.7098089265953887E-10 2.31353439169575E-16 2.871424650613063E-7 1.9048750178009626E-10 1.2197337482854024E-16 3.3546262790251185E-4
3.8 -1.4 -4.0 2.164046348976332E-4 3.286415676451725E-27 6.827599963572116E-19 2.567109570286311E-13 6.157252814002589E-25 1.0553256849581265E-16 2.6452796298155085E-11 3.854001198198379E-25 5.004232009695965E-17 7.602187409607342E-12 1.6110272932682781E-22 2.7612289654375516E-14 6.9212972257169344E-9 7.841711694114211E-23 1.3440344281487465E-14 3.368957038787211E-9 4.1342806192842857E-23 6.441793351312751E-15 1.0764667233120378E-9 3.873997628687189E-21 6.639859243269895E-13 1.6643472864740315E-7 1.633948703057377E-21 2.652003068221429E-13 5.3180092512219895E-8 3.520329803231932E-20 5.485171232719416E-12 9.166087736247614E-7 2.969562125557771E-20 3.855831142565219E-12 4.83251366038757E-7 0.37531109885139957
3.8 -1.4 -1.4 0.002012301925863406 1.6266646214532314E-18 5.080769149993044E-13 2.87204783352526E-10 3.0476319199813464E-16 7.853222526126541E-11 2.9595034500349723E-8 1.9076002603895674E-16 3.7239070463889225E-11 8.505225539469527E-9 7.974040292384671E-14 2.0547728365038006E-8 7.743454713577994E-6 3.88138210143395E-14 1.0001653136534946E-8 3.769144050757964E-6 2.0463290954753607E-14 4.793670558410481E-9 1.2043365644909184E-6 1.9174978172520684E-12 4.94106160048835E-7 1.8620494713898254E-4 8.087493519390986E-13 1.9734922149212726E-7 5.9497175833191773E-5 1.7424442038164422E-11 4.081798718484009E-6 0.0010254896296404022 1.4698328289584038E-11 2.8693227519554687E-6 5.406551613318706E-4 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.004186317640735878 9.333463883457657E-13 4.3828858558482145E-10 3.7248452456421565E-10 1.7486679233120852E-10 6.774521123959297E-8 3.838269065941506E-8 1.0945414254177502E-10 3.212399351411952E-8 1.1030682896016145E-8 4.5753387694457995E-8 1.7725337515297175E-5 1.0042719392773184E-5 2.227056467808863E-8 8.627848023222818E-6 4.888316320405303E-6 1.1741411508184684E-8 4.135222496397436E-6 1.561940324959964E-6 1.1002204380606938E-6 4.262368228575153E-4 2.4149479823055604E-4 4.6404358756814503E-7 1.7024176576526518E-4 7.716367740971005E-5 9.997783089873683E-6 0.0035211318092827396 0.001329988354244377 8.433595618221345E-6 0.002475198879174882 7.011919452424396E-4 0.37531109885139957
3.8 -1.4 3.8 0.0068645332980936955 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 1.1631118381640927E-7 6.774521123959297E-8 5.770604146929881E-11 7.280250711370656E-8 3.212399351411952E-8 1.6583960991178033E-11 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 1.4813079758803949E-5 8.627848023222818E-6 7.3492863437692484E-9 7.80970162479982E-6 4.135222496397436E-6 2.348282301636937E-9 7.318024188804728E-4 4.262368228575153E-4 3.63072744560029E-7 3.086547096388402E-4 1.7024176576526518E-4 1.1601089689203626E-7 0.0066499417712218845 0.0035211318092827396 1.9995566179747367E-6 0.005609535561939517 0.002475198879174882 1.0541994522776681E-6 0.37531109885139957
3.8 -1.4 6.4 0.0054053806631733465 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 8.968192272833243E-8 7.853222526126527E-11 1.0057185335938444E-16 5.613448856049884E-8 3.7239070463889154E-11 2.890303424832678E-17 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 1.142164863866049E-5 1.0001653136534931E-8 1.2808560934732036E-14 6.021682822454587E-6 4.793670558410473E-9 4.092658190950722E-15 5.64257415572674E-4 4.941061600488342E-7 6.327742796931826E-13 2.3798870333276696E-4 1.9734922149212692E-7 2.0218733798477464E-13 0.005127448148202008 4.0817987184840025E-6 3.4848884076328846E-12 0.0043252412906549615 2.869322751955464E-6 1.8372910361980047E-12 0.37531109885139957
3.8 1.2000000000000002 -4.0 5.356698507845659E-4 1.8856770853470695E-21 3.917535110203989E-13 1.4729541752583488E-7 2.6240140916146534E-20 4.4974431816010965E-12 1.127329222069875E-6 1.642445707308593E-20 2.132635398905322E-12 3.2397966256216773E-7 5.099368797562266E-19 8.740090803000822E-11 2.190791382551568E-5 2.4821292661853207E-19 4.25425891565565E-11 1.0663726477946408E-5 1.308619765688473E-19 2.039014494247806E-11 3.407329499263416E-6 9.107668645013996E-19 1.5610138062404734E-10 3.912837602993185E-5 3.8413713163371116E-19 6.234790907475677E-11 1.2502502777127999E-5 6.14703336847797E-19 9.577946523188376E-11 1.6005388790954323E-5 5.185308904527474E-19 6.732869935880658E-11 8.438306745224832E-6 0.48675225595997157
3.8 1.2000000000000002 -1.4 0.25466969868358624 9.333463883457657E-13 2.9152398556059304E-7 1.6479214198328546E-4 1.2987982377301673E-11 3.3467793504073916E-6 0.0012612408474463339 8.129550816959362E-12 1.5870039546476752E-6 3.6246411089657584E-4 2.524015106845201E-10 6.503952187730642E-5 0.02451028081073019 1.2285700473339503E-10 3.165813400033683E-5 0.01193043447883532 6.477225297556255E-11 1.5173358125895274E-5 0.0038120746460287487 4.50798798061928E-10 1.1616308559047941E-4 0.04377630347644017 1.901348895979378E-10 4.639629367328408E-5 0.013987632795389095 3.0425736400428474E-10 7.127443827250621E-5 0.01790661479114932 2.566552553512673E-10 5.010275652301224E-5 0.009440664662997457 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 0.5013032340027925 5.35534780279311E-7 2.5148089102921975E-4 2.137238869842205E-4 7.452234642518471E-6 0.002887073087657509 0.001635741201584985 4.66456747986791E-6 0.0013690165761634735 4.7009061075832786E-4 1.4482274668268805E-4 0.05610583596532173 0.031788120616062704 7.04927986621179E-5 0.027309642228640463 0.015472939422596201 3.7164974010285084E-5 0.01308917897121876 0.0049439943010504556 2.5865899122206357E-4 0.10020717922342423 0.05677480506160667 1.0909545223047591E-4 0.040023400650804064 0.01814098181357297 1.745765587299132E-4 0.06148433793503919 0.023223627472975874 1.4726345705979498E-4 0.043220751902104795 0.01224388203956378 0.48675225595997157
3.8 1.2000000000000002 3.8 0.7426615716206166 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.0049567915199545 0.002887073087657509 2.4592374320310954E-6 0.003102598031004964 0.0013690165761634735 7.06752648464835E-7 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 0.046887695219988486 0.027309642228640463 2.3262623558498908E-5 0.024719971505252274 0.01308917897121876 7.432994802057017E-6 0.1720448638230505 0.10020717922342423 8.535746710328098E-5 0.07256392725429188 0.040023400650804064 2.7273863057618978E-5 0.11611813736487936 0.06148433793503919 3.4915311745982646E-5 0.09795105631651024 0.043220751902104795 1.8407932132474372E-5 0.48675225595997157
3.8 1.2000000000000002 6.4 0.6185227301911822 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.0038219419619585854 3.3467793504073853E-6 4.286034184509552E-12 0.002392263131917399 1.5870039546476725E-6 1.2317501237817214E-12 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 0.036152831754046405 3.1658134000336774E-5 4.054281156202036E-11 0.019060373230143732 1.5173358125895245E-5 1.2954450595112511E-11 0.13265546508012166 1.161630855904792E-4 1.4876360336043625E-10 0.05595053118155635 4.6396293673284E-5 4.753372239948445E-11 0.08953307395574654 7.127443827250609E-5 6.085147280085695E-11 0.07552531730397961 5.010275652301215E-5 3.208190691890841E-11 0.48675225595997157
3.8 3.8 -4.0 0.5229129880476612 1.2542423359420818E-18 2.605715700709704E-10 9.797231455308489E-5 1.2963272261318683E-18 2.2218470788406172E-10 5.569282451848389E-5 8.114084046390922E-19 1.0535741175507215E-10 1.6005388790954323E-5 1.8711096935856773E-18 3.2069986058140523E-10 8.038663519445424E-5 9.107668645013996E-19 1.5610138062404734E-10 3.912837602993185E-5 4.801714145421389E-19 7.481749088970812E-11 1.2502502777127999E-5 2.4821292661853207E-19 4.25425891565565E-11 1.0663726477946408E-5 1.0468958125507784E-19 1.699178745206505E-11 3.407329499263416E-6 1.2442770509913582E-20 1.9387594535502925E-12 3.2397966256216773E-7 1.0496056366458614E-20 1.3628615701821503E-12 1.7080745788937503E-7 7.318024188804728E-4
3.8 3.8 -1.4 3.8708485141247406 6.208075409403594E-10 1.9390473982737369E-4 0.10961011443164077 6.416381383781682E-10 1.653390965259404E-4 0.062308386775783216 4.016197204856559E-10 7.840188209975684E-5 0.01790661479114932 9.261360220567725E-10 2.386493008879494E-4 0.08993549170122418 4.50798798061928E-10 1.1616308559047941E-4 0.04377630347644017 2.3766861199742226E-10 5.5675552407940894E-5 0.013987632795389095 1.2285700473339503E-10 3.165813400033683E-5 0.01193043447883532 5.1817802380450045E-11 1.2644465104912728E-5 0.0038120746460287487 6.158750618908607E-12 1.4427308678615227E-6 3.6246411089657584E-4 5.195192950920669E-12 1.0141755607295127E-6 1.910970980979294E-4 7.318024188804728E-4
3.8 3.8 1.2000000000000002 3.6034684977610336 3.562064783070342E-4 0.16727041053862637 0.142156655209273 3.681586426494874E-4 0.14262848127694583 0.08080962146112095 2.3044105752348544E-4 0.06763277172854311 0.023223627472975874 5.313976217982533E-4 0.20586895694154697 0.11664004504639745 2.5865899122206357E-4 0.10020717922342423 0.05677480506160667 1.3636931528809488E-4 0.04802808078096488 0.01814098181357297 7.04927986621179E-5 0.027309642228640463 0.015472939422596201 2.973197920822807E-5 0.0109076491426823 0.0049439943010504556 3.5337632423241746E-6 0.0012445605237849757 4.7009061075832786E-4 2.9808938570073884E-6 8.748706326234875E-4 2.47839575997725E-4 7.318024188804728E-4
3.8 3.8 3.8 3.521900629791554 0.23692775868212168 0.16727041053862637 2.137238869842205E-4 0.24487764079127558 0.14262848127694583 1.2149235207433084E-4 0.15327594132164077 0.06763277172854311 3.4915311745982646E-5 0.35345468195878016 0.20586895694154697 1.753612151934236E-4 0.1720448638230505 0.10020717922342423 8.535746710328098E-5 0.09070490906786484 0.04802808078096488 2.7273863057618978E-5 0.046887695219988486 0.027309642228640463 2.3262623558498908E-5 0.019775977204201822 0.0109076491426823 7.432994802057017E-6 0.0023504530537916393 0.0012445605237849757 7.06752648464835E-7 0.0019827166079818 8.748706326234875E-4 3.7261173212592355E-7 7.318024188804728E-4
3.8 3.8 6.4 3.511344850379243 0.18268352405273452 1.9390473982737336E-4 3.7248452456421565E-10 0.18881329325994903 1.653390965259401E-4 2.1174058566479553E-10 0.11818365762158545 7.84018820997567E-5 6.085147280085695E-11 0.27253179303401254 2.3864930088794895E-4 3.0562488727873496E-10 0.13265546508012166 1.161630855904792E-4 1.4876360336043625E-10 0.06993816397694544 5.56755524079408E-5 4.753372239948445E-11 0.036152831754046405 3.1658134000336774E-5 4.054281156202036E-11 0.015248298584114986 1.2644465104912705E-5 1.2954450595112511E-11 0.0018123205544828779 1.4427308678615202E-6 1.2317501237817214E-12 0.0015287767847834343 1.0141755607295108E-6 6.493991188650837E-13 7.318024188804728E-4
3.8 6.4 -4.0 4.683673728542989 9.670855421101112E-19 2.0091412231854673E-10 7.554170850100189E-5 7.42390531389442E-20 1.2724242770465213E-11 3.1894590158557936E-6 4.646835340266146E-20 6.033688355991353E-12 9.166087736247606E-7 7.958869386330616E-21 1.3641147343374442E-12 3.4192903393772813E-7 3.8739976286871815E-21 6.639859243269883E-13 1.6643472864740286E-7 2.0424358788217172E-21 3.182403681865709E-13 5.318009251221979E-8 7.841711694114211E-23 1.3440344281487465E-14 3.368957038787211E-9 3.3074244954274285E-23 5.368161126093959E-15 1.0764667233120378E-9 2.9196978774230145E-25 4.549301826996331E-17 7.602187409607342E-12 2.462901125601036E-25 3.197956621085232E-17 4.007999439114407E-12 1.2754076295260396E-9
3.8 6.4 -1.4 4.969119223401883 4.786746389208764E-10 1.495105571387244E-4 0.08451505255262697 3.674582072396006E-11 9.468765081453039E-6 0.003568324064790343 2.3000263490377015E-11 4.489978590332408E-6 0.0010254896296404013 3.939371197099807E-12 1.0151081047880492E-6 3.8254562738851465E-4 1.917497817252065E-12 4.941061600488342E-7 1.8620494713898221E-4 1.0109366899238713E-12 2.3681906579055228E-7 5.9497175833191665E-5 3.88138210143395E-14 1.0001653136534946E-8 3.769144050757964E-6 1.6370632763802887E-14 3.994725465342068E-9 1.2043365644909184E-6 1.445151712416339E-16 3.3853700421717474E-11 8.505225539469527E-9 1.2190527679925385E-16 2.3797644018565275E-11 4.484096136416625E-9 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.925863475771374 2.7465356997214227E-4 0.12897411530382139 0.10961011443164072 2.1083989045553344E-5 0.008168156301277103 0.004627866838600098 1.3197073678633252E-5 0.0038732449902110105 0.0013299883542443759 2.2603294069810525E-6 8.756750844778135E-4 4.961349336825982E-4 1.1002204380606921E-6 4.262368228575144E-4 2.414947982305556E-4 5.800544844601803E-7 2.0429011891831778E-4 7.716367740970992E-5 2.227056467808863E-8 8.627848023222818E-6 4.888316320405303E-6 9.393129206547748E-9 3.4460187469978634E-6 1.561940324959964E-6 8.291980495589016E-11 2.920363046738138E-8 1.1030682896016145E-8 6.994671693248341E-11 2.052885189078575E-8 5.815559190820464E-9 1.2754076295260396E-9
3.8 6.4 3.8 4.881436296612853 0.18268352405273452 0.12897411530382139 1.6479214198328535E-4 0.01402383890484878 0.008168156301277103 6.957716385032604E-6 0.00877792313801288 0.0038732449902110105 1.999556617974735E-6 0.0015034391929775704 8.756750844778135E-4 7.459087043037473E-7 7.318024188804715E-4 4.262368228575144E-4 3.630727445600284E-7 3.858183870485495E-4 2.0429011891831778E-4 1.1601089689203607E-7 1.4813079758803949E-5 8.627848023222818E-6 7.3492863437692484E-9 6.247761299839856E-6 3.4460187469978634E-6 2.348282301636937E-9 5.515341448008072E-8 2.920363046738138E-8 1.6583960991178033E-11 4.652447352656371E-8 2.052885189078575E-8 8.743339616560426E-12 1.2754076295260396E-9
3.8 6.4 6.4 4.884879261681911 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.010813103226637396 9.468765081453023E-6 1.212612083890682E-11 0.006768231555626646 4.489978590332399E-6 3.4848884076328814E-12 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 5.64257415572673E-4 4.941061600488333E-7 6.327742796931816E-13 2.9748587916595814E-4 2.3681906579055189E-7 2.0218733798477426E-13 1.142164863866049E-5 1.0001653136534931E-8 1.2808560934732036E-14 4.81734625796367E-6 3.994725465342061E-9 4.092658190950722E-15 4.2526127697347605E-8 3.3853700421717415E-11 2.890303424832678E-17 3.587276909133297E-8 2.3797644018565233E-11 1.523815959990673E-17 1.2754076295260396E-9
6.4 -4.0 -4.0 0.11814588988316833 6.639677199580735E-36 1.3794073625809013E-27 5.186434267821657E-22 2.0385147204726077E-32 3.493923359436503E-24 8.757869179712679E-19 8.083238727575837E-33 1.049569003804626E-24 1.5944545038635047E-19 5.900090541597062E-29 1.0112492178274336E-20 2.5347975461166915E-15 2.2143694644895412E-29 3.795330551554322E-21 9.513376523988862E-16 1.8428605385537604E-29 2.8714371030545516E-21 4.79836331429638E-16 1.4728693217741423E-26 2.5244323610040006E-18 6.327742796931846E-13 9.806106144693283E-27 1.5915936365915635E-18 3.191591210811251E-13 1.801996201588033E-24 2.807764692201803E-16 4.69196246015835E-11 2.399475060533044E-24 3.1156009785362165E-16 3.904783101854538E-11 3.3546262790251185E-4
6.4 -4.0 -1.4 0.11815051973076414 3.286415676451725E-27 1.026488137920661E-21 5.802513252660671E-19 1.0089958491448682E-23 2.6000085113038037E-18 9.798186838236948E-16 4.000929815154926E-24 7.810384093636201E-19 1.7838543615281037E-16 2.920345291727967E-20 7.525226809281491E-15 2.8358975732919886E-12 1.0960380004639336E-20 2.8243011428974462E-15 1.0643438344622332E-12 9.121536456320166E-21 2.1367843938109294E-15 5.368344663120472E-13 7.290205055072579E-18 1.8785602743958647E-12 7.079393961749077E-10 4.853690923544382E-18 1.1843868843025903E-12 3.570709535972935E-10 8.919271807640483E-16 2.089402470076037E-10 5.249304811394048E-8 1.187659010695383E-15 2.3184793221472032E-10 4.3686190795574755E-8 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.11815674880151704 1.8856770853470695E-21 8.85491981247484E-19 7.5254540156524905E-19 5.7894087031529375E-18 2.242877051261819E-15 1.2707563305282067E-15 2.295650463992774E-18 6.737566884491663E-16 2.313534391695758E-16 1.6756334986383216E-14 6.4915781785706195E-12 3.67796088550483E-12 6.288838496461632E-15 2.4363613394791046E-12 1.3803795414712834E-12 5.233748244960792E-15 1.843280381405234E-12 6.962367709192564E-13 4.18296830748874E-12 1.6205253600272804E-9 9.181479024352368E-10 2.7849470836770274E-12 1.0217021025387595E-9 4.6309606279476743E-10 5.117692988797076E-10 1.802406734673848E-7 6.807981343976333E-8 6.814540831585373E-10 2.0000180933490054E-7 5.665793521460219E-8 3.3546262790251185E-4
6.4 -4.0 3.8 0.11816457197697199 1.2542423359420818E-18 8.85491981247484E-19 1.1314062512082418E-21 3.850776759176384E-15 2.242877051261819E-15 1.9105048720404694E-18 1.5269326985192003E-15 6.737566884491663E-16 3.4782582787769304E-19 1.1145336016681301E-11 6.4915781785706195E-12 5.529590545506461E-15 4.182968307488737E-12 2.4363613394791046E-12 2.075316703832339E-15 3.481183854596282E-12 1.843280381405234E-12 1.0467496489921584E-15 2.782266371015869E-9 1.6205253600272804E-9 1.3803795414712842E-12 1.8523842511790697E-9 1.0217021025387595E-9 6.962367709192568E-13 3.4039906719881664E-7 1.802406734673848E-7 1.0235385977594153E-10 4.532634817168175E-7 2.0000180933490054E-7 8.518176039481716E-11 3.3546262790251185E-4
6.4 -4.0 6.4 0.1181602941754421 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 2.9691475267384674E-15 2.6000085113037995E-18 3.329686302178065E-24 1.1773438786085476E-15 7.810384093636188E-19 6.062014871446857E-25 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 3.22528434685525E-12 2.824301142897441E-15 3.616925401530981E-21 2.684172331560234E-12 2.1367843938109255E-15 1.824307291264033E-21 2.1452708974997186E-9 1.8785602743958615E-12 2.405767668173951E-18 1.4282838143891731E-9 1.1843868843025883E-12 1.2134227308860955E-18 2.624652405697022E-7 2.0894024700760334E-10 1.7838543615280966E-16 3.4948952636459783E-7 2.318479322147199E-10 1.4845737633692289E-16 3.3546262790251185E-4
6.4 -1.4 -4.0 2.1595567103765393E-4 3.286415676451725E-27 6.827599963572116E-19 2.567109570286311E-13 7.494173211939976E-25 1.2844678815363115E-16 3.219647517927228E-11 2.971633735559704E-25 3.8585210268050395E-17 5.8616786577536455E-12 1.6110272932682781E-22 2.7612289654375516E-14 6.9212972257169344E-9 6.04636423716113E-23 1.0363198771986802E-14 2.5976396672609644E-9 5.0319543477608255E-23 7.840495855631233E-15 1.3101992601381728E-9 2.9870520149985116E-21 5.119673999035719E-13 1.2832976145636641E-7 1.988726948532848E-21 3.227830812249526E-13 6.47270522670795E-8 2.714355877333537E-20 4.2293499773924074E-12 7.067526484648342E-7 3.614341266276486E-20 4.6930453127832256E-12 5.881794286187207E-7 0.37531109885139957
6.4 -1.4 -1.4 0.0016153813924435114 1.6266646214532314E-18 5.080769149993044E-13 2.87204783352526E-10 3.70936230564331E-16 9.55838775189788E-11 3.602098556917676E-8 1.4708582058526717E-16 2.8713244335032526E-11 6.557967639824969E-9 7.974040292384671E-14 2.0547728365038006E-8 7.743454713577994E-6 2.9927458244202245E-14 7.711790511582482E-9 2.906204497459005E-6 2.490647233982223E-14 5.834517206116147E-9 1.465833398823936E-6 1.478489732767084E-12 3.809813382609649E-7 1.4357361977598168E-4 9.843525857330924E-13 2.4019953277541143E-7 7.241575988258598E-5 1.3435143665268997E-11 3.147277374818059E-6 7.907054051593438E-4 1.7889766987900377E-11 3.4923369810661022E-6 6.580472735723998E-4 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.003312495632295527 9.333463883457657E-13 4.3828858558482145E-10 3.7248452456421565E-10 2.1283550803146582E-10 8.245468598502135E-8 4.6716699936018914E-8 8.439479017960519E-11 2.4769256141405325E-8 8.505225539469522E-9 4.5753387694457995E-8 1.7725337515297175E-5 1.0042719392773184E-5 1.7171754211781124E-8 6.652515900378115E-6 3.769144050757962E-6 1.4290816741338435E-8 5.033100734054117E-6 1.9010834369743098E-6 8.483267135001921E-7 3.286505781921608E-4 1.8620494713898243E-4 5.648010773923457E-7 2.0720625238092972E-4 9.391817774048993E-5 7.708806505967179E-6 0.002714974265370836 0.0010254896296404018 1.0264776885346548E-5 0.003012637241782817 8.534413075444272E-4 0.37531109885139957
6.4 -1.4 3.8 0.005405636868369749 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 1.415657573818755E-7 8.245468598502135E-8 7.023571765038373E-11 5.613448856049884E-8 2.4769256141405325E-8 1.2787089421152303E-11 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 1.142164863866049E-5 6.652515900378115E-6 5.666678889887771E-9 9.505417184871548E-6 5.033100734054117E-6 2.858163348267687E-9 5.64257415572674E-4 3.286505781921608E-4 2.799478154550634E-7 3.7567271096195973E-4 2.0720625238092972E-4 1.4120026934808643E-7 0.0051274481482020086 0.002714974265370836 1.5417613011934358E-6 0.006827530460355418 0.003012637241782817 1.2830971106683185E-6 0.37531109885139957
6.4 -1.4 6.4 0.004264993880885309 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 1.0915450172477799E-7 9.558387751897864E-11 1.2240895608622924E-16 4.3282586422844775E-8 2.8713244335032474E-11 2.2285730391707147E-17 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 8.806680295330312E-6 7.711790511582468E-9 9.876061220586741E-15 7.329166994119676E-6 5.834517206116137E-9 4.981294467964446E-15 4.350715750787321E-4 3.809813382609642E-7 4.879016118131377E-13 2.896630395303437E-4 2.40199532775411E-7 2.460881464332731E-13 0.003953527025796716 3.147277374818054E-6 2.6870287330537994E-12 0.005264378188579195 3.4923369810660963E-6 2.236220873487547E-12 0.37531109885139957
6.4 1.2000000000000002 -4.0 4.6297350997180175E-4 1.8856770853470695E-21 3.917535110203989E-13 1.4729541752583488E-7 3.193764606905627E-20 5.473970166878201E-12 1.3721055009890384E-6 1.2664103672165502E-20 1.644371906266769E-12 2.498050325866636E-7 5.099368797562266E-19 8.740090803000822E-11 2.190791382551568E-5 1.913849706861633E-19 3.2802530833352484E-11 8.222283211396053E-6 1.5927595453503165E-19 2.4817444180298066E-11 4.1471607921574625E-6 7.022482351711457E-19 1.2036221707629776E-10 3.0169996387797192E-5 4.675445833658126E-19 7.588547102466191E-11 1.5217163274744562E-5 4.739679826752375E-19 7.38509085544615E-11 1.2340980408667952E-5 6.311191737907949E-19 8.194773714375475E-11 1.0270510936368018E-5 0.48675225595997157
6.4 1.2000000000000002 -1.4 0.20886401716115185 9.333463883457657E-13 2.9152398556059304E-7 1.6479214198328546E-4 1.5808054752562536E-11 4.0734634278875E-6 0.0015350932726428755 6.2683030492871925E-12 1.2236619159076206E-6 2.794785275036844E-4 2.524015106845201E-10 6.503952187730642E-5 0.02451028081073019 9.472908832676046E-11 2.44100544245415E-5 0.009198980424231515 7.883621117887982E-11 1.846793975125678E-5 0.004639787995908689 3.475891281239911E-10 8.956773135637453E-5 0.03375378821612735 2.3141875757311254E-10 5.64702914892329E-5 0.017024758631847523 2.3459812300791656E-10 5.49562686572743E-5 0.013806923731089275 3.123826481483618E-10 6.098153626650018E-5 0.01149051019302748 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 0.41971686109965206 5.35534780279311E-7 2.5148089102921975E-4 2.137238869842205E-4 9.070333623470007E-6 0.003513941435900026 0.0019909086511287677 3.596622152439896E-6 0.0010555824020422146 3.624641108965756E-4 1.4482274668268805E-4 0.05610583596532173 0.031788120616062704 5.4353584196157506E-5 0.02105714294812126 0.011930434478835314 4.523458124326528E-5 0.015931224098727485 0.006017480647644663 1.9943942536412276E-4 0.0772649044491234 0.04377630347644015 1.3278327857365222E-4 0.04871365624713013 0.02207992168786585 1.3460753245282897E-4 0.04740759626689432 0.01790661479114931 1.7923867808146237E-4 0.05260524634753471 0.014902388380477059 0.48675225595997157
6.4 1.2000000000000002 3.8 0.6354184334749563 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.006033056518572022 0.003513941435900026 2.9932100957451026E-6 0.0023922631319173993 0.0010555824020422146 5.449427503696812E-7 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 0.036152831754046405 0.02105714294812126 1.7936682784731978E-5 0.030087403238223315 0.015931224098727485 9.046916248653056E-6 0.13265546508012166 0.0772649044491234 6.581501037016052E-5 0.0883196867514634 0.04871365624713013 3.3195819643413054E-5 0.08953307395574654 0.04740759626689432 2.6921506490565795E-5 0.11921910704381647 0.05260524634753471 2.2404834760182796E-5 0.48675225595997157
6.4 1.2000000000000002 6.4 0.5232009732919237 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.004651797795887498 4.073463427887494E-6 5.216658068345637E-12 0.0018445582815243158 1.2236619159076185E-6 9.497428862556352E-13 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 0.027875698255246998 2.441005442454146E-5 3.1260599147830954E-11 0.023198939979543434 1.846793975125675E-5 1.5767242235775964E-11 0.10228420671553738 8.956773135637437E-5 1.1470441228091708E-10 0.06809903452739006 5.6470291489232795E-5 5.7854689393278135E-11 0.06903461865544633 5.49562686572742E-5 4.6919624601583315E-11 0.09192408154421979 6.098153626650007E-5 3.9047831018545226E-11 0.48675225595997157
6.4 3.8 -4.0 0.5229129880476612 1.2542423359420818E-18 2.605715700709704E-10 9.797231455308489E-5 1.5777979344769873E-18 2.7042753257439066E-10 6.778537218002892E-5 6.256377371313136E-19 8.123599940990764E-11 1.2340980408667952E-5 1.8711096935856773E-18 3.2069986058140523E-10 8.038663519445424E-5 7.022482351711457E-19 1.2036221707629776E-10 3.0169996387797192E-5 5.844307292072658E-19 9.106256522959429E-11 1.5217163274744562E-5 1.913849706861633E-19 3.2802530833352484E-11 8.222283211396053E-6 1.2742076362802533E-19 2.0681203483581722E-11 4.1471607921574625E-6 9.594017933458713E-21 1.494883551151608E-12 2.498050325866636E-7 1.2775058427622509E-20 1.6587788384479398E-12 2.07894772877127E-7 7.318024188804728E-4
6.4 3.8 -1.4 3.8708485141247406 6.208075409403594E-10 1.9390473982737369E-4 0.10961011443164077 7.809566203709045E-10 2.0123906967945058E-4 0.07583736727398137 3.096695223704499E-10 6.0451895523001736E-5 0.013806923731089275 9.261360220567725E-10 2.386493008879494E-4 0.08993549170122418 3.475891281239911E-10 8.956773135637453E-5 0.03375378821612735 2.892734469663907E-10 6.776434978707948E-5 0.017024758631847523 9.472908832676046E-11 2.44100544245415E-5 0.009198980424231515 6.306896894310386E-11 1.5389949792713985E-5 0.004639787995908689 4.748714431278176E-12 1.1124199235523823E-6 2.794785275036844E-4 6.323221901025015E-12 1.2343828569356062E-6 2.3258988979437507E-4 7.318024188804728E-4
6.4 3.8 1.2000000000000002 3.6290855123861623 3.562064783070342E-4 0.16727041053862637 0.142156655209273 4.4809669520365586E-4 0.17359731294686453 0.09835576331114859 1.7768194283773425E-4 0.052148355893583756 0.01790661479114931 5.313976217982533E-4 0.20586895694154697 0.11664004504639745 1.9943942536412276E-4 0.0772649044491234 0.04377630347644015 1.6597909821706525E-4 0.058456387496556156 0.02207992168786585 5.4353584196157506E-5 0.02105714294812126 0.011930434478835314 3.6187664994612223E-5 0.013276020082272905 0.006017480647644663 2.724713751848406E-6 9.596203654929223E-4 3.624641108965756E-4 3.628133449388003E-6 0.001064830738151523 3.0165282592860114E-4 7.318024188804728E-4
6.4 3.8 3.8 3.635762453547546 0.23692775868212168 0.16727041053862637 2.137238869842205E-4 0.29804776760954116 0.17359731294686453 1.4787190941720644E-4 0.11818365762158545 0.052148355893583756 2.6921506490565795E-5 0.35345468195878016 0.20586895694154697 1.753612151934236E-4 0.13265546508012166 0.0772649044491234 6.581501037016052E-5 0.11039960843932925 0.058456387496556156 3.3195819643413054E-5 0.036152831754046405 0.02105714294812126 1.7936682784731978E-5 0.024069922590578652 0.013276020082272905 9.046916248653056E-6 0.001812320554482878 9.596203654929223E-4 5.449427503696812E-7 0.002413222607428809 0.001064830738151523 4.535166811735004E-7 7.318024188804728E-4
6.4 3.8 6.4 3.6301789409912497 0.18268352405273452 1.9390473982737336E-4 3.7248452456421565E-10 0.22981020386054946 2.0123906967945023E-4 2.5771568472239847E-10 0.09112569662518916 6.045189552300163E-5 4.6919624601583315E-11 0.27253179303401254 2.3864930088794895E-4 3.0562488727873496E-10 0.10228420671553738 8.956773135637437E-5 1.1470441228091708E-10 0.08512379315923757 6.776434978707936E-5 5.7854689393278135E-11 0.027875698255246998 2.441005442454146E-5 3.1260599147830954E-11 0.01855915198363475 1.5389949792713958E-5 1.5767242235775964E-11 0.001397392637518421 1.1124199235523804E-6 9.497428862556352E-13 0.0018607191183549995 1.234382856935604E-6 7.904027376281269E-13 7.318024188804728E-4
6.4 6.4 -4.0 4.688709643938755 9.670855421101112E-19 2.0091412231854673E-10 7.554170850100189E-5 9.035853165691208E-20 1.548704953218463E-11 3.881984228883553E-6 3.582949758080266E-20 4.652284975131645E-12 7.067526484648335E-7 7.958869386330616E-21 1.3641147343374442E-12 3.4192903393772813E-7 2.987052014998506E-21 5.119673999035709E-13 1.2832976145636618E-7 2.4859086856660552E-21 3.8733969746994236E-13 6.472705226707939E-8 6.04636423716113E-23 1.0363198771986802E-14 2.5976396672609644E-9 4.025563478208661E-23 6.533746546359361E-15 1.3101992601381728E-9 2.251237678454321E-25 3.507746388004581E-17 5.8616786577536455E-12 2.9976692847759905E-25 3.892326913746398E-17 4.878253815041255E-12 1.2754076295260396E-9
6.4 6.4 -1.4 4.975688524460382 4.786746389208764E-10 1.495105571387244E-4 0.08451505255262697 4.472441746975089E-11 1.1524712037518126E-5 0.004343112005577836 1.773438963815506E-11 3.4620051122998623E-6 7.907054051593432E-4 3.939371197099807E-12 1.0151081047880492E-6 3.8254562738851465E-4 1.478489732767081E-12 3.809813382609642E-7 1.435736197759814E-4 1.230440732166363E-12 2.882394393304932E-7 7.241575988258583E-5 2.9927458244202245E-14 7.711790511582482E-9 2.906204497459005E-6 1.9925177871857785E-14 4.862097671763456E-9 1.465833398823936E-6 1.1142865195853572E-16 2.6102949395484113E-11 6.557967639824969E-9 1.483744922257324E-16 2.8964811369387517E-11 5.4577250862389026E-9 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.941678884038287 2.7465356997214227E-4 0.12897411530382139 0.10961011443164072 2.5661942213366343E-5 0.009941702897883289 0.005632712629793214 1.017562458787667E-5 0.002986471691907917 0.0010254896296404009 2.2603294069810525E-6 8.756750844778135E-4 4.961349336825982E-4 8.483267135001907E-7 3.2865057819216016E-4 1.862049471389821E-4 7.060013467404309E-7 2.4864750285711517E-4 9.391817774048974E-5 1.7171754211781124E-8 6.652515900378115E-6 3.769144050757962E-6 1.1432653393070749E-8 4.194250611711765E-6 1.9010834369743098E-6 6.393544710576151E-11 2.251750558309575E-8 8.505225539469522E-9 8.513420321258633E-11 2.4986268480309502E-8 7.078287869093775E-9 1.2754076295260396E-9
6.4 6.4 3.8 4.906434745201772 0.18268352405273452 0.12897411530382139 1.6479214198328535E-4 0.017068826150888526 0.009941702897883289 8.468440930410894E-6 0.006768231555626646 0.002986471691907917 1.5417613011934347E-6 0.0015034391929775704 8.756750844778135E-4 7.459087043037473E-7 5.64257415572673E-4 3.2865057819216016E-4 2.799478154550629E-7 4.695908887024487E-4 2.4864750285711517E-4 1.412002693480862E-7 1.142164863866049E-5 6.652515900378115E-6 5.666678889887771E-9 7.604333747897239E-6 4.194250611711765E-6 2.858163348267687E-9 4.2526127697347605E-8 2.251750558309575E-8 1.2787089421152303E-11 5.66263029527502E-8 2.4986268480309502E-8 1.0641775401573292E-11 1.2754076295260396E-9
6.4 6.4 6.4 4.9092061517502215 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.013160945471447976 1.1524712037518108E-5 1.4759057765017797E-11 0.005218655674051662 3.462005112299857E-6 2.687028733053797E-12 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 4.350715750787313E-4 3.809813382609635E-7 4.879016118131368E-13 3.6207879941292897E-4 2.8823943933049264E-7 2.4608814643327263E-13 8.806680295330312E-6 7.711790511582468E-9 9.876061220586741E-15 5.863333595295741E-6 4.8620976717634475E-9 4.981294467964446E-15 3.2789838199124826E-8 2.6102949395484064E-11 2.2285730391707147E-17 4.3661800689911194E-8 2.8964811369387465E-11 1.854681152821655E-17 1.2754076295260396E-9

55
Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fcl Normal file
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FUNCTION_BLOCK MamdaniQoSFewRules
VAR_INPUT
commitment : REAL;
clarity : REAL;
influence : REAL;
END_VAR
VAR_OUTPUT
service_quality : REAL;
END_VAR
FUZZIFY commitment
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for commitment
END_FUZZIFY
FUZZIFY clarity
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for clarity
END_FUZZIFY
FUZZIFY influence
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for influence
END_FUZZIFY
DEFUZZIFY service_quality
TERM inadequate := GAUSS 0 1;
TERM sufficient := GAUSS 2.5 1;
TERM excellent := GAUSS 5 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-4.0 .. 9.0); // Added range for service_quality
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : MIN;
RULE 1 : IF commitment IS fully AND influence IS high THEN service_quality IS excellent;
RULE 2 : IF commitment IS partially AND clarity IS high AND influence IS low THEN service_quality IS sufficient;
RULE 3 : IF commitment IS nothing THEN service_quality IS inadequate;
END_RULEBLOCK
END_FUNCTION_BLOCK

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clarity commitment influence service_quality No1.1 No1.2 No1.3
-4.0 -4.0 -4.0 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 -1.4 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 3.8 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 6.4 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -1.4 -4.0 2.1440130067640695E-4 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 -1.4 2.1441791333979342E-4 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 1.2000000000000002 2.1441791333979342E-4 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 3.8 2.1441791333979342E-4 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 6.4 2.1441791333979342E-4 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 1.2000000000000002 -4.0 1.9531210161944423E-4 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 -1.4 1.9532595962674313E-4 1.2754076295260396E-9 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 1.2000000000000002 0.013466242916050405 7.318024188804728E-4 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 3.8 0.013466242916050405 7.318024188804728E-4 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 6.4 0.013466242916050405 7.318024188804728E-4 2.576757109154981E-18 0.48675225595997157
-4.0 3.8 -4.0 0.05592821568344743 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 -1.4 0.05593232172333802 1.2754076295260396E-9 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 1.2000000000000002 2.4969922645135156 7.318024188804728E-4 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 3.8 4.98650133474635 0.48675225595997157 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 6.4 4.983844332639953 0.37531109885139935 2.576757109154981E-18 7.318024188804728E-4
-4.0 6.4 -4.0 1.274193733749591 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 -1.4 2.4934999999999605 1.2754076295260396E-9 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 1.2000000000000002 4.941040359589573 7.318024188804728E-4 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 3.8 4.999773597633503 0.37531109885139935 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 6.4 4.999773597633503 0.37531109885139935 2.576757109154981E-18 1.2754076295260396E-9
-1.4 -4.0 -4.0 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 -1.4 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 1.2000000000000002 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 3.8 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 6.4 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -1.4 -4.0 2.144177309941489E-4 2.576757109154981E-18 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 -1.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 1.2000000000000002 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 3.8 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 6.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 1.2000000000000002 -4.0 1.9532580751709105E-4 2.576757109154981E-18 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 -1.4 1.9532595962674313E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 1.2000000000000002 0.013466242916050405 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 3.8 0.013466242916050405 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 6.4 0.013466242916050405 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
-1.4 3.8 -4.0 0.05593227661090372 2.576757109154981E-18 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 -1.4 0.05593232172333802 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 1.2000000000000002 2.4969922645135156 7.318024188804728E-4 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 3.8 4.98650133474635 0.48675225595997157 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 6.4 4.983844332639953 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
-1.4 6.4 -4.0 2.481925003446635 2.576757109154981E-18 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 -1.4 2.4934999999999605 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 1.2000000000000002 4.941040359589573 7.318024188804728E-4 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 3.8 4.999773597633503 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 6.4 4.999773597633503 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
1.2000000000000002 -4.0 -4.0 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 -1.4 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 1.2000000000000002 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 3.8 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 6.4 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
1.2000000000000002 -1.4 -4.0 0.00337352439733487 2.576757109154981E-18 3.3546262790251185E-4 0.37531109885139957
1.2000000000000002 -1.4 -1.4 0.006864070932523873 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
1.2000000000000002 -1.4 1.2000000000000002 0.006864070932523873 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
1.2000000000000002 -1.4 3.8 0.006864070932523873 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
1.2000000000000002 -1.4 6.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
1.2000000000000002 1.2000000000000002 -4.0 0.002830854341554809 2.576757109154981E-18 3.3546262790251185E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 -1.4 0.0057435169812833 1.2754076295260396E-9 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 1.2000000000000002 0.013466242916050405 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 3.8 0.013466242916050405 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 6.4 0.013466242916050405 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
1.2000000000000002 3.8 -4.0 0.7371439769851642 2.576757109154981E-18 3.3546262790251185E-4 7.318024188804728E-4
1.2000000000000002 3.8 -1.4 1.3051507596381537 1.2754076295260396E-9 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 1.2000000000000002 2.4969922645135156 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 3.8 4.98650133474635 0.48675225595997157 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 6.4 4.983844332639953 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
1.2000000000000002 6.4 -4.0 2.4999994818236524 2.576757109154981E-18 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 -1.4 2.4999998133236305 1.2754076295260396E-9 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 1.2000000000000002 4.7897223007885525 7.318024188804728E-4 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 3.8 4.9991437345489995 0.37531109885139935 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 6.4 4.999773597633503 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
3.8 -4.0 -4.0 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 -1.4 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 3.8 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 6.4 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
3.8 -1.4 -4.0 0.00337352439733487 2.576757109154981E-18 3.3546262790251185E-4 0.37531109885139957
3.8 -1.4 -1.4 0.026192490780329366 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.026192490780329366 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
3.8 -1.4 3.8 0.006864070932523873 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
3.8 -1.4 6.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
3.8 1.2000000000000002 -4.0 0.002830854341554809 2.576757109154981E-18 3.3546262790251185E-4 0.48675225595997157
3.8 1.2000000000000002 -1.4 1.1153641551681739 1.2754076295260396E-9 0.37531109885139957 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 1.2539682405047998 7.318024188804728E-4 0.48675225595997157 0.48675225595997157
3.8 1.2000000000000002 3.8 0.013466242916050405 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
3.8 1.2000000000000002 6.4 0.013466242916050405 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
3.8 3.8 -4.0 0.7371439769851642 2.576757109154981E-18 3.3546262790251185E-4 7.318024188804728E-4
3.8 3.8 -1.4 2.4897435637821252 1.2754076295260396E-9 0.19789869908361474 7.318024188804728E-4
3.8 3.8 1.2000000000000002 2.4999683652434324 7.318024188804728E-4 0.19789869908361474 7.318024188804728E-4
3.8 3.8 3.8 4.98650133474635 0.48675225595997157 7.318024188804728E-4 7.318024188804728E-4
3.8 3.8 6.4 4.983844332639953 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
3.8 6.4 -4.0 2.4999994818236524 2.576757109154981E-18 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 -1.4 2.4999998133236305 1.2754076295260396E-9 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.7897223007885525 7.318024188804728E-4 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 3.8 4.9991437345489995 0.37531109885139935 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 6.4 4.999773597633503 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
6.4 -4.0 -4.0 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 -1.4 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 3.8 0.11824597576586889 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 6.4 0.11815440484561586 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
6.4 -1.4 -4.0 0.00337352439733487 2.576757109154981E-18 3.3546262790251185E-4 0.37531109885139957
6.4 -1.4 -1.4 0.026192490780329366 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.026192490780329366 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
6.4 -1.4 3.8 0.006864070932523873 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
6.4 -1.4 6.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
6.4 1.2000000000000002 -4.0 0.002830854341554809 2.576757109154981E-18 3.3546262790251185E-4 0.48675225595997157
6.4 1.2000000000000002 -1.4 1.1153641551681732 1.2754076295260396E-9 0.37531109885139935 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 1.1196711092043583 7.318024188804728E-4 0.37531109885139935 0.48675225595997157
6.4 1.2000000000000002 3.8 0.013466242916050405 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
6.4 1.2000000000000002 6.4 0.013466242916050405 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
6.4 3.8 -4.0 0.7371439769851642 2.576757109154981E-18 3.3546262790251185E-4 7.318024188804728E-4
6.4 3.8 -1.4 2.4897435637821252 1.2754076295260396E-9 0.19789869908361474 7.318024188804728E-4
6.4 3.8 1.2000000000000002 2.4999683652434324 7.318024188804728E-4 0.19789869908361474 7.318024188804728E-4
6.4 3.8 3.8 4.98650133474635 0.48675225595997157 7.318024188804728E-4 7.318024188804728E-4
6.4 3.8 6.4 4.983844332639953 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
6.4 6.4 -4.0 2.4999994818236524 2.576757109154981E-18 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 -1.4 2.4999998133236305 1.2754076295260396E-9 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.7897223007885525 7.318024188804728E-4 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 3.8 4.9991437345489995 0.37531109885139935 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 6.4 4.999773597633503 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9

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FUNCTION_BLOCK MamdaniQoSFewRules
VAR_INPUT
commitment : REAL;
clarity : REAL;
influence : REAL;
END_VAR
VAR_OUTPUT
service_quality : REAL;
END_VAR
FUZZIFY commitment
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for commitment
END_FUZZIFY
FUZZIFY clarity
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for clarity
END_FUZZIFY
FUZZIFY influence
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for influence
END_FUZZIFY
DEFUZZIFY service_quality
TERM inadequate := GAUSS 0 1;
TERM sufficient := GAUSS 2.5 1;
TERM excellent := GAUSS 5 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-4.0 .. 9.0); // Added range for service_quality
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : MIN;
RULE 1 : IF commitment IS fully AND influence IS high THEN service_quality IS excellent;
RULE 2 : IF commitment IS fully AND influence IS medium THEN service_quality IS excellent WITH 0.8;
RULE 3 : IF commitment IS fully AND influence IS low THEN service_quality IS excellent WITH 0.6;
RULE 4 : IF commitment IS largely AND influence IS high AND clarity IS NOT high THEN service_quality IS excellent;
RULE 5 : IF commitment IS largely AND influence IS medium AND clarity IS NOT high THEN service_quality IS excellent WITH 0.66;
RULE 6 : IF commitment IS largely AND influence IS low AND clarity IS NOT high THEN service_quality IS excellent WITH 0.33;
RULE 7 : IF commitment IS largely AND influence IS high AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 8 : IF commitment IS largely AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 9 : IF commitment IS largely AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.1;
RULE 10 : IF commitment IS satISfactory AND influence IS high THEN service_quality IS sufficient;
RULE 11 : IF commitment IS satISfactory AND influence IS medium THEN service_quality IS sufficient WITH 0.66;
RULE 12 : IF commitment IS satISfactory AND influence IS low THEN service_quality IS sufficient WITH 0.33;
RULE 13 : IF commitment IS satISfactory AND influence IS high AND clarity IS high THEN service_quality IS sufficient;
RULE 14 : IF commitment IS satISfactory AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 15 : IF commitment IS satISfactory AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 16 : IF commitment IS satISfactory AND influence IS high AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.5;
RULE 17 : IF commitment IS satISfactory AND influence IS medium AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.3;
RULE 18 : IF commitment IS satISfactory AND influence IS low AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.1;
RULE 19 : IF commitment IS partially AND influence IS high AND clarity IS high THEN service_quality IS sufficient;
RULE 20 : IF commitment IS partially AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 21 : IF commitment IS partially AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 22 : IF commitment IS partially AND influence IS high AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.4;
RULE 23 : IF commitment IS partially AND influence IS medium AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.25;
RULE 24 : IF commitment IS partially AND influence IS low AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.1;
RULE 25 : IF commitment IS minimal AND influence IS high AND clarity IS high THEN service_quality IS inadequate WITH 0.5;
RULE 26 : IF commitment IS minimal AND influence IS medium AND clarity IS high THEN service_quality IS inadequate WITH 0.3;
RULE 27 : IF commitment IS minimal AND influence IS low AND clarity IS high THEN service_quality IS inadequate WITH 0.1;
RULE 28 : IF commitment IS minimal AND influence IS high AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.4;
RULE 29 : IF commitment IS minimal AND influence IS medium AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.2;
RULE 30 : IF commitment IS minimal AND influence IS low AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.05;
RULE 31 : IF commitment IS nothing THEN service_quality IS inadequate;
END_RULEBLOCK
END_FUNCTION_BLOCK

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clarity commitment influence service_quality No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7 No1.8 No1.9 No1.10 No1.11 No1.12 No1.13 No1.14 No1.15 No1.16 No1.17 No1.18 No1.19 No1.20 No1.21 No1.22 No1.23 No1.24 No1.25 No1.26 No1.27 No1.28 No1.29 No1.30 No1.31
-4.0 -4.0 -4.0 0.11815608791282411 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.0307028436619925E-18 1.6728965228231954E-10 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 1.3383172182585565E-10 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 -1.4 0.11817141532486804 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 5.101630518104158E-10 3.807494936178157E-9 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 5.101630518104158E-10 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 0.1181865280667859 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 3.8 0.1181865280667859 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 6.4 0.1181865280667859 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.091991897885052E-9 3.807494936178157E-9 1.2754076295260396E-10 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 7.453306344157342E-7 6.377038147630198E-11 3.3546262790251185E-4
-4.0 -1.4 -4.0 5.607142338016129E-4 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 2.0074758273878343E-10 6.252150377482016E-6 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.37531109885139957
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1.2000000000000002 3.8 -1.4 4.102793568985088 1.2754076295260396E-9 3.9836433720261914E-4 0.22518665931083973 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 8.417690354871861E-10 1.643252890960804E-4 7.318024188804729E-5 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 1.2754076295260396E-9 3.286505781921608E-4 2.4149479823055604E-4 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 1.2754076295260396E-9 3.286505781921608E-4 2.4149479823055604E-4 5.101630518104158E-10 1.2448885537581848E-4 0.019789869908361474 6.377038147630198E-10 1.4938662645098218E-4 7.318024188804729E-5 5.101630518104158E-10 9.959108430065478E-5 9.92054737218515E-4 7.318024188804728E-4
1.2000000000000002 3.8 1.2000000000000002 3.8309441946419502 7.318024188804728E-4 0.3436458865685914 0.29205135357598294 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 3.659012094402364E-4 0.12886720746322178 0.04867522559599716 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 2.9272096755218914E-4 0.049474674770903684 0.019789869908361474 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 2.9272096755218914E-4 0.00396821894887406 9.92054737218515E-4 7.318024188804728E-4
1.2000000000000002 3.8 3.8 3.710740189055049 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.0791594796334459 0.049474674770903684 7.318024188804729E-5 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 0.00793643789774812 0.00396821894887406 3.659012094402364E-5 7.318024188804728E-4
1.2000000000000002 3.8 6.4 3.701537173785213 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.0791594796334459 1.2448885537581826E-4 1.2754076295260396E-10 3.659012094402364E-4 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 9.959108430065462E-5 6.377038147630198E-11 7.318024188804728E-4
1.2000000000000002 6.4 -4.0 4.03006270824493 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.0307028436619925E-18 1.6728965228231954E-10 6.252150377482016E-6 1.2883785545774905E-18 2.0074758273878343E-10 4.6557157157830784E-8 1.0307028436619925E-18 1.3383172182585565E-10 2.3278578578915392E-8 1.2754076295260396E-9
1.2000000000000002 6.4 -1.4 4.98625374927366 1.2754076295260396E-9 3.9836433720261914E-4 0.2251866593108396 1.2754076295260396E-9 3.286505781921608E-4 0.018524471735264114 8.417690354871861E-10 1.643252890960804E-4 7.318024188804729E-5 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 1.2754076295260396E-9 3.286505781921608E-4 2.4149479823055604E-4 6.377038147630198E-10 1.4938662645098218E-4 3.0887154082367665E-4 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 5.101630518104158E-10 1.5630375943705038E-5 6.252150377482016E-6 6.377038147630198E-10 1.3967147147349234E-7 4.6557157157830784E-8 5.101630518104158E-10 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
1.2000000000000002 6.4 1.2000000000000002 4.979005883759914 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 3.659012094402364E-4 9.266146224710298E-4 3.0887154082367665E-4 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
1.2000000000000002 6.4 3.8 4.973794415273984 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.001544357704118383 9.266146224710298E-4 7.318024188804729E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
1.2000000000000002 6.4 6.4 4.973794415273984 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 1.493866264509819E-4 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.5008601509928064E-5 1.5630375943705038E-5 1.2754076295260396E-10 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 9.311431431566157E-8 6.377038147630198E-11 1.2754076295260396E-9
3.8 -4.0 -4.0 0.11817949341112592 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 4.416446820253236E-10 5.025893315755168E-9 1.0307028436619925E-18 1.6728965228231954E-10 1.522997974471263E-9 1.2883785545774905E-18 2.0074758273878343E-10 3.726653172078671E-7 1.0307028436619925E-18 1.3383172182585565E-10 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 -1.4 0.11821244175982118 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.2754076295260396E-9 1.0051786631510336E-8 5.025893315755168E-9 5.101630518104158E-10 3.807494936178157E-9 1.522997974471263E-9 6.377038147630198E-10 1.1179959516236013E-6 3.726653172078671E-7 5.101630518104158E-10 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 0.11824597576586889 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 3.8 0.11824597576586889 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 6.4 0.11824597576586889 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 4.2088451774359307E-10 6.091991897885052E-9 3.807494936178157E-9 1.2754076295260396E-10 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 7.453306344157342E-7 6.377038147630198E-11 3.3546262790251185E-4
3.8 -1.4 -4.0 0.00130634613143027 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 4.6557157157830784E-8 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.2883785545774905E-18 2.0074758273878343E-10 6.252150377482016E-6 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.37531109885139957
3.8 -1.4 -1.4 0.00932831265194775 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 1.2754076295260396E-9 3.072772372416832E-7 1.536386186208416E-7 8.417690354871861E-10 1.536386186208416E-7 4.6557157157830784E-8 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 6.377038147630198E-10 1.8756451132446044E-5 6.252150377482016E-6 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181348 5.101630518104158E-10 1.2448885537581848E-4 3.088715408236772E-4 6.377038147630198E-10 1.4938662645098218E-4 0.005613476283413373 5.101630518104158E-10 9.959108430065478E-5 0.0028067381417066863 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.01778386544879001 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 7.318024188804728E-4 0.0020385521694362696 0.0010192760847181348 2.9272096755218914E-4 7.721788520591929E-4 3.088715408236772E-4 3.659012094402364E-4 0.016840428850240115 0.005613476283413373 2.9272096755218914E-4 0.011226952566826745 0.0028067381417066863 0.37531109885139957
3.8 -1.4 3.8 0.02619496147046741 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 0.0030887154082367718 0.0020385521694362696 2.4149479823055604E-4 0.0012354861632947088 7.721788520591929E-4 7.318024188804729E-5 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.011226952566826745 3.659012094402364E-5 0.37531109885139957
3.8 -1.4 6.4 0.02619496147046741 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 1.8756451132446044E-5 1.2754076295260396E-10 0.0030887154082367718 3.286505781921602E-4 4.2088451774359307E-10 0.0012354861632947088 1.2448885537581826E-4 1.2754076295260396E-10 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 9.959108430065462E-5 6.377038147630198E-11 0.37531109885139957
3.8 1.2000000000000002 -4.0 0.0037609776242212584 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.48675225595997157
3.8 1.2000000000000002 -1.4 0.6112624914549254 1.2754076295260396E-9 3.9836433720261914E-4 4.3908145132828363E-4 1.2754076295260396E-9 3.286505781921608E-4 0.006547561265642199 8.417690354871861E-10 1.643252890960804E-4 0.00198410947443703 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 6.377038147630198E-10 1.4938662645098218E-4 0.019789869908361474 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 5.101630518104158E-10 1.2448885537581848E-4 0.03753110988513996 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 5.101630518104158E-10 9.959108430065478E-5 0.01876555494256998 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 1.0425407980229744 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 7.318024188804728E-4 0.013095122531284397 0.006547561265642199 4.829895964611121E-4 0.006547561265642199 0.00198410947443703 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 3.659012094402364E-4 0.05936960972508442 0.019789869908361474 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 2.9272096755218914E-4 0.10738933955268481 0.04867522559599716 3.659012094402364E-4 0.12886720746322178 0.04867522559599716 2.9272096755218914E-4 0.08591147164214785 0.02433761279799858 0.48675225595997157
3.8 1.2000000000000002 3.8 1.3425868428254457 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.05936960972508442 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.19470090238398863 0.10738933955268481 7.318024188804729E-5 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19470090238398863 0.08591147164214785 3.659012094402364E-5 0.48675225595997157
3.8 1.2000000000000002 6.4 1.2196245188194168 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 1.493866264509819E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.15012443954055976 1.2448885537581826E-4 1.2754076295260396E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 9.959108430065462E-5 6.377038147630198E-11 0.48675225595997157
3.8 3.8 -4.0 0.9663752852395127 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 7.318024188804728E-4
3.8 3.8 -1.4 4.090487675317852 1.2754076295260396E-9 3.9836433720261914E-4 0.22518665931083973 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 8.417690354871861E-10 1.643252890960804E-4 0.03753110988513996 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 5.101630518104158E-10 1.2448885537581848E-4 0.019789869908361474 6.377038147630198E-10 1.4938662645098218E-4 0.00198410947443703 5.101630518104158E-10 9.959108430065478E-5 9.92054737218515E-4 7.318024188804728E-4
3.8 3.8 1.2000000000000002 3.817074116634072 7.318024188804728E-4 0.3436458865685914 0.29205135357598294 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 4.829895964611121E-4 0.14175392820954397 0.04867522559599716 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 3.659012094402364E-4 0.12886720746322178 0.04867522559599716 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 2.9272096755218914E-4 0.049474674770903684 0.019789869908361474 3.659012094402364E-4 0.005952328423311089 0.00198410947443703 2.9272096755218914E-4 0.00396821894887406 9.92054737218515E-4 7.318024188804728E-4
3.8 3.8 3.8 3.7015444515220532 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.3212564889335813 0.14175392820954397 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.0791594796334459 0.049474674770903684 7.318024188804729E-5 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.00396821894887406 3.659012094402364E-5 7.318024188804728E-4
3.8 3.8 6.4 3.690206482447506 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.0791594796334459 1.2448885537581826E-4 1.2754076295260396E-10 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 9.959108430065462E-5 6.377038147630198E-11 7.318024188804728E-4
3.8 6.4 -4.0 4.03006270824493 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.0307028436619925E-18 1.6728965228231954E-10 6.252150377482016E-6 1.2883785545774905E-18 2.0074758273878343E-10 4.6557157157830784E-8 1.0307028436619925E-18 1.3383172182585565E-10 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 -1.4 4.9330976812916925 1.2754076295260396E-9 3.9836433720261914E-4 0.2251866593108396 1.2754076295260396E-9 3.286505781921608E-4 0.018524471735264114 8.417690354871861E-10 1.643252890960804E-4 0.005613476283413368 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 6.377038147630198E-10 1.4938662645098218E-4 3.0887154082367665E-4 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 5.101630518104158E-10 1.5630375943705038E-5 6.252150377482016E-6 6.377038147630198E-10 1.3967147147349234E-7 4.6557157157830784E-8 5.101630518104158E-10 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.840793607186286 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 0.018524471735264114 0.005613476283413368 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 3.659012094402364E-4 9.266146224710298E-4 3.0887154082367665E-4 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 3.8 4.750683615645646 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 9.266146224710298E-4 7.318024188804729E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 6.4 4.750683615645646 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 1.493866264509819E-4 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.5008601509928064E-5 1.5630375943705038E-5 1.2754076295260396E-10 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 9.311431431566157E-8 6.377038147630198E-11 1.2754076295260396E-9
6.4 -4.0 -4.0 0.11817949341112592 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 4.416446820253236E-10 5.025893315755168E-9 1.0307028436619925E-18 1.6728965228231954E-10 1.522997974471263E-9 1.2883785545774905E-18 2.0074758273878343E-10 3.726653172078671E-7 1.0307028436619925E-18 1.3383172182585565E-10 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 -1.4 0.11821244175982118 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.2754076295260396E-9 1.0051786631510336E-8 5.025893315755168E-9 5.101630518104158E-10 3.807494936178157E-9 1.522997974471263E-9 6.377038147630198E-10 1.1179959516236013E-6 3.726653172078671E-7 5.101630518104158E-10 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.11824597576586889 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 3.8 0.11824597576586889 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 6.4 0.11824597576586889 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 4.2088451774359307E-10 6.091991897885052E-9 3.807494936178157E-9 1.2754076295260396E-10 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 7.453306344157342E-7 6.377038147630198E-11 3.3546262790251185E-4
6.4 -1.4 -4.0 0.00130634613143027 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 4.6557157157830784E-8 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.2883785545774905E-18 2.0074758273878343E-10 6.252150377482016E-6 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.37531109885139957
6.4 -1.4 -1.4 0.00932831265194775 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 1.2754076295260396E-9 3.072772372416832E-7 1.536386186208416E-7 8.417690354871861E-10 1.536386186208416E-7 4.6557157157830784E-8 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 6.377038147630198E-10 1.8756451132446044E-5 6.252150377482016E-6 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181348 5.101630518104158E-10 1.2448885537581848E-4 3.088715408236772E-4 6.377038147630198E-10 1.4938662645098218E-4 0.005613476283413373 5.101630518104158E-10 9.959108430065478E-5 0.0028067381417066863 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.01778386544879001 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 7.318024188804728E-4 0.0020385521694362696 0.0010192760847181348 2.9272096755218914E-4 7.721788520591929E-4 3.088715408236772E-4 3.659012094402364E-4 0.016840428850240115 0.005613476283413373 2.9272096755218914E-4 0.011226952566826745 0.0028067381417066863 0.37531109885139957
6.4 -1.4 3.8 0.02619496147046741 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 0.0030887154082367718 0.0020385521694362696 2.4149479823055604E-4 0.0012354861632947088 7.721788520591929E-4 7.318024188804729E-5 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.011226952566826745 3.659012094402364E-5 0.37531109885139957
6.4 -1.4 6.4 0.02619496147046741 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 1.8756451132446044E-5 1.2754076295260396E-10 0.0030887154082367718 3.286505781921602E-4 4.2088451774359307E-10 0.0012354861632947088 1.2448885537581826E-4 1.2754076295260396E-10 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 9.959108430065462E-5 6.377038147630198E-11 0.37531109885139957
6.4 1.2000000000000002 -4.0 0.0037609776242212584 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.48675225595997157
6.4 1.2000000000000002 -1.4 0.6112624914549251 1.2754076295260396E-9 3.9836433720261914E-4 4.3908145132828363E-4 1.2754076295260396E-9 3.286505781921608E-4 0.006547561265642199 8.417690354871861E-10 1.643252890960804E-4 0.00198410947443703 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 6.377038147630198E-10 1.4938662645098218E-4 0.019789869908361474 1.2754076295260396E-9 3.286505781921608E-4 0.1238526626209618 5.101630518104158E-10 1.2448885537581848E-4 0.03753110988513996 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513994 5.101630518104158E-10 9.959108430065478E-5 0.01876555494256998 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 0.9739714136639687 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 7.318024188804728E-4 0.013095122531284397 0.006547561265642199 4.829895964611121E-4 0.006547561265642199 0.00198410947443703 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 3.659012094402364E-4 0.05936960972508442 0.019789869908361474 7.318024188804728E-4 0.2477053252419236 0.1238526626209618 2.9272096755218914E-4 0.10738933955268481 0.04867522559599716 3.659012094402364E-4 0.1125933296554198 0.03753110988513994 2.9272096755218914E-4 0.08591147164214785 0.02433761279799858 0.48675225595997157
6.4 1.2000000000000002 3.8 1.2196245188194168 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.05936960972508442 7.318024188804729E-5 0.37531109885139935 0.2477053252419236 2.4149479823055604E-4 0.19470090238398863 0.10738933955268481 7.318024188804729E-5 0.18765554942569967 0.1125933296554198 7.318024188804729E-5 0.19470090238398863 0.08591147164214785 3.659012094402364E-5 0.48675225595997157
6.4 1.2000000000000002 6.4 1.2196245188194168 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 1.493866264509819E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.15012443954055976 1.2448885537581826E-4 1.2754076295260396E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 9.959108430065462E-5 6.377038147630198E-11 0.48675225595997157
6.4 3.8 -4.0 0.9663752852395127 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 7.318024188804728E-4
6.4 3.8 -1.4 4.090487675317852 1.2754076295260396E-9 3.9836433720261914E-4 0.22518665931083973 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 8.417690354871861E-10 1.643252890960804E-4 0.03753110988513994 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 1.2754076295260396E-9 3.286505781921608E-4 0.1238526626209618 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 5.101630518104158E-10 1.2448885537581848E-4 0.019789869908361474 6.377038147630198E-10 1.4938662645098218E-4 0.00198410947443703 5.101630518104158E-10 9.959108430065478E-5 9.92054737218515E-4 7.318024188804728E-4
6.4 3.8 1.2000000000000002 3.817074116634072 7.318024188804728E-4 0.3436458865685914 0.29205135357598294 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 4.829895964611121E-4 0.1238526626209618 0.03753110988513994 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 7.318024188804728E-4 0.2477053252419236 0.1238526626209618 3.659012094402364E-4 0.12886720746322178 0.04867522559599716 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 2.9272096755218914E-4 0.049474674770903684 0.019789869908361474 3.659012094402364E-4 0.005952328423311089 0.00198410947443703 2.9272096755218914E-4 0.00396821894887406 9.92054737218515E-4 7.318024188804728E-4
6.4 3.8 3.8 3.7015444515220532 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.2477053252419236 0.1238526626209618 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.37531109885139935 0.2477053252419236 2.4149479823055604E-4 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.0791594796334459 0.049474674770903684 7.318024188804729E-5 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.00396821894887406 3.659012094402364E-5 7.318024188804728E-4
6.4 3.8 6.4 3.690206482447506 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.0791594796334459 1.2448885537581826E-4 1.2754076295260396E-10 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 9.959108430065462E-5 6.377038147630198E-11 7.318024188804728E-4
6.4 6.4 -4.0 4.03006270824493 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.0307028436619925E-18 1.6728965228231954E-10 6.252150377482016E-6 1.2883785545774905E-18 2.0074758273878343E-10 4.6557157157830784E-8 1.0307028436619925E-18 1.3383172182585565E-10 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 -1.4 4.9330976812916925 1.2754076295260396E-9 3.9836433720261914E-4 0.2251866593108396 1.2754076295260396E-9 3.286505781921608E-4 0.018524471735264114 8.417690354871861E-10 1.643252890960804E-4 0.005613476283413368 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 6.377038147630198E-10 1.4938662645098218E-4 3.0887154082367665E-4 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 5.101630518104158E-10 1.5630375943705038E-5 6.252150377482016E-6 6.377038147630198E-10 1.3967147147349234E-7 4.6557157157830784E-8 5.101630518104158E-10 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.840793607186286 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 0.018524471735264114 0.005613476283413368 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 3.659012094402364E-4 9.266146224710298E-4 3.0887154082367665E-4 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 3.8 4.750683615645646 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 9.266146224710298E-4 7.318024188804729E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 6.4 4.750683615645646 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 1.493866264509819E-4 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.5008601509928064E-5 1.5630375943705038E-5 1.2754076295260396E-10 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 9.311431431566157E-8 6.377038147630198E-11 1.2754076295260396E-9

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Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fcl Normal file
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@ -0,0 +1,55 @@
FUNCTION_BLOCK SingletonQoSFewRules
VAR_INPUT
commitment : REAL;
clarity : REAL;
influence : REAL;
END_VAR
VAR_OUTPUT
service_quality : REAL;
END_VAR
FUZZIFY commitment
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for commitment
END_FUZZIFY
FUZZIFY clarity
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for clarity
END_FUZZIFY
FUZZIFY influence
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for influence
END_FUZZIFY
DEFUZZIFY service_quality
TERM inadequate := 0;
TERM sufficient := 2.5;
TERM excellent := 5;
METHOD : COGS;
DEFAULT := 0;
RANGE := (0.0 .. 5.0); // Added range for service_quality
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN;
ACCU : MAX;
RULE 1 : IF commitment IS fully AND influence IS high THEN service_quality IS excellent;
RULE 2 : IF commitment IS partially AND clarity IS high AND influence IS low THEN service_quality IS sufficient;
RULE 3 : IF commitment IS nothing THEN service_quality IS inadequate;
END_RULEBLOCK
END_FUNCTION_BLOCK

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clarity commitment influence service_quality No1.1 No1.2 No1.3
-4.0 -4.0 -4.0 5.760903513901482E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 -1.4 5.760903513901482E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 5.760903513901482E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 3.8 5.760903513901482E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 6.4 5.760903513901482E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -1.4 -4.0 5.1492424225679916E-17 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 -1.4 1.6991339056898312E-8 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 1.2000000000000002 1.6991339056898312E-8 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 3.8 1.6991339056898312E-8 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 6.4 1.6991339056898312E-8 1.2754076295260396E-9 2.576757109154981E-18 0.37531109885139957
-4.0 1.2000000000000002 -4.0 3.970331535608048E-17 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 -1.4 1.3101198934940618E-8 1.2754076295260396E-9 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 1.2000000000000002 0.0075059112836849745 7.318024188804728E-4 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 3.8 0.0075059112836849745 7.318024188804728E-4 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 6.4 0.0075059112836849745 7.318024188804728E-4 2.576757109154981E-18 0.48675225595997157
-4.0 3.8 -4.0 2.6408328013218462E-14 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 -1.4 8.714137689993001E-6 1.2754076295260396E-9 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 1.2000000000000002 2.5 7.318024188804728E-4 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 3.8 4.992494088716315 0.48675225595997157 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 6.4 4.990269695074573 0.37531109885139935 2.576757109154981E-18 7.318024188804728E-4
-4.0 6.4 -4.0 1.5152550285241103E-8 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 -1.4 2.5000000000000004 1.2754076295260396E-9 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 1.2000000000000002 4.99999128586231 7.318024188804728E-4 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 3.8 4.999999983008662 0.37531109885139935 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 6.4 4.999999983008662 0.37531109885139935 2.576757109154981E-18 1.2754076295260396E-9
-1.4 -4.0 -4.0 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 -1.4 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 1.2000000000000002 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 3.8 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -4.0 6.4 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
-1.4 -1.4 -4.0 8.495669554195368E-9 2.576757109154981E-18 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 -1.4 2.5487008472989575E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 1.2000000000000002 2.5487008472989575E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 3.8 2.5487008472989575E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 6.4 2.5487008472989575E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 1.2000000000000002 -4.0 6.5505994873219665E-9 2.576757109154981E-18 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 -1.4 1.9651798331066846E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 1.2000000000000002 0.007505917804813016 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 3.8 0.007505917804813016 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 6.4 0.007505917804813016 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
-1.4 3.8 -4.0 4.3570688582006414E-6 2.576757109154981E-18 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 -1.4 1.3071183740966453E-5 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 1.2000000000000002 2.5 7.318024188804728E-4 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 3.8 4.992494082195187 0.48675225595997157 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 6.4 4.990269686628437 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
-1.4 6.4 -4.0 1.2500000037881376 2.576757109154981E-18 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 -1.4 2.5000000000000004 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 1.2000000000000002 4.999986928816259 7.318024188804728E-4 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 3.8 4.999999974512992 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 6.4 4.999999974512992 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
1.2000000000000002 -4.0 -4.0 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 -1.4 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 1.2000000000000002 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 3.8 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
1.2000000000000002 -4.0 6.4 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
1.2000000000000002 -1.4 -4.0 0.002232568205745448 2.576757109154981E-18 3.3546262790251185E-4 0.37531109885139957
1.2000000000000002 -1.4 -1.4 0.004865169404485839 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
1.2000000000000002 -1.4 1.2000000000000002 0.004865169404485839 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
1.2000000000000002 -1.4 3.8 0.004865169404485839 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
1.2000000000000002 -1.4 6.4 2.5487008472989575E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
1.2000000000000002 1.2000000000000002 -4.0 0.0017217772851831675 2.576757109154981E-18 3.3546262790251185E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 -1.4 0.0037529687135552333 1.2754076295260396E-9 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 1.2000000000000002 0.011241990648635307 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 3.8 0.011241990648635307 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 6.4 0.007505917804813016 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
1.2000000000000002 3.8 -4.0 0.7857997151543754 2.576757109154981E-18 3.3546262790251185E-4 7.318024188804728E-4
1.2000000000000002 3.8 -1.4 1.2500032678044781 1.2754076295260396E-9 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 1.2000000000000002 2.5000000000000004 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 3.8 4.988758009351365 0.48675225595997157 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 6.4 4.990269686628437 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
1.2000000000000002 6.4 -4.0 2.499949002281903 2.576757109154981E-18 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 -1.4 2.5 1.2754076295260396E-9 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 1.2000000000000002 4.803216444780573 7.318024188804728E-4 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 3.8 4.999583587850963 0.37531109885139935 6.252150377482015E-5 1.2754076295260396E-9
1.2000000000000002 6.4 6.4 4.999999974512992 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
3.8 -4.0 -4.0 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 -1.4 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 3.8 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
3.8 -4.0 6.4 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
3.8 -1.4 -4.0 0.002232568205745448 2.576757109154981E-18 3.3546262790251185E-4 0.37531109885139957
3.8 -1.4 -1.4 0.020406444666765312 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.020406444666765312 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
3.8 -1.4 3.8 0.004865169404485839 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
3.8 -1.4 6.4 2.5487008472989575E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
3.8 1.2000000000000002 -4.0 0.0017217772851831675 2.576757109154981E-18 3.3546262790251185E-4 0.48675225595997157
3.8 1.2000000000000002 -1.4 1.0884092762795572 1.2754076295260396E-9 0.37531109885139957 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 1.2528168310197556 7.318024188804728E-4 0.48675225595997157 0.48675225595997157
3.8 1.2000000000000002 3.8 0.011241990648635307 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
3.8 1.2000000000000002 6.4 0.007505917804813016 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
3.8 3.8 -4.0 0.7857997151543754 2.576757109154981E-18 3.3546262790251185E-4 7.318024188804728E-4
3.8 3.8 -1.4 2.490789416363066 1.2754076295260396E-9 0.19789869908361474 7.318024188804728E-4
3.8 3.8 1.2000000000000002 2.4999999999999996 7.318024188804728E-4 0.19789869908361474 7.318024188804728E-4
3.8 3.8 3.8 4.988758009351365 0.48675225595997157 7.318024188804728E-4 7.318024188804728E-4
3.8 3.8 6.4 4.990269686628437 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
3.8 6.4 -4.0 2.499949002281903 2.576757109154981E-18 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 -1.4 2.5 1.2754076295260396E-9 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.803216444780573 7.318024188804728E-4 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 3.8 4.999583587850963 0.37531109885139935 6.252150377482015E-5 1.2754076295260396E-9
3.8 6.4 6.4 4.999999974512992 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9
6.4 -4.0 -4.0 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 -1.4 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 3.8 1.134946717944894E-4 2.576757109154981E-18 1.522997974471263E-8 3.3546262790251185E-4
6.4 -4.0 6.4 9.504805301663191E-6 2.576757109154981E-18 1.2754076295260396E-9 3.3546262790251185E-4
6.4 -1.4 -4.0 0.002232568205745448 2.576757109154981E-18 3.3546262790251185E-4 0.37531109885139957
6.4 -1.4 -1.4 0.020406444666765312 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.020406444666765312 1.2754076295260396E-9 0.0030887154082367718 0.37531109885139957
6.4 -1.4 3.8 0.004865169404485839 1.2754076295260396E-9 7.318024188804728E-4 0.37531109885139957
6.4 -1.4 6.4 2.5487008472989575E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
6.4 1.2000000000000002 -4.0 0.0017217772851831675 2.576757109154981E-18 3.3546262790251185E-4 0.48675225595997157
6.4 1.2000000000000002 -1.4 1.088409276279557 1.2754076295260396E-9 0.37531109885139935 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 1.0917269891114247 7.318024188804728E-4 0.37531109885139935 0.48675225595997157
6.4 1.2000000000000002 3.8 0.011241990648635307 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
6.4 1.2000000000000002 6.4 0.007505917804813016 7.318024188804728E-4 1.2754076295260396E-9 0.48675225595997157
6.4 3.8 -4.0 0.7857997151543754 2.576757109154981E-18 3.3546262790251185E-4 7.318024188804728E-4
6.4 3.8 -1.4 2.490789416363066 1.2754076295260396E-9 0.19789869908361474 7.318024188804728E-4
6.4 3.8 1.2000000000000002 2.4999999999999996 7.318024188804728E-4 0.19789869908361474 7.318024188804728E-4
6.4 3.8 3.8 4.988758009351365 0.48675225595997157 7.318024188804728E-4 7.318024188804728E-4
6.4 3.8 6.4 4.990269686628437 0.37531109885139935 1.2754076295260396E-9 7.318024188804728E-4
6.4 6.4 -4.0 2.499949002281903 2.576757109154981E-18 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 -1.4 2.5 1.2754076295260396E-9 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.803216444780573 7.318024188804728E-4 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 3.8 4.999583587850963 0.37531109885139935 6.252150377482015E-5 1.2754076295260396E-9
6.4 6.4 6.4 4.999999974512992 0.37531109885139935 1.2754076295260396E-9 1.2754076295260396E-9

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FUNCTION_BLOCK SingletonQoSFewRules
VAR_INPUT
commitment : REAL;
clarity : REAL;
influence : REAL;
END_VAR
VAR_OUTPUT
service_quality : REAL;
END_VAR
FUZZIFY commitment
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for commitment
END_FUZZIFY
FUZZIFY clarity
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for clarity
END_FUZZIFY
FUZZIFY influence
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high:= GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for influence
END_FUZZIFY
DEFUZZIFY service_quality
TERM inadequate := 0;
TERM sufficient := 2.5;
TERM excellent := 5;
METHOD : COGS;
DEFAULT := 0;
RANGE := (0.0 .. 5.0); // Added range for service_quality
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : MIN;
RULE 1 : IF commitment IS fully AND influence IS high THEN service_quality IS excellent;
RULE 2 : IF commitment IS fully AND influence IS medium THEN service_quality IS excellent WITH 0.8;
RULE 3 : IF commitment IS fully AND influence IS low THEN service_quality IS excellent WITH 0.6;
RULE 4 : IF commitment IS largely AND influence IS high AND clarity IS NOT high THEN service_quality IS excellent;
RULE 5 : IF commitment IS largely AND influence IS medium AND clarity IS NOT high THEN service_quality IS excellent WITH 0.66;
RULE 6 : IF commitment IS largely AND influence IS low AND clarity IS NOT high THEN service_quality IS excellent WITH 0.33;
RULE 7 : IF commitment IS largely AND influence IS high AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 8 : IF commitment IS largely AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 9 : IF commitment IS largely AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.1;
RULE 10 : IF commitment IS satISfactory AND influence IS high THEN service_quality IS sufficient;
RULE 11 : IF commitment IS satISfactory AND influence IS medium THEN service_quality IS sufficient WITH 0.66;
RULE 12 : IF commitment IS satISfactory AND influence IS low THEN service_quality IS sufficient WITH 0.33;
RULE 13 : IF commitment IS satISfactory AND influence IS high AND clarity IS high THEN service_quality IS sufficient;
RULE 14 : IF commitment IS satISfactory AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 15 : IF commitment IS satISfactory AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 16 : IF commitment IS satISfactory AND influence IS high AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.5;
RULE 17 : IF commitment IS satISfactory AND influence IS medium AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.3;
RULE 18 : IF commitment IS satISfactory AND influence IS low AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.1;
RULE 19 : IF commitment IS partially AND influence IS high AND clarity IS high THEN service_quality IS sufficient;
RULE 20 : IF commitment IS partially AND influence IS medium AND clarity IS high THEN service_quality IS sufficient WITH 0.66;
RULE 21 : IF commitment IS partially AND influence IS low AND clarity IS high THEN service_quality IS sufficient WITH 0.33;
RULE 22 : IF commitment IS partially AND influence IS high AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.4;
RULE 23 : IF commitment IS partially AND influence IS medium AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.25;
RULE 24 : IF commitment IS partially AND influence IS low AND clarity IS NOT high THEN service_quality IS sufficient WITH 0.1;
RULE 25 : IF commitment IS minimal AND influence IS high AND clarity IS high THEN service_quality IS inadequate WITH 0.5;
RULE 26 : IF commitment IS minimal AND influence IS medium AND clarity IS high THEN service_quality IS inadequate WITH 0.3;
RULE 27 : IF commitment IS minimal AND influence IS low AND clarity IS high THEN service_quality IS inadequate WITH 0.1;
RULE 28 : IF commitment IS minimal AND influence IS high AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.4;
RULE 29 : IF commitment IS minimal AND influence IS medium AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.2;
RULE 30 : IF commitment IS minimal AND influence IS low AND clarity IS NOT high THEN service_quality IS inadequate WITH 0.05;
RULE 31 : IF commitment IS nothing THEN service_quality IS inadequate;
END_RULEBLOCK
END_FUNCTION_BLOCK

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clarity commitment influence service_quality No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7 No1.8 No1.9 No1.10 No1.11 No1.12 No1.13 No1.14 No1.15 No1.16 No1.17 No1.18 No1.19 No1.20 No1.21 No1.22 No1.23 No1.24 No1.25 No1.26 No1.27 No1.28 No1.29 No1.30 No1.31
-4.0 -4.0 -4.0 1.1350055490606169E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.0307028436619925E-18 1.6728965228231954E-10 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 1.3383172182585565E-10 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 -1.4 2.8374822803468476E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 5.101630518104158E-10 3.807494936178157E-9 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 5.101630518104158E-10 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 4.5399294067593406E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 3.8 4.5399294067593406E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
-4.0 -4.0 6.4 4.5399294067593406E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.091991897885052E-9 3.807494936178157E-9 1.2754076295260396E-10 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 7.453306344157342E-7 6.377038147630198E-11 3.3546262790251185E-4
-4.0 -1.4 -4.0 2.2548294354937814E-4 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 2.0074758273878343E-10 6.252150377482016E-6 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.37531109885139957
-4.0 -1.4 -1.4 0.0020598335186958615 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 1.2754076295260396E-9 3.072772372416832E-7 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.377038147630198E-10 1.8756451132446044E-5 6.252150377482016E-6 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 5.101630518104158E-10 1.2448885537581848E-4 3.088715408236772E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 5.101630518104158E-10 9.959108430065478E-5 0.0028067381417066863 0.37531109885139957
-4.0 -1.4 1.2000000000000002 0.005139214397237144 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 2.9272096755218914E-4 7.721788520591929E-4 3.088715408236772E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 2.9272096755218914E-4 0.011226952566826745 0.0028067381417066863 0.37531109885139957
-4.0 -1.4 3.8 0.008208916419029324 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.0012354861632947088 7.721788520591929E-4 7.318024188804729E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.02245390513365349 0.011226952566826745 3.659012094402364E-5 0.37531109885139957
-4.0 -1.4 6.4 0.008208916419029324 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.1260751887410076E-5 1.8756451132446044E-5 1.2754076295260396E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.0012354861632947088 1.2448885537581826E-4 1.2754076295260396E-10 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.02245390513365349 9.959108430065462E-5 6.377038147630198E-11 0.37531109885139957
-4.0 1.2000000000000002 -4.0 0.002634446252362264 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.48675225595997157
-4.0 1.2000000000000002 -1.4 0.35088075845480393 1.2754076295260396E-9 3.9836433720261914E-4 4.3908145132828363E-4 1.2754076295260396E-9 3.286505781921608E-4 0.006547561265642199 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.377038147630198E-10 1.4938662645098218E-4 0.019789869908361474 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 5.101630518104158E-10 1.2448885537581848E-4 0.03753110988513996 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 5.101630518104158E-10 9.959108430065478E-5 0.01876555494256998 0.48675225595997157
-4.0 1.2000000000000002 1.2000000000000002 0.621781148508704 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 7.318024188804728E-4 0.013095122531284397 0.006547561265642199 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.659012094402364E-4 0.05936960972508442 0.019789869908361474 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 2.9272096755218914E-4 0.10738933955268481 0.04867522559599716 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 2.9272096755218914E-4 0.08591147164214785 0.02433761279799858 0.48675225595997157
-4.0 1.2000000000000002 3.8 0.8430928661432882 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.09894934954180737 0.05936960972508442 7.318024188804729E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.19470090238398863 0.10738933955268481 7.318024188804729E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.19470090238398863 0.08591147164214785 3.659012094402364E-5 0.48675225595997157
-4.0 1.2000000000000002 6.4 0.8430928661432882 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.09894934954180737 1.493866264509819E-4 1.2754076295260396E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.15012443954055976 1.2448885537581826E-4 1.2754076295260396E-10 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.15012443954055976 9.959108430065462E-5 6.377038147630198E-11 0.48675225595997157
-4.0 3.8 -4.0 1.2293215795023484 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 7.318024188804728E-4
-4.0 3.8 -1.4 4.101246484723092 1.2754076295260396E-9 3.9836433720261914E-4 0.22518665931083973 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 5.101630518104158E-10 1.2448885537581848E-4 0.019789869908361474 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 5.101630518104158E-10 9.959108430065478E-5 9.92054737218515E-4 7.318024188804728E-4
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1.2000000000000002 6.4 6.4 4.979590508749082 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 1.493866264509819E-4 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.5008601509928064E-5 1.5630375943705038E-5 1.2754076295260396E-10 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 9.311431431566157E-8 6.377038147630198E-11 1.2754076295260396E-9
3.8 -4.0 -4.0 3.745450549002364E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 4.416446820253236E-10 5.025893315755168E-9 1.0307028436619925E-18 1.6728965228231954E-10 1.522997974471263E-9 1.2883785545774905E-18 2.0074758273878343E-10 3.726653172078671E-7 1.0307028436619925E-18 1.3383172182585565E-10 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 -1.4 7.490782832730384E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.2754076295260396E-9 1.0051786631510336E-8 5.025893315755168E-9 5.101630518104158E-10 3.807494936178157E-9 1.522997974471263E-9 6.377038147630198E-10 1.1179959516236013E-6 3.726653172078671E-7 5.101630518104158E-10 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 1.1349486049995967E-4 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 3.8 1.1349486049995967E-4 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
3.8 -4.0 6.4 1.1349486049995967E-4 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 4.2088451774359307E-10 6.091991897885052E-9 3.807494936178157E-9 1.2754076295260396E-10 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 7.453306344157342E-7 6.377038147630198E-11 3.3546262790251185E-4
3.8 -1.4 -4.0 7.392344994752629E-4 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 4.6557157157830784E-8 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.2883785545774905E-18 2.0074758273878343E-10 6.252150377482016E-6 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.37531109885139957
3.8 -1.4 -1.4 0.006775229122923378 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 1.2754076295260396E-9 3.072772372416832E-7 1.536386186208416E-7 8.417690354871861E-10 1.536386186208416E-7 4.6557157157830784E-8 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 6.377038147630198E-10 1.8756451132446044E-5 6.252150377482016E-6 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181348 5.101630518104158E-10 1.2448885537581848E-4 3.088715408236772E-4 6.377038147630198E-10 1.4938662645098218E-4 0.005613476283413373 5.101630518104158E-10 9.959108430065478E-5 0.0028067381417066863 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.01351187678830349 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 7.318024188804728E-4 0.0020385521694362696 0.0010192760847181348 2.9272096755218914E-4 7.721788520591929E-4 3.088715408236772E-4 3.659012094402364E-4 0.016840428850240115 0.005613476283413373 2.9272096755218914E-4 0.011226952566826745 0.0028067381417066863 0.37531109885139957
3.8 -1.4 3.8 0.020412554615169148 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 0.0030887154082367718 0.0020385521694362696 2.4149479823055604E-4 0.0012354861632947088 7.721788520591929E-4 7.318024188804729E-5 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.011226952566826745 3.659012094402364E-5 0.37531109885139957
3.8 -1.4 6.4 0.020412554615169148 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 1.8756451132446044E-5 1.2754076295260396E-10 0.0030887154082367718 3.286505781921602E-4 4.2088451774359307E-10 0.0012354861632947088 1.2448885537581826E-4 1.2754076295260396E-10 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 9.959108430065462E-5 6.377038147630198E-11 0.37531109885139957
3.8 1.2000000000000002 -4.0 0.002634446252362264 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.48675225595997157
3.8 1.2000000000000002 -1.4 0.5547566834143304 1.2754076295260396E-9 3.9836433720261914E-4 4.3908145132828363E-4 1.2754076295260396E-9 3.286505781921608E-4 0.006547561265642199 8.417690354871861E-10 1.643252890960804E-4 0.00198410947443703 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 6.377038147630198E-10 1.4938662645098218E-4 0.019789869908361474 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 5.101630518104158E-10 1.2448885537581848E-4 0.03753110988513996 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 5.101630518104158E-10 9.959108430065478E-5 0.01876555494256998 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 0.9883705619108492 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 7.318024188804728E-4 0.013095122531284397 0.006547561265642199 4.829895964611121E-4 0.006547561265642199 0.00198410947443703 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 3.659012094402364E-4 0.05936960972508442 0.019789869908361474 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 2.9272096755218914E-4 0.10738933955268481 0.04867522559599716 3.659012094402364E-4 0.12886720746322178 0.04867522559599716 2.9272096755218914E-4 0.08591147164214785 0.02433761279799858 0.48675225595997157
3.8 1.2000000000000002 3.8 1.324902536229299 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.05936960972508442 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.19470090238398863 0.10738933955268481 7.318024188804729E-5 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19470090238398863 0.08591147164214785 3.659012094402364E-5 0.48675225595997157
3.8 1.2000000000000002 6.4 1.176412276151891 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 1.493866264509819E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.15012443954055976 1.2448885537581826E-4 1.2754076295260396E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 9.959108430065462E-5 6.377038147630198E-11 0.48675225595997157
3.8 3.8 -4.0 1.2293215795023484 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 7.318024188804728E-4
3.8 3.8 -1.4 4.0896556316926524 1.2754076295260396E-9 3.9836433720261914E-4 0.22518665931083973 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 8.417690354871861E-10 1.643252890960804E-4 0.03753110988513996 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 5.101630518104158E-10 1.2448885537581848E-4 0.019789869908361474 6.377038147630198E-10 1.4938662645098218E-4 0.00198410947443703 5.101630518104158E-10 9.959108430065478E-5 9.92054737218515E-4 7.318024188804728E-4
3.8 3.8 1.2000000000000002 3.8334793859766254 7.318024188804728E-4 0.3436458865685914 0.29205135357598294 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 4.829895964611121E-4 0.14175392820954397 0.04867522559599716 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 3.659012094402364E-4 0.12886720746322178 0.04867522559599716 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 2.9272096755218914E-4 0.049474674770903684 0.019789869908361474 3.659012094402364E-4 0.005952328423311089 0.00198410947443703 2.9272096755218914E-4 0.00396821894887406 9.92054737218515E-4 7.318024188804728E-4
3.8 3.8 3.8 3.7121709328085735 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.3212564889335813 0.14175392820954397 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.0791594796334459 0.049474674770903684 7.318024188804729E-5 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.00396821894887406 3.659012094402364E-5 7.318024188804728E-4
3.8 3.8 6.4 3.7010848629000774 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.0791594796334459 1.2448885537581826E-4 1.2754076295260396E-10 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 9.959108430065462E-5 6.377038147630198E-11 7.318024188804728E-4
3.8 6.4 -4.0 4.11228954428557 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.0307028436619925E-18 1.6728965228231954E-10 6.252150377482016E-6 1.2883785545774905E-18 2.0074758273878343E-10 4.6557157157830784E-8 1.0307028436619925E-18 1.3383172182585565E-10 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 -1.4 4.939192494247536 1.2754076295260396E-9 3.9836433720261914E-4 0.2251866593108396 1.2754076295260396E-9 3.286505781921608E-4 0.018524471735264114 8.417690354871861E-10 1.643252890960804E-4 0.005613476283413368 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 6.377038147630198E-10 1.4938662645098218E-4 3.0887154082367665E-4 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 5.101630518104158E-10 1.5630375943705038E-5 6.252150377482016E-6 6.377038147630198E-10 1.3967147147349234E-7 4.6557157157830784E-8 5.101630518104158E-10 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.8547171232203254 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 0.018524471735264114 0.005613476283413368 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 3.659012094402364E-4 9.266146224710298E-4 3.0887154082367665E-4 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 3.8 4.775382042848198 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 9.266146224710298E-4 7.318024188804729E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
3.8 6.4 6.4 4.775382042848198 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 1.493866264509819E-4 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.5008601509928064E-5 1.5630375943705038E-5 1.2754076295260396E-10 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 9.311431431566157E-8 6.377038147630198E-11 1.2754076295260396E-9
6.4 -4.0 -4.0 3.745450549002364E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 6.869204536936659E-12 2.289734845645553E-12 2.576757109154981E-18 4.416446820253236E-10 5.025893315755168E-9 1.0307028436619925E-18 1.6728965228231954E-10 1.522997974471263E-9 1.2883785545774905E-18 2.0074758273878343E-10 3.726653172078671E-7 1.0307028436619925E-18 1.3383172182585565E-10 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 -1.4 7.490782832730384E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.2754076295260396E-9 1.0051786631510336E-8 5.025893315755168E-9 5.101630518104158E-10 3.807494936178157E-9 1.522997974471263E-9 6.377038147630198E-10 1.1179959516236013E-6 3.726653172078671E-7 5.101630518104158E-10 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 1.1349486049995967E-4 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 3.8 1.1349486049995967E-4 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 5.025893315755168E-9 6.091991897885052E-9 3.807494936178157E-9 1.522997974471263E-9 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 7.453306344157342E-7 1.8633265860393355E-7 3.3546262790251185E-4
6.4 -4.0 6.4 1.1349486049995967E-4 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 6.869204536936659E-12 2.289734845645553E-12 1.522997974471263E-8 1.0051786631510336E-8 4.2088451774359307E-10 6.091991897885052E-9 3.807494936178157E-9 1.2754076295260396E-10 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 7.453306344157342E-7 6.377038147630198E-11 3.3546262790251185E-4
6.4 -1.4 -4.0 7.392344994752629E-4 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 4.6557157157830784E-8 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.2883785545774905E-18 2.0074758273878343E-10 6.252150377482016E-6 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.37531109885139957
6.4 -1.4 -1.4 0.006775229122923378 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 1.2754076295260396E-9 3.072772372416832E-7 1.536386186208416E-7 8.417690354871861E-10 1.536386186208416E-7 4.6557157157830784E-8 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 6.377038147630198E-10 1.8756451132446044E-5 6.252150377482016E-6 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181348 5.101630518104158E-10 1.2448885537581848E-4 3.088715408236772E-4 6.377038147630198E-10 1.4938662645098218E-4 0.005613476283413373 5.101630518104158E-10 9.959108430065478E-5 0.0028067381417066863 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.01351187678830349 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 7.318024188804728E-4 0.0020385521694362696 0.0010192760847181348 2.9272096755218914E-4 7.721788520591929E-4 3.088715408236772E-4 3.659012094402364E-4 0.016840428850240115 0.005613476283413373 2.9272096755218914E-4 0.011226952566826745 0.0028067381417066863 0.37531109885139957
6.4 -1.4 3.8 0.020412554615169148 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 1.8756451132446044E-5 6.252150377482016E-6 0.0030887154082367718 0.0020385521694362696 2.4149479823055604E-4 0.0012354861632947088 7.721788520591929E-4 7.318024188804729E-5 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.011226952566826745 3.659012094402364E-5 0.37531109885139957
6.4 -1.4 6.4 0.020412554615169148 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 1.8756451132446044E-5 1.2754076295260396E-10 0.0030887154082367718 3.286505781921602E-4 4.2088451774359307E-10 0.0012354861632947088 1.2448885537581826E-4 1.2754076295260396E-10 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 9.959108430065462E-5 6.377038147630198E-11 0.37531109885139957
6.4 1.2000000000000002 -4.0 0.002634446252362264 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 0.48675225595997157
6.4 1.2000000000000002 -1.4 0.55475668341433 1.2754076295260396E-9 3.9836433720261914E-4 4.3908145132828363E-4 1.2754076295260396E-9 3.286505781921608E-4 0.006547561265642199 8.417690354871861E-10 1.643252890960804E-4 0.00198410947443703 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 6.377038147630198E-10 1.4938662645098218E-4 0.019789869908361474 1.2754076295260396E-9 3.286505781921608E-4 0.1238526626209618 5.101630518104158E-10 1.2448885537581848E-4 0.03753110988513996 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513994 5.101630518104158E-10 9.959108430065478E-5 0.01876555494256998 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 0.915974114389173 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 7.318024188804728E-4 0.013095122531284397 0.006547561265642199 4.829895964611121E-4 0.006547561265642199 0.00198410947443703 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 3.659012094402364E-4 0.05936960972508442 0.019789869908361474 7.318024188804728E-4 0.2477053252419236 0.1238526626209618 2.9272096755218914E-4 0.10738933955268481 0.04867522559599716 3.659012094402364E-4 0.1125933296554198 0.03753110988513994 2.9272096755218914E-4 0.08591147164214785 0.02433761279799858 0.48675225595997157
6.4 1.2000000000000002 3.8 1.176412276151891 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.05936960972508442 7.318024188804729E-5 0.37531109885139935 0.2477053252419236 2.4149479823055604E-4 0.19470090238398863 0.10738933955268481 7.318024188804729E-5 0.18765554942569967 0.1125933296554198 7.318024188804729E-5 0.19470090238398863 0.08591147164214785 3.659012094402364E-5 0.48675225595997157
6.4 1.2000000000000002 6.4 1.176412276151891 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 1.493866264509819E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.15012443954055976 1.2448885537581826E-4 1.2754076295260396E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 9.959108430065462E-5 6.377038147630198E-11 0.48675225595997157
6.4 3.8 -4.0 1.2293215795023484 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.0307028436619925E-18 1.6728965228231954E-10 3.354626279025119E-5 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 1.3383172182585565E-10 1.6773131395125595E-5 7.318024188804728E-4
6.4 3.8 -1.4 4.0896556316926524 1.2754076295260396E-9 3.9836433720261914E-4 0.22518665931083973 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 8.417690354871861E-10 1.643252890960804E-4 0.03753110988513994 1.2754076295260396E-9 3.286505781921608E-4 0.12385266262096187 1.2754076295260396E-9 3.286505781921608E-4 0.1238526626209618 6.377038147630198E-10 1.4938662645098218E-4 0.03753110988513996 1.2754076295260396E-9 3.286505781921608E-4 0.06530657069759287 5.101630518104158E-10 1.2448885537581848E-4 0.019789869908361474 6.377038147630198E-10 1.4938662645098218E-4 0.00198410947443703 5.101630518104158E-10 9.959108430065478E-5 9.92054737218515E-4 7.318024188804728E-4
6.4 3.8 1.2000000000000002 3.8334793859766254 7.318024188804728E-4 0.3436458865685914 0.29205135357598294 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 4.829895964611121E-4 0.1238526626209618 0.03753110988513994 7.318024188804728E-4 0.28350785641908793 0.16062824446679064 7.318024188804728E-4 0.2477053252419236 0.1238526626209618 3.659012094402364E-4 0.12886720746322178 0.04867522559599716 7.318024188804728E-4 0.13061314139518573 0.06530657069759287 2.9272096755218914E-4 0.049474674770903684 0.019789869908361474 3.659012094402364E-4 0.005952328423311089 0.00198410947443703 2.9272096755218914E-4 0.00396821894887406 9.92054737218515E-4 7.318024188804728E-4
6.4 3.8 3.8 3.7121709328085735 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.2477053252419236 0.1238526626209618 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.37531109885139935 0.2477053252419236 2.4149479823055604E-4 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.0791594796334459 0.049474674770903684 7.318024188804729E-5 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.00396821894887406 3.659012094402364E-5 7.318024188804728E-4
6.4 3.8 6.4 3.7010848629000774 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.0791594796334459 1.2448885537581826E-4 1.2754076295260396E-10 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 9.959108430065462E-5 6.377038147630198E-11 7.318024188804728E-4
6.4 6.4 -4.0 4.11228954428557 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 2.208223410126618E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.0307028436619925E-18 1.6728965228231954E-10 6.252150377482016E-6 1.2883785545774905E-18 2.0074758273878343E-10 4.6557157157830784E-8 1.0307028436619925E-18 1.3383172182585565E-10 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 -1.4 4.939192494247536 1.2754076295260396E-9 3.9836433720261914E-4 0.2251866593108396 1.2754076295260396E-9 3.286505781921608E-4 0.018524471735264114 8.417690354871861E-10 1.643252890960804E-4 0.005613476283413368 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 1.2754076295260396E-9 3.286505781921608E-4 0.0010192760847181328 6.377038147630198E-10 1.4938662645098218E-4 3.0887154082367665E-4 1.2754076295260396E-9 4.1264192491381304E-5 2.0632096245690652E-5 5.101630518104158E-10 1.5630375943705038E-5 6.252150377482016E-6 6.377038147630198E-10 1.3967147147349234E-7 4.6557157157830784E-8 5.101630518104158E-10 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.8547171232203254 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 0.018524471735264114 0.005613476283413368 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 3.659012094402364E-4 9.266146224710298E-4 3.0887154082367665E-4 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 3.8 4.775382042848198 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 9.266146224710298E-4 7.318024188804729E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.5008601509928064E-5 1.5630375943705038E-5 6.252150377482016E-6 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 9.311431431566157E-8 2.3278578578915392E-8 1.2754076295260396E-9
6.4 6.4 6.4 4.775382042848198 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 1.493866264509819E-4 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.5008601509928064E-5 1.5630375943705038E-5 1.2754076295260396E-10 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 9.311431431566157E-8 6.377038147630198E-11 1.2754076295260396E-9

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Examples/jFuzzyLogic/bathTub.fcl Normal file
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FUNCTION_BLOCK Example
VAR_INPUT
temp: REAL;
END_VAR
VAR_OUTPUT
hot_valve: REAL;
cold_valve: REAL;
END_VAR
FUZZIFY temp
TERM hot := (1,0)(70,0)(90,1);
TERM cold := (1,1)(50,1)(80,0);
RANGE := (1.0 .. 90.0); // Added range for temp
END_FUZZIFY
DEFUZZIFY hot_valve
TERM open := 100;
TERM closed := 0;
METHOD : COGS;
DEFAULT := 0;
RANGE := (0.0 .. 100.0); // Added range for hot_valve
END_DEFUZZIFY
DEFUZZIFY cold_valve
TERM open := 100;
TERM closed := 0;
METHOD : COGS;
DEFAULT := 0;
RANGE := (0.0 .. 100.0); // Added range for cold_valve
END_DEFUZZIFY
RULEBLOCK Control
AND: MIN;
ACCU: MAX;
RULE 1: IF temp IS cold THEN hot_valve IS open;
RULE 2: IF temp IS hot THEN cold_valve IS open;
END_RULEBLOCK
END_FUNCTION_BLOCK

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temp hot_valve cold_valve Control.1 Control.2
1.0 100.0 0.0 1.0 0.0
18.8 100.0 0.0 1.0 0.0
36.6 100.0 0.0 1.0 0.0
54.4 100.0 0.0 0.8533333333333334 0.0
72.2 100.0 100.0 0.2599999999999999 0.11000000000000014

51
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FUNCTION_BLOCK ip // Block definition (there may be more than one block per file)
VAR_OUTPUT // Define input variables
force : REAL;
END_VAR
VAR_INPUT // Define output variable
x : REAL;
dxdt : REAL;
END_VAR
FUZZIFY x
TERM ok := (-0.1,0) (0,1) (0.1,0) ;
TERM left := (-2,1) (0,0);
TERM right := ( 0, 0) (2,1) ;
RANGE := (-2.0 .. 2.0) WITH 0.1; // Added range for x
END_FUZZIFY
FUZZIFY dxdt
TERM ok := TRIAN -1 0 1 ;
TERM left := (-1,1) (0,0);
TERM right := (0,0) (1,1) ;
TERM tooRight := (3,0) (4,1);
TERM tooLeft := (-4,1) (-3,0);
RANGE := (-4.0 .. 4.0) WITH 0.1; // Added range for dxdt
END_FUZZIFY
DEFUZZIFY force
TERM zero := TRIAN -1 0 1 ;
TERM left := (-101,0) (-100,1) (-99,0);
TERM right := (99,0) (100,1) (101,0);
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (-101.0 .. 101.0); // Added range for force
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN; // Use 'min' for 'and' (also implicit use 'max' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF x IS right AND dxdt IS NOT tooLeft THEN force IS left ;
RULE 2 : IF x IS left AND dxdt IS NOT tooRight THEN force IS right ;
RULE 3 : IF x IS ok AND dxdt IS right THEN force IS left ;
RULE 4 : IF x IS ok AND dxdt IS left THEN force IS right ;
RULE 5 : IF x IS ok AND dxdt IS ok THEN force IS zero ;
END_RULEBLOCK
END_FUNCTION_BLOCK

26
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x dxdt force No1.1 No1.2 No1.3 No1.4 No1.5
-2.0 -4.0 99.99816161616144 0.0 1.0 0.0 0.0 0.0
-2.0 -2.4 99.99816161616144 0.0 1.0 0.0 0.0 0.0
-2.0 -0.7999999999999998 99.99816161616144 0.0 1.0 0.0 0.0 0.0
-2.0 0.7999999999999998 99.99816161616144 0.0 1.0 0.0 0.0 0.0
-2.0 2.4000000000000004 99.99816161616144 0.0 1.0 0.0 0.0 0.0
-1.2 -4.0 99.99816161616141 0.0 0.6 0.0 0.0 0.0
-1.2 -2.4 99.99816161616141 0.0 0.6 0.0 0.0 0.0
-1.2 -0.7999999999999998 99.99816161616141 0.0 0.6 0.0 0.0 0.0
-1.2 0.7999999999999998 99.99816161616141 0.0 0.6 0.0 0.0 0.0
-1.2 2.4000000000000004 99.99816161616141 0.0 0.6 0.0 0.0 0.0
-0.3999999999999999 -4.0 99.9981616161611 0.0 0.19999999999999996 0.0 0.0 0.0
-0.3999999999999999 -2.4 99.9981616161611 0.0 0.19999999999999996 0.0 0.0 0.0
-0.3999999999999999 -0.7999999999999998 99.9981616161611 0.0 0.19999999999999996 0.0 0.0 0.0
-0.3999999999999999 0.7999999999999998 99.9981616161611 0.0 0.19999999999999996 0.0 0.0 0.0
-0.3999999999999999 2.4000000000000004 99.9981616161611 0.0 0.19999999999999996 0.0 0.0 0.0
0.3999999999999999 -4.0 0.0 0.0 0.0 0.0 0.0 0.0
0.3999999999999999 -2.4 -99.99816161616162 0.19999999999999996 0.0 0.0 0.0 0.0
0.3999999999999999 -0.7999999999999998 -99.99816161616162 0.19999999999999996 0.0 0.0 0.0 0.0
0.3999999999999999 0.7999999999999998 -99.99816161616162 0.19999999999999996 0.0 0.0 0.0 0.0
0.3999999999999999 2.4000000000000004 -99.99816161616162 0.19999999999999996 0.0 0.0 0.0 0.0
1.2000000000000002 -4.0 0.0 0.0 0.0 0.0 0.0 0.0
1.2000000000000002 -2.4 -99.99816161616161 0.6000000000000001 0.0 0.0 0.0 0.0
1.2000000000000002 -0.7999999999999998 -99.99816161616161 0.6000000000000001 0.0 0.0 0.0 0.0
1.2000000000000002 0.7999999999999998 -99.99816161616161 0.6000000000000001 0.0 0.0 0.0 0.0
1.2000000000000002 2.4000000000000004 -99.99816161616161 0.6000000000000001 0.0 0.0 0.0 0.0

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FUNCTION_BLOCK ipPhi0 // control block fOR the angle
VAR_OUTPUT
fORce : REAL;
END_VAR
VAR_INPUT
phi : REAL;
dphidt : REAL;
END_VAR
FUZZIFY phi
TERM Z := TRIAN -5 0 5;
TERM PS := TRIAN 0 5 10;
TERM NS := TRIAN -10 -5 0;
TERM PB := (5,0) (10,1) (60,1) (70,0);
TERM NB := (-70,0) (-60,1) (-10, 1) (-5,0);
RANGE := (-70.0 .. 70.0); // Added range for phi
RANGE := (-70.0 .. 70.0); // Added range for phi
END_FUZZIFY
FUZZIFY dphidt
TERM Z := TRIAN -8 0 8;
TERM PS := TRIAN 0 8 200 ;
TERM NS := TRIAN -200 -8 0 ;
TERM PB := (8,0) (200,1) (500,1) ;
TERM NB := (-500,1) (-200,1) (-8,0);
RANGE := (-500.0 .. 500.0); // Added range for dphidt
END_FUZZIFY
DEFUZZIFY fORce
TERM Z := TRIAN -20 0 20;
TERM PS := TRIAN 30 50 70;
TERM NS := TRIAN -70 -50 -30;
TERM PB := TRIAN 190 200 210;
TERM NB := TRIAN -210 -200 -190;
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (-210.0 .. 210.0); // Added range for fORce
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN;
ACT : MIN;
ACCU : MAX;
RULE 1 : IF phi IS PS AND (dphidt IS PS OR dphidt IS Z) THEN fORce IS PS;
RULE 2 : IF phi IS PS AND dphidt IS PB THEN fORce IS PB ;
RULE 3 : IF phi IS NS AND (dphidt IS NS OR dphidt IS Z) THEN fORce IS NS;
RULE 4 : IF phi IS NS AND dphidt IS NB THEN fORce IS NB ;
RULE 5 : IF phi IS PB AND (dphidt IS NOT NB) AND (dphidt IS NOT NS) THEN fORce IS PB ;
RULE 6 : IF phi IS NB AND (dphidt IS NOT PB) AND (dphidt IS NOT PS) THEN fORce IS NB ;
RULE 7 : IF phi IS Z AND dphidt IS Z THEN fORce IS Z;
END_RULEBLOCK
END_FUNCTION_BLOCK

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phi dphidt fORce No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7
-70.0 -500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-70.0 -300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-70.0 -100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-70.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-70.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-42.0 -500.0 -199.9979032258064 0.0 0.0 0.0 0.0 0.0 1.0 0.0
-42.0 -300.0 -199.9979032258064 0.0 0.0 0.0 0.0 0.0 1.0 0.0
-42.0 -100.0 -199.9979032258064 0.0 0.0 0.0 0.0 0.0 1.0 0.0
-42.0 100.0 -199.99670186293636 0.0 0.0 0.0 0.0 0.0 0.47916666666666674 0.0
-42.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-14.0 -500.0 -199.9979032258064 0.0 0.0 0.0 0.0 0.0 1.0 0.0
-14.0 -300.0 -199.9979032258064 0.0 0.0 0.0 0.0 0.0 1.0 0.0
-14.0 -100.0 -199.9979032258064 0.0 0.0 0.0 0.0 0.0 1.0 0.0
-14.0 100.0 -199.99670186293636 0.0 0.0 0.0 0.0 0.0 0.47916666666666674 0.0
-14.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14.0 -500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14.0 -300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14.0 -100.0 199.99670186293625 0.0 0.0 0.0 0.0 0.47916666666666663 0.0 0.0
14.0 100.0 199.99790322580637 0.0 0.0 0.0 0.0 1.0 0.0 0.0
14.0 300.0 199.99790322580637 0.0 0.0 0.0 0.0 1.0 0.0 0.0
42.0 -500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
42.0 -300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
42.0 -100.0 199.99670186293625 0.0 0.0 0.0 0.0 0.47916666666666663 0.0 0.0
42.0 100.0 199.99790322580637 0.0 0.0 0.0 0.0 1.0 0.0 0.0
42.0 300.0 199.99790322580637 0.0 0.0 0.0 0.0 1.0 0.0 0.0

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FUNCTION_BLOCK qualify
VAR_INPUT
scoring : REAL; // Account owner's scoring
sel : REAL; // Social economic level
occupation_type: REAL; // enum_occupation_type.rid
city: REAL; // City (actually Zip code)
scoring_partner : REAL; // Partner's scoring
END_VAR
VAR_OUTPUT
qualify : REAL; // Qualify
credLimMul : REAL; // Credit limit multiplier
END_VAR
// Scoring : [0,1000]
FUZZIFY scoring
TERM veryHigh := TRAPE 850 900 1000 1000;
TERM high := TRAPE 650 700 900 1000;
TERM midHigh := TRAPE 500 550 700 900;
TERM midLow := TRAPE 380 420 550 700;
TERM low := TRAPE 0 0 420 550;
RANGE := (0.0 .. 1000.0); // Added range for scoring
END_FUZZIFY
// Social economic level: {A,B,C1,C2,C3,D1,D2,E}
FUZZIFY sel
TERM a := SIGM 0.01 4500; // Use sigm
TERM b := TRAPE 2000 3000 5000 10000;
TERM c1 := TRAPE 1300 2000 3000 5000;
TERM c2 := TRAPE 900 1300 2000 3000;
TERM c3 := TRAPE 600 900 1300 2000;
TERM d1 := TRAPE 400 600 900 1300;
TERM low := TRAPE -100 0 600 900;
RANGE := (-100.0 .. 10000.0); // Added range for sel
END_FUZZIFY
// City (zip code < 1900 => BsAs)
FUZZIFY city
TERM bsas := TRAPE 0 0 1899 1900;
TERM other := TRAPE 1900 1900 10000 10000;
RANGE := (0.0 .. 10000.0); // Added range for city
END_FUZZIFY
// Occupation type rid
// Good ones: { Hired State Employee, Hired Private company Employee, Contractor State Employee}
// Rids: { 9, 10 ,11 }
FUZZIFY occupation_type
TERM good := TRAPE 8.5 9 11 11.5;
TERM other_1 := TRAPE 0 0 8 8.5;
TERM other_2 := TRAPE 11.5 12 20 20;
RANGE := (0.0 .. 20.0); // Added range for occupation_type
END_FUZZIFY
// Partner's Scoring : [0,1000]
FUZZIFY scoring_partner
TERM midLow := TRAPE 0 420 550 700;
TERM low := TRIAN 0 0 420;
TERM noPartner := TRAPE -100 -50 -1 0; // No partner
TERM fakeRange := (0,0) (1000,0); // Fake term: it's always 0. Used to simulate 'range' (0..1000)
RANGE := (-100.0 .. 1000.0); // Added range for scoring_partner
END_FUZZIFY
// Qualify : {accept,manual_accept,manual_reject,reject}
DEFUZZIFY qualify
METHOD : COG;
DEFAULT := 0;
TERM accept := TRIAN 3 4 5; // Center in 4
TERM manual_accept := TRIAN 2 3 4; // Center in 3
TERM manual_reject := TRIAN 1 2 3; // Center in 2
TERM reject := TRIAN 0 1 2; // Center in 1
RANGE := (0.0 .. 5.0) WITH 0.1; // Added range for qualify
END_DEFUZZIFY
// Credit limit multiplier
DEFUZZIFY credLimMul
METHOD : COG;
DEFAULT := 0;
TERM veryHigh := TRIAN 3 3.5 4;
TERM high := TRIAN 2 3 3.5;
TERM midHigh := TRIAN 1 2 3;
TERM midLow := TRIAN 0 0.5 1;
TERM low := TRIAN -2 -1 0;
RANGE := (-2.0 .. 4.0) WITH 0.01; // Added range for credLimMul
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : MIN;
ACT : MIN;
// Scoring rules
RULE 1 : IF scoring IS veryHigh THEN qualify IS accept , credLimMul IS veryHigh;
RULE 2 : IF scoring IS high THEN qualify IS accept , credLimMul IS high;
RULE 3 : IF scoring IS midHigh THEN qualify IS manual_accept , credLimMul IS midHigh;
RULE 4 : IF scoring IS midLow THEN qualify IS manual_reject , credLimMul IS midLow;
RULE 5 : IF scoring IS low THEN qualify IS reject , credLimMul IS low;
// Social economic level
RULE 6 : IF sel IS a OR sel IS b THEN qualify IS accept , credLimMul IS veryHigh;
RULE 7 : IF sel IS c1 THEN qualify IS accept , credLimMul IS high;
RULE 8 : IF sel IS c2 THEN qualify IS manual_accept , credLimMul IS midHigh;
RULE 9 : IF sel IS c3 THEN qualify IS manual_reject , credLimMul IS midLow;
RULE 10 : IF sel IS d1 OR sel IS low THEN qualify IS reject , credLimMul IS low;
// Ocupation type
RULE 11 : IF NOT occupation_type IS good THEN qualify IS reject , credLimMul IS low;
// City
RULE 12 : IF city IS other AND (scoring IS midLow OR scoring IS low) THEN qualify IS reject , credLimMul IS low;
// Partner's scoring
RULE 13 : IF scoring_partner IS midLow THEN qualify IS manual_reject , credLimMul IS midLow;
RULE 14 : IF scoring_partner IS low THEN qualify IS reject , credLimMul IS low;
END_RULEBLOCK
END_FUNCTION_BLOCK

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FUNCTION_BLOCK qualify
VAR_INPUT
city : REAL;
occupation_type : REAL;
scoring : REAL;
scoring_partner : REAL;
sel : REAL;
END_VAR
VAR_OUTPUT
credLimMul : REAL;
qualify : REAL;
END_VAR
FUZZIFY city
TERM bsas := TRAPE 0.0 0.0 1899.0 1900.0;
TERM other := TRAPE 1900.0 1900.0 10000.0 10000.0;
RANGE := (0.0 .. 10000.0); // Added range for city
END_FUZZIFY
FUZZIFY occupation_type
TERM good := TRAPE 8.5 9.0 11.0 11.5;
TERM other_1 := TRAPE 0.0 0.0 8.0 8.5;
TERM other_2 := TRAPE 11.5 12.0 20.0 20.0;
RANGE := (0.0 .. 20.0); // Added range for occupation_type
END_FUZZIFY
FUZZIFY scoring
TERM high := TRAPE 648.9499999999999 889.7 889.8499999999999 904.0999999999996;
TERM low := TRAPE 0.0 0.0 384.25000000000006 541.1999999999998;
TERM midHigh := TRAPE 342.8000000000001 438.40000000000003 522.0000000000001 999.9999999999998;
TERM midLow := TRAPE 52.31999999999999 399.52 646.9599999999996 720.48;
TERM veryHigh := TRAPE 893.65 914.8500000000001 1000.0 1000.0;
RANGE := (0.0 .. 1000.0); // Added range for scoring
END_FUZZIFY
FUZZIFY scoring_partner
TERM fakeRange := (0.0, 0.0) (1000.0, 0.0) ;
TERM low := TRIAN 0.0 0.0 420.0;
TERM midLow := TRAPE 0.0 420.0 550.0 700.0;
TERM noPartner := TRAPE -100.0 -50.0 -1.0 0.0;
RANGE := (-100.0 .. 1000.0); // Added range for scoring_partner
END_FUZZIFY
FUZZIFY sel
TERM a := SIGM 0.01 4500.0;
TERM b := TRAPE 2000.0 3000.0 5000.0 10000.0;
TERM c1 := TRAPE 1300.0 2000.0 3000.0 5000.0;
TERM c2 := TRAPE 900.0 1300.0 2000.0 3000.0;
TERM c3 := TRAPE 600.0 900.0 1300.0 2000.0;
TERM d1 := TRAPE 400.0 600.0 900.0 1300.0;
TERM low := TRAPE -100.0 0.0 600.0 900.0;
RANGE := (-100.0 .. 10000.0); // Added range for sel
END_FUZZIFY
DEFUZZIFY credLimMul
TERM high := TRIAN 2.7050000000000005 2.7059999999999995 3.7085000000000004;
TERM low := TRIAN -1.6839999999999997 -1.5280000000000005 -0.40800000000000014;
TERM midHigh := TRIAN 0.5799999999999998 2.188 3.5240000000000005;
TERM midLow := TRIAN -0.5480000000000002 -0.12000000000000002 0.8239999999999994;
TERM veryHigh := TRIAN 3.519 3.9860000000000007 4.0;
METHOD : COG;
DEFAULT := 0.0;
RANGE := (-2.0 .. 4.0) WITH 0.01;
END_DEFUZZIFY
DEFUZZIFY qualify
TERM accept := TRIAN 3.0 4.0 5.0;
TERM manual_accept := TRIAN 2.0 3.0 4.0;
TERM manual_reject := TRIAN 1.0 2.0 3.0;
TERM reject := TRIAN 0.0 1.0 2.0;
METHOD : COG;
DEFAULT := 0.0;
RANGE := (0.0 .. 5.0) WITH 0.1;
END_DEFUZZIFY
RULEBLOCK No1
ACT : MIN;
ACCU : MAX;
AND : MIN;
RULE 1 : IF scoring IS veryHigh THEN qualify IS accept , credLimMul IS veryHigh WITH 0.9999999999999999;
RULE 2 : IF scoring IS high THEN qualify IS accept , credLimMul IS high WITH 0.6299999999999997;
RULE 3 : IF scoring IS midHigh THEN qualify IS manual_accept , credLimMul IS midHigh WITH 0.2799999999999998;
RULE 4 : IF scoring IS midLow THEN qualify IS manual_reject , credLimMul IS midLow WITH 0.3299999999999999;
RULE 5 : IF scoring IS low THEN qualify IS reject , credLimMul IS low;
RULE 6 : IF (sel IS a) OR (sel IS b) THEN qualify IS accept , credLimMul IS veryHigh WITH 0.43999999999999984;
RULE 7 : IF sel IS c1 THEN qualify IS accept , credLimMul IS high;
RULE 8 : IF sel IS c2 THEN qualify IS manual_accept , credLimMul IS midHigh WITH 0.6699999999999998;
RULE 9 : IF sel IS c3 THEN qualify IS manual_reject , credLimMul IS midLow WITH 0.7199999999999999;
RULE 10 : IF (sel IS d1) OR (sel IS low) THEN qualify IS reject , credLimMul IS low WITH 0.16999999999999982;
RULE 11 : IF NOT occupation_type IS good THEN qualify IS reject , credLimMul IS low;
RULE 12 : IF (city IS other) AND ((scoring IS midLow) OR (scoring IS low)) THEN qualify IS reject , credLimMul IS low;
RULE 13 : IF scoring_partner IS midLow THEN qualify IS manual_reject , credLimMul IS midLow;
RULE 14 : IF scoring_partner IS low THEN qualify IS reject , credLimMul IS low;
END_RULEBLOCK
END_FUNCTION_BLOCK

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FUNCTION_BLOCK Libcfuz // Block definition (there may be more than one block per file)
VAR_INPUT // Define input variables
Age : REAL;
Lactation : REAL;
END_VAR
VAR_OUTPUT // Define output variable
rISk : REAL;
END_VAR
FUZZIFY Age // Fuzzify input variable 'Age': {'young', 'mature' , 'old'}
TERM young := (0, 1)(10,1) (30, 1) (40, 0) ;
TERM mature:= (30, 0) (40,1) (60,1) (70,0);
TERM old := (60, 0) (70, 1) (90,1);
RANGE := (0.0 .. 90.0); // Added range for Age
END_FUZZIFY
FUZZIFY Lactation // Fuzzify input variable 'Lactation': { 'low', 'medium','full' }
TERM low := (0, 1) (2,1) (6, 1) (10,0);
TERM medium := (6,0) (10,1) (16,1) (20,0);
TERM full := (16,0) (20,1) (24,0);
RANGE := (0.0 .. 24.0); // Added range for Lactation
END_FUZZIFY
DEFUZZIFY rISk // Defzzzify output variable 'rISk' : {'verylow',low', 'medium', 'high','veryhigh' }
TERM verylow := (0,1) (5,1) (10,0);
TERM low := (5,0) (10,1) (25,1) (30,0);
TERM medium := (25,0) (30,1) (45,1) (50,0);
TERM high := (45,0) (50,1) (75,1) (80,0);
TERM veryhigh := (75,0) (80,1) (95,1) (100,0);
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value IS 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 100.0); // Added range for rISk
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX; // Use 'max' accumulation method
AND : MIN; // Use 'min' for 'and' (also implicit use 'max' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
RULE 1 : IF Age IS old AND Lactation IS low THEN rISk IS veryhigh;
RULE 2 : IF Age IS old THEN rISk IS high;
RULE 3 : IF Age IS old AND Lactation IS full THEN rISk IS low;
/* In thIS it picks Rule2 When given the in put it should pick Rule3 with the given input */
RULE 4 : IF Age IS young THEN rISk IS low;
RULE 5 : IF Age IS mature THEN rISk IS veryhigh;
RULE 6 : IF Lactation IS medium THEN rISk IS medium;
RULE 7 : IF Lactation IS full THEN rISk IS low;
RULE 8 : IF Lactation IS low THEN rISk IS veryhigh;
RULE 9 : IF Age IS young AND Lactation IS medium THEN rISk IS low;
RULE 10 : IF Age IS young AND Lactation IS full THEN rISk IS verylow;
RULE 11 : IF Age IS mature AND Lactation IS low THEN rISk IS veryhigh;
RULE 12 : IF Age IS mature AND Lactation IS medium THEN rISk IS high;
RULE 13 : IF Age IS mature AND Lactation IS full THEN rISk IS low ;
RULE 14: IF Age IS old AND Lactation IS low THEN rISk IS high;
RULE 15: IF Age IS old AND Lactation IS medium THEN rISk IS medium;
RULE 16: IF Age IS old AND Lactation IS full THEN rISk IS low;
RULE 17 : IF Age IS young AND Lactation IS low THEN rISk IS medium;
END_RULEBLOCK
END_FUNCTION_BLOCK

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Lactation Age rISk No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7 No1.8 No1.9 No1.10 No1.11 No1.12 No1.13 No1.14 No1.15 No1.16 No1.17
0.0 0.0 47.92553191489446 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
0.0 18.0 47.92553191489446 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
0.0 36.0 59.758064516129764 0.0 0.0 0.0 0.4 0.6 0.0 0.0 1.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.4
0.0 54.0 87.49999999999996 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 72.0 72.37179487179526 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
4.800000000000001 0.0 47.92553191489446 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
4.800000000000001 18.0 47.92553191489446 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
4.800000000000001 36.0 59.758064516129764 0.0 0.0 0.0 0.4 0.6 0.0 0.0 1.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.4
4.800000000000001 54.0 87.49999999999996 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
4.800000000000001 72.0 72.37179487179526 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
9.600000000000001 0.0 30.8165195460276 0.0 0.0 0.0 1.0 0.0 0.9000000000000004 0.0 0.09999999999999964 0.9000000000000004 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.09999999999999964
9.600000000000001 18.0 30.8165195460276 0.0 0.0 0.0 1.0 0.0 0.9000000000000004 0.0 0.09999999999999964 0.9000000000000004 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.09999999999999964
9.600000000000001 36.0 53.569210292812286 0.0 0.0 0.0 0.4 0.6 0.9000000000000004 0.0 0.09999999999999964 0.4 0.0 0.09999999999999964 0.6 0.0 0.0 0.0 0.0 0.09999999999999964
9.600000000000001 54.0 63.11119873817127 0.0 0.0 0.0 0.0 1.0 0.9000000000000004 0.0 0.09999999999999964 0.0 0.0 0.09999999999999964 0.9000000000000004 0.0 0.0 0.0 0.0 0.0
9.600000000000001 72.0 54.61382113821117 0.09999999999999964 1.0 0.0 0.0 0.0 0.9000000000000004 0.0 0.09999999999999964 0.0 0.0 0.0 0.0 0.0 0.09999999999999964 0.9000000000000004 0.0 0.0
14.399999999999999 0.0 27.499999999999943 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14.399999999999999 18.0 27.499999999999943 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14.399999999999999 36.0 53.13903281519825 0.0 0.0 0.0 0.4 0.6 1.0 0.0 0.0 0.4 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0
14.399999999999999 54.0 62.50000000000055 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0
14.399999999999999 72.0 52.62820512820545 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
19.200000000000003 0.0 17.97728674203499 0.0 0.0 0.0 1.0 0.0 0.1999999999999993 0.8000000000000007 0.0 0.1999999999999993 0.8000000000000007 0.0 0.0 0.0 0.0 0.0 0.0 0.0
19.200000000000003 18.0 17.97728674203499 0.0 0.0 0.0 1.0 0.0 0.1999999999999993 0.8000000000000007 0.0 0.1999999999999993 0.8000000000000007 0.0 0.0 0.0 0.0 0.0 0.0 0.0
19.200000000000003 36.0 46.59456991830936 0.0 0.0 0.0 0.4 0.6 0.1999999999999993 0.8000000000000007 0.0 0.1999999999999993 0.4 0.0 0.1999999999999993 0.6 0.0 0.0 0.0 0.0
19.200000000000003 54.0 54.934782608697176 0.0 0.0 0.0 0.0 1.0 0.1999999999999993 0.8000000000000007 0.0 0.0 0.0 0.0 0.1999999999999993 0.8000000000000007 0.0 0.0 0.0 0.0
19.200000000000003 72.0 45.78000000000053 0.0 1.0 0.8000000000000007 0.0 0.0 0.1999999999999993 0.8000000000000007 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1999999999999993 0.8000000000000007 0.0

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/*
Robot controller.
References:
"Learning Weighted LinguIStic Rules to Control an Autonomous Robot"
, M. Mucientes, R. Alcala , J. Alcala-Fdez, J. Casillas
Pablo Cingolani
pcingola@users.sourceforge.net
*/
FUNCTION_BLOCK robot
VAR_INPUT
rd : REAL; // Right dIStance
dq : REAL; // DIStance quotient
o : REAL; // Orientation. Note: I cannot use 'or' (like the reference paper) because it IS a reserved word in FCL
v : REAL; // Velocity
END_VAR
VAR_OUTPUT
la : REAL; // Linear acceleration
av : REAL; // Angular velocity
END_VAR
FUZZIFY rd
TERM L := TRIAN 0 0 1;
TERM M := TRIAN 0 1 2;
TERM H := TRIAN 1 2 3;
TERM VH := TRIAN 2 3 3;
RANGE := (0.0 .. 3.0) WITH 0.1; // Added range for rd
END_FUZZIFY
FUZZIFY dq
TERM L := TRIAN 0 0 2;
TERM H := TRIAN 0 2 2;
RANGE := (0.0 .. 2.0) WITH 0.1; // Added range for dq
END_FUZZIFY
FUZZIFY o
TERM HL := TRIAN -45 -45 -22.5;
TERM LL := TRIAN -45 -22.5 0;
TERM Z := TRIAN -22.5 0 22.5;
TERM LR := TRIAN 0 22.5 45;
TERM HR := TRIAN 22.5 45 45;
RANGE := (-45.0 .. 45.0) WITH 0.1; // Added range for o
END_FUZZIFY
FUZZIFY v
TERM L := TRIAN 0 0 1;
TERM H := TRIAN 0 1 1;
RANGE := (0.0 .. 1.0) WITH 0.1; // Added range for v
END_FUZZIFY
DEFUZZIFY la
TERM VHB := TRIAN -1 -1 -0.75;
TERM HB := TRIAN -1 -0.75 -0.5;
TERM MB := TRIAN -0.75 -0.5 -0.25;
TERM SB := TRIAN -0.5 -0.25 0;
TERM Z := TRIAN -0.25 0 0.25;
TERM SA := TRIAN 0 0.25 0.5;
TERM MA := TRIAN 0.25 0.5 0.75;
TERM HA := TRIAN 0.5 0.75 1;
TERM VHA := TRIAN 0.75 1 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-1.0 .. 1.0) WITH 0.01; // Added range for la
END_DEFUZZIFY
DEFUZZIFY av
TERM VHR := TRIAN -1 -1 -0.55;
TERM HR := TRIAN -1 -0.75 -0.5;
TERM MR := TRIAN -0.75 -0.5 -0.25;
TERM SR := TRIAN -0.5 -0.25 0;
TERM Z := TRIAN -0.25 0 0.25;
TERM SL := TRIAN 0 0.25 0.5;
TERM ML := TRIAN 0.25 0.5 0.75;
TERM HL := TRIAN 0.5 0.75 1;
TERM VHL := TRIAN 0.75 1 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-1.0 .. 1.0) WITH 0.01; // Added range for av
END_DEFUZZIFY
RULEBLOCK rules
AND : MIN; // Use 'min' for 'AND' (also implicit use 'max' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1: IF rd IS L AND dq IS L AND o IS LL AND v IS L THEN la IS VHB , av IS VHR WITH 0.4610;
RULE 2: IF rd IS L AND dq IS L AND o IS LL AND v IS H THEN la IS VHB , av IS VHR WITH 0.4896;
RULE 3: IF rd IS L AND dq IS L AND o IS Z AND v IS L THEN la IS Z , av IS MR WITH 0.6664;
RULE 4: IF rd IS L AND dq IS L AND o IS Z AND v IS H THEN la IS HB , av IS SR WITH 0.5435;
RULE 5: IF rd IS L AND dq IS H AND o IS LL AND v IS L THEN la IS MA , av IS HR WITH 0.7276;
RULE 6: IF rd IS L AND dq IS H AND o IS Z AND v IS L THEN la IS MA , av IS HL WITH 0.4845;
RULE 7: IF rd IS L AND dq IS H AND o IS Z AND v IS H THEN la IS HB , av IS ML WITH 0.5023;
RULE 8: IF rd IS L AND dq IS H AND o IS LR AND v IS H THEN la IS VHB , av IS VHL WITH 0.7363;
RULE 9: IF rd IS L AND dq IS H AND o IS HR AND v IS L THEN la IS VHB , av IS VHL WITH 0.9441;
RULE 10: IF rd IS M AND dq IS L AND o IS Z AND v IS H THEN la IS SA , av IS HR WITH 0.3402;
RULE 11: IF rd IS M AND dq IS L AND o IS LR AND v IS H THEN la IS Z , av IS VHL WITH 0.4244;
RULE 12: IF rd IS M AND dq IS L AND o IS HR AND v IS L THEN la IS SA , av IS HL WITH 0.5472;
RULE 13: IF rd IS M AND dq IS L AND o IS HR AND v IS H THEN la IS MB , av IS VHL WITH 0.4369;
RULE 14: IF rd IS M AND dq IS H AND o IS HL AND v IS L THEN la IS Z , av IS VHR WITH 0.1770;
RULE 15: IF rd IS M AND dq IS H AND o IS HL AND v IS H THEN la IS VHB , av IS VHR WITH 0.4526;
RULE 16: IF rd IS M AND dq IS H AND o IS LL AND v IS H THEN la IS SA , av IS VHR WITH 0.2548;
RULE 17: IF rd IS M AND dq IS H AND o IS Z AND v IS L THEN la IS HA , av IS Z WITH 0.2084;
RULE 18: IF rd IS M AND dq IS H AND o IS LR AND v IS L THEN la IS HA , av IS VHL WITH 0.6242;
RULE 19: IF rd IS M AND dq IS H AND o IS LR AND v IS H THEN la IS SA , av IS VHL WITH 0.3779;
RULE 20: IF rd IS M AND dq IS H AND o IS HR AND v IS L THEN la IS Z , av IS VHL WITH 0.6931;
RULE 21: IF rd IS M AND dq IS H AND o IS HR AND v IS H THEN la IS VHB , av IS VHL WITH 0.7580;
RULE 22: IF rd IS H AND dq IS L AND o IS Z AND v IS L THEN la IS HA , av IS VHR WITH 0.5758;
RULE 23: IF rd IS H AND dq IS L AND o IS LR AND v IS H THEN la IS SA , av IS MR WITH 0.2513;
RULE 24: IF rd IS H AND dq IS L AND o IS HR AND v IS L THEN la IS HA , av IS VHL WITH 0.5471;
RULE 25: IF rd IS H AND dq IS L AND o IS HR AND v IS H THEN la IS SA , av IS HL WITH 0.5595;
RULE 26: IF rd IS H AND dq IS H AND o IS HL AND v IS L THEN la IS VHB , av IS VHR WITH 0.9999;
RULE 27: IF rd IS H AND dq IS H AND o IS HL AND v IS H THEN la IS VHB , av IS VHR WITH 0.9563;
RULE 28: IF rd IS H AND dq IS H AND o IS LL AND v IS L THEN la IS HA , av IS VHR WITH 0.9506;
RULE 29: IF rd IS H AND dq IS H AND o IS Z AND v IS L THEN la IS HA , av IS VHR WITH 0.4529;
RULE 30: IF rd IS H AND dq IS H AND o IS Z AND v IS H THEN la IS SA , av IS VHR WITH 0.2210;
RULE 31: IF rd IS H AND dq IS H AND o IS LR AND v IS L THEN la IS HA , av IS MR WITH 0.3612;
RULE 32: IF rd IS H AND dq IS H AND o IS LR AND v IS H THEN la IS SA , av IS MR WITH 0.2122;
RULE 33: IF rd IS H AND dq IS H AND o IS HR AND v IS L THEN la IS HA , av IS HL WITH 0.7878;
RULE 34: IF rd IS H AND dq IS H AND o IS HR AND v IS H THEN la IS SA , av IS VHL WITH 0.3859;
RULE 35: IF rd IS VH AND dq IS L AND o IS LR AND v IS L THEN la IS HA , av IS VHR WITH 0.5530;
RULE 36: IF rd IS VH AND dq IS L AND o IS HR AND v IS L THEN la IS HA , av IS HR WITH 0.4223;
RULE 37: IF rd IS VH AND dq IS L AND o IS HR AND v IS H THEN la IS SA , av IS HR WITH 0.3854;
RULE 38: IF rd IS VH AND dq IS H AND o IS LL AND v IS L THEN la IS HA , av IS VHR WITH 0.0936;
RULE 39: IF rd IS VH AND dq IS H AND o IS LR AND v IS L THEN la IS HA , av IS VHR WITH 0.7325;
RULE 40: IF rd IS VH AND dq IS H AND o IS LR AND v IS H THEN la IS SA , av IS VHR WITH 0.5631;
RULE 41: IF rd IS VH AND dq IS H AND o IS HR AND v IS L THEN la IS HA , av IS HR WITH 0.5146;
END_RULEBLOCK
END_FUNCTION_BLOCK

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rd v dq o la av rules.1 rules.2 rules.3 rules.4 rules.5 rules.6 rules.7 rules.8 rules.9 rules.10 rules.11 rules.12 rules.13 rules.14 rules.15 rules.16 rules.17 rules.18 rules.19 rules.20 rules.21 rules.22 rules.23 rules.24 rules.25 rules.26 rules.27 rules.28 rules.29 rules.30 rules.31 rules.32 rules.33 rules.34 rules.35 rules.36 rules.37 rules.38 rules.39 rules.40 rules.41
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2.4000000000000004 0.6 0.4 27.0 0.5426310663737375 -0.1274500537307169 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.15077999999999991 0.10942000000000002 0.1119 0.0 0.0 0.0 0.0 0.0 0.07224000000000001 0.042440000000000005 0.15756 0.07718000000000001 0.22120000000000004 0.08446000000000001 0.07708000000000001 0.0 0.14650000000000002 0.11262000000000001 0.10292
2.4000000000000004 0.6 0.8 -45.0 -0.8984258511091379 -0.8984258511091379 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.39996000000000004 0.38252 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.6 0.8 -27.0 0.37840042866676843 -0.8974166904498947 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.38024 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03744 0.0 0.0 0.0
2.4000000000000004 0.6 0.8 -9.0 0.6423492477677656 -0.8974166904498947 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.23032 0.0 0.0 0.0 0.0 0.0 0.38024 0.18116000000000002 0.0884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03744 0.0 0.0 0.0
2.4000000000000004 0.6 0.8 9.0 0.527892065311855 -0.6900173210481826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.23032 0.10052000000000001 0.0 0.0 0.0 0.0 0.0 0.18116000000000002 0.0884 0.14448000000000003 0.08488000000000001 0.0 0.0 0.22120000000000004 0.0 0.0 0.0 0.29300000000000004 0.22524000000000002 0.0
2.4000000000000004 0.6 0.8 27.0 0.527892065311855 -0.1774627783618012 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.15077999999999991 0.10942000000000002 0.1119 0.0 0.0 0.0 0.0 0.0 0.14448000000000003 0.08488000000000001 0.15756 0.07718000000000001 0.22120000000000004 0.08446000000000001 0.07708000000000001 0.0 0.29300000000000004 0.22524000000000002 0.10292
2.4000000000000004 0.6 1.2 -45.0 -0.9065801223048938 -0.9065801223048938 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.39996000000000004 0.5737799999999997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.6 1.2 -27.0 0.37840042866676843 -0.8974166904498947 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.38024 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03744 0.0 0.0 0.0
2.4000000000000004 0.6 1.2 -9.0 0.6066262783109148 -0.8974166904498947 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.23032 0.0 0.0 0.0 0.0 0.0 0.38024 0.18116000000000002 0.1325999999999999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03744 0.0 0.0 0.0
2.4000000000000004 0.6 1.2 9.0 0.527892065311855 -0.6900173210481826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.23032 0.10052000000000001 0.0 0.0 0.0 0.0 0.0 0.18116000000000002 0.1325999999999999 0.14448000000000003 0.08488000000000001 0.0 0.0 0.22120000000000004 0.0 0.0 0.0 0.29300000000000004 0.22524000000000002 0.0
2.4000000000000004 0.6 1.2 27.0 0.5278920653118551 -0.17326966380306769 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.10052000000000001 0.10942000000000002 0.1119 0.0 0.0 0.0 0.0 0.0 0.14448000000000003 0.12731999999999993 0.15756 0.07718000000000001 0.22120000000000004 0.08446000000000001 0.07708000000000001 0.0 0.29300000000000004 0.22524000000000022 0.10292
2.4000000000000004 0.6 1.6 -45.0 -0.9065801223048938 -0.9065801223048938 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.39996000000000004 0.5737799999999997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.6 1.6 -27.0 0.37840042866676843 -0.8974166904498947 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.38024 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03744 0.0 0.0 0.0
2.4000000000000004 0.6 1.6 -9.0 0.6066262783109148 -0.8974166904498947 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.38024 0.18116000000000002 0.1325999999999999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03744 0.0 0.0 0.0
2.4000000000000004 0.6 1.6 9.0 0.527892065311855 -0.6900173210481826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.05025999999999999 0.0 0.0 0.0 0.0 0.0 0.18116000000000002 0.1325999999999999 0.14448000000000003 0.08488000000000001 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.29300000000000004 0.22524000000000002 0.0
2.4000000000000004 0.6 1.6 27.0 0.5278920653118551 -0.17326966380306769 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05025999999999999 0.10941999999999998 0.11189999999999997 0.0 0.0 0.0 0.0 0.0 0.14448000000000003 0.12731999999999993 0.15756 0.07718000000000001 0.11059999999999999 0.08445999999999998 0.07707999999999998 0.0 0.29300000000000004 0.22524000000000022 0.10292
2.4000000000000004 0.8 0.0 -45.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 0.0 -27.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 0.0 -9.0 0.7500000000000027 -0.8825595107012113 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 0.0 9.0 0.5160081230919774 -0.6390191465160894 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.10052000000000001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 0.0 27.0 0.46419525112699656 -0.12074897545137622 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.15077999999999991 0.10941999999999998 0.1119 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11059999999999999 0.08445999999999998 0.07708000000000001 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 0.4 -45.0 -0.8875387092120623 -0.8875387092120623 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19126 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 0.4 -27.0 0.1859700763652909 -0.8875387092120623 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.19011999999999996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 0.4 -9.0 0.6496193566897628 -0.8869719854964562 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.19011999999999996 0.09057999999999998 0.0442 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 0.4 9.0 0.5304558687624641 -0.6601596489727047 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.10052000000000001 0.0 0.0 0.0 0.0 0.0 0.09057999999999998 0.0442 0.07223999999999998 0.042440000000000005 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.14649999999999996 0.11262000000000001 0.0
2.4000000000000004 0.8 0.4 27.0 0.5050361062340039 -0.0619947374874516 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.15077999999999991 0.10941999999999998 0.1119 0.0 0.0 0.0 0.0 0.0 0.07223999999999998 0.042440000000000005 0.15755999999999995 0.07718000000000001 0.11059999999999999 0.08445999999999998 0.07708000000000001 0.0 0.14649999999999996 0.11262000000000001 0.10291999999999997
2.4000000000000004 0.8 0.8 -45.0 -0.8975337689420841 -0.8975337689420841 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.38252 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 0.8 -27.0 0.1859700763652909 -0.8875387092120623 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.19011999999999996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 0.8 -9.0 0.5853207607421803 -0.8869719854964562 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.19011999999999996 0.09057999999999998 0.0884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 0.8 9.0 0.4522487296178078 -0.6993846794854975 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.10052000000000001 0.0 0.0 0.0 0.0 0.0 0.09057999999999998 0.0884 0.07223999999999998 0.08488000000000001 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.14649999999999996 0.22524000000000002 0.0
2.4000000000000004 0.8 0.8 27.0 0.46034270550829315 -0.1305666426199114 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.15077999999999991 0.10941999999999998 0.1119 0.0 0.0 0.0 0.0 0.0 0.07223999999999998 0.08488000000000001 0.15755999999999995 0.07718000000000001 0.11059999999999999 0.08445999999999998 0.07708000000000001 0.0 0.14649999999999996 0.22524000000000002 0.10291999999999997
2.4000000000000004 0.8 1.2 -45.0 -0.9065801223048938 -0.9065801223048938 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.5737799999999997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 1.2 -27.0 0.1859700763652909 -0.8875387092120623 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.19011999999999996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.2 -9.0 0.5407655106992174 -0.8869719854964562 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.19011999999999996 0.09057999999999998 0.1325999999999999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.2 9.0 0.4522487296178078 -0.6993846794854975 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.10052000000000001 0.0 0.0 0.0 0.0 0.0 0.09057999999999998 0.1325999999999999 0.07223999999999998 0.08488000000000001 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.14649999999999996 0.22524000000000002 0.0
2.4000000000000004 0.8 1.2 27.0 0.4603427055082932 -0.11055364331008116 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.10052000000000001 0.10941999999999998 0.1119 0.0 0.0 0.0 0.0 0.0 0.07223999999999998 0.12731999999999993 0.15755999999999995 0.07718000000000001 0.11059999999999999 0.08445999999999998 0.07708000000000001 0.0 0.14649999999999996 0.22524000000000022 0.10291999999999997
2.4000000000000004 0.8 1.6 -45.0 -0.9065801223048938 -0.9065801223048938 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.5737799999999997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 1.6 -27.0 0.1859700763652909 -0.8875387092120623 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.19011999999999996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.6 -9.0 0.5407655106992174 -0.8869719854964562 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.19011999999999996 0.09057999999999998 0.1325999999999999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.6 9.0 0.4522487296178078 -0.7149595303630241 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.05025999999999999 0.0 0.0 0.0 0.0 0.0 0.09057999999999998 0.1325999999999999 0.07223999999999998 0.08488000000000001 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.14649999999999996 0.22524000000000002 0.0
2.4000000000000004 0.8 1.6 27.0 0.4603427055082932 -0.11055364331008116 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05025999999999999 0.10941999999999998 0.11189999999999997 0.0 0.0 0.0 0.0 0.0 0.07223999999999998 0.12731999999999993 0.15755999999999995 0.07718000000000001 0.11059999999999999 0.08445999999999998 0.07707999999999998 0.0 0.14649999999999996 0.22524000000000022 0.10291999999999997

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Examples/jFuzzyLogic/robot_optimized.fcl Normal file
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FUNCTION_BLOCK robot
VAR_INPUT
dq : REAL;
o : REAL;
rd : REAL;
v : REAL;
END_VAR
VAR_OUTPUT
av : REAL;
la : REAL;
END_VAR
FUZZIFY dq
TERM H := TRIAN 0.0 2.0 2.0;
TERM L := TRIAN 0.0 0.0 2.0;
RANGE := (0.0 .. 2.0) WITH 0.1; // Added range for dq
END_FUZZIFY
FUZZIFY o
TERM HL := TRIAN -45.0 -45.0 -22.5;
TERM HR := TRIAN 22.5 45.0 45.0;
TERM LL := TRIAN -45.0 -22.5 0.0;
TERM LR := TRIAN 0.0 22.5 45.0;
TERM Z := TRIAN -22.5 0.0 22.5;
RANGE := (-45.0 .. 45.0) WITH 0.1; // Added range for o
END_FUZZIFY
FUZZIFY rd
TERM H := TRIAN 1.0 2.0 3.0;
TERM L := TRIAN 0.0 0.0 1.0;
TERM M := TRIAN 0.0 1.0 2.0;
TERM VH := TRIAN 2.0 3.0 3.0;
RANGE := (0.0 .. 3.0) WITH 0.1; // Added range for rd
END_FUZZIFY
FUZZIFY v
TERM H := TRIAN 0.0 1.0 1.0;
TERM L := TRIAN 0.0 0.0 1.0;
RANGE := (0.0 .. 1.0) WITH 0.1; // Added range for v
END_FUZZIFY
DEFUZZIFY av
TERM HL := TRIAN 0.5 0.75 1.0;
TERM HR := TRIAN -1.0 -0.75 -0.5;
TERM ML := TRIAN 0.25 0.5 0.75;
TERM MR := TRIAN -0.75 -0.5 -0.25;
TERM SL := TRIAN 0.0 0.25 0.5;
TERM SR := TRIAN -0.5 -0.25 0.0;
TERM VHL := TRIAN 0.75 1.0 1.0;
TERM VHR := TRIAN -1.0 -1.0 -0.75;
TERM Z := TRIAN -0.25 0.0 0.25;
METHOD : COG;
DEFAULT := 0.0;
RANGE := (-1.0 .. 1.0) WITH 0.01;
END_DEFUZZIFY
DEFUZZIFY la
TERM HA := TRIAN 0.5 0.75 1.0;
TERM HB := TRIAN -1.0 -0.75 -0.5;
TERM MA := TRIAN 0.25 0.5 0.75;
TERM MB := TRIAN -0.75 -0.5 -0.25;
TERM SA := TRIAN 0.0 0.25 0.5;
TERM SB := TRIAN -0.5 -0.25 0.0;
TERM VHA := TRIAN 0.75 1.0 1.0;
TERM VHB := TRIAN -1.0 -1.0 -0.75;
TERM Z := TRIAN -0.25 0.0 0.25;
METHOD : COG;
DEFAULT := 0.0;
RANGE := (-1.0 .. 1.0) WITH 0.01;
END_DEFUZZIFY
RULEBLOCK rules
ACT : MIN;
ACCU : MAX;
AND : MIN;
RULE 1 : IF (((rd IS L) AND (dq IS L)) AND (o IS LL)) AND (v IS L) THEN la IS VHB , av IS VHR WITH 0.461;
RULE 2 : IF (((rd IS L) AND (dq IS L)) AND (o IS LL)) AND (v IS H) THEN la IS VHB , av IS VHR WITH 0.4896;
RULE 3 : IF (((rd IS L) AND (dq IS L)) AND (o IS Z)) AND (v IS L) THEN la IS Z , av IS MR WITH 0.6664;
RULE 4 : IF (((rd IS L) AND (dq IS L)) AND (o IS Z)) AND (v IS H) THEN la IS HB , av IS SR WITH 0.5435;
RULE 5 : IF (((rd IS L) AND (dq IS H)) AND (o IS LL)) AND (v IS L) THEN la IS MA , av IS HR WITH 0.7276;
RULE 6 : IF (((rd IS L) AND (dq IS H)) AND (o IS Z)) AND (v IS L) THEN la IS MA , av IS HL WITH 0.4845;
RULE 7 : IF (((rd IS L) AND (dq IS H)) AND (o IS Z)) AND (v IS H) THEN la IS HB , av IS ML WITH 0.5023;
RULE 8 : IF (((rd IS L) AND (dq IS H)) AND (o IS LR)) AND (v IS H) THEN la IS VHB , av IS VHL WITH 0.7363;
RULE 9 : IF (((rd IS L) AND (dq IS H)) AND (o IS HR)) AND (v IS L) THEN la IS VHB , av IS VHL WITH 0.9441;
RULE 10 : IF (((rd IS M) AND (dq IS L)) AND (o IS Z)) AND (v IS H) THEN la IS SA , av IS HR WITH 0.3402;
RULE 11 : IF (((rd IS M) AND (dq IS L)) AND (o IS LR)) AND (v IS H) THEN la IS Z , av IS VHL WITH 0.4244;
RULE 12 : IF (((rd IS M) AND (dq IS L)) AND (o IS HR)) AND (v IS L) THEN la IS SA , av IS HL WITH 0.5472;
RULE 13 : IF (((rd IS M) AND (dq IS L)) AND (o IS HR)) AND (v IS H) THEN la IS MB , av IS VHL WITH 0.4369;
RULE 14 : IF (((rd IS M) AND (dq IS H)) AND (o IS HL)) AND (v IS L) THEN la IS Z , av IS VHR WITH 0.177;
RULE 15 : IF (((rd IS M) AND (dq IS H)) AND (o IS HL)) AND (v IS H) THEN la IS VHB , av IS VHR WITH 0.4526;
RULE 16 : IF (((rd IS M) AND (dq IS H)) AND (o IS LL)) AND (v IS H) THEN la IS SA , av IS VHR WITH 0.2548;
RULE 17 : IF (((rd IS M) AND (dq IS H)) AND (o IS Z)) AND (v IS L) THEN la IS HA , av IS Z WITH 0.2084;
RULE 18 : IF (((rd IS M) AND (dq IS H)) AND (o IS LR)) AND (v IS L) THEN la IS HA , av IS VHL WITH 0.6242;
RULE 19 : IF (((rd IS M) AND (dq IS H)) AND (o IS LR)) AND (v IS H) THEN la IS SA , av IS VHL WITH 0.3779;
RULE 20 : IF (((rd IS M) AND (dq IS H)) AND (o IS HR)) AND (v IS L) THEN la IS Z , av IS VHL WITH 0.6931;
RULE 21 : IF (((rd IS M) AND (dq IS H)) AND (o IS HR)) AND (v IS H) THEN la IS VHB , av IS VHL WITH 0.758;
RULE 22 : IF (((rd IS H) AND (dq IS L)) AND (o IS Z)) AND (v IS L) THEN la IS HA , av IS VHR WITH 0.5758;
RULE 23 : IF (((rd IS H) AND (dq IS L)) AND (o IS LR)) AND (v IS H) THEN la IS SA , av IS MR WITH 0.2513;
RULE 24 : IF (((rd IS H) AND (dq IS L)) AND (o IS HR)) AND (v IS L) THEN la IS HA , av IS VHL WITH 0.5471;
RULE 25 : IF (((rd IS H) AND (dq IS L)) AND (o IS HR)) AND (v IS H) THEN la IS SA , av IS HL WITH 0.5595;
RULE 26 : IF (((rd IS H) AND (dq IS H)) AND (o IS HL)) AND (v IS L) THEN la IS VHB , av IS VHR WITH 0.9999;
RULE 27 : IF (((rd IS H) AND (dq IS H)) AND (o IS HL)) AND (v IS H) THEN la IS VHB , av IS VHR WITH 0.9563;
RULE 28 : IF (((rd IS H) AND (dq IS H)) AND (o IS LL)) AND (v IS L) THEN la IS HA , av IS VHR WITH 0.9506;
RULE 29 : IF (((rd IS H) AND (dq IS H)) AND (o IS Z)) AND (v IS L) THEN la IS HA , av IS VHR WITH 0.4529;
RULE 30 : IF (((rd IS H) AND (dq IS H)) AND (o IS Z)) AND (v IS H) THEN la IS SA , av IS VHR WITH 0.221;
RULE 31 : IF (((rd IS H) AND (dq IS H)) AND (o IS LR)) AND (v IS L) THEN la IS HA , av IS MR WITH 0.3612;
RULE 32 : IF (((rd IS H) AND (dq IS H)) AND (o IS LR)) AND (v IS H) THEN la IS SA , av IS MR WITH 0.2122;
RULE 33 : IF (((rd IS H) AND (dq IS H)) AND (o IS HR)) AND (v IS L) THEN la IS HA , av IS HL WITH 0.7878;
RULE 34 : IF (((rd IS H) AND (dq IS H)) AND (o IS HR)) AND (v IS H) THEN la IS SA , av IS VHL WITH 0.3859;
RULE 35 : IF (((rd IS VH) AND (dq IS L)) AND (o IS LR)) AND (v IS L) THEN la IS HA , av IS VHR WITH 0.553;
RULE 36 : IF (((rd IS VH) AND (dq IS L)) AND (o IS HR)) AND (v IS L) THEN la IS HA , av IS HR WITH 0.4223;
RULE 37 : IF (((rd IS VH) AND (dq IS L)) AND (o IS HR)) AND (v IS H) THEN la IS SA , av IS HR WITH 0.3854;
RULE 38 : IF (((rd IS VH) AND (dq IS H)) AND (o IS LL)) AND (v IS L) THEN la IS HA , av IS VHR WITH 0.0936;
RULE 39 : IF (((rd IS VH) AND (dq IS H)) AND (o IS LR)) AND (v IS L) THEN la IS HA , av IS VHR WITH 0.7325;
RULE 40 : IF (((rd IS VH) AND (dq IS H)) AND (o IS LR)) AND (v IS H) THEN la IS SA , av IS VHR WITH 0.5631;
RULE 41 : IF (((rd IS VH) AND (dq IS H)) AND (o IS HR)) AND (v IS L) THEN la IS HA , av IS HR WITH 0.5146;
END_RULEBLOCK
END_FUNCTION_BLOCK

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rd v dq o av la rules.1 rules.2 rules.3 rules.4 rules.5 rules.6 rules.7 rules.8 rules.9 rules.10 rules.11 rules.12 rules.13 rules.14 rules.15 rules.16 rules.17 rules.18 rules.19 rules.20 rules.21 rules.22 rules.23 rules.24 rules.25 rules.26 rules.27 rules.28 rules.29 rules.30 rules.31 rules.32 rules.33 rules.34 rules.35 rules.36 rules.37 rules.38 rules.39 rules.40 rules.41
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2.4000000000000004 0.8 1.2 -27.0 -0.8875387092120623 0.1859700763652909 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.19011999999999996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.2 -9.0 -0.8869719854964562 0.5407655106992174 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.19011999999999996 0.09057999999999998 0.1325999999999999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.2 9.0 -0.6993846794854975 0.4522487296178078 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.10052000000000001 0.0 0.0 0.0 0.0 0.0 0.09057999999999998 0.1325999999999999 0.07223999999999998 0.08488000000000001 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.14649999999999996 0.22524000000000002 0.0
2.4000000000000004 0.8 1.2 27.0 -0.11055364331008116 0.4603427055082932 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.10052000000000001 0.10941999999999998 0.1119 0.0 0.0 0.0 0.0 0.0 0.07223999999999998 0.12731999999999993 0.15755999999999995 0.07718000000000001 0.11059999999999999 0.08445999999999998 0.07708000000000001 0.0 0.14649999999999996 0.22524000000000022 0.10291999999999997
2.4000000000000004 0.8 1.6 -45.0 -0.9065801223048938 -0.9065801223048938 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.5737799999999997 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.4000000000000004 0.8 1.6 -27.0 -0.8875387092120623 0.1859700763652909 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19997999999999996 0.19125999999999996 0.19011999999999996 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.6 -9.0 -0.8869719854964562 0.5407655106992174 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.0 0.0 0.0 0.0 0.0 0.19011999999999996 0.09057999999999998 0.1325999999999999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018719999999999997 0.0 0.0 0.0
2.4000000000000004 0.8 1.6 9.0 -0.7149595303630241 0.4522487296178078 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.11515999999999997 0.05025999999999999 0.0 0.0 0.0 0.0 0.0 0.09057999999999998 0.1325999999999999 0.07223999999999998 0.08488000000000001 0.0 0.0 0.11059999999999999 0.0 0.0 0.0 0.14649999999999996 0.22524000000000002 0.0
2.4000000000000004 0.8 1.6 27.0 -0.11055364331008116 0.4603427055082932 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05025999999999999 0.10941999999999998 0.11189999999999997 0.0 0.0 0.0 0.0 0.0 0.07223999999999998 0.12731999999999993 0.15755999999999995 0.07718000000000001 0.11059999999999999 0.08445999999999998 0.07707999999999998 0.0 0.14649999999999996 0.22524000000000022 0.10291999999999997

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// Block definition (there may be more than one block per file)
FUNCTION_BLOCK tipper
// Define input variables
VAR_INPUT
service : REAL;
food : REAL;
END_VAR
// Define output variable
VAR_OUTPUT
tip : REAL;
END_VAR
// Fuzzify input variable 'service'
FUZZIFY service
TERM poor := (0, 1) (4, 0) ;
TERM good := (1, 0) (4,1) (6,1) (9,0);
TERM excellent := (6, 0) (9, 1);
RANGE := (0.0 .. 9.0); // Added range for service
END_FUZZIFY
// Fuzzify input variable 'food'
FUZZIFY food
TERM rancid := (0, 1) (1, 1) (3,0) ;
TERM delicious := (7,0) (9,1);
RANGE := (0.0 .. 9.0); // Added range for food
END_FUZZIFY
// Defzzzify output variable 'tip'
DEFUZZIFY tip
TERM cheap := (0,0) (5,1) (10,0);
TERM average := (10,0) (15,1) (20,0);
TERM generous := (20,0) (25,1) (30,0);
// Use 'Center Of Gravity' defuzzification method
METHOD : COG;
// Default value is 0 (if no rule activates defuzzifier)
DEFAULT := 0;
RANGE := (0.0 .. 30.0); // Added range for tip
END_DEFUZZIFY
RULEBLOCK No1
// Use 'min' for 'and' (also implicit use 'max'
// for 'or' to fulfill DeMorgan's Law)
AND : MIN;
// Use 'min' activation method
ACT : MIN;
// Use 'max' accumulation method
ACCU : MAX;
RULE 1 : IF service IS poor OR food IS rancid
THEN tip IS cheap;
RULE 2 : IF service IS good
THEN tip IS average;
RULE 3 : IF service IS excellent AND food IS delicious
THEN tip is generous;
END_RULEBLOCK
END_FUNCTION_BLOCK

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service food tip No1.1 No1.2 No1.3
0.0 0.0 5.000020040080156 1.0 0.0 0.0
0.0 1.8 5.000020040080156 1.0 0.0 0.0
0.0 3.6 5.000020040080156 1.0 0.0 0.0
0.0 5.3999999999999995 5.000020040080156 1.0 0.0 0.0
0.0 7.2 5.000020040080156 1.0 0.0 0.0
1.8 0.0 8.161124892286946 1.0 0.26666666666666666 0.0
1.8 1.8 8.54950607575949 0.6 0.26666666666666666 0.0
1.8 3.6 8.66927574381595 0.55 0.26666666666666666 0.0
1.8 5.3999999999999995 8.66927574381595 0.55 0.26666666666666666 0.0
1.8 7.2 8.66927574381595 0.55 0.26666666666666666 0.0
3.6 0.0 9.955181584284231 1.0 0.8666666666666667 0.0
3.6 1.8 10.390254985117041 0.6 0.8666666666666667 0.0
3.6 3.6 13.37919506577268 0.09999999999999998 0.8666666666666667 0.0
3.6 5.3999999999999995 13.37919506577268 0.09999999999999998 0.8666666666666667 0.0
3.6 7.2 13.37919506577268 0.09999999999999998 0.8666666666666667 0.0
5.3999999999999995 0.0 10.000040019919915 1.0 1.0 0.0
5.3999999999999995 1.8 10.434808979091365 0.6 1.0 0.0
5.3999999999999995 3.6 15.000000000000037 0.0 1.0 0.0
5.3999999999999995 5.3999999999999995 15.000000000000037 0.0 1.0 0.0
5.3999999999999995 7.2 15.000000000000037 0.0 1.0 0.0
7.2 0.0 9.565238207039531 1.0 0.5999999999999999 0.0
7.2 1.8 10.000007142857134 0.6 0.5999999999999999 0.0
7.2 3.6 15.000000000000021 0.0 0.5999999999999999 0.0
7.2 5.3999999999999995 15.000000000000021 0.0 0.5999999999999999 0.0
7.2 7.2 16.84460906644311 0.0 0.5999999999999999 0.10000000000000009

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FUNCTION_BLOCK testDeMorgan1
VAR_INPUT
x1 : REAL;
x2 : REAL;
END_VAR
VAR_OUTPUT
y : REAL;
END_VAR
FUZZIFY x1
TERM value := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x1
END_FUZZIFY
FUZZIFY x2
TERM value2 := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x2
END_FUZZIFY
DEFUZZIFY y
TERM value := (0, 1) (1, 0) ;
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 1.0); // Added range for y
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN; // Use 'min' for 'and' (also implicit use 'max' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF NOT(x1 IS value OR x2 IS value2) THEN y IS value;
RULE 2 : IF NOT(x1 IS value) AND NOT(x2 IS value2) THEN y IS value;
RULE 3 : IF NOT(x1 IS value AND x2 IS value2) THEN y IS value;
RULE 4 : IF NOT(x1 IS value) OR NOT(x2 IS value2) THEN y IS value;
END_RULEBLOCK
END_FUNCTION_BLOCK

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x1 x2 y No1.1 No1.2 No1.3 No1.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0

42
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FUNCTION_BLOCK testDeMorgan2
VAR_INPUT
x1 : REAL;
x2 : REAL;
END_VAR
VAR_OUTPUT
y : REAL;
END_VAR
FUZZIFY x1
TERM value := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x1
END_FUZZIFY
FUZZIFY x2
TERM value2 := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x2
END_FUZZIFY
DEFUZZIFY y
TERM value := (0, 1) (1, 0) ;
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 1.0); // Added range for y
END_DEFUZZIFY
RULEBLOCK No1
AND : PROD; // Use 'PROD' for 'and' (also implicit use 'ASUM' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF NOT(x1 IS value OR x2 IS value2) THEN y IS value;
RULE 2 : IF NOT(x1 IS value) AND NOT(x2 IS value2) THEN y IS value;
RULE 3 : IF NOT(x1 IS value AND x2 IS value2) THEN y IS value;
RULE 4 : IF NOT(x1 IS value) OR NOT(x2 IS value2) THEN y IS value;
END_RULEBLOCK
END_FUNCTION_BLOCK

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x1 x2 y No1.1 No1.2 No1.3 No1.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0

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FUNCTION_BLOCK testDeMorgan2
VAR_INPUT
x1 : REAL;
x2 : REAL;
END_VAR
VAR_OUTPUT
y : REAL;
END_VAR
FUZZIFY x1
TERM value := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x1
END_FUZZIFY
FUZZIFY x2
TERM value2 := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x2
END_FUZZIFY
DEFUZZIFY y
TERM value := (0, 1) (1, 0) ;
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 1.0); // Added range for y
END_DEFUZZIFY
RULEBLOCK No1
AND : BDIF; // Use 'BDIF' for 'and' (also implicit use 'BSUM' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF NOT(x1 IS value OR x2 IS value2) THEN y IS value;
RULE 2 : IF NOT(x1 IS value) AND NOT(x2 IS value2) THEN y IS value;
RULE 3 : IF NOT(x1 IS value AND x2 IS value2) THEN y IS value;
RULE 4 : IF NOT(x1 IS value) OR NOT(x2 IS value2) THEN y IS value;
END_RULEBLOCK
END_FUNCTION_BLOCK

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x1 x2 y No1.1 No1.2 No1.3 No1.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0

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FUNCTION_BLOCK testDeMorgan1
VAR_INPUT
x1 : REAL;
x2 : REAL;
END_VAR
VAR_OUTPUT
y : REAL;
END_VAR
FUZZIFY x1
TERM value := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x1
END_FUZZIFY
FUZZIFY x2
TERM value2 := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x2
END_FUZZIFY
DEFUZZIFY y
TERM value := (0, 1) (1, 0) ;
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 1.0); // Added range for y
END_DEFUZZIFY
RULEBLOCK No1
AND : DMIN; // Use 'DMIN' for 'and' (also implicit use 'DMAX' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF NOT(x1 IS value OR x2 IS value2) THEN y IS value;
RULE 2 : IF NOT(x1 IS value) AND NOT(x2 IS value2) THEN y IS value;
RULE 3 : IF NOT(x1 IS value AND x2 IS value2) THEN y IS value;
RULE 4 : IF NOT(x1 IS value) OR NOT(x2 IS value2) THEN y IS value;
END_RULEBLOCK
END_FUNCTION_BLOCK

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x1 x2 y No1.1 No1.2 No1.3 No1.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0

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FUNCTION_BLOCK testDeMorgan1
VAR_INPUT
x1 : REAL;
x2 : REAL;
END_VAR
VAR_OUTPUT
y : REAL;
END_VAR
FUZZIFY x1
TERM value := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x1
END_FUZZIFY
FUZZIFY x2
TERM value2 := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x2
END_FUZZIFY
DEFUZZIFY y
TERM value := (0, 1) (1, 0) ;
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 1.0); // Added range for y
END_DEFUZZIFY
RULEBLOCK No1
AND : NIPMIN; // Use 'NIPMIN' for 'and' (also implicit use 'NIPMAX' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF NOT(x1 IS value OR x2 IS value2) THEN y IS value;
RULE 2 : IF NOT(x1 IS value) AND NOT(x2 IS value2) THEN y IS value;
RULE 3 : IF NOT(x1 IS value AND x2 IS value2) THEN y IS value;
RULE 4 : IF NOT(x1 IS value) OR NOT(x2 IS value2) THEN y IS value;
END_RULEBLOCK
END_FUNCTION_BLOCK

26
Examples/jFuzzyLogic/testDeMorgan_5.fld Normal file
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x1 x2 y No1.1 No1.2 No1.3 No1.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0

42
Examples/jFuzzyLogic/testDeMorgan_6.fcl Normal file
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FUNCTION_BLOCK testDeMorgan1
VAR_INPUT
x1 : REAL;
x2 : REAL;
END_VAR
VAR_OUTPUT
y : REAL;
END_VAR
FUZZIFY x1
TERM value := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x1
END_FUZZIFY
FUZZIFY x2
TERM value2 := (0, 1) (1, 0) ;
RANGE := (0.0 .. 1.0); // Added range for x2
END_FUZZIFY
DEFUZZIFY y
TERM value := (0, 1) (1, 0) ;
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 1.0); // Added range for y
END_DEFUZZIFY
RULEBLOCK No1
AND : HAMACHER; // Use 'HAMACHER' for 'and' (also implicit use 'EINSTEIN' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF NOT(x1 IS value OR x2 IS value2) THEN y IS value;
RULE 2 : IF NOT(x1 IS value) AND NOT(x2 IS value2) THEN y IS value;
RULE 3 : IF NOT(x1 IS value AND x2 IS value2) THEN y IS value;
RULE 4 : IF NOT(x1 IS value) OR NOT(x2 IS value2) THEN y IS value;
END_RULEBLOCK
END_FUNCTION_BLOCK

26
Examples/jFuzzyLogic/testDeMorgan_6.fld Normal file
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x1 x2 y No1.1 No1.2 No1.3 No1.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0

56
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/*
Example: A tip calculation FIS (fuzzy inference system)
Calculates tip based on 'servie' and 'food'
If you want to about this example (and fuzzy logic), please
read Matlab's tutorial on fuzzy logic toolbox
http://www.mathworks.com/access/helpdesk/help/pdf_doc/fuzzy/fuzzy.pdf
Pablo Cingolani
pcingola@users.sourceforge.net
*/
FUNCTION_BLOCK tipper // Block definition (there may be more than one block per file)
VAR_INPUT // Define input variables
service : REAL;
food : REAL;
END_VAR
VAR_OUTPUT // Define output variable
tip : REAL;
END_VAR
FUZZIFY service // Fuzzify input variable 'service': {'poor', 'good' , 'excellent'}
TERM poor := (0, 1) (4, 0) ;
TERM good := (1, 0) (4,1) (6,1) (9,0);
TERM excellent := (6, 0) (9, 1);
RANGE := (0.0 .. 9.0); // Added range for service
END_FUZZIFY
FUZZIFY food // Fuzzify input variable 'food': { 'rancid', 'delicious' }
TERM rancid := (0, 1) (1, 1) (3,0) ;
TERM delicious := (7,0) (9,1);
RANGE := (0.0 .. 9.0); // Added range for food
END_FUZZIFY
DEFUZZIFY tip // Defzzzify output variable 'tip' : {'cheap', 'average', 'generous' }
TERM cheap := (0,0) (5,1) (10,0);
TERM average := (10,0) (15,1) (20,0);
TERM generous := (20,0) (25,1) (30,0);
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 30.0); // Added range for tip
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN; // Use 'min' for 'and' (also implicit use 'max' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF service IS poor OR food IS rancid THEN tip IS cheap;
RULE 2 : IF service IS good THEN tip IS average;
RULE 3 : IF service IS excellent AND food IS delicious THEN tip IS generous;
END_RULEBLOCK
END_FUNCTION_BLOCK

26
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service food tip No1.1 No1.2 No1.3
0.0 0.0 5.000020040080156 1.0 0.0 0.0
0.0 1.8 5.000020040080156 1.0 0.0 0.0
0.0 3.6 5.000020040080156 1.0 0.0 0.0
0.0 5.3999999999999995 5.000020040080156 1.0 0.0 0.0
0.0 7.2 5.000020040080156 1.0 0.0 0.0
1.8 0.0 8.161124892286946 1.0 0.26666666666666666 0.0
1.8 1.8 8.54950607575949 0.6 0.26666666666666666 0.0
1.8 3.6 8.66927574381595 0.55 0.26666666666666666 0.0
1.8 5.3999999999999995 8.66927574381595 0.55 0.26666666666666666 0.0
1.8 7.2 8.66927574381595 0.55 0.26666666666666666 0.0
3.6 0.0 9.955181584284231 1.0 0.8666666666666667 0.0
3.6 1.8 10.390254985117041 0.6 0.8666666666666667 0.0
3.6 3.6 13.37919506577268 0.09999999999999998 0.8666666666666667 0.0
3.6 5.3999999999999995 13.37919506577268 0.09999999999999998 0.8666666666666667 0.0
3.6 7.2 13.37919506577268 0.09999999999999998 0.8666666666666667 0.0
5.3999999999999995 0.0 10.000040019919915 1.0 1.0 0.0
5.3999999999999995 1.8 10.434808979091365 0.6 1.0 0.0
5.3999999999999995 3.6 15.000000000000037 0.0 1.0 0.0
5.3999999999999995 5.3999999999999995 15.000000000000037 0.0 1.0 0.0
5.3999999999999995 7.2 15.000000000000037 0.0 1.0 0.0
7.2 0.0 9.565238207039531 1.0 0.5999999999999999 0.0
7.2 1.8 10.000007142857134 0.6 0.5999999999999999 0.0
7.2 3.6 15.000000000000021 0.0 0.5999999999999999 0.0
7.2 5.3999999999999995 15.000000000000021 0.0 0.5999999999999999 0.0
7.2 7.2 16.84460906644311 0.0 0.5999999999999999 0.10000000000000009

56
Examples/jFuzzyLogic/tipper_with.fcl Normal file
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/*
Example: A tip calculation FIS (fuzzy inference system)
Calculates tip based on 'servie' and 'food'
If you want to about this example (and fuzzy logic), please
read Matlab's tutorial on fuzzy logic toolbox
http://www.mathworks.com/access/helpdesk/help/pdf_doc/fuzzy/fuzzy.pdf
Pablo Cingolani
pcingola@users.sourceforge.net
*/
FUNCTION_BLOCK tipper // Block definition (there may be more than one block per file)
VAR_INPUT // Define input variables
service : REAL;
food : REAL;
END_VAR
VAR_OUTPUT // Define output variable
tip : REAL;
END_VAR
FUZZIFY service // Fuzzify input variable 'service': {'poor', 'good' , 'excellent'}
TERM poor := (0, 1) (4, 0) ;
TERM good := (1, 0) (4,1) (6,1) (9,0);
TERM excellent := (6, 0) (9, 1);
RANGE := (0.0 .. 9.0); // Added range for service
END_FUZZIFY
FUZZIFY food // Fuzzify input variable 'food': { 'rancid', 'delicious' }
TERM rancid := (0, 1) (1, 1) (3,0) ;
TERM delicious := (7,0) (9,1);
RANGE := (0.0 .. 9.0); // Added range for food
END_FUZZIFY
DEFUZZIFY tip // Defzzzify output variable 'tip' : {'cheap', 'average', 'generous' }
TERM cheap := (0,0) (5,1) (10,0);
TERM average := (10,0) (15,1) (20,0);
TERM generous := (20,0) (25,1) (30,0);
METHOD : COG; // Use 'Center Of Gravity' defuzzification method
DEFAULT := 0; // Default value is 0 (if no rule activates defuzzifier)
RANGE := (0.0 .. 30.0); // Added range for tip
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN; // Use 'min' for 'and' (also implicit use 'max' for 'or' to fulfill DeMorgan's Law)
ACT : MIN; // Use 'min' activation method
ACCU : MAX; // Use 'max' accumulation method
RULE 1 : IF service IS poor OR food IS rancid THEN tip IS cheap WITH 0.8;
RULE 2 : IF service IS good THEN tip IS average WITH 0.5;
RULE 3 : IF service IS excellent AND food IS delicious THEN tip IS generous WITH 0.9;
END_RULEBLOCK
END_FUNCTION_BLOCK

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service food tip No1.1 No1.2 No1.3
0.0 0.0 5.0000250000000035 0.8 0.0 0.0
0.0 1.8 5.0000250000000035 0.8 0.0 0.0
0.0 3.6 5.0000250000000035 0.8 0.0 0.0
0.0 5.3999999999999995 5.0000250000000035 0.8 0.0 0.0
0.0 7.2 5.0000250000000035 0.8 0.0 0.0
1.8 0.0 7.058837543262971 0.8 0.13333333333333333 0.0
1.8 1.8 7.543607688597161 0.48 0.13333333333333333 0.0
1.8 3.6 7.661117431208402 0.44000000000000006 0.13333333333333333 0.0
1.8 5.3999999999999995 7.661117431208402 0.44000000000000006 0.13333333333333333 0.0
1.8 7.2 7.661117431208402 0.44000000000000006 0.13333333333333333 0.0
3.6 0.0 9.142384348411793 0.8 0.43333333333333335 0.0
3.6 1.8 9.819983982824105 0.48 0.43333333333333335 0.0
3.6 3.6 13.154974486118704 0.07999999999999999 0.43333333333333335 0.0
3.6 5.3999999999999995 13.154974486118704 0.07999999999999999 0.43333333333333335 0.0
3.6 7.2 13.154974486118704 0.07999999999999999 0.43333333333333335 0.0
5.3999999999999995 0.0 9.385978947368391 0.8 0.5 0.0
5.3999999999999995 1.8 10.068944038929448 0.48 0.5 0.0
5.3999999999999995 3.6 14.999999999999893 0.0 0.5 0.0
5.3999999999999995 5.3999999999999995 14.999999999999893 0.0 0.5 0.0
5.3999999999999995 7.2 14.999999999999893 0.0 0.5 0.0
7.2 0.0 8.469404081632552 0.8 0.29999999999999993 0.0
7.2 1.8 9.114238141335782 0.48 0.29999999999999993 0.0
7.2 3.6 14.999999999999948 0.0 0.29999999999999993 0.0
7.2 5.3999999999999995 14.999999999999948 0.0 0.29999999999999993 0.0
7.2 7.2 17.52089485261743 0.0 0.29999999999999993 0.09000000000000008

61
Examples/jFuzzyLogic/trust/LarsenFewRules.fcl Normal file
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FUNCTION_BLOCK LarsenTrustFewRules
VAR_INPUT
WTV : REAL;
OW : REAL;
AC : REAL;
END_VAR
VAR_OUTPUT
trustworthiness : REAL;
END_VAR
FUZZIFY WTV
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for WTV
END_FUZZIFY
FUZZIFY OW
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for OW
END_FUZZIFY
FUZZIFY AC
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for AC
END_FUZZIFY
DEFUZZIFY trustworthiness
METHOD : COG;
DEFAULT := 0;
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for trustworthiness
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : PROD;
RULE 1 : IF WTV IS fully AND OW IS high AND AC IS high THEN trustworthiness IS fully;
RULE 2 : IF WTV IS satISfactory AND OW IS high THEN trustworthiness IS satISfactory;
RULE 3 : IF WTV IS nothing AND AC IS NOT low THEN trustworthiness IS nothing;
END_RULEBLOCK
END_FUNCTION_BLOCK

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AC WTV OW trustworthiness No1.1 No1.2 No1.3
-4.0 -4.0 -4.0 0.11818521788846277 1.7108835426513894E-53 5.900090541597062E-29 3.353500927277926E-4
-4.0 -4.0 -1.4 0.11818521788846277 8.468294957935359E-45 2.920345291727967E-20 3.353500927277926E-4
-4.0 -4.0 1.2000000000000002 0.11818521793462167 4.8589318352387055E-39 1.6756334986383216E-14 3.353500927277926E-4
-4.0 -4.0 3.8 0.11818527438861667 3.231877855741909E-36 1.1145336016681301E-11 3.353500927277926E-4
-4.0 -4.0 6.4 0.11818526065974372 2.491944545793228E-36 8.593629009975718E-12 3.353500927277926E-4
-4.0 -1.4 -4.0 2.1506070932800485E-4 8.468294957935359E-45 1.6110272932682781E-22 0.3751851960038979
-4.0 -1.4 -1.4 2.150607098365393E-4 4.1915196275405104E-36 7.974040292384671E-14 0.3751851960038979
-4.0 -1.4 1.2000000000000002 2.1575008953929393E-4 2.4050069414740774E-30 4.5753387694457995E-8 0.3751851960038979
-4.0 -1.4 3.8 6.083944903499572E-4 1.5996702445350932E-27 3.0432483008403587E-5 0.3751851960038979
-4.0 -1.4 6.4 5.209621778103196E-4 1.2334282788115618E-27 2.3465014283569664E-5 0.3751851960038979
-4.0 1.2000000000000002 -4.0 1.9445568147248457E-4 4.8589318352387055E-39 5.099368797562266E-19 0.4865889687690498
-4.0 1.2000000000000002 -1.4 1.9445819024731551E-4 2.4050069414740774E-30 2.524015106845201E-10 0.4865889687690498
-4.0 1.2000000000000002 1.2000000000000002 0.0016710434696031785 1.3799430522844653E-24 1.4482274668268805E-4 0.4865889687690498
-4.0 1.2000000000000002 3.8 0.577164026339291 9.178575753047099E-22 0.09632763823049303 0.4865889687690498
-4.0 1.2000000000000002 6.4 0.4714804883570884 7.077155389805126E-22 0.07427357821433386 0.4865889687690498
-4.0 3.8 -4.0 0.055944801306810045 3.231877855741909E-36 1.8711096935856773E-18 7.315569265179297E-4
-4.0 3.8 -1.4 0.05594773365732699 1.5996702445350932E-27 9.261360220567725E-10 7.315569265179297E-4
-4.0 3.8 1.2000000000000002 1.249613600101666 9.178575753047099E-22 5.313976217982533E-4 7.315569265179297E-4
-4.0 3.8 3.8 2.99167697165677 6.105052865403128E-19 0.35345468195878016 7.315569265179297E-4
-4.0 3.8 6.4 2.9898878656220282 4.707310693283687E-19 0.27253179303401254 7.315569265179297E-4
-4.0 6.4 -4.0 1.2742182863113973 2.491944545793228E-36 7.958869386330616E-21 1.2749797779309919E-9
-4.0 6.4 -1.4 1.2774731103630723 1.2334282788115618E-27 3.939371197099807E-12 1.2749797779309919E-9
-4.0 6.4 1.2000000000000002 2.9991668530542963 7.077155389805126E-22 2.2603294069810525E-6 1.2749797779309919E-9
-4.0 6.4 3.8 2.9999906324435406 4.707310693283687E-19 0.0015034391929775704 1.2749797779309919E-9
-4.0 6.4 6.4 3.000001373992968 3.629579374926471E-19 0.0011592291739045892 1.2749797779309919E-9
-1.4 -4.0 -4.0 0.1729219238763693 8.468294957935358E-45 5.900090541597062E-29 2.0955978040084196E-4
-1.4 -4.0 -1.4 0.1729219238763693 4.1915196275405104E-36 2.920345291727967E-20 2.0955978040084196E-4
-1.4 -4.0 1.2000000000000002 0.17292192394878977 2.4050069414740774E-30 1.6756334986383216E-14 2.0955978040084196E-4
-1.4 -4.0 3.8 0.17292201249142544 1.599670244535093E-27 1.1145336016681301E-11 2.0955978040084196E-4
-1.4 -4.0 6.4 0.17292199096003727 1.2334282788115618E-27 8.593629009975718E-12 2.0955978040084196E-4
-1.4 -1.4 -4.0 3.210329871719679E-4 4.1915196275405104E-36 1.6110272932682781E-22 0.23445267793035454
-1.4 -1.4 -1.4 3.210329878971802E-4 2.0746604688815383E-27 7.974040292384671E-14 0.23445267793035454
-1.4 -1.4 1.2000000000000002 3.2201609985845305E-4 1.1903971046867634E-21 4.5753387694457995E-8 0.23445267793035454
-1.4 -1.4 3.8 8.81942728137606E-4 7.917826741826336E-19 3.0432483008403587E-5 0.23445267793035454
-1.4 -1.4 6.4 7.572634780808301E-4 6.105052865403078E-19 2.3465014283569664E-5 0.23445267793035454
-1.4 1.2000000000000002 -4.0 2.5077535291380453E-4 2.4050069414740774E-30 5.099368797562266E-19 0.30406873190723693
-1.4 1.2000000000000002 -1.4 2.507788651150245E-4 1.1903971046867634E-21 2.524015106845201E-10 0.30406873190723693
-1.4 1.2000000000000002 1.2000000000000002 0.002317738374462926 6.830251446447845E-16 1.4482274668268805E-4 0.30406873190723693
-1.4 1.2000000000000002 3.8 0.7645424405498797 4.543084601193931E-13 0.09632763823049303 0.30406873190723693
-1.4 1.2000000000000002 6.4 0.6314390511205998 3.5029525861903424E-13 0.07427357821433386 0.30406873190723693
-1.4 3.8 -4.0 0.08760995430479933 1.599670244535093E-27 1.8711096935856773E-18 4.5714884890833034E-4
-1.4 3.8 -1.4 0.08761453230590201 7.917826741826336E-19 9.261360220567725E-10 4.5714884890833034E-4
-1.4 3.8 1.2000000000000002 1.7309481614646514 4.543084601193931E-13 5.313976217982533E-4 4.5714884890833034E-4
-1.4 3.8 3.8 2.9948330988875123 3.0217947106968236E-10 0.35345468195878016 4.5714884890833034E-4
-1.4 3.8 6.4 2.9937213386441157 2.3299596036556137E-10 0.27253179303401254 4.5714884890833034E-4
-1.4 6.4 -4.0 1.3098271374500312 1.2334282788115618E-27 7.958869386330616E-21 7.967329906051629E-10
-1.4 6.4 -1.4 1.3149726846911323 6.105052865403078E-19 3.939371197099807E-12 7.967329906051629E-10
-1.4 6.4 1.2000000000000002 2.9992438212950003 3.5029525861903424E-13 2.2603294069810525E-6 7.967329906051629E-10
-1.4 6.4 3.8 2.9999907907692545 2.3299596036556137E-10 0.0015034391929775704 7.967329906051629E-10
-1.4 6.4 6.4 3.0000015758372998 1.7965190472569094E-10 0.0011592291739045892 7.967329906051629E-10
1.2000000000000002 -4.0 -4.0 0.19541886391891883 4.8589318352387055E-39 5.900090541597062E-29 1.7217543698070374E-4
1.2000000000000002 -4.0 -1.4 0.19541886391891883 2.4050069414740774E-30 2.920345291727967E-20 1.7217543698070374E-4
1.2000000000000002 -4.0 1.2000000000000002 0.1954188640063562 1.3799430522844651E-24 1.6756334986383216E-14 1.7217543698070374E-4
1.2000000000000002 -4.0 3.8 0.1954189708912178 9.178575753047099E-22 1.1145336016681301E-11 1.7217543698070374E-4
1.2000000000000002 -4.0 6.4 0.19541894489996384 7.077155389805127E-22 8.593629009975718E-12 1.7217543698070374E-4
1.2000000000000002 -1.4 -4.0 3.658255907130346E-4 2.4050069414740774E-30 1.6110272932682781E-22 0.19262757479866496
1.2000000000000002 -1.4 -1.4 3.658255915603294E-4 1.1903971046867632E-21 7.974040292384671E-14 0.19262757479866496
1.2000000000000002 -1.4 1.2000000000000002 3.669742005721377E-4 6.830251446447845E-16 4.5753387694457995E-8 0.19262757479866496
1.2000000000000002 -1.4 3.8 0.0010211482726604514 4.5430846011939316E-13 3.0432483008403587E-5 0.19262757479866496
1.2000000000000002 -1.4 6.4 8.754846634126227E-4 3.5029525861903424E-13 2.3465014283569664E-5 0.19262757479866496
1.2000000000000002 1.2000000000000002 -4.0 3.0973878205314495E-4 1.3799430522844651E-24 5.099368797562266E-19 0.24982449727784992
1.2000000000000002 1.2000000000000002 -1.4 3.097428639046232E-4 6.830251446447845E-16 2.524015106845201E-10 0.24982449727784992
1.2000000000000002 1.2000000000000002 1.2000000000000002 0.0027118045499758743 3.919056476030223E-10 1.4482274668268805E-4 0.24982449727784992
1.2000000000000002 1.2000000000000002 3.8 0.8622400664125656 2.6067276244598225E-7 0.09632763823049303 0.24982449727784992
1.2000000000000002 1.2000000000000002 6.4 0.7162527173769472 2.009921468597709E-7 0.07427357821433386 0.24982449727784992
1.2000000000000002 3.8 -4.0 0.10592800851539386 9.178575753047099E-22 1.8711096935856773E-18 3.755959405734386E-4
1.2000000000000002 3.8 -1.4 0.1059335284221209 4.5430846011939316E-13 9.261360220567725E-10 3.755959405734386E-4
1.2000000000000002 3.8 1.2000000000000002 1.9191107387019437 2.6067276244598225E-7 5.313976217982533E-4 3.755959405734386E-4
1.2000000000000002 3.8 3.8 2.9968203623618237 1.7338430690350557E-4 0.35345468195878016 3.755959405734386E-4
1.2000000000000002 3.8 6.4 2.9958183639500877 1.3368824479140015E-4 0.27253179303401254 3.755959405734386E-4
1.2000000000000002 6.4 -4.0 1.3246718366863455 7.077155389805127E-22 7.958869386330616E-21 6.546000885856803E-10
1.2000000000000002 6.4 -1.4 1.3309026891174796 3.5029525861903424E-13 3.939371197099807E-12 6.546000885856803E-10
1.2000000000000002 6.4 1.2000000000000002 3.012855370211627 2.009921468597709E-7 2.2603294069810525E-6 6.546000885856803E-10
1.2000000000000002 6.4 3.8 3.090872642180729 1.3368824479140015E-4 0.0015034391929775704 6.546000885856803E-10
1.2000000000000002 6.4 6.4 3.0865277521955643 1.0308053314970442E-4 0.0011592291739045892 6.546000885856803E-10
3.8 -4.0 -4.0 0.11823171892053663 3.231877855741909E-36 5.900090541597062E-29 3.352171355399688E-4
3.8 -4.0 -1.4 0.11823171892053663 1.5996702445350932E-27 2.920345291727967E-20 3.352171355399688E-4
3.8 -4.0 1.2000000000000002 0.11823171896671306 9.178575753047099E-22 1.6756334986383216E-14 3.352171355399688E-4
3.8 -4.0 3.8 0.1182317754421317 6.105052865403128E-19 1.1145336016681301E-11 3.352171355399688E-4
3.8 -4.0 6.4 0.11823176170804936 4.707310693283687E-19 8.593629009975718E-12 3.352171355399688E-4
3.8 -1.4 -4.0 2.158401953449656E-4 1.5996702445350932E-27 1.6110272932682781E-22 0.37503644528142743
3.8 -1.4 -1.4 2.15840195853646E-4 7.917826741826335E-19 7.974040292384671E-14 0.37503644528142743
3.8 -1.4 1.2000000000000002 2.16529773403126E-4 4.5430846011939316E-13 4.5753387694457995E-8 0.37503644528142743
3.8 -1.4 3.8 6.09286845352862E-4 3.0217947106968236E-10 3.0432483008403587E-5 0.37503644528142743
3.8 -1.4 6.4 5.218294452864596E-4 2.3299596036556137E-10 2.3465014283569664E-5 0.37503644528142743
3.8 1.2000000000000002 -4.0 1.9344334783557006E-4 9.178575753047099E-22 5.099368797562266E-19 0.48639604948166454
3.8 1.2000000000000002 -1.4 1.9344585727809589E-4 4.5430846011939316E-13 2.524015106845201E-10 0.48639604948166454
3.8 1.2000000000000002 1.2000000000000002 0.001670845937881152 2.606727624459823E-7 1.4482274668268805E-4 0.48639604948166454
3.8 1.2000000000000002 3.8 0.5783338998811626 1.7338430690350557E-4 0.09632763823049303 0.48639604948166454
3.8 1.2000000000000002 6.4 0.4724026329155365 1.3368824479140015E-4 0.07427357821433386 0.48639604948166454
3.8 3.8 -4.0 0.05596441041914192 6.105052865403128E-19 1.8711096935856773E-18 7.312668841001935E-4
3.8 3.8 -1.4 0.0559673438691831 3.0217947106968236E-10 9.261360220567725E-10 7.312668841001935E-4
3.8 3.8 1.2000000000000002 1.4995949560697341 1.7338430690350557E-4 5.313976217982533E-4 7.312668841001935E-4
3.8 3.8 3.8 3.4872129273604524 0.11532512103806247 0.35345468195878016 7.312668841001935E-4
3.8 3.8 6.4 3.472382363106076 0.08892161745938626 0.27253179303401254 7.312668841001935E-4
3.8 6.4 -4.0 1.274247315431523 4.707310693283687E-19 7.958869386330616E-21 1.2744742831376938E-9
3.8 6.4 -1.4 1.5157226202419296 2.3299596036556137E-10 3.939371197099807E-12 1.2744742831376938E-9
3.8 6.4 1.2000000000000002 4.745343081506798 1.3368824479140015E-4 2.2603294069810525E-6 1.2744742831376938E-9
3.8 6.4 3.8 4.967707742115026 0.08892161745938626 0.0015034391929775704 1.2744742831376938E-9
3.8 6.4 6.4 4.968274483980691 0.06856315415427784 0.0011592291739045892 1.2744742831376938E-9
6.4 -4.0 -4.0 0.11814588747172593 2.4919445457932275E-36 5.900090541597062E-29 3.3546262747466025E-4
6.4 -4.0 -1.4 0.11814588747172593 1.2334282788115618E-27 2.920345291727967E-20 3.3546262747466025E-4
6.4 -4.0 1.2000000000000002 0.11814588751787 7.077155389805126E-22 1.6756334986383216E-14 3.3546262747466025E-4
6.4 -4.0 3.8 0.11814594395374475 4.707310693283687E-19 1.1145336016681301E-11 3.3546262747466025E-4
6.4 -4.0 6.4 0.11814593022927795 3.629579374926471E-19 8.593629009975718E-12 3.3546262747466025E-4
6.4 -1.4 -4.0 2.144013031828958E-4 1.2334282788115618E-27 1.6110272932682781E-22 0.3753110983727249
6.4 -1.4 -1.4 2.144013036913068E-4 6.105052865403078E-19 7.974040292384671E-14 0.3753110983727249
6.4 -1.4 1.2000000000000002 2.1509051602560476E-4 3.5029525861903424E-13 4.5753387694457995E-8 0.3753110983727249
6.4 -1.4 3.8 6.076396026685148E-4 2.3299596036556137E-10 3.0432483008403587E-5 0.3753110983727249
6.4 -1.4 6.4 5.202285129267842E-4 1.796519047256909E-10 2.3465014283569664E-5 0.3753110983727249
6.4 1.2000000000000002 -4.0 1.9531209836435727E-4 7.077155389805126E-22 5.099368797562266E-19 0.486752255339164
6.4 1.2000000000000002 -1.4 1.953146065743299E-4 3.5029525861903424E-13 2.524015106845201E-10 0.486752255339164
6.4 1.2000000000000002 1.2000000000000002 0.0016718500626351936 2.0099214685977092E-7 1.4482274668268805E-4 0.486752255339164
6.4 1.2000000000000002 3.8 0.577827786469533 1.3368824479140015E-4 0.09632763823049303 0.486752255339164
6.4 1.2000000000000002 6.4 0.47199161125427697 1.0308053314970442E-4 0.07427357821433386 0.486752255339164
6.4 3.8 -4.0 0.05592821574648544 4.707310693283687E-19 1.8711096935856773E-18 7.318024179471264E-4
6.4 3.8 -1.4 0.0559311471670107 2.3299596036556137E-10 9.261360220567725E-10 7.318024179471264E-4
6.4 3.8 1.2000000000000002 1.437132610774914 1.3368824479140015E-4 5.313976217982533E-4 7.318024179471264E-4
6.4 3.8 3.8 3.398268456240319 0.08892161745938626 0.35345468195878016 7.318024179471264E-4
6.4 3.8 6.4 3.3842499889485675 0.06856315415427784 0.27253179303401254 7.318024179471264E-4
6.4 6.4 -4.0 1.2741937338428944 3.629579374926471E-19 7.958869386330616E-21 1.2754076278993749E-9
6.4 6.4 -1.4 1.4591715092476663 1.796519047256909E-10 3.939371197099807E-12 1.2754076278993749E-9
6.4 6.4 1.2000000000000002 4.709551732065541 1.0308053314970442E-4 2.2603294069810525E-6 1.2754076278993749E-9
6.4 6.4 3.8 4.959747714315154 0.06856315415427784 0.0015034391929775704 1.2754076278993749E-9
6.4 6.4 6.4 4.9603435569236325 0.052865728738350284 0.0011592291739045892 1.2754076278993749E-9

86
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FUNCTION_BLOCK LarsenTrustManyRules
VAR_INPUT
WTV : REAL;
OW : REAL;
AC : REAL;
END_VAR
VAR_OUTPUT
trustworthiness : REAL;
END_VAR
FUZZIFY WTV
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for WTV
END_FUZZIFY
FUZZIFY OW
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for OW
END_FUZZIFY
FUZZIFY AC
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for AC
END_FUZZIFY
DEFUZZIFY trustworthiness
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-4.0 .. 9.0); // Added range for trustworthiness
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : PROD;
RULE 1 : IF WTV IS fully AND AC IS high THEN trustworthiness IS fully;
RULE 2 : IF WTV IS fully AND AC IS medium THEN trustworthiness IS fully WITH 0.8;
RULE 3 : IF WTV IS fully AND AC IS low THEN trustworthiness IS fully WITH 0.6;
RULE 4 : IF WTV IS largely AND AC IS high AND OW IS NOT high THEN trustworthiness IS fully;
RULE 5 : IF WTV IS largely AND AC IS medium AND OW IS NOT high THEN trustworthiness IS fully WITH 0.66;
RULE 6 : IF WTV IS largely AND AC IS low AND OW IS NOT high THEN trustworthiness IS largely WITH 0.33;
RULE 7 : IF WTV IS largely AND AC IS high AND OW IS high THEN trustworthiness IS largely WITH 0.66;
RULE 8 : IF WTV IS largely AND AC IS medium AND OW IS high THEN trustworthiness IS largely WITH 0.33;
RULE 9 : IF WTV IS largely AND AC IS low AND OW IS high THEN trustworthiness IS largely WITH 0.1;
RULE 10 : IF WTV IS satISfactory AND AC IS high THEN trustworthiness IS largely;
RULE 11 : IF WTV IS satISfactory AND AC IS medium THEN trustworthiness IS largely WITH 0.66;
RULE 12 : IF WTV IS satISfactory AND AC IS low THEN trustworthiness IS satISfactory WITH 0.33;
RULE 13 : IF WTV IS satISfactory AND AC IS high AND OW IS high THEN trustworthiness IS satISfactory;
RULE 14 : IF WTV IS satISfactory AND AC IS medium AND OW IS high THEN trustworthiness IS satISfactory WITH 0.66;
RULE 15 : IF WTV IS satISfactory AND AC IS low AND OW IS high THEN trustworthiness IS satISfactory WITH 0.33;
RULE 16 : IF WTV IS satISfactory AND AC IS high AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.5;
RULE 17 : IF WTV IS satISfactory AND AC IS medium AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.7;
RULE 18 : IF WTV IS satISfactory AND AC IS low AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.9;
RULE 19 : IF WTV IS partially AND AC IS high AND OW IS high THEN trustworthiness IS satISfactory;
RULE 20 : IF WTV IS partially AND AC IS medium AND OW IS high THEN trustworthiness IS satISfactory WITH 0.33;
RULE 21 : IF WTV IS partially AND AC IS low AND OW IS high THEN trustworthiness IS partially WITH 0.66;
RULE 22 : IF WTV IS partially AND AC IS high AND OW IS NOT high THEN trustworthiness IS partially WITH 0.6;
RULE 23 : IF WTV IS partially AND AC IS medium AND OW IS NOT high THEN trustworthiness IS partially WITH 0.75;
RULE 24 : IF WTV IS partially AND AC IS low AND OW IS NOT high THEN trustworthiness IS partially WITH 0.9;
RULE 25 : IF WTV IS minimal AND AC IS high AND OW IS high THEN trustworthiness IS minimal WITH 0.5;
RULE 26 : IF WTV IS minimal AND AC IS medium AND OW IS high THEN trustworthiness IS minimal WITH 0.3;
RULE 27 : IF WTV IS minimal AND AC IS low AND OW IS high THEN trustworthiness IS minimal WITH 0.1;
RULE 28 : IF WTV IS minimal AND AC IS high AND OW IS NOT high THEN trustworthiness IS minimal WITH 0.4;
RULE 29 : IF WTV IS minimal AND AC IS medium AND OW IS NOT high THEN trustworthiness IS nothing WITH 0.8;
RULE 30 : IF WTV IS minimal AND AC IS low AND OW IS NOT high THEN trustworthiness IS nothing WITH 0.95;
RULE 31 : IF WTV IS nothing THEN trustworthiness IS nothing;
END_RULEBLOCK
END_FUNCTION_BLOCK

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AC WTV OW trustworthiness No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7 No1.8 No1.9 No1.10 No1.11 No1.12 No1.13 No1.14 No1.15 No1.16 No1.17 No1.18 No1.19 No1.20 No1.21 No1.22 No1.23 No1.24 No1.25 No1.26 No1.27 No1.28 No1.29 No1.30 No1.31
-4.0 -4.0 -4.0 0.11814590917809341 6.639677199580735E-36 1.3794073625809013E-27 5.186434267821657E-22 3.263247861014401E-32 5.593061367045755E-24 1.4019569042462244E-18 5.549674102370404E-50 7.205980319737613E-42 1.0946977029531417E-36 5.900090541597062E-29 1.0112492178274336E-20 2.5347975461166915E-15 1.5203100247718292E-46 2.6057436111642533E-38 6.531557597224785E-33 2.950045270798531E-29 1.0725370492109144E-20 6.913084216681886E-15 1.0112214926104486E-43 8.665942804123523E-36 8.688821773082804E-30 2.3546375147684775E-26 7.643454047279237E-18 4.5981801252569924E-12 1.2371887048697986E-41 1.927714808756546E-33 3.221340285992516E-28 3.8410720218034705E-24 1.9949776426683007E-15 1.1876452230674055E-9 3.3546262790251185E-4
-4.0 -4.0 -1.4 0.11814590917809335 6.639677199580735E-36 1.3794073625809013E-27 5.186434267821657E-22 3.2632478568524295E-32 5.593061359912321E-24 1.4019569024581579E-18 2.7469010045217157E-41 3.5667165699687486E-33 5.418383430128308E-28 5.900090541597062E-29 1.0112492178274336E-20 2.5347975461166915E-15 7.525020491647316E-38 1.2897549677693489E-29 3.2329001296211116E-24 2.9500452670360205E-29 1.0725370478429922E-20 6.913084207864885E-15 5.005204418506582E-35 4.289348627445915E-27 4.3006729433710306E-21 2.354637511765355E-26 7.643454037530716E-18 4.598180119392439E-12 6.1236657027089455E-33 9.54153639822384E-25 1.5944545038634991E-19 3.8410720169045376E-24 1.9949776401238914E-15 1.1876452215526736E-9 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 0.11814590916090019 6.639677199580735E-36 1.3794073625809013E-27 5.186434267821657E-22 3.260859808336304E-32 5.5889683512084044E-24 1.4009309487925308E-18 1.5761147675439324E-35 2.0465079186755038E-27 3.1089559202835345E-22 5.900090541597062E-29 1.0112492178274336E-20 2.5347975461166915E-15 4.317700529954529E-32 7.40034623697102E-24 1.8549709756204818E-18 2.947886420533554E-29 1.0717521640039629E-20 6.908025204930193E-15 2.8718823814904615E-29 2.4611391905763012E-21 2.4676368479482006E-15 2.352914385339583E-26 7.637860549118834E-18 4.594815165918881E-12 3.51363224581236E-27 5.474735491939222E-19 9.148648915921748E-14 3.838261116006821E-24 1.9935177132037836E-15 1.1867761014203929E-9 3.3546262790251185E-4
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3.8 -1.4 -4.0 0.0326090422973306 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 2.266180127765707E-7 1.3199319826783202E-7 1.1243311274018632E-10 3.854001198198379E-25 1.7005720599836953E-25 8.779176441142381E-29 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 7.841711694114211E-23 4.5673889454713485E-23 3.8905471113174165E-26 1.5216241504201794E-5 1.8799600395012153E-5 4.11780489250122E-8 3.873997628687189E-21 1.1282009996190496E-21 3.8440511131905296E-24 9.020635157865439E-4 9.950853232702445E-4 2.0342964662829508E-6 3.520329803231932E-20 1.8640080884570515E-20 1.058520359677183E-23 0.010929488978917025 0.0192904803414535 3.902557746403569E-5 0.37531109885139957
3.8 -1.4 -1.4 0.032609042259867264 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 2.2661801248754035E-7 1.3199319809948686E-7 1.1243311259678828E-10 1.9076002603895674E-16 8.417256605816809E-17 4.345395448490758E-20 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 3.88138210143395E-14 2.2607030702934155E-14 1.92568925149176E-17 1.5216241484794883E-5 1.8799600371035E-5 4.1178048872493396E-8 1.917497817252069E-12 5.584213418644324E-13 1.9026753047977673E-15 9.020635146360452E-4 9.950853220011052E-4 2.0342964636883936E-6 1.742444203816442E-11 9.226209676766952E-12 5.239317815188483E-15 0.01092948896497747 0.01929048031685027 3.902557741426216E-5 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.03258754652279105 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 2.264521731666589E-7 1.3189660532606383E-7 1.1235083391632079E-10 1.0945414254177502E-10 4.8296470884084643E-11 2.493297692894826E-14 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 2.227056467808863E-8 1.2971444869167263E-8 1.1049205129637537E-11 1.520510622186275E-5 1.8785842801969098E-5 4.11479147292041E-8 1.100220438060694E-6 3.204105724871138E-7 1.0917155881469651E-9 9.014033835237074E-4 9.94357117423683E-4 2.032807763208205E-6 9.997783089873683E-6 5.293807565715704E-6 3.0062117880409034E-9 0.010921490752445126 0.019276363521278258 3.8997018452049296E-5 0.37531109885139957
3.8 -1.4 3.8 0.024405332772771893 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 1.1631118381640927E-7 6.774521123959297E-8 5.770604146929881E-11 7.280250711370656E-8 3.212399351411952E-8 1.6583960991178033E-11 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 1.4813079758803949E-5 8.627848023222818E-6 7.3492863437692484E-9 7.80970162479982E-6 9.648852491594016E-6 2.1134540714732432E-8 7.318024188804728E-4 2.1311841142875764E-4 7.26145489120058E-7 4.6298206445826026E-4 5.107252972957955E-4 1.0440980720283263E-6 0.0066499417712218845 0.0035211318092827396 1.9995566179747367E-6 0.005609535561939517 0.009900795516699527 2.0029789593275692E-5 0.37531109885139957
3.8 -1.4 6.4 0.023725780630822733 6.208075409403594E-10 4.3828858558482145E-10 5.600078330074594E-13 1.415657573818755E-7 8.245468598502135E-8 7.023571765038373E-11 5.613448856049884E-8 2.4769256141405325E-8 1.2787089421152303E-11 3.0432483008403587E-5 1.7725337515297175E-5 1.509861793917114E-8 1.142164863866049E-5 6.652515900378115E-6 5.666678889887771E-9 9.505417184871548E-6 1.174390171279294E-5 2.5723470134409186E-8 5.64257415572674E-4 1.643252890960804E-4 5.598956309101268E-7 5.635090664429395E-4 6.216187571427892E-4 1.270802424132778E-6 0.0051274481482020086 0.002714974265370836 1.5417613011934358E-6 0.006827530460355418 0.012050548967131269 2.4378845102698046E-5 0.37531109885139957
3.8 1.2000000000000002 -4.0 1.090334644033002 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.009657697627537778 0.005625106239984455 4.7915211719650505E-6 1.642445707308593E-20 7.247266246815995E-21 3.741389770765924E-24 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 2.4821292661853207E-19 1.4457111168872598E-19 1.2314710388704924E-22 0.04816381911524652 0.05950618966018971 1.3034047201441925E-4 9.107668645013996E-19 2.652371491767259E-19 9.037265158394215E-22 0.21207280917526808 0.23394199652448516 4.782578596184279E-4 6.14703336847797E-19 3.254842744659578E-19 1.8483381773421468E-22 0.19084556620841372 0.336841242101857 6.81446172103848E-4 0.48675225595997157
3.8 1.2000000000000002 -1.4 1.0903346436015653 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.009657697615220277 0.005625106232810152 4.791521165853908E-6 8.129550816959362E-12 3.587151707685354E-12 1.851861412048417E-15 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 1.2285700473339503E-10 7.15578112511078E-11 6.095365189584572E-14 0.048163819053818016 0.05950618958429506 1.30340471848182E-4 4.5079879806192804E-10 1.3128341918290835E-10 4.473140635613002E-13 0.21207280890478883 0.23394199622611378 4.7825785900845416E-4 3.0425736400428474E-10 1.6110370879340108E-10 9.148648915921711E-14 0.19084556596500782 0.3368412416722471 6.814461712347263E-4 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 1.0900870551993387 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.00965063010105313 0.0056209897736315744 4.788014725181289E-6 4.66456747986791E-6 2.058233176440133E-6 1.0625596314427338E-9 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 7.04927986621179E-5 4.1058386472733474E-5 3.497393998933068E-8 0.04812857271591546 0.05946264288665802 1.3024508854172107E-4 2.586589912220635E-4 7.532770033111198E-5 2.56659522912733E-7 0.21191761378053484 0.23377079720555083 4.779078693599106E-4 1.745765587299132E-4 9.24379634058157E-5 5.249304811394053E-8 0.1907059049614298 0.3365947408661082 6.809474881467656E-4 0.48675225595997157
3.8 1.2000000000000002 3.8 1.0979575866323525 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.0049567915199545 0.002887073087657509 2.4592374320310954E-6 0.003102598031004964 0.0013690165761634735 7.06752648464835E-7 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 0.046887695219988486 0.027309642228640463 2.3262623558498908E-5 0.024719971505252274 0.03054141759951044 6.689695321851315E-5 0.1720448638230505 0.050103589611712115 1.7071493420656197E-4 0.1088458908814378 0.12007020195241219 2.4546476751857076E-4 0.11611813736487936 0.06148433793503919 3.4915311745982646E-5 0.09795105631651024 0.17288300760841918 3.4975071051701297E-4 0.48675225595997157
3.8 1.2000000000000002 6.4 1.0016548294649432 3.562064783070342E-4 2.5148089102921975E-4 3.213208681675866E-7 0.006033056518572022 0.003513941435900026 2.9932100957451026E-6 0.0023922631319173993 0.0010555824020422146 5.449427503696812E-7 0.09632763823049303 0.05610583596532173 4.779150640528706E-5 0.036152831754046405 0.02105714294812126 1.7936682784731978E-5 0.030087403238223315 0.037172856230364135 8.142224623787751E-5 0.13265546508012166 0.0386324522245617 1.3163002074032104E-4 0.1324795301271951 0.14614096874139038 2.9876237679071744E-4 0.08953307395574654 0.04740759626689432 2.6921506490565795E-5 0.11921910704381647 0.21042098539013884 4.2569186044347305E-4 0.48675225595997157
3.8 3.8 -4.0 4.1068411046761115 0.23692775868212168 0.16727041053862637 2.137238869842205E-4 0.47711391552103427 0.277894024734032 2.3671288083607357E-4 8.114084046390922E-19 3.580327019125536E-19 1.8483381773421468E-22 0.35345468195878016 0.20586895694154697 1.753612151934236E-4 9.107668645013996E-19 5.304742983534518E-19 4.518632579197107E-22 0.17672734097939008 0.2183458634228528 4.782578596184279E-4 2.4821292661853207E-19 7.228555584436299E-20 2.4629420777409848E-22 0.057796582938295815 0.06375663177877469 1.3034047201441925E-4 1.2442770509913582E-20 6.5884238607418124E-21 3.741389770765924E-24 0.0038630790510151116 0.006818310593920552 1.3793773070808478E-5 7.318024188804728E-4
3.8 3.8 -1.4 4.106841104577381 0.23692775868212168 0.16727041053862637 2.137238869842205E-4 0.47711391491251953 0.27789402437960387 2.3671288053416813E-4 4.016197204856559E-10 1.772140796727412E-10 9.148648915921711E-14 0.35345468195878016 0.20586895694154697 1.753612151934236E-4 4.5079879806192804E-10 2.625668383658167E-10 2.236570317806501E-13 0.17672734075399069 0.21834586314437285 4.7825785900845416E-4 1.2285700473339503E-10 3.57789056255539E-11 1.2190730379169143E-13 0.057796582864581614 0.063756631697459 1.30340471848182E-4 6.158750618908607E-12 3.261047006986685E-12 1.851861412048417E-15 0.003863079046088111 0.0068183105852244255 1.3793773053215793E-5 7.318024188804728E-4
3.8 3.8 1.2000000000000002 4.106784416185404 0.23692775868212168 0.16727041053862637 2.137238869842205E-4 0.47676476240357446 0.2776906612145392 2.3653965377729756E-4 2.3044105752348544E-4 1.016817597463973E-4 5.249304811394053E-8 0.35345468195878016 0.20586895694154697 1.753612151934236E-4 2.586589912220635E-4 1.5065540066222397E-4 1.283297614563665E-7 0.17659801148377904 0.21818607739184742 4.779078693599106E-4 7.04927986621179E-5 2.0529193236366737E-5 6.994787997866136E-8 0.05775428725909855 0.06370997452141931 1.3024508854172107E-4 3.5337632423241746E-6 1.8711210694910295E-6 1.0625596314427338E-9 0.003860252040421252 0.006813320937735241 1.3783678754309771E-5 7.318024188804728E-4
3.8 3.8 3.8 3.9856469641250407 0.23692775868212168 0.16727041053862637 2.137238869842205E-4 0.24487764079127558 0.14262848127694583 1.2149235207433084E-4 0.15327594132164077 0.06763277172854311 3.4915311745982646E-5 0.35345468195878016 0.20586895694154697 1.753612151934236E-4 0.1720448638230505 0.10020717922342423 8.535746710328098E-5 0.09070490906786484 0.11206552182225137 2.4546476751857076E-4 0.046887695219988486 0.013654821114320231 4.6525247116997817E-5 0.029663965806302726 0.0327229474280469 6.689695321851315E-5 0.0023504530537916393 0.0012445605237849757 7.06752648464835E-7 0.0019827166079818 0.00349948253049395 7.0796229103925475E-6 7.318024188804728E-4
3.8 3.8 6.4 4.071487676563545 0.23692775868212168 0.16727041053862637 2.137238869842205E-4 0.29804776760954116 0.17359731294686453 1.4787190941720644E-4 0.11818365762158545 0.052148355893583756 2.6921506490565795E-5 0.35345468195878016 0.20586895694154697 1.753612151934236E-4 0.13265546508012166 0.0772649044491234 6.581501037016052E-5 0.11039960843932925 0.13639823749196436 2.9876237679071744E-4 0.036152831754046405 0.01052857147406063 3.5873365569463956E-5 0.036104883885867974 0.03982806024681872 8.142224623787751E-5 0.001812320554482878 9.596203654929223E-4 5.449427503696812E-7 0.002413222607428809 0.004259322952606092 8.616816942296507E-6 7.318024188804728E-4
3.8 6.4 -4.0 4.985531166774085 0.18268352405273452 0.12897411530382139 1.6479214198328535E-4 0.027323722447292535 0.015914646281699123 1.3556253224349228E-5 4.646835340266146E-20 2.050408897302755E-20 1.058520359677182E-23 0.0015034391929775704 8.756750844778135E-4 7.459087043037473E-7 3.873997628687182E-21 2.2564019992380948E-21 1.922025556595262E-24 7.517195964887852E-4 9.287463017188931E-4 2.0342964662829474E-6 7.841711694114211E-23 2.2836944727356742E-23 7.781094222634833E-26 1.8259489805042153E-5 2.014242899465588E-5 4.11780489250122E-8 2.9196978774230145E-25 1.5459745999851774E-25 8.779176441142381E-29 9.064720511062828E-8 1.5999175547616E-7 3.2367108213083937E-10 1.2754076295260396E-9
3.8 6.4 -1.4 4.985531166781584 0.18268352405273452 0.12897411530382139 1.6479214198328535E-4 0.02732372241244365 0.01591464626140146 1.3556253207059478E-5 2.3000263490377015E-11 1.014883064444364E-11 5.239317815188479E-15 0.0015034391929775704 8.756750844778135E-4 7.459087043037473E-7 1.917497817252065E-12 1.1168426837288626E-12 9.51337652398882E-16 7.517195955300363E-4 9.28746300534363E-4 2.03429646368839E-6 3.88138210143395E-14 1.1303515351467077E-14 3.85137850298352E-17 1.825948978175386E-5 2.0142428968966073E-5 4.1178048872493396E-8 1.445151712416339E-16 7.652051459833462E-17 4.345395448490758E-20 9.064720499501614E-8 1.5999175527210532E-7 3.236710817180268E-10 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.9855354698108 0.18268352405273452 0.12897411530382139 1.6479214198328535E-4 0.027303726881112787 0.01590299990505455 1.3546332725448693E-5 1.3197073678633248E-5 5.82318832228727E-6 3.0062117880409013E-9 0.0015034391929775704 8.756750844778135E-4 7.459087043037473E-7 1.100220438060692E-6 6.408211449742263E-7 5.458577940734816E-10 7.511694862697548E-4 9.280666429287689E-4 2.0328077632082017E-6 2.227056467808863E-8 6.485722434583632E-9 2.2098410259275074E-11 1.8246127466235298E-5 2.0127688716395463E-5 4.11479147292041E-8 8.291980495589016E-11 4.390588262189513E-11 2.493297692894826E-14 9.058086926666357E-8 1.598746731225016E-7 3.2343421885001435E-10 1.2754076295260396E-9
3.8 6.4 3.8 4.954528678224563 0.18268352405273452 0.12897411530382139 1.6479214198328535E-4 0.01402383890484878 0.008168156301277103 6.957716385032604E-6 0.00877792313801288 0.0038732449902110105 1.999556617974735E-6 0.0015034391929775704 8.756750844778135E-4 7.459087043037473E-7 7.318024188804715E-4 4.262368228575144E-4 3.630727445600284E-7 3.858183870485495E-4 4.7667694414274146E-4 1.0440980720283247E-6 1.4813079758803949E-5 4.313924011611409E-6 1.4698572687538497E-8 9.371641949759784E-6 1.033805624099359E-5 2.1134540714732432E-8 5.515341448008072E-8 2.920363046738138E-8 1.6583960991178033E-11 4.652447352656371E-8 8.2115407563143E-8 1.6612345271464809E-10 1.2754076295260396E-9
3.8 6.4 6.4 4.963960087349446 0.18268352405273452 0.12897411530382139 1.6479214198328535E-4 0.017068826150888526 0.009941702897883289 8.468440930410894E-6 0.006768231555626646 0.002986471691907917 1.5417613011934347E-6 0.0015034391929775704 8.756750844778135E-4 7.459087043037473E-7 5.64257415572673E-4 3.2865057819216016E-4 2.799478154550629E-7 4.695908887024487E-4 5.801775066666021E-4 1.2708024241327757E-6 1.142164863866049E-5 3.3262579501890577E-6 1.1333357779775543E-8 1.1406500621845857E-5 1.2582751835135294E-5 2.5723470134409186E-8 4.2526127697347605E-8 2.251750558309575E-8 1.2787089421152303E-11 5.66263029527502E-8 9.994507392123801E-8 2.0219373262989252E-10 1.2754076295260396E-9
6.4 -4.0 -4.0 0.11935466136816546 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 4.75300188826657E-15 4.162085330031037E-18 5.330151209755528E-24 8.083238727575837E-33 5.362341481533565E-36 4.161971218972297E-42 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 2.2143694644895412E-29 1.9390681678819702E-32 2.4832567622436145E-38 4.296814504987859E-12 7.981301161359141E-15 2.6283107625551702E-20 1.4728693217741423E-26 6.448774838846762E-30 3.303434916214345E-35 3.4295882600835904E-9 5.687888237448516E-12 1.748198912754018E-17 1.801996201588033E-24 1.4345119782311823E-27 1.2247331405417894E-33 5.594617188203596E-7 1.4845657208791554E-9 4.515351793853226E-15 3.3546262790251185E-4
6.4 -4.0 -1.4 0.1193546613666681 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 4.753001882204555E-15 4.162085324722682E-18 5.3301512029574124E-24 4.000929815154925E-24 2.6541776923299946E-27 2.0600350058938336E-33 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 1.0960380004639338E-20 9.597731686471491E-24 1.229128119753698E-29 4.296814499507669E-12 7.981301151179729E-15 2.6283107592030028E-20 7.290205055072577E-18 3.1919254637306326E-21 1.6350885695874413E-26 3.4295882557094676E-9 5.687888230194139E-12 1.7481989105243518E-17 8.919271807640483E-16 7.100349176032873E-19 6.062014871446836E-25 5.594617181068179E-7 1.4845657189857288E-9 4.515351788094312E-15 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.11935380279200354 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 4.749523629987793E-15 4.1590395059189335E-18 5.32625059220723E-24 2.295650463992774E-18 1.522912056051822E-21 1.182005317665992E-27 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 6.288838496461633E-15 5.5069791816563666E-18 7.052481969893996E-24 4.293670085739628E-12 7.975460425863445E-15 2.626387358381563E-20 4.18296830748874E-12 1.83146056301411E-15 9.381798748942423E-21 3.4270784790990973E-9 5.683725827078029E-12 1.7469195765609804E-17 5.117692988797076E-10 4.074032945723995E-13 3.478258278776919E-19 5.590523033812558E-7 1.483479312093629E-9 4.512047448488388E-15 3.3546262790251185E-4
6.4 -4.0 3.8 0.11889919346002248 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 2.4394674965708124E-15 2.1361809061405274E-18 2.735688083799253E-24 1.5269326985192E-15 1.0129522119452551E-18 7.862009472594772E-25 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 4.182968307488738E-12 3.662921126028218E-15 4.6908993744712085E-21 2.2053303512434904E-12 4.096384815571639E-15 1.348974569517568E-20 2.782266371015869E-9 1.218180669739551E-12 6.2402249407651105E-18 1.7602284374740693E-9 2.919295806222265E-12 8.972591481042304E-18 3.4039906719881664E-7 2.7098089265953887E-10 2.31353439169575E-16 2.871424650613063E-7 7.61950007120385E-10 2.317494121742264E-15 3.3546262790251185E-4
6.4 -4.0 6.4 0.11891526867249601 9.670855421101112E-19 1.0264881379206591E-21 1.971849405871035E-27 2.9691475267384674E-15 2.6000085113037995E-18 3.329686302178065E-24 1.1773438786085476E-15 7.810384093636188E-19 6.062014871446857E-25 8.593629009975718E-12 7.525226809281477E-15 9.637139462702292E-21 3.22528434685525E-12 2.824301142897441E-15 3.616925401530981E-21 2.684172331560234E-12 4.9858302522254925E-15 1.6418765621376298E-20 2.1452708974997186E-9 9.392801371979307E-13 4.811535336347902E-18 2.1424257215837593E-9 3.553160652907765E-12 1.0920804577974859E-17 2.624652405697022E-7 2.0894024700760334E-10 1.7838543615280966E-16 3.4948952636459783E-7 9.273917288588796E-10 2.8206901504015346E-15 3.3546262790251185E-4
6.4 -1.4 -4.0 0.025465288479923753 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 1.7473417812302764E-7 1.5301036618904356E-10 1.9595186637886281E-16 2.971633735559704E-25 1.9713527442601248E-28 1.5300617113452173E-34 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 6.04636423716113E-23 5.294650514159705E-26 6.780564453989853E-32 1.1732507141784832E-5 2.179304523564633E-8 7.176636263146204E-14 2.9870520149985116E-21 1.3078435127867228E-24 6.699529806900028E-30 6.955375043427548E-4 1.1535319372591467E-6 3.545434077389833E-12 2.714355877333536E-20 2.1608125565335405E-23 1.8448216457199674E-29 0.008427199809216569 2.2362087590445863E-5 6.801496956027448E-11 0.37531109885139957
6.4 -1.4 -1.4 0.025465288450525914 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 1.7473417790017033E-7 1.53010365993893E-10 1.9595186612894428E-16 1.4708582058526717E-16 9.757529421703967E-20 7.573288042408141E-26 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 2.9927458244202245E-14 2.6206729526198044E-17 3.356150102928302E-23 1.1732507126821102E-5 2.1793045207851314E-8 7.176636253993066E-14 1.478489732767084E-12 6.473383108201983E-16 3.3160406930087126E-21 6.955375034556609E-4 1.1535319357879233E-6 3.54543407286796E-12 1.3435143665268995E-11 1.0695291421869083E-14 9.131243273595307E-21 0.008427199798468453 2.2362087561925084E-5 6.801496947352766E-11 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.025448420502541532 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 1.746063072288161E-7 1.5289839283295264E-10 1.9580846832906261E-16 8.439479017960519E-11 5.59866780454645E-14 4.345395448490757E-20 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 1.7171754211781124E-8 1.503687732003369E-11 1.92568925149176E-17 1.1723921264678942E-5 2.1777097032428112E-8 7.171384383369408E-14 8.483267135001922E-7 3.714292832545334E-10 1.9026753047977673E-15 6.950285083146547E-4 1.1526877797972046E-6 3.542839520156018E-12 7.708806505967179E-6 6.136736171214481E-9 5.239317815188483E-15 0.008421032764011795 2.2345722960655958E-5 6.796519604103019E-11 0.37531109885139957
6.4 -1.4 3.8 0.019202095652600698 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 8.968192272833243E-8 7.853222526126527E-11 1.0057185335938444E-16 5.613448856049884E-8 3.7239070463889154E-11 2.890303424832678E-17 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 1.142164863866049E-5 1.0001653136534931E-8 1.2808560934732036E-14 6.021682822454587E-6 1.118523130295777E-8 3.683392371855649E-14 5.64257415572674E-4 2.470530800244171E-7 1.2655485593863651E-12 3.569830549991504E-4 5.920476644763808E-7 1.8196860418629714E-12 0.005127448148202008 4.0817987184840025E-6 3.4848884076328846E-12 0.0043252412906549615 1.1477291007821856E-5 3.490852968776208E-11 0.37531109885139957
6.4 -1.4 6.4 0.018511420634402802 4.786746389208764E-10 5.080769149993035E-13 9.759987728719389E-19 1.0915450172477799E-7 9.558387751897864E-11 1.2240895608622924E-16 4.3282586422844775E-8 2.8713244335032474E-11 2.2285730391707147E-17 2.3465014283569664E-5 2.054772836503797E-8 2.6314332964869414E-14 8.806680295330312E-6 7.711790511582468E-9 9.876061220586741E-15 7.329166994119676E-6 1.3613873480937652E-8 4.4831650211680016E-14 4.350715750787321E-4 1.904906691304821E-7 9.758032236262753E-13 4.3449455929551556E-4 7.20598598326233E-7 2.2147933178994578E-12 0.003953527025796716 3.147277374818054E-6 2.6870287330537994E-12 0.005264378188579195 1.3969347924264385E-5 4.248819659626339E-11 0.37531109885139957
6.4 1.2000000000000002 -4.0 0.8940778107712544 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.007446583070924341 6.52078725970273E-6 8.350809592989233E-12 1.2664103672165502E-20 8.401242464363119E-24 6.520608480586249E-30 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 1.913849706861633E-19 1.6759105037338995E-22 2.1462453771587204E-28 0.03713678910716693 6.898131108199154E-5 2.271613596160681E-10 7.022482351711458E-19 3.074706413289443E-22 1.5750422020603399E-27 0.1635190758204075 2.711923873726693E-4 8.335224198510952E-10 4.739679826752375E-19 3.773108666081069E-22 3.2213402859925036E-28 0.14715177646857686 3.904761948187315E-4 1.1876452230674007E-9 0.48675225595997157
6.4 1.2000000000000002 -1.4 0.8940778103684599 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.007446583061426912 6.520787251386068E-6 8.350809582338546E-12 6.268303049287191E-12 4.158330910770529E-15 3.227480687156881E-21 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 9.472908832676046E-11 8.295190242304224E-14 1.0623188810220862E-19 0.03713678905980239 6.898131099401225E-5 2.2716135932634478E-10 3.4758912812399117E-10 1.5218756956677276E-13 7.795926260166689E-19 0.16351907561185405 2.711923870267884E-4 8.335224187880144E-10 2.3459812300791656E-10 1.867561192575399E-13 1.5944545038634926E-19 0.14715177628089834 3.904761943207152E-4 1.1876452215526688E-9 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 0.8938466693824656 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.007441133643420644 6.516015331813075E-6 8.344698450329473E-12 3.596622152439896E-6 2.3859639448277136E-9 1.851861412048417E-15 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 5.4353584196157506E-5 4.759607943264218E-8 6.095365189584572E-14 0.03710961231506885 6.893083039168419E-5 2.269951223836249E-10 1.9943942536412276E-4 8.732206782696741E-8 4.4731406356130015E-13 0.16339941216518905 2.7099392812760794E-4 8.329124461280571E-10 1.3460753245282897E-4 1.071567839563464E-7 9.148648915921711E-14 0.14704409044261457 3.9019044339484786E-4 1.1867761014203881E-9 0.48675225595997157
6.4 1.2000000000000002 3.8 0.9239885008569845 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.0038219419619585854 3.3467793504073853E-6 4.286034184509552E-12 0.002392263131917399 1.5870039546476725E-6 1.2317501237817214E-12 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 0.036152831754046405 3.1658134000336774E-5 4.054281156202036E-11 0.019060373230143732 3.540450229375557E-5 1.165900553560126E-10 0.13265546508012166 5.80815427952396E-5 2.975272067208725E-10 0.08392579677233453 1.39188881019852E-4 4.278035015953601E-10 0.08953307395574654 7.127443827250609E-5 6.085147280085695E-11 0.07552531730397961 2.004110260920486E-4 6.095562314592598E-10 0.48675225595997157
6.4 1.2000000000000002 6.4 0.8134855356983868 2.7465356997214227E-4 2.915239855605925E-7 5.600078330074594E-13 0.004651797795887498 4.073463427887494E-6 5.216658068345637E-12 0.0018445582815243158 1.2236619159076185E-6 9.497428862556352E-13 0.07427357821433386 6.503952187730632E-5 8.329249852589164E-11 0.027875698255246998 2.441005442454146E-5 3.1260599147830954E-11 0.023198939979543434 4.309185941959908E-5 1.419051801219837E-10 0.10228420671553738 4.478386567818718E-5 2.2940882456183416E-10 0.10214855179108508 1.694108744676984E-4 5.206922045395033E-10 0.06903461865544633 5.49562686572742E-5 4.6919624601583315E-11 0.09192408154421979 2.4392614506600028E-4 7.419087893523592E-10 0.48675225595997157
6.4 3.8 -4.0 4.202179093168432 0.18268352405273452 1.9390473982737336E-4 3.7248452456421565E-10 0.3678794411714421 3.221428607254535E-4 4.125504459076234E-10 6.256377371313136E-19 4.150419532689176E-22 3.2213402859925036E-28 0.27253179303401254 2.3864930088794895E-4 3.0562488727873496E-10 7.022482351711458E-19 6.149412826578886E-22 7.875211010301699E-28 0.13626589651700627 2.5311289488115794E-4 8.335224198510952E-10 1.913849706861633E-19 8.379552518669497E-23 4.292490754317441E-28 0.044564146928600314 7.390854758784808E-5 2.271613596160681E-10 9.594017933458713E-21 7.637493149421016E-24 6.520608480586249E-30 0.0029786332283697364 7.90398455721543E-6 2.404020943436294E-11 7.318024188804728E-4
6.4 3.8 -1.4 4.202179092993034 0.18268352405273452 1.9390473982737336E-4 3.7248452456421565E-10 0.3678794407022459 3.2214286031459E-4 4.125504453814534E-10 3.096695223704499E-10 2.0543173118329388E-13 1.5944545038634926E-19 0.27253179303401254 2.3864930088794895E-4 3.0562488727873496E-10 3.4758912812399117E-10 3.0437513913354553E-13 3.8979631300833443E-19 0.1362658963432117 2.531128945583358E-4 8.335224187880144E-10 9.472908832676046E-11 4.147595121152112E-14 2.1246377620441723E-19 0.04456414687176286 7.390854749358455E-5 2.2716135932634478E-10 4.748714431278175E-12 3.780300827973208E-15 3.227480687156881E-21 0.002978633224570765 7.903984547134629E-6 2.4040209403701872E-11 7.318024188804728E-4
6.4 3.8 1.2000000000000002 4.2020783959872565 0.18268352405273452 1.9390473982737336E-4 3.7248452456421565E-10 0.36761022610653643 3.219071158007495E-4 4.12248540493398E-10 1.7768194283773425E-4 1.1787246235198105E-7 9.148648915921711E-14 0.27253179303401254 2.3864930088794895E-4 3.0562488727873496E-10 1.9943942536412276E-4 1.7464413565393482E-7 2.2365703178065008E-13 0.13616617680432422 2.529276662524341E-4 8.329124461280571E-10 5.4353584196157506E-5 2.379803971632109E-8 1.2190730379169143E-13 0.044531534778082624 7.385446113394735E-5 2.269951223836249E-10 2.724713751848406E-6 2.169058131661558E-9 1.851861412048417E-15 0.002976453457368258 7.898200402197667E-6 2.402261675094848E-11 7.318024188804728E-4
6.4 3.8 3.8 4.009455959725444 0.18268352405273452 1.9390473982737336E-4 3.7248452456421565E-10 0.18881329325994903 1.653390965259401E-4 2.1174058566479553E-10 0.11818365762158545 7.84018820997567E-5 6.085147280085695E-11 0.27253179303401254 2.3864930088794895E-4 3.0562488727873496E-10 0.13265546508012166 1.161630855904792E-4 1.4876360336043625E-10 0.06993816397694544 1.299096222851952E-4 4.278035015953601E-10 0.036152831754046405 1.5829067000168387E-5 8.108562312404072E-11 0.02287244787617248 3.7933395314738115E-5 1.165900553560126E-10 0.0018123205544828779 1.4427308678615202E-6 1.2317501237817214E-12 0.0015287767847834343 4.056702242918043E-6 1.2338583258436589E-11 7.318024188804728E-4
6.4 3.8 6.4 4.108689179278561 0.18268352405273452 1.9390473982737336E-4 3.7248452456421565E-10 0.22981020386054946 2.0123906967945023E-4 2.5771568472239847E-10 0.09112569662518916 6.045189552300163E-5 4.6919624601583315E-11 0.27253179303401254 2.3864930088794895E-4 3.0562488727873496E-10 0.10228420671553738 8.956773135637437E-5 1.1470441228091708E-10 0.08512379315923757 1.581168161698518E-4 5.206922045395033E-10 0.027875698255246998 1.220502721227073E-5 6.252119829566191E-11 0.02783872797545212 4.6169849378141874E-5 1.419051801219837E-10 0.001397392637518421 1.1124199235523804E-6 9.497428862556352E-13 0.0018607191183549995 4.937531427742416E-6 1.501765201493441E-11 7.318024188804728E-4
6.4 6.4 -4.0 4.986972414105274 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.021067999523041402 1.844872226211782E-5 2.3626252584095325E-11 3.582949758080266E-20 2.376893812186893E-23 1.844821645719966E-29 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 2.987052014998506E-21 2.6156870255734405E-24 3.349764903450008E-30 5.796145869522946E-4 1.076629808108535E-6 3.5454340773898267E-12 6.04636423716113E-23 2.6473252570798523E-26 1.3561128907979706E-31 1.4079008570141797E-5 2.3349691323906783E-8 7.176636263146204E-14 2.251237678454321E-25 1.792138858418295E-28 1.5300617113452173E-34 6.989367124921106E-8 1.8546711053217402E-10 5.641038577573323E-16 1.2754076295260396E-9
6.4 6.4 -1.4 4.9869724141112926 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.021067999496171115 1.844872223858818E-5 2.362625255396222E-11 1.773438963815506E-11 1.1764820564055982E-14 9.1312432735953E-21 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 1.4784897327670813E-12 1.2946766216403942E-15 1.6580203465043535E-21 5.796145862130497E-4 1.076629806735393E-6 3.545434072867953E-12 2.9927458244202245E-14 1.3103364763099022E-17 6.712300205856604E-23 1.4079008552185322E-5 2.3349691294126412E-8 7.176636253993066E-14 1.1142865195853572E-16 8.870481292458152E-20 7.573288042408141E-26 6.989367116006813E-8 1.8546711029562787E-10 5.641038570378698E-16 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.98697586721879 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.02105258191002947 1.843522144254115E-5 2.3608962835305205E-11 1.0175624587876668E-5 6.7504097883359245E-9 5.239317815188479E-15 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 8.483267135001907E-7 7.428585665090656E-10 9.513376523988819E-16 5.791904235955445E-4 1.0758419278107222E-6 3.5428395201560114E-12 1.7171754211781124E-8 7.518438660016844E-12 3.85137850298352E-17 1.406870551761473E-5 2.3332603963315834E-8 7.171384383369408E-14 6.393544710576151E-11 5.089698004133136E-14 4.345395448490757E-20 6.984252289152645E-8 1.8533138525206381E-10 5.636910451897256E-16 1.2754076295260396E-9
6.4 6.4 3.8 4.955706309624084 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.010813103226637396 9.468765081453023E-6 1.212612083890682E-11 0.006768231555626646 4.489978590332399E-6 3.4848884076328814E-12 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 5.64257415572673E-4 4.941061600488333E-7 6.327742796931816E-13 2.9748587916595814E-4 5.525778201779543E-7 1.8196860418629682E-12 1.142164863866049E-5 5.0008265682674655E-9 2.561712186946407E-14 7.2260193869455045E-6 1.1984176396026183E-8 3.683392371855649E-14 4.2526127697347605E-8 3.3853700421717415E-11 2.890303424832678E-17 3.587276909133297E-8 9.519057607426093E-11 2.895250323982279E-16 1.2754076295260396E-9
6.4 6.4 6.4 4.964995629579528 0.14085842092104486 1.4951055713872412E-4 2.872047833525258E-10 0.013160945471447976 1.1524712037518108E-5 1.4759057765017797E-11 0.005218655674051662 3.462005112299857E-6 2.687028733053797E-12 0.0011592291739045892 1.0151081047880475E-6 1.2999924950429364E-12 4.350715750787313E-4 3.809813382609635E-7 4.879016118131368E-13 3.6207879941292897E-4 6.725586917711495E-7 2.2147933178994533E-12 8.806680295330312E-6 3.855895255791234E-9 1.9752122441173482E-14 8.79500039294361E-6 1.4586293015290342E-8 4.4831650211680016E-14 3.2789838199124826E-8 2.6102949395484064E-11 2.2285730391707147E-17 4.3661800689911194E-8 1.1585924547754986E-10 3.5238941903611443E-16 1.2754076295260396E-9

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FUNCTION_BLOCK MamdaniTrustFewRules
VAR_INPUT
WTV : REAL;
OW : REAL;
AC : REAL;
END_VAR
VAR_OUTPUT
trustworthiness : REAL;
END_VAR
FUZZIFY WTV
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for WTV
END_FUZZIFY
FUZZIFY OW
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for OW
END_FUZZIFY
FUZZIFY AC
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for AC
END_FUZZIFY
DEFUZZIFY trustworthiness
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-4.0 .. 9.0); // Added range for trustworthiness
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : MIN;
RULE 1 : IF WTV IS fully AND OW IS high AND AC IS high THEN trustworthiness IS fully;
RULE 2 : IF WTV IS satISfactory AND OW IS high THEN trustworthiness IS satISfactory;
RULE 3 : IF WTV IS nothing AND AC IS NOT low THEN trustworthiness IS nothing;
END_RULEBLOCK
END_FUNCTION_BLOCK

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AC WTV OW trustworthiness No1.1 No1.2 No1.3
-4.0 -4.0 -4.0 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 -1.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -4.0 3.8 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -4.0 6.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -1.4 -4.0 2.1440130067640695E-4 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 -1.4 2.1441791333979342E-4 2.576757109154981E-18 1.2754076295260396E-9 0.37531109885139957
-4.0 -1.4 1.2000000000000002 0.0010024559404113941 2.576757109154981E-18 6.252150377482015E-5 0.37531109885139957
-4.0 -1.4 3.8 0.0010024559404113941 2.576757109154981E-18 6.252150377482015E-5 0.37531109885139957
-4.0 -1.4 6.4 0.0010024559404113941 2.576757109154981E-18 6.252150377482015E-5 0.37531109885139957
-4.0 1.2000000000000002 -4.0 1.9531210161944423E-4 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 -1.4 1.9532595962674313E-4 2.576757109154981E-18 1.2754076295260396E-9 0.48675225595997157
-4.0 1.2000000000000002 1.2000000000000002 0.007165935525054486 2.576757109154981E-18 7.318024188804728E-4 0.48675225595997157
-4.0 1.2000000000000002 3.8 0.9534088270515392 2.576757109154981E-18 0.19789869908361474 0.48675225595997157
-4.0 1.2000000000000002 6.4 0.9534088270515392 2.576757109154981E-18 0.19789869908361474 0.48675225595997157
-4.0 3.8 -4.0 0.05592821568344743 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 -1.4 0.05593232172333802 2.576757109154981E-18 1.2754076295260396E-9 7.318024188804728E-4
-4.0 3.8 1.2000000000000002 1.5550452747415202 2.576757109154981E-18 7.318024188804728E-4 7.318024188804728E-4
-4.0 3.8 3.8 2.993347126786305 2.576757109154981E-18 0.48675225595997157 7.318024188804728E-4
-4.0 3.8 6.4 2.9920305992170753 2.576757109154981E-18 0.37531109885139935 7.318024188804728E-4
-4.0 6.4 -4.0 1.274193733749591 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 -1.4 2.4934999999999605 2.576757109154981E-18 1.2754076295260396E-9 1.2754076295260396E-9
-4.0 6.4 1.2000000000000002 2.9999895280198197 2.576757109154981E-18 7.318024188804728E-4 1.2754076295260396E-9
-4.0 6.4 3.8 3.0000085271004036 2.576757109154981E-18 0.003088715408236766 1.2754076295260396E-9
-4.0 6.4 6.4 3.0000085271004036 2.576757109154981E-18 0.003088715408236766 1.2754076295260396E-9
-1.4 -4.0 -4.0 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-1.4 -4.0 -1.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -4.0 1.2000000000000002 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -4.0 3.8 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -4.0 6.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -1.4 -4.0 2.1440130067640695E-4 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
-1.4 -1.4 -1.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 1.2000000000000002 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
-1.4 -1.4 3.8 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
-1.4 -1.4 6.4 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
-1.4 1.2000000000000002 -4.0 1.9531210161944423E-4 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
-1.4 1.2000000000000002 -1.4 1.9532595962674313E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 1.2000000000000002 0.007165935525054486 1.2754076295260396E-9 7.318024188804728E-4 0.48675225595997157
-1.4 1.2000000000000002 3.8 0.9534088270515392 1.2754076295260396E-9 0.19789869908361474 0.48675225595997157
-1.4 1.2000000000000002 6.4 0.9534088270515392 1.2754076295260396E-9 0.19789869908361474 0.48675225595997157
-1.4 3.8 -4.0 0.05592821568344743 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
-1.4 3.8 -1.4 0.05593232172333802 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 1.2000000000000002 1.5550452747415202 1.2754076295260396E-9 7.318024188804728E-4 7.318024188804728E-4
-1.4 3.8 3.8 2.993347126786305 1.2754076295260396E-9 0.48675225595997157 7.318024188804728E-4
-1.4 3.8 6.4 2.9920305992170753 1.2754076295260396E-9 0.37531109885139935 7.318024188804728E-4
-1.4 6.4 -4.0 1.274193733749591 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
-1.4 6.4 -1.4 2.4934999999999605 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 1.2000000000000002 2.9999895280198197 1.2754076295260396E-9 7.318024188804728E-4 1.2754076295260396E-9
-1.4 6.4 3.8 3.0000085271004036 1.2754076295260396E-9 0.003088715408236766 1.2754076295260396E-9
-1.4 6.4 6.4 3.0000085271004036 1.2754076295260396E-9 0.003088715408236766 1.2754076295260396E-9
1.2000000000000002 -4.0 -4.0 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
1.2000000000000002 -4.0 -1.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -4.0 1.2000000000000002 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -4.0 3.8 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -4.0 6.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -1.4 -4.0 2.1440130067640695E-4 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
1.2000000000000002 -1.4 -1.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
1.2000000000000002 -1.4 1.2000000000000002 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
1.2000000000000002 -1.4 3.8 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
1.2000000000000002 -1.4 6.4 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
1.2000000000000002 1.2000000000000002 -4.0 1.9531210161944423E-4 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
1.2000000000000002 1.2000000000000002 -1.4 1.9532595962674313E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
1.2000000000000002 1.2000000000000002 1.2000000000000002 0.013466242916050405 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 3.8 0.9575577777095805 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
1.2000000000000002 1.2000000000000002 6.4 0.9575577777095805 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
1.2000000000000002 3.8 -4.0 0.05592821568344743 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
1.2000000000000002 3.8 -1.4 0.05593232172333802 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
1.2000000000000002 3.8 1.2000000000000002 2.4969922645135156 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 3.8 2.997287292052104 7.318024188804728E-4 0.48675225595997157 7.318024188804728E-4
1.2000000000000002 3.8 6.4 2.996753318597033 7.318024188804728E-4 0.37531109885139935 7.318024188804728E-4
1.2000000000000002 6.4 -4.0 1.274193733749591 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
1.2000000000000002 6.4 -1.4 2.4934999999999605 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
1.2000000000000002 6.4 1.2000000000000002 3.9416994880514222 7.318024188804728E-4 7.318024188804728E-4 1.2754076295260396E-9
1.2000000000000002 6.4 3.8 3.285807095912562 7.318024188804728E-4 0.003088715408236766 1.2754076295260396E-9
1.2000000000000002 6.4 6.4 3.285807095912562 7.318024188804728E-4 0.003088715408236766 1.2754076295260396E-9
3.8 -4.0 -4.0 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
3.8 -4.0 -1.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -4.0 3.8 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -4.0 6.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -1.4 -4.0 2.1440130067640695E-4 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
3.8 -1.4 -1.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
3.8 -1.4 3.8 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
3.8 -1.4 6.4 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
3.8 1.2000000000000002 -4.0 1.9531210161944423E-4 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
3.8 1.2000000000000002 -1.4 1.9532595962674313E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 0.013466242916050405 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
3.8 1.2000000000000002 3.8 0.9575577777095805 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
3.8 1.2000000000000002 6.4 0.9575577777095805 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
3.8 3.8 -4.0 0.05592821568344743 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
3.8 3.8 -1.4 0.05593232172333802 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
3.8 3.8 1.2000000000000002 2.4969922645135156 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
3.8 3.8 3.8 3.994789184782769 0.48675225595997157 0.48675225595997157 7.318024188804728E-4
3.8 3.8 6.4 3.99359436150837 0.37531109885139935 0.37531109885139935 7.318024188804728E-4
3.8 6.4 -4.0 1.274193733749591 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
3.8 6.4 -1.4 2.4934999999999605 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 3.9416994880514222 7.318024188804728E-4 7.318024188804728E-4 1.2754076295260396E-9
3.8 6.4 3.8 4.980025078729596 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9
3.8 6.4 6.4 4.980025078729596 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9
6.4 -4.0 -4.0 0.11814588732224174 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
6.4 -4.0 -1.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -4.0 3.8 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -4.0 6.4 0.11814600922885106 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -1.4 -4.0 2.1440130067640695E-4 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
6.4 -1.4 -1.4 2.1441791333979342E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
6.4 -1.4 3.8 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
6.4 -1.4 6.4 0.0010024559404113941 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
6.4 1.2000000000000002 -4.0 1.9531210161944423E-4 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
6.4 1.2000000000000002 -1.4 1.9532595962674313E-4 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 0.013466242916050405 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
6.4 1.2000000000000002 3.8 0.9575577777095805 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
6.4 1.2000000000000002 6.4 0.9575577777095805 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
6.4 3.8 -4.0 0.05592821568344743 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
6.4 3.8 -1.4 0.05593232172333802 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
6.4 3.8 1.2000000000000002 2.4969922645135156 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
6.4 3.8 3.8 3.878487887632338 0.37531109885139935 0.48675225595997157 7.318024188804728E-4
6.4 3.8 6.4 3.99359436150837 0.37531109885139935 0.37531109885139935 7.318024188804728E-4
6.4 6.4 -4.0 1.274193733749591 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
6.4 6.4 -1.4 2.4934999999999605 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 3.9416994880514222 7.318024188804728E-4 7.318024188804728E-4 1.2754076295260396E-9
6.4 6.4 3.8 4.980025078729596 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9
6.4 6.4 6.4 4.980025078729596 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9

85
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FUNCTION_BLOCK MamdaniTrustManyRules
VAR_INPUT
WTV : REAL;
OW : REAL;
AC : REAL;
END_VAR
VAR_OUTPUT
trustworthiness : REAL;
END_VAR
FUZZIFY WTV
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for WTV
END_FUZZIFY
FUZZIFY OW
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for OW
END_FUZZIFY
FUZZIFY AC
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for AC
END_FUZZIFY
DEFUZZIFY trustworthiness
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
METHOD : COG;
DEFAULT := 0;
RANGE := (-4.0 .. 9.0); // Added range for trustworthiness
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : MIN;
RULE 1 : IF WTV IS fully AND AC IS high THEN trustworthiness IS fully;
RULE 2 : IF WTV IS fully AND AC IS medium THEN trustworthiness IS fully WITH 0.8;
RULE 3 : IF WTV IS fully AND AC IS low THEN trustworthiness IS fully WITH 0.6;
RULE 4 : IF WTV IS largely AND AC IS high AND OW IS NOT high THEN trustworthiness IS fully;
RULE 5 : IF WTV IS largely AND AC IS medium AND OW IS NOT high THEN trustworthiness IS fully WITH 0.66;
RULE 6 : IF WTV IS largely AND AC IS low AND OW IS NOT high THEN trustworthiness IS largely WITH 0.33;
RULE 7 : IF WTV IS largely AND AC IS high AND OW IS high THEN trustworthiness IS largely WITH 0.66;
RULE 8 : IF WTV IS largely AND AC IS medium AND OW IS high THEN trustworthiness IS largely WITH 0.33;
RULE 9 : IF WTV IS largely AND AC IS low AND OW IS high THEN trustworthiness IS largely WITH 0.1;
RULE 10 : IF WTV IS satISfactory AND AC IS high THEN trustworthiness IS largely;
RULE 11 : IF WTV IS satISfactory AND AC IS medium THEN trustworthiness IS largely WITH 0.66;
RULE 12 : IF WTV IS satISfactory AND AC IS low THEN trustworthiness IS satISfactory WITH 0.33;
RULE 13 : IF WTV IS satISfactory AND AC IS high AND OW IS high THEN trustworthiness IS satISfactory;
RULE 14 : IF WTV IS satISfactory AND AC IS medium AND OW IS high THEN trustworthiness IS satISfactory WITH 0.66;
RULE 15 : IF WTV IS satISfactory AND AC IS low AND OW IS high THEN trustworthiness IS satISfactory WITH 0.33;
RULE 16 : IF WTV IS satISfactory AND AC IS high AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.5;
RULE 17 : IF WTV IS satISfactory AND AC IS medium AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.7;
RULE 18 : IF WTV IS satISfactory AND AC IS low AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.9;
RULE 19 : IF WTV IS partially AND AC IS high AND OW IS high THEN trustworthiness IS satISfactory;
RULE 20 : IF WTV IS partially AND AC IS medium AND OW IS high THEN trustworthiness IS satISfactory WITH 0.33;
RULE 21 : IF WTV IS partially AND AC IS low AND OW IS high THEN trustworthiness IS partially WITH 0.66;
RULE 22 : IF WTV IS partially AND AC IS high AND OW IS NOT high THEN trustworthiness IS partially WITH 0.6;
RULE 23 : IF WTV IS partially AND AC IS medium AND OW IS NOT high THEN trustworthiness IS partially WITH 0.75;
RULE 24 : IF WTV IS partially AND AC IS low AND OW IS NOT high THEN trustworthiness IS partially WITH 0.9;
RULE 25 : IF WTV IS minimal AND AC IS high AND OW IS high THEN trustworthiness IS minimal WITH 0.5;
RULE 26 : IF WTV IS minimal AND AC IS medium AND OW IS high THEN trustworthiness IS minimal WITH 0.3;
RULE 27 : IF WTV IS minimal AND AC IS low AND OW IS high THEN trustworthiness IS minimal WITH 0.1;
RULE 28 : IF WTV IS minimal AND AC IS high AND OW IS NOT high THEN trustworthiness IS minimal WITH 0.4;
RULE 29 : IF WTV IS minimal AND AC IS medium AND OW IS NOT high THEN trustworthiness IS nothing WITH 0.8;
RULE 30 : IF WTV IS minimal AND AC IS low AND OW IS NOT high THEN trustworthiness IS nothing WITH 0.95;
RULE 31 : IF WTV IS nothing THEN trustworthiness IS nothing;
END_RULEBLOCK
END_FUNCTION_BLOCK

126
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AC WTV OW trustworthiness No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7 No1.8 No1.9 No1.10 No1.11 No1.12 No1.13 No1.14 No1.15 No1.16 No1.17 No1.18 No1.19 No1.20 No1.21 No1.22 No1.23 No1.24 No1.25 No1.26 No1.27 No1.28 No1.29 No1.30 No1.31
-4.0 -4.0 -4.0 0.11821587903342956 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 1.5460542654929886E-18 5.018689568469586E-10 1.3706981770241366E-8 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 5.353268873034226E-10 3.5403205134747374E-6 3.3546262790251185E-4
-4.0 -4.0 -1.4 0.1182160143017219 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 2.208223410126618E-10 8.417690354871861E-10 1.5460542654929886E-18 5.018689568469586E-10 1.3706981770241366E-8 1.2883785545774905E-18 2.0074758273878343E-10 1.2754076295260396E-10 1.0307028436619925E-18 5.353268873034226E-10 3.5403205134747374E-6 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 0.11899213161348593 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 2.208223410126618E-10 1.0051786631510336E-8 1.5460542654929886E-18 5.018689568469586E-10 1.3706981770241366E-8 1.2883785545774905E-18 2.0074758273878343E-10 3.726653172078671E-7 1.0307028436619925E-18 5.353268873034226E-10 3.5403205134747374E-6 3.3546262790251185E-4
-4.0 -4.0 3.8 0.11899213161348593 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 2.208223410126618E-10 1.0051786631510336E-8 1.5460542654929886E-18 5.018689568469586E-10 1.3706981770241366E-8 1.2883785545774905E-18 2.0074758273878343E-10 3.726653172078671E-7 1.0307028436619925E-18 5.353268873034226E-10 3.5403205134747374E-6 3.3546262790251185E-4
-4.0 -4.0 6.4 0.11899213161348593 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 4.179174631201078E-15 1.2664165549094177E-15 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 1.2883785545774905E-18 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 2.208223410126618E-10 1.0051786631510336E-8 1.5460542654929886E-18 5.018689568469586E-10 1.3706981770241366E-8 1.2883785545774905E-18 2.0074758273878343E-10 3.726653172078671E-7 1.0307028436619925E-18 5.353268873034226E-10 3.5403205134747374E-6 3.3546262790251185E-4
-4.0 -1.4 -4.0 0.0026719733718906275 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 4.684110263904947E-10 5.626935339733814E-5 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 1.5460542654929886E-18 5.018689568469586E-10 3.019163651122607E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 5.353268873034226E-10 3.1868949650738624E-4 0.37531109885139957
-4.0 -1.4 -1.4 0.0026719733718906275 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 1.2754076295260396E-10 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 4.2088451774359307E-10 1.2883785545774905E-18 4.684110263904947E-10 5.626935339733814E-5 2.576757109154981E-18 2.208223410126618E-10 8.417690354871861E-10 1.5460542654929886E-18 5.018689568469586E-10 3.019163651122607E-4 1.2883785545774905E-18 2.0074758273878343E-10 1.2754076295260396E-10 1.0307028436619925E-18 5.353268873034226E-10 3.1868949650738624E-4 0.37531109885139957
-4.0 -1.4 1.2000000000000002 0.0026719733718906275 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 4.6557157157830784E-8 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.2883785545774905E-18 4.684110263904947E-10 5.626935339733814E-5 2.576757109154981E-18 2.208223410126618E-10 2.2140533441565783E-4 1.5460542654929886E-18 5.018689568469586E-10 3.019163651122607E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 5.353268873034226E-10 3.1868949650738624E-4 0.37531109885139957
-4.0 -1.4 3.8 0.0026719733718906275 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 4.6557157157830784E-8 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.2883785545774905E-18 4.684110263904947E-10 5.626935339733814E-5 2.576757109154981E-18 2.208223410126618E-10 2.2140533441565783E-4 1.5460542654929886E-18 5.018689568469586E-10 3.019163651122607E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 5.353268873034226E-10 3.1868949650738624E-4 0.37531109885139957
-4.0 -1.4 6.4 0.0026719733718906275 2.576757109154981E-18 5.353268873034226E-10 7.652445777156238E-10 2.576757109154981E-18 4.416446820253236E-10 1.536386186208416E-7 1.7006596920422875E-18 2.208223410126618E-10 4.6557157157830784E-8 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 2.576757109154981E-18 4.416446820253236E-10 2.0632096245690652E-5 1.2883785545774905E-18 4.684110263904947E-10 5.626935339733814E-5 2.576757109154981E-18 2.208223410126618E-10 2.2140533441565783E-4 1.5460542654929886E-18 5.018689568469586E-10 3.019163651122607E-4 1.2883785545774905E-18 2.0074758273878343E-10 3.354626279025119E-5 1.0307028436619925E-18 5.353268873034226E-10 3.1868949650738624E-4 0.37531109885139957
-4.0 1.2000000000000002 -4.0 0.004723883399507445 2.576757109154981E-18 5.353268873034226E-10 2.012775767415071E-4 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 4.416446820253236E-10 1.1070266720782891E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 4.684110263904947E-10 3.019163651122607E-4 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 1.5460542654929886E-18 5.018689568469586E-10 3.019163651122607E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 5.353268873034226E-10 3.1868949650738624E-4 0.48675225595997157
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1.2000000000000002 6.4 1.2000000000000002 4.9341082622086905 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 3.659012094402364E-4 0.002162100785765736 0.0027798438674130894 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
1.2000000000000002 6.4 3.8 4.9341082622086905 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 0.018524471735264114 0.005613476283413368 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 3.659012094402364E-4 0.002162100785765736 0.0027798438674130894 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
1.2000000000000002 6.4 6.4 4.9341082622086905 7.318024188804728E-4 0.3002488790811195 0.2251866593108396 7.318024188804728E-4 0.03704894347052823 0.018524471735264114 4.829895964611121E-4 0.018524471735264114 0.005613476283413368 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 7.318024188804728E-4 0.0020385521694362657 0.0010192760847181328 3.659012094402364E-4 0.002162100785765736 0.0027798438674130894 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 -4.0 -4.0 0.12128052218537347 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 9.137987846827578E-9 1.1422484808534473E-8 1.3706981770241366E-8 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 2.9813225376629368E-6 3.5403205134747374E-6 3.3546262790251185E-4
3.8 -4.0 -1.4 0.12128228598872379 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.3706981770241366E-8 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 3.5403205134747374E-6 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 0.12206865304093126 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 1.0051786631510336E-8 9.137987846827578E-9 1.1422484808534473E-8 1.3706981770241366E-8 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 2.9813225376629368E-6 3.5403205134747374E-6 3.3546262790251185E-4
3.8 -4.0 3.8 0.12206865304093126 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 1.0051786631510336E-8 9.137987846827578E-9 1.1422484808534473E-8 1.3706981770241366E-8 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 2.9813225376629368E-6 3.5403205134747374E-6 3.3546262790251185E-4
3.8 -4.0 6.4 0.12206865304093126 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 1.0051786631510336E-8 9.137987846827578E-9 1.1422484808534473E-8 1.3706981770241366E-8 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 2.9813225376629368E-6 3.5403205134747374E-6 3.3546262790251185E-4
3.8 -1.4 -4.0 0.06339896610180008 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 -1.4 0.06339896610180008 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.06630485529036885 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 7.318024188804728E-4 2.4149479823055604E-4 4.829895964611121E-4 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 3.8 0.09016420293594168 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 0.0030887154082367718 0.0010192760847181348 4.829895964611121E-4 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 6.4 0.09016420293594168 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 0.0030887154082367718 0.0010192760847181348 4.829895964611121E-4 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 1.2000000000000002 -4.0 1.4890107656972384 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 -1.4 1.4890107656972384 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 1.4890107656972384 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 7.318024188804728E-4 2.4149479823055604E-4 4.829895964611121E-4 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 3.8 1.7856294634227654 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 0.48675225595997157 0.14175392820954397 4.829895964611121E-4 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 6.4 1.671673800335508 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 0.37531109885139935 0.1238526626209618 4.829895964611121E-4 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 0.18765554942569967 0.1125933296554198 7.318024188804729E-5 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 3.8 -4.0 3.843335867683807 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 -1.4 3.843335867683807 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 1.2000000000000002 3.843335867683807 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 7.318024188804728E-4 2.4149479823055604E-4 4.829895964611121E-4 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 3.8 3.733446363865519 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.3212564889335813 0.14175392820954397 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 0.19789869908361474 0.06530657069759287 4.829895964611121E-4 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 6.4 3.7946881788966484 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.2477053252419236 0.1238526626209618 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.37531109885139935 0.2477053252419236 2.4149479823055604E-4 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 0.19789869908361474 0.06530657069759287 4.829895964611121E-4 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 6.4 -4.0 4.982875937365356 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 -1.4 4.982875937365356 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.982875937365356 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 3.8 4.9042678629781244 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 6.4 4.9042678629781244 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
6.4 -4.0 -4.0 0.1212744024386403 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 -1.4 0.12127616624469809 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.12206865304093126 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 3.8 0.12206865304093126 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 6.4 0.12206865304093126 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -1.4 -4.0 0.06184925957820933 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 -1.4 0.06184925957820933 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.06475675839588418 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 7.318024188804728E-4 1.643252890960801E-4 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 3.659012094402364E-4 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 3.8 0.09016420293594168 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 0.0030887154082367718 1.643252890960801E-4 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 6.4 0.09016420293594168 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 0.0030887154082367718 1.643252890960801E-4 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 1.2000000000000002 -4.0 1.405595580103225 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 -1.4 1.405595580103225 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 1.405595580103225 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 7.318024188804728E-4 1.643252890960801E-4 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 3.659012094402364E-4 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 3.8 1.671673800335508 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 0.37531109885139935 1.643252890960801E-4 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 6.4 1.671673800335508 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 0.37531109885139935 1.643252890960801E-4 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 3.8 -4.0 3.9393014423745836 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 -1.4 3.9393014423745836 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 1.2000000000000002 3.9393014423745836 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 7.318024188804728E-4 1.643252890960801E-4 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 3.659012094402364E-4 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 3.8 3.770802817936482 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 0.19789869908361474 1.643252890960801E-4 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 6.4 3.770802817936482 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 0.19789869908361474 1.643252890960801E-4 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 6.4 -4.0 4.985049098493575 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 -1.4 4.985049098493575 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.985049098493575 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 6.252150377482015E-5 2.0632096245690652E-5 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 3.8 4.904311031631794 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 6.252150377482015E-5 2.0632096245690652E-5 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 6.4 4.904311031631794 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 6.252150377482015E-5 2.0632096245690652E-5 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9

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Examples/jFuzzyLogic/trust/SingletonFewRules.fcl Normal file
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@ -0,0 +1,57 @@
FUNCTION_BLOCK LarsenTrustFewRules
VAR_INPUT
WTV : REAL;
OW : REAL;
AC : REAL;
END_VAR
VAR_OUTPUT
trustworthiness : REAL;
END_VAR
FUZZIFY WTV
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for WTV
END_FUZZIFY
FUZZIFY OW
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for OW
END_FUZZIFY
FUZZIFY AC
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for AC
END_FUZZIFY
DEFUZZIFY trustworthiness
TERM nothing := 0;
TERM minimal := 1;
TERM partially := 2;
TERM satISfactory := 3;
TERM largely := 4;
TERM fully := 5;
METHOD : COGS;
DEFAULT := 0;
RANGE := (0.0 .. 5.0); // Added range for trustworthiness
END_DEFUZZIFY
RULEBLOCK No1
ACCU : MAX;
AND : MIN;
RULE 1 : IF WTV IS fully AND OW IS high AND AC IS high THEN trustworthiness IS fully;
RULE 2 : IF WTV IS satISfactory AND OW IS high THEN trustworthiness IS satISfactory;
RULE 3 : IF WTV IS nothing AND AC IS NOT low THEN trustworthiness IS nothing;
END_RULEBLOCK
END_FUNCTION_BLOCK

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AC WTV OW trustworthiness No1.1 No1.2 No1.3
-4.0 -4.0 -4.0 6.14496374816158E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-4.0 -4.0 -1.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -4.0 1.2000000000000002 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -4.0 3.8 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -4.0 6.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-4.0 -1.4 -4.0 5.4925252507391914E-17 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
-4.0 -1.4 -1.4 1.0194803458168783E-8 2.576757109154981E-18 1.2754076295260396E-9 0.37531109885139957
-4.0 -1.4 1.2000000000000002 4.996741943320413E-4 2.576757109154981E-18 6.252150377482015E-5 0.37531109885139957
-4.0 -1.4 3.8 4.996741943320413E-4 2.576757109154981E-18 6.252150377482015E-5 0.37531109885139957
-4.0 -1.4 6.4 4.996741943320413E-4 2.576757109154981E-18 6.252150377482015E-5 0.37531109885139957
-4.0 1.2000000000000002 -4.0 4.235020304648585E-17 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
-4.0 1.2000000000000002 -1.4 7.860719379492583E-9 2.576757109154981E-18 1.2754076295260396E-9 0.48675225595997157
-4.0 1.2000000000000002 1.2000000000000002 0.004503546770211002 2.576757109154981E-18 7.318024188804728E-4 0.48675225595997157
-4.0 1.2000000000000002 3.8 0.8671514921249884 2.576757109154981E-18 0.19789869908361474 0.48675225595997157
-4.0 1.2000000000000002 6.4 0.8671514921249884 2.576757109154981E-18 0.19789869908361474 0.48675225595997157
-4.0 3.8 -4.0 2.8168883214099693E-14 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
-4.0 3.8 -1.4 5.2284826263196654E-6 2.576757109154981E-18 1.2754076295260396E-9 7.318024188804728E-4
-4.0 3.8 1.2000000000000002 1.500000000000006 2.576757109154981E-18 7.318024188804728E-4 7.318024188804728E-4
-4.0 3.8 3.8 2.995496453229789 2.576757109154981E-18 0.48675225595997157 7.318024188804728E-4
-4.0 3.8 6.4 2.994161817044743 2.576757109154981E-18 0.37531109885139935 7.318024188804728E-4
-4.0 6.4 -4.0 1.616272030425718E-8 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
-4.0 6.4 -1.4 1.500000003535595 2.576757109154981E-18 1.2754076295260396E-9 1.2754076295260396E-9
-4.0 6.4 1.2000000000000002 2.999994771517398 2.576757109154981E-18 7.318024188804728E-4 1.2754076295260396E-9
-4.0 6.4 3.8 2.9999987612256884 2.576757109154981E-18 0.003088715408236766 1.2754076295260396E-9
-4.0 6.4 6.4 2.9999987612256884 2.576757109154981E-18 0.003088715408236766 1.2754076295260396E-9
-1.4 -4.0 -4.0 6.14496374816158E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
-1.4 -4.0 -1.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -4.0 1.2000000000000002 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -4.0 3.8 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -4.0 6.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
-1.4 -1.4 -4.0 5.4925252507391914E-17 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
-1.4 -1.4 -1.4 2.7186142371188878E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
-1.4 -1.4 1.2000000000000002 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
-1.4 -1.4 3.8 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
-1.4 -1.4 6.4 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
-1.4 1.2000000000000002 -4.0 4.235020304648585E-17 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
-1.4 1.2000000000000002 -1.4 2.0961918219804633E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
-1.4 1.2000000000000002 1.2000000000000002 0.004503559839959952 1.2754076295260396E-9 7.318024188804728E-4 0.48675225595997157
-1.4 1.2000000000000002 3.8 0.867151499823899 1.2754076295260396E-9 0.19789869908361474 0.48675225595997157
-1.4 1.2000000000000002 6.4 0.867151499823899 1.2754076295260396E-9 0.19789869908361474 0.48675225595997157
-1.4 3.8 -4.0 2.8168883214099693E-14 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
-1.4 3.8 -1.4 1.3942595990364214E-5 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
-1.4 3.8 1.2000000000000002 1.5000030499508459 1.2754076295260396E-9 7.318024188804728E-4 7.318024188804728E-4
-1.4 3.8 3.8 2.9954964584741846 1.2754076295260396E-9 0.48675225595997157 7.318024188804728E-4
-1.4 3.8 6.4 2.9941618238478536 1.2754076295260396E-9 0.37531109885139935 7.318024188804728E-4
-1.4 6.4 -4.0 1.616272030425718E-8 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
-1.4 6.4 -1.4 2.666666666666667 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
-1.4 6.4 1.2000000000000002 2.9999982571755006 1.2754076295260396E-9 7.318024188804728E-4 1.2754076295260396E-9
-1.4 6.4 3.8 2.9999995870753993 1.2754076295260396E-9 0.003088715408236766 1.2754076295260396E-9
-1.4 6.4 6.4 2.9999995870753993 1.2754076295260396E-9 0.003088715408236766 1.2754076295260396E-9
1.2000000000000002 -4.0 -4.0 6.14496374816158E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
1.2000000000000002 -4.0 -1.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -4.0 1.2000000000000002 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -4.0 3.8 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -4.0 6.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
1.2000000000000002 -1.4 -4.0 5.4925252507391914E-17 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
1.2000000000000002 -1.4 -1.4 2.7186142371188878E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
1.2000000000000002 -1.4 1.2000000000000002 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
1.2000000000000002 -1.4 3.8 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
1.2000000000000002 -1.4 6.4 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
1.2000000000000002 1.2000000000000002 -4.0 4.235020304648585E-17 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
1.2000000000000002 1.2000000000000002 -1.4 2.0961918219804633E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
1.2000000000000002 1.2000000000000002 1.2000000000000002 0.01199145669187766 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
1.2000000000000002 1.2000000000000002 3.8 0.8715642505464681 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
1.2000000000000002 1.2000000000000002 6.4 0.8715642505464681 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
1.2000000000000002 3.8 -4.0 2.8168883214099693E-14 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
1.2000000000000002 3.8 -1.4 1.3942595990364214E-5 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
1.2000000000000002 3.8 1.2000000000000002 2.666666666666667 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
1.2000000000000002 3.8 3.8 2.998501067913516 7.318024188804728E-4 0.48675225595997157 7.318024188804728E-4
1.2000000000000002 3.8 6.4 2.9980577188125554 7.318024188804728E-4 0.37531109885139935 7.318024188804728E-4
1.2000000000000002 6.4 -4.0 1.616272030425718E-8 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
1.2000000000000002 6.4 -1.4 2.666666666666667 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
1.2000000000000002 6.4 1.2000000000000002 3.99999651434189 7.318024188804728E-4 7.318024188804728E-4 1.2754076295260396E-9
1.2000000000000002 6.4 3.8 3.3830895677424584 7.318024188804728E-4 0.003088715408236766 1.2754076295260396E-9
1.2000000000000002 6.4 6.4 3.3830895677424584 7.318024188804728E-4 0.003088715408236766 1.2754076295260396E-9
3.8 -4.0 -4.0 6.14496374816158E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
3.8 -4.0 -1.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -4.0 1.2000000000000002 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -4.0 3.8 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -4.0 6.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
3.8 -1.4 -4.0 5.4925252507391914E-17 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
3.8 -1.4 -1.4 2.7186142371188878E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
3.8 -1.4 1.2000000000000002 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
3.8 -1.4 3.8 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
3.8 -1.4 6.4 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
3.8 1.2000000000000002 -4.0 4.235020304648585E-17 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
3.8 1.2000000000000002 -1.4 2.0961918219804633E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 0.01199145669187766 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
3.8 1.2000000000000002 3.8 0.8715642505464681 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
3.8 1.2000000000000002 6.4 0.8715642505464681 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
3.8 3.8 -4.0 2.8168883214099693E-14 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
3.8 3.8 -1.4 1.3942595990364214E-5 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
3.8 3.8 1.2000000000000002 2.666666666666667 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
3.8 3.8 3.8 3.9969953802455946 0.48675225595997157 0.48675225595997157 7.318024188804728E-4
3.8 3.8 6.4 3.996104087187866 0.37531109885139935 0.37531109885139935 7.318024188804728E-4
3.8 6.4 -4.0 1.616272030425718E-8 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
3.8 6.4 -1.4 2.666666666666667 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 3.99999651434189 7.318024188804728E-4 7.318024188804728E-4 1.2754076295260396E-9
3.8 6.4 3.8 4.983674840896059 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9
3.8 6.4 6.4 4.983674840896059 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9
6.4 -4.0 -4.0 6.14496374816158E-14 2.576757109154981E-18 2.576757109154981E-18 3.3546262790251185E-4
6.4 -4.0 -1.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -4.0 3.8 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -4.0 6.4 2.0476812571940782E-7 2.576757109154981E-18 2.289734845645553E-11 3.3546262790251185E-4
6.4 -1.4 -4.0 5.4925252507391914E-17 2.576757109154981E-18 2.576757109154981E-18 0.37531109885139957
6.4 -1.4 -1.4 2.7186142371188878E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.37531109885139957
6.4 -1.4 1.2000000000000002 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
6.4 -1.4 3.8 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
6.4 -1.4 6.4 4.996911811432583E-4 1.2754076295260396E-9 6.252150377482015E-5 0.37531109885139957
6.4 1.2000000000000002 -4.0 4.235020304648585E-17 2.576757109154981E-18 2.576757109154981E-18 0.48675225595997157
6.4 1.2000000000000002 -1.4 2.0961918219804633E-8 1.2754076295260396E-9 1.2754076295260396E-9 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 0.01199145669187766 7.318024188804728E-4 7.318024188804728E-4 0.48675225595997157
6.4 1.2000000000000002 3.8 0.8715642505464681 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
6.4 1.2000000000000002 6.4 0.8715642505464681 7.318024188804728E-4 0.19789869908361474 0.48675225595997157
6.4 3.8 -4.0 2.8168883214099693E-14 2.576757109154981E-18 2.576757109154981E-18 7.318024188804728E-4
6.4 3.8 -1.4 1.3942595990364214E-5 1.2754076295260396E-9 1.2754076295260396E-9 7.318024188804728E-4
6.4 3.8 1.2000000000000002 2.666666666666667 7.318024188804728E-4 7.318024188804728E-4 7.318024188804728E-4
6.4 3.8 3.8 3.8674443570693646 0.37531109885139935 0.48675225595997157 7.318024188804728E-4
6.4 3.8 6.4 3.996104087187866 0.37531109885139935 0.37531109885139935 7.318024188804728E-4
6.4 6.4 -4.0 1.616272030425718E-8 2.576757109154981E-18 2.576757109154981E-18 1.2754076295260396E-9
6.4 6.4 -1.4 2.666666666666667 1.2754076295260396E-9 1.2754076295260396E-9 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 3.99999651434189 7.318024188804728E-4 7.318024188804728E-4 1.2754076295260396E-9
6.4 6.4 3.8 4.983674840896059 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9
6.4 6.4 6.4 4.983674840896059 0.37531109885139935 0.003088715408236766 1.2754076295260396E-9

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FUNCTION_BLOCK LarsenTrustManyRules
VAR_INPUT
WTV : REAL;
OW : REAL;
AC : REAL;
END_VAR
VAR_OUTPUT
trustworthiness : REAL;
END_VAR
FUZZIFY WTV
TERM nothing := GAUSS 0 1;
TERM minimal := GAUSS 1 1;
TERM partially := GAUSS 2 1;
TERM satISfactory := GAUSS 3 1;
TERM largely := GAUSS 4 1;
TERM fully := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for WTV
END_FUZZIFY
FUZZIFY OW
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for OW
END_FUZZIFY
FUZZIFY AC
TERM low := GAUSS 0 1;
TERM medium := GAUSS 2.5 1;
TERM high := GAUSS 5 1;
RANGE := (-4.0 .. 9.0); // Added range for AC
END_FUZZIFY
DEFUZZIFY trustworthiness
TERM nothing := 0;
TERM minimal := 1;
TERM partially := 2;
TERM satISfactory := 3;
TERM largely := 4;
TERM fully := 5;
METHOD : COGS;
DEFAULT := 0;
RANGE := (0.0 .. 5.0); // Added range for trustworthiness
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN;
ACCU : MAX;
RULE 1 : IF WTV IS fully AND AC IS high THEN trustworthiness IS fully;
RULE 2 : IF WTV IS fully AND AC IS medium THEN trustworthiness IS fully WITH 0.8;
RULE 3 : IF WTV IS fully AND AC IS low THEN trustworthiness IS fully WITH 0.6;
RULE 4 : IF WTV IS largely AND AC IS high AND OW IS NOT high THEN trustworthiness IS fully;
RULE 5 : IF WTV IS largely AND AC IS medium AND OW IS NOT high THEN trustworthiness IS fully WITH 0.66;
RULE 6 : IF WTV IS largely AND AC IS low AND OW IS NOT high THEN trustworthiness IS largely WITH 0.33;
RULE 7 : IF WTV IS largely AND AC IS high AND OW IS high THEN trustworthiness IS largely WITH 0.66;
RULE 8 : IF WTV IS largely AND AC IS medium AND OW IS high THEN trustworthiness IS largely WITH 0.33;
RULE 9 : IF WTV IS largely AND AC IS low AND OW IS high THEN trustworthiness IS largely WITH 0.1;
RULE 10 : IF WTV IS satISfactory AND AC IS high THEN trustworthiness IS largely;
RULE 11 : IF WTV IS satISfactory AND AC IS medium THEN trustworthiness IS largely WITH 0.66;
RULE 12 : IF WTV IS satISfactory AND AC IS low THEN trustworthiness IS satISfactory WITH 0.33;
RULE 13 : IF WTV IS satISfactory AND AC IS high AND OW IS high THEN trustworthiness IS satISfactory;
RULE 14 : IF WTV IS satISfactory AND AC IS medium AND OW IS high THEN trustworthiness IS satISfactory WITH 0.66;
RULE 15 : IF WTV IS satISfactory AND AC IS low AND OW IS high THEN trustworthiness IS satISfactory WITH 0.33;
RULE 16 : IF WTV IS satISfactory AND AC IS high AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.5;
RULE 17 : IF WTV IS satISfactory AND AC IS medium AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.7;
RULE 18 : IF WTV IS satISfactory AND AC IS low AND OW IS NOT high THEN trustworthiness IS satISfactory WITH 0.9;
RULE 19 : IF WTV IS partially AND AC IS high AND OW IS high THEN trustworthiness IS satISfactory;
RULE 20 : IF WTV IS partially AND AC IS medium AND OW IS high THEN trustworthiness IS satISfactory WITH 0.33;
RULE 21 : IF WTV IS partially AND AC IS low AND OW IS high THEN trustworthiness IS partially WITH 0.66;
RULE 22 : IF WTV IS partially AND AC IS high AND OW IS NOT high THEN trustworthiness IS partially WITH 0.6;
RULE 23 : IF WTV IS partially AND AC IS medium AND OW IS NOT high THEN trustworthiness IS partially WITH 0.75;
RULE 24 : IF WTV IS partially AND AC IS low AND OW IS NOT high THEN trustworthiness IS partially WITH 0.9;
RULE 25 : IF WTV IS minimal AND AC IS high AND OW IS high THEN trustworthiness IS minimal WITH 0.5;
RULE 26 : IF WTV IS minimal AND AC IS medium AND OW IS high THEN trustworthiness IS minimal WITH 0.3;
RULE 27 : IF WTV IS minimal AND AC IS low AND OW IS high THEN trustworthiness IS minimal WITH 0.1;
RULE 28 : IF WTV IS minimal AND AC IS high AND OW IS NOT high THEN trustworthiness IS minimal WITH 0.4;
RULE 29 : IF WTV IS minimal AND AC IS medium AND OW IS NOT high THEN trustworthiness IS nothing WITH 0.8;
RULE 30 : IF WTV IS minimal AND AC IS low AND OW IS NOT high THEN trustworthiness IS nothing WITH 0.95;
RULE 31 : IF WTV IS nothing THEN trustworthiness IS nothing;
END_RULEBLOCK
END_FUNCTION_BLOCK

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AC WTV OW trustworthiness No1.1 No1.2 No1.3 No1.4 No1.5 No1.6 No1.7 No1.8 No1.9 No1.10 No1.11 No1.12 No1.13 No1.14 No1.15 No1.16 No1.17 No1.18 No1.19 No1.20 No1.21 No1.22 No1.23 No1.24 No1.25 No1.26 No1.27 No1.28 No1.29 No1.30 No1.31
-4.0 -4.0 -4.0 8.208112280456528E-5 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 2.576757109154981E-18 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.576757109154981E-18 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.2883785545774905E-18 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 1.5460542654929886E-18 5.018689568469586E-10 1.3706981770241366E-8 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.0307028436619925E-18 5.353268873034226E-10 3.5403205134747374E-6 3.3546262790251185E-4
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3.8 -4.0 6.4 0.005740311046607863 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 1.0051786631510336E-8 9.137987846827578E-9 1.1422484808534473E-8 1.3706981770241366E-8 1.8633265860393355E-6 1.1179959516236013E-6 3.726653172078671E-7 1.4906612688314684E-6 2.9813225376629368E-6 3.5403205134747374E-6 3.3546262790251185E-4
3.8 -1.4 -4.0 0.06873599549883373 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 -1.4 0.06873599549883373 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 1.2000000000000002 0.07367558811216139 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 7.318024188804728E-4 2.4149479823055604E-4 4.829895964611121E-4 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 3.8 0.10326367470558286 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 0.0030887154082367718 0.0010192760847181348 4.829895964611121E-4 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 -1.4 6.4 0.10326367470558286 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 1.536386186208416E-7 3.072772372416832E-7 1.536386186208416E-7 4.6557157157830784E-8 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 6.252150377482015E-5 4.1264192491381304E-5 2.0632096245690652E-5 3.1260751887410076E-5 4.37650526423741E-5 5.626935339733814E-5 0.0030887154082367718 0.0010192760847181348 4.829895964611121E-4 0.001853229244942063 0.002316536556177579 6.586221769924255E-4 0.028067381417066863 0.016840428850240115 7.318024188804729E-5 0.02245390513365349 0.04490781026730698 6.952122979364491E-4 0.37531109885139957
3.8 1.2000000000000002 -4.0 1.5776455322679288 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 -1.4 1.5776455322679288 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 1.2000000000000002 1.5776455322679288 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 7.318024188804728E-4 2.4149479823055604E-4 4.829895964611121E-4 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 3.8 1.8435738229230794 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 0.48675225595997157 0.14175392820954397 4.829895964611121E-4 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 0.24337612797998578 0.12886720746322178 7.318024188804729E-5 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 1.2000000000000002 6.4 1.788576728144589 7.318024188804728E-4 5.854419351043783E-4 4.3908145132828363E-4 0.019841094744370298 0.013095122531284397 2.4149479823055604E-4 0.013095122531284397 0.006547561265642199 7.318024188804729E-5 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.19789869908361474 0.13061314139518573 2.4149479823055604E-4 0.09894934954180737 0.1385290893585303 6.586221769924255E-4 0.37531109885139935 0.1238526626209618 4.829895964611121E-4 0.29205135357598294 0.32216801865805444 6.586221769924255E-4 0.18765554942569967 0.1125933296554198 7.318024188804729E-5 0.19470090238398863 0.3436458865685914 6.952122979364491E-4 0.48675225595997157
3.8 3.8 -4.0 3.863051074650122 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 -1.4 3.863051074650122 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 1.2000000000000002 3.863051074650122 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 7.318024188804728E-4 2.4149479823055604E-4 4.829895964611121E-4 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 3.659012094402364E-4 2.1954072566414182E-4 7.318024188804729E-5 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 3.8 3.7613292456632803 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.3212564889335813 0.14175392820954397 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 0.19789869908361474 0.06530657069759287 4.829895964611121E-4 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 3.8 6.4 3.817036120584763 0.48675225595997157 0.3436458865685914 4.3908145132828363E-4 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.2477053252419236 0.1238526626209618 7.318024188804729E-5 0.48675225595997157 0.28350785641908793 2.4149479823055604E-4 0.37531109885139935 0.2477053252419236 2.4149479823055604E-4 0.24337612797998578 0.3006901507475175 6.586221769924255E-4 0.19789869908361474 0.06530657069759287 4.829895964611121E-4 0.11873921945016884 0.14842402431271107 6.586221769924255E-4 0.009920547372185149 0.005952328423311089 7.318024188804729E-5 0.00793643789774812 0.01587287579549624 6.952122979364491E-4 7.318024188804728E-4
3.8 6.4 -4.0 4.980072762664452 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 -1.4 4.980072762664452 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 1.2000000000000002 4.980072275823434 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 4.829895964611121E-4 2.4149479823055604E-4 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 7.318024188804728E-4 4.829895964611121E-4 2.4149479823055604E-4 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 3.8 4.895552997878364 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
3.8 6.4 6.4 4.895552997878364 0.37531109885139935 0.3002488790811195 4.3908145132828363E-4 0.05613476283413368 0.03704894347052823 2.4149479823055604E-4 0.03704894347052823 0.018524471735264114 7.318024188804729E-5 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.003088715408236766 0.0020385521694362657 2.4149479823055604E-4 0.001544357704118383 0.002162100785765736 6.586221769924255E-4 6.252150377482015E-5 2.0632096245690652E-5 4.1264192491381304E-5 3.751290226489209E-5 4.6891127831115114E-5 5.626935339733814E-5 2.327857857891539E-7 1.3967147147349234E-7 4.6557157157830784E-8 1.8622862863132314E-7 3.7245725726264627E-7 4.422929929993924E-7 1.2754076295260396E-9
6.4 -4.0 -4.0 0.0044920416475705445 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 -1.4 0.0045031963733368225 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 1.2000000000000002 0.005726806245408842 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 3.8 0.005726806245408842 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -4.0 6.4 0.005726806245408842 2.576757109154981E-18 2.061405687323985E-18 1.5460542654929886E-18 1.2664165549094176E-14 8.358349262402156E-15 4.179174631201078E-15 8.358349262402156E-15 4.179174631201078E-15 1.2664165549094177E-15 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 2.289734845645553E-11 1.511224998126065E-11 7.556124990630325E-12 1.1448674228227765E-11 1.602814391951887E-11 2.0607613610809977E-11 1.522997974471263E-8 5.025893315755168E-9 8.417690354871861E-10 9.137987846827578E-9 1.1422484808534473E-8 1.1478668665734357E-9 1.8633265860393355E-6 1.1179959516236013E-6 1.2754076295260396E-10 1.4906612688314684E-6 2.9813225376629368E-6 1.2116372480497376E-9 3.3546262790251185E-4
6.4 -1.4 -4.0 0.06640583808052351 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 -1.4 0.06640583808052351 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 1.2000000000000002 0.07144668911740074 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 7.318024188804728E-4 1.643252890960801E-4 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 3.659012094402364E-4 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 3.8 0.10111184438833831 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 0.0030887154082367718 1.643252890960801E-4 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 -1.4 6.4 0.10111184438833831 1.2754076295260396E-9 1.0203261036208317E-9 7.652445777156238E-10 4.655715715783078E-7 3.072772372416832E-7 4.2088451774359307E-10 3.072772372416832E-7 1.536386186208416E-7 1.2754076295260396E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 6.252150377482015E-5 4.1264192491381304E-5 4.2088451774359307E-10 3.1260751887410076E-5 4.37650526423741E-5 1.1478668665734357E-9 0.0030887154082367718 1.643252890960801E-4 8.417690354871861E-10 0.001853229244942063 3.734665661274548E-4 1.1478668665734357E-9 0.028067381417066863 1.493866264509819E-4 1.2754076295260396E-10 0.02245390513365349 3.983643372026185E-4 1.2116372480497376E-9 0.37531109885139957
6.4 1.2000000000000002 -4.0 1.5169817918546202 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 -1.4 1.5169817918546202 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 1.2000000000000002 1.5169817918546202 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 7.318024188804728E-4 1.643252890960801E-4 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 3.659012094402364E-4 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 3.8 1.778562082104366 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 0.37531109885139935 1.643252890960801E-4 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 1.2000000000000002 6.4 1.778562082104366 7.318024188804728E-4 3.983643372026185E-4 7.652445777156238E-10 0.019841094744370298 3.286505781921602E-4 4.2088451774359307E-10 0.013095122531284397 1.643252890960801E-4 1.2754076295260396E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.19789869908361474 3.286505781921602E-4 4.2088451774359307E-10 0.09894934954180737 3.485687950522911E-4 1.1478668665734357E-9 0.37531109885139935 1.643252890960801E-4 8.417690354871861E-10 0.2251866593108396 3.734665661274548E-4 1.1478668665734357E-9 0.18765554942569967 1.493866264509819E-4 1.2754076295260396E-10 0.15012443954055976 3.983643372026185E-4 1.2116372480497376E-9 0.48675225595997157
6.4 3.8 -4.0 3.9281594486363876 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 -1.4 3.9281594486363876 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 1.2000000000000002 3.9281594486363876 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 7.318024188804728E-4 1.643252890960801E-4 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 3.659012094402364E-4 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 3.8 3.784782968892101 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 0.19789869908361474 1.643252890960801E-4 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 3.8 6.4 3.784782968892101 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.2477053252419236 1.643252890960801E-4 1.2754076295260396E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.37531109885139935 3.286505781921602E-4 4.2088451774359307E-10 0.18765554942569967 3.485687950522911E-4 1.1478668665734357E-9 0.19789869908361474 1.643252890960801E-4 8.417690354871861E-10 0.11873921945016884 3.734665661274548E-4 1.1478668665734357E-9 0.009920547372185149 1.493866264509819E-4 1.2754076295260396E-10 0.00793643789774812 3.983643372026185E-4 1.2116372480497376E-9 7.318024188804728E-4
6.4 6.4 -4.0 4.9833661822036985 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 1.7006596920422875E-18 8.503298460211438E-19 2.576757109154981E-19 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 2.576757109154981E-18 1.7006596920422875E-18 8.503298460211438E-19 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 2.576757109154981E-18 8.503298460211438E-19 1.7006596920422875E-18 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 1.2883785545774905E-18 7.730271327464943E-19 2.576757109154981E-19 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 -1.4 4.9833661822036985 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 8.417690354871861E-10 4.2088451774359307E-10 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 1.2754076295260396E-9 8.417690354871861E-10 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 1.2754076295260396E-9 4.2088451774359307E-10 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 6.377038147630198E-10 3.826222888578119E-10 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 1.2000000000000002 4.983365694155616 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 4.829895964611121E-4 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 7.318024188804728E-4 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 6.252150377482015E-5 2.0632096245690652E-5 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 3.8 4.8956191766547725 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 6.252150377482015E-5 2.0632096245690652E-5 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9
6.4 6.4 6.4 4.8956191766547725 0.37531109885139935 3.983643372026185E-4 7.652445777156238E-10 0.05613476283413368 3.286505781921602E-4 4.2088451774359307E-10 0.03704894347052823 1.643252890960801E-4 1.2754076295260396E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.003088715408236766 3.286505781921602E-4 4.2088451774359307E-10 0.001544357704118383 3.485687950522911E-4 1.1478668665734357E-9 6.252150377482015E-5 2.0632096245690652E-5 8.417690354871861E-10 3.751290226489209E-5 4.6891127831115114E-5 1.1478668665734357E-9 2.327857857891539E-7 1.3967147147349234E-7 1.2754076295260396E-10 1.8622862863132314E-7 3.7245725726264627E-7 1.2116372480497376E-9 1.2754076295260396E-9

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FUNCTION_BLOCK Zzz
VAR_INPUT
temperature : REAL; // RANGE := ( 100 .. 150 );
END_VAR
VAR_OUTPUT
out : REAL;
END_VAR
FUZZIFY temperature
TERM low := SIGM -4 125;
TERM high := SIGM 4 125;
RANGE := (100.0 .. 150.0); // Added range for temperature
END_FUZZIFY
DEFUZZIFY out
TERM low := (0,0) (5,1) (10,0);
TERM mid := (10,0) (15,1) (20,0);
TERM high := (20,0) (25,1) (30,0);
METHOD : COG;
DEFAULT := 0;
RANGE := (0.0 .. 30.0); // Added range for out
END_DEFUZZIFY
RULEBLOCK No1
AND : MIN;
ACT : MIN;
ACCU : MAX;
RULE 1 : IF temperature IS low THEN out IS low;
RULE 2 : IF temperature IS high THEN out IS high;
END_RULEBLOCK
END_FUNCTION_BLOCK

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temperature out No1.1 No1.2
100.0 5.000020040080156 1.0 3.7200759760208356E-44
110.0 5.000020040080156 1.0 8.75651076269652E-27
120.0 5.000020122403032 0.9999999979388463 2.0611536181902037E-9
130.0 24.999979877597212 2.0611536181902037E-9 0.9999999979388463
140.0 24.99997995991997 8.75651076269652E-27 1.0

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An FCL parser with a scikit-fuzzy back-end
=======================================
This is a parser for the Fuzzy Control Language
[FCL](https://en.wikipedia.org/wiki/Fuzzy_Control_Language)
along with a back-end for
[scikit-fuzzy](https://github.com/scikit-fuzzy/scikit-fuzzy),
a fuzzy logic toolkit for SciPy.
The basic use-case is to parse a FCL file and then use the fuzzy rules
in your `scikit-fuzzy` code. For example:
```python
from fcl_parser import FCLParser
p = FCLParser() # Create the parser
p.read_fcl_file('tipper.fcl') # Parse a file
# ... and so on, as usual for skfuzzy:
cs = ctrl.ControlSystem(p.rules)
```
After reading a file the parser object has attributes to supply the
`rules` (as above) or the `antecedents`, the `consequents`, or all the
`fuzzy_variables` All these are represented via lists of their
corresponding `scikit-fuzzy` objects.
Other Entry Points
------------------
The parser can be used to accept program fragments, so you can
interleave its use with regular `scikit-fuzzy` code.
For example, in the following `scikit-fuzzy` code we set up the
tipping example in the usual way by specifying the variables and
defining some membership functions for the inputs:
```python
# First we set up the variables in the usual way:
food = ctrl.Antecedent(np.linspace(0, 10, 11), 'quality')
service = ctrl.Antecedent(np.linspace(0, 10, 11), 'service')
tip = ctrl.Consequent(np.linspace(0, 25, 26), 'tip')
# Auto-generate the membership functions for the inputs:
food.automf(3)
service.automf(3)
```
We can define the output variable using FCL code, in this case getting
the parser to parse a membership function `mf` definition:
```python
# Define a FCL parser-object:
p = FCLParser()
# Use FCL to define membership functions for the output:
tip['bad'] = p.mf('Triangle 0 0 13', tip.universe)
tip['middling'] = p.mf('Triangle 0 13 25', tip.universe)
tip['lots'] = p.mf('Triangle 13 25 25', tip.universe)
```
Last, we can define the rules in FCL, and get a `scikit-fuzzy` rule
object for each of them if we like:
```python
# We need to tell the parser about the variables before we parse any rules:
p.add_vars([food, service, tip])
# Now use FCL to define three rules:
rule1 = p.rule('IF quality is poor OR service is poor THEN tip is bad')
rule2 = p.rule('IF service is average THEN tip is middling')
rule3 = p.rule('IF service is good OR quality is good THEN tip is lots')
# To get the control system, just add the rules (from the parser):
tipping = ctrl.ControlSystem(p.rules)
```
There are some more examples of mixed FCL/skfuzzy use in the file
[tests/test_fcl_parser.py](./tests/test_fcl_parser.py)
Dependencies
------------
The scanner is written using
[PLY](http://www.dabeaz.com/ply/ply.html) (Python Lex-Yacc),
so you need to install PLY before the code here will work.
$ pip install ply
You don't need to import this anywhere, my scanner code just needs it.
The parser is hand-written so we don't actually use the
parser-generation features of PLY.
What's implemented
------------------
Much of FCL is implemented, concentrating on
the subset of FCL that can be translated easily into
`scikit-fuzzy`. That includes most parts of a standard
(Mamdani-style) fuzzy system.
At the moment the main options are for:
* defuzzification methods: cog, coa, lm, rm, mom
* membership functions: quite a collection; have a look in
[fcl_symbols.py](./fcl_symbols.py) for a list.
* and/or methods (norms and co-norms): again, quite a few,
including (norms) min, prod, bdif, drp, eprod, hprod, nilmin
and their co-norm duals.
I was doing this with an eye on the XML standard, hence the rather
large selection of membership functions and norms.
I've also implemented the *hedge functions* listed in the IEEE standard,
so you can write things like:
```python
rule1 = p.rule('IF quality is slightly poor OR service is very poor THEN tip is extremely bad')
```
What happens here is that when the rule is processed, the hedge
functions are applied to the corresponding membership function, and a
new membership function is generated and added to the variable. For
example, a membership function called `_slightly_poor` would be added
to the variable `quality` above.
What's not implemented
------------------
Most notably _not_ implemented (yet) are options for:
* activation method (this is hard-wired to `MIN`).
At the moment `scikit-fuzzy` doesn't have an option to change this;
its CrispValueCalculator always uses np.minimum.
* accumulation method (well, not exactly).
This is a small incompatibility: FCL sees the accumulation as a
property of the rule-base, whereas `scikit-fuzzy` sees it as a
property of the output variables. I could fix the parser to
propagate the setting from the rules to the variables used in those
rules, but this might cause unexpected behaviour if the variables
are used in more than one rule base.
You can set an 'ACCU' option as part of an (output) variable
definition, and this will be propagated through to `scikit-fuzzy`.
* default values for variables.
In FCL these values are used in defuzzification when all the
memberships have been cut to zero area. As far as I can see this
case will raise an exception in `scikit-fuzzy`.
The parser accepts these, I just haven't figured out how to get them
into the `scikit-fuzzy` code, so they are ignored for the moment.
Compliance
----------
First of all, I'm working from the draft of the FCL standard (IEC
TC65/WG 7/TF8), plus any examples I could find, so I may have missed a
few things.
Second, the parser does not enforce strict conformance to the FCL standard,
and is somewhat liberal in the kind of FCL code it will accept.
This is intended as a feature, not a bug.
In particular:
* Case is not relevant for keywords
(so `rule` and `RULE` are the same)
but note that case _is_ relevant for identifiers (e.g. variable names).
* The semi-colon at the end of lines can be left out in most cases.
* The parser doesn't impose a strict ordering on the contents of
variable definitions, so you can mix `TERM`, `RANGE`, `METHOD`
etc. in your preferred order.
I only made one real change to the FCL language
to better support `scikit-fuzzy`:
* When defining a variable range (universe) you can specify
the granularity using an optional `WITH` setting, thus:
```
RANGE := (0 .. 2.1) WITH 0.01
```
This maps directly to a NumPy `arange(1, 2.1, 0.01)` expression.
This is due to the way `scikit-fuzzy` calculates its membership
functions: these get worked out to point-lists when they are defined,
so I need to know the granularity to get this right.
This working-out is also the reason we can't really generate FCL from
a `scikit-fuzzy` program, since the information on the original
definition of the membership functions is not retained once the
point-sets have been calculated.
Reading the code
----------------
The main functionality is in [fcl_parser.py](./fcl_parser.py)
which contains the
hand-written top-down parser. This is essentially a context-free
grammar, with a Python method for each non-terminal.
This can be called from the command-line if you just want to parse a file;
for example:
```
$ python fcl_parser.py tests/tipper.fcl
```
The scanner code is in [fcl_scanner.py](./fcl_scanner.py).
This uses a few tricks related
to PLY, but us essentially a list of regular expressions plus some
extra code to check tokens etc.
The symbol table is in [fcl_symbols.py](./fcl_symbols.py)
and contains a list of the
variables and rules, added in as they are processed. The mappings
between option names (membership functions, defuzzification method
etc.) is also kept here.
The other files are simple auxiliary definitions: some extra
membership functions (that are not in `scikit-fuzzy`) are defined in
[extramf.py](./extramf.py)
and the t-norms and their duals are defined in
[norms.py](./norms.py).
The set of hedge functions as defined in the IEEE standard is implemented in
[hedges.py](./hedges.py).
[James Power](http://www.cs.nuim.ie/~jpower/),
27 August 2018.

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# -*- coding: utf-8 -*-
"""
Some extra membership functions to augment those in skfuzzy.membership
@author: james.power@mu.ie Created on Fri Jul 27 16:10:03 2018
"""
from collections import OrderedDict
import numpy as np
import skfuzzy
import skfuzzy.membership as skmemb
import scipy.interpolate as interp
def singletonmf(x, xpt):
''' Find which x-val is nearest the given point, and set it to 1'''
mf = np.zeros(len(x))
diffs = np.abs(x - xpt)
idx = np.nonzero(diffs == diffs.min())[0][0]
mf[idx] = 1
return mf
def pointsetmf(x, pointset, method='linear'):
'''Interpolate from a point-set using the chosen interpolation method'''
# Make sure we're in ascending order first:
pointset = sorted(pointset, key=lambda p: p[0])
x_min, x_max = x[0], x[-1]
# Lead on left from y=0, unless otherwise specified:
if pointset[0][0] > x_min:
pointset = [(x_min, 0)] + pointset
# Trail on right from last given y value
if pointset[-1][0] < x_max:
pointset = pointset + [(x_max, pointset[-1][1])]
px, py = [p[0] for p in pointset], [p[1] for p in pointset]
if method == 'linear':
f = interp.interp1d(px, py)
elif method == 'lagrange':
f = interp.lagrange(px, py)
elif method == 'spline':
f = interp.make_interp_spline(px, py)
elif method == 'cubic':
f = interp.CubicSpline(px, py, bc_type='natural')
# Sometimes interpoliation can go outside the bounds:
return np.clip(f(x), 0, 1)
def gaussprod(x, mean1, sigma1, mean2, sigma2):
'''Ensure the means are in correct order before calling gauss2mf'''
if mean1 > mean2:
mean1, sigma1, mean2, sigma2 = mean2, sigma2, mean1, sigma1
return skmemb.gauss2mf(x, mean1, sigma1, mean2, sigma2)
def rectanglemf(x, a, b):
'''Zero before and after given end points, one in between them'''
mf = np.ones(len(x))
mf[np.nonzero(x < a)] = 0
mf[np.nonzero(x > b)] = 0
return mf
def leftlinearmf(x, a, b):
'''One to the left, zero to the right, slope down in-between'''
mf = np.ones(len(x))
midpts = np.nonzero(np.logical_and(a < x, x < b))
mf[midpts] = (((b - x[midpts]) / (b - a)))
mf[np.nonzero(x >= b)] = 0
return mf
def rightlinearmf(x, a, b):
'''Zero to the left, one to the right, slope up in-between'''
mf = np.zeros(len(x))
midpts = np.nonzero(np.logical_and(a < x, x < b))
mf[midpts] = 1 - (((b - x[midpts]) / (b - a)))
mf[np.nonzero(x >= b)] = 1
return mf
def rampmf(x, a, b):
'''A line from a up/down to b (depending on the order of a and b)'''
if a < b:
return rightlinearmf(x, a, b)
elif a > b:
return leftlinearmf(x, b, a)
else: # a == b
return np.zeros(len(x))
def cosinemf(x, center, width):
'''A cosine curve distributed about the center with the given width'''
mf = np.zeros(len(x))
# Only plot the curve within the given width either side the center:
midpts = np.nonzero(np.logical_and(center - 0.5 * width <= x,
x <= center + 0.5 * width))
to_angle = 2.0 * np.pi / width
mf[midpts] = (0.5 * (1.0 + np.cos(to_angle * (x[midpts] - center))))
return mf
def concavemf(x, infl, end):
'''A curve rising/falling to end point, bent according to inflexion pt'''
mf = np.ones(len(x))
if infl <= end: # Concave increasing
incpts = np.nonzero(x < end)
mf[incpts] = (end - infl) / (2.0 * end - infl - x[incpts])
else: # Concave decreasing
decpts = np.nonzero(x > end)
mf[decpts] = (infl - end) / (infl - 2.0 * end + x[decpts])
return mf
def leftgaussmf(x, mean, sigma):
''' Like Gaussian, but always 1 when <= mean (so, slopes down only)'''
mf = skmemb.gaussmf(x, mean, sigma)
mf[np.nonzero(x <= mean)] = 1
return mf
def rightgaussmf(x, mean, sigma):
''' Like Gaussian, but always 1 when >= mean (so, slopes up only)'''
mf = skmemb.gaussmf(x, mean, sigma)
mf[np.nonzero(x >= mean)] = 1
return mf
def spikemf(x, center, width):
'''A symmetrical curved (exp) spike centered at the given location'''
return np.exp(-np.abs(10.0 / width * (x - center)))
def jfl_sigmf(x, gain, center):
'''Like sigmf, but jFuzzyLogic supplies parameters in a different order'''
return skmemb.sigmf(x, center, gain)
def fl_bellmf(x, center, width, slope):
'''Like gbellmf, but fuzzylite supplies parameters in a different order'''
return skmemb.gbellmf(x, width, slope, center)
# ### Sanity check: plot some examples of the membership functions
import matplotlib.pyplot as plt
def visualise_all(x, y_list, titles, ncols=3):
'''
Just display the given plot-data on a grid of separate graphs.
Also show the centroid as a vertical red line.
'''
nrows = np.int(np.ceil(len(y_list) / ncols))
fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8, 9))
fig.tight_layout()
fig.subplots_adjust(bottom=-.25)
for i, p in enumerate(y_list):
r, c = divmod(i, ncols)
axes[r][c].plot(x, p)
cog = skfuzzy.centroid(x, p)
axes[r][c].axvline(x=cog, color='red', linestyle='--')
axes[r][c].set_title(titles[i])
axes[r][c].set_ylim([-0.05, 1.05]) # so all have the same (0,1) y-axis
def _plot_mf_for(x):
'''Given the x-values, plot a series of example membership functions'''
tests = OrderedDict([
# Gaussians:
('gauss', skmemb.gaussmf(x, 50, 10)),
('left gauss', leftgaussmf(x, 50, 10)),
('right gauss', rightgaussmf(x, 50, 10)),
# Triangles:
('triangular', skmemb.trimf(x, [25, 50, 75])),
('left linear', leftlinearmf(x, 25, 75)),
('right linear', rightlinearmf(x, 25, 75)),
# fuzzylite:
('cosine', cosinemf(x, 50, 50)),
('inc concave', concavemf(x, 50, 75)),
('dec concave', concavemf(x, 50, 25)),
('spike', spikemf(x, 50, 50)),
('inc ramp', rampmf(x, 25, 75)),
('dec ramp', rampmf(x, 75, 25)),
# Rectangle-ish
('trapezoid', skmemb.trapmf(x, [20, 40, 60, 80])),
('rectangle', rectanglemf(x, 25, 75)),
('singleton', singletonmf(x, 50)),
])
# Example point sets:
ps_tests = [
[(40, 0.5), (60, 1)],
[(10, 0.5), (25, 0.25), (40, 0.75), (80, .5)],
[(0, 1), (40, 0.25), (50, .5), (99, 0)]
]
# Now try some interpolation methods on these:
for method in ['linear', 'lagrange', 'spline', 'cubic']:
tests.update([('{} ex{}'.format(method, i), pointsetmf(x, ps, method))
for i, ps in enumerate(ps_tests)])
return tests
if __name__ == '__main__':
x = np.arange(0, 100)
plots = _plot_mf_for(x)
visualise_all(x, plots.values(), list(plots.keys()))

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# -*- coding: utf-8 -*-
'''
Top-down recursive descent parser for the Fuzzy Control Language (FCL).
This is a bare-bones parser that just collects things as it goes through,
then returns the file contents as a tuple - really a simple AST.
I'm working from the draft IEC 61131-7 standard, but I have widened
the grammar slightly in places, as usage seems to be more liberal.
In particular, in blocks (variables, rules) I'm not fussy about
the order of decls where it doesn't matter. Also, I've made the
terminating semi-colon optional in most places (again reflecting usage).
References: https://en.wikipedia.org/wiki/Fuzzy_Control_Language
@author: james.power@mu.ie, Created on Tue Aug 14 09:58:10 2018
'''
import os
import sys
import codecs
import numpy as np
import skfuzzy.control as ctrl
import skfuzzy.control.term as fuzzterm
from fcl_scanner import BufferedFCLLexer
from fcl_symbols import NameMapper, SymbolTable
# A universe is given this no. of points unless specified:
_DEFAULT_UNIVERSE_SIZE = 1000
class ParsingError(Exception):
'''The parser raises this to flag an error while parsing an FCL file.'''
def __init__(self, pos, error_kind, msg):
Exception.__init__(self, '{} {}: {}'.format(pos, error_kind, msg))
self.pos = pos # filename[line,col]
self.error_kind = error_kind # e.g. 'lexical error', 'syntax error'
class FCLParser(NameMapper, SymbolTable):
'''
A top-down parser for the Fuzzy Control Language (FCL).
The main entry point is fcl_file with a filename, or you can call
any non-terminal with a string.
The relationship with NameMapper and SymbolTable should
really be "has-a" rather than "is-a", but it's simpler this way.
'''
def __init__(self, vars=None):
'''
Set up parser by initialising symbol table and lexer
Optionally supply an initial list of variables (or add them later)
'''
NameMapper.__init__(self)
self.load_ieee_names()
self.load_fcl_names_too()
self.load_jfl_names()
SymbolTable.__init__(self, vars)
self.lex = BufferedFCLLexer(self._report_error)
def _report_error(self, msg, error_kind='syntax error', pos=None):
'''
Raise an error; report the current position if none given.
All errors (lexical, syntax, scope) go through this method.
'''
if not pos: # No user-supplied position, get it from lexer:
tok = self.lex.token()
pos = self.lex.get_pos(tok)
got = tok.value if tok else '[EOF]'
msg += ' while reading token "{}"'.format(got)
raise ParsingError(pos, error_kind, msg)
def _calc_universe(self, start, stop, step=None):
'''
Return an np array corresponding to the given RANGE bounds.
Optionally specify the step, otherwise we guess.
'''
if start >= stop:
self._report_error('invalid range bounds ({}, {})'
.format(start, stop))
if not step: # Guess some "reasonable" step:
urange = 1 + (stop - start)
scale_by = urange / _DEFAULT_UNIVERSE_SIZE
step = np.power(10, np.round(np.log10(scale_by), 0))
universe = np.arange(start, stop, step)
return universe
def _make_mf(self, universe, mfunc, params):
'''
Given a function name and parameters, make a membership function.
'''
assert len(universe) > 0,\
'No current universe has been set for this mf'
skfunc, split_params = self.translate_mf(mfunc)
if split_params:
return skfunc(universe, *params)
else: # Takes parameters as an array
return skfunc(universe, params)
def _finalise_ante_var(self, universe, varname):
'''
Have just finished an input var definition, so add it to the list.
'''
fuzzyvar = ctrl.Antecedent(universe, varname)
self.add_vars([fuzzyvar])
return fuzzyvar
def _finalise_cons_var(self, universe, varname, options):
'''
Have just finished an output var definition, so add it to the list.
Make sure any declared options (e.g. defuzz method) are registered.
Default values are ignored at the moment.
'''
fuzzyvar = ctrl.Consequent(universe, varname)
for key, val in options.items():
key = key.upper()
if key == 'METHOD':
fuzzyvar.defuzzify_method = self.translate_defuzz(val)
elif key == 'ACCU':
fuzzyvar.accumulation_method = self.translate_accu(val)
elif key == 'DEFAULT':
pass
self.add_vars([fuzzyvar])
return fuzzyvar
def _finalise_terms(self, fuzzyvar, termlist):
'''
Propagate range values to any terms declared before the range.
That is, make sure all term definitions are skfuzzy Term objects.
'''
universe = fuzzyvar.universe
for term in termlist:
if not isinstance(term, fuzzterm.Term):
(term_name, fname, params) = term
mf_def = self._make_mf(universe, fname, params)
term = fuzzterm.Term(term_name, mf_def)
self.add_term_to_var(fuzzyvar, term)
def _add_hedges(self, fvar, hedges, membfun):
'''
Apply one or more hedge functions to the variable's member func.
Create a new mf for the overall result, and add it to the variable.
Return the term corresponding to this new membership function.
'''
if len(hedges) == 0:
return membfun
mf_name = '_{}_{}'.format('_'.join(hedges), membfun)
if mf_name in fvar.terms: # Already done it (some previous rule)
return fvar[mf_name]
mf_vals = fvar[membfun].mf
# Now apply each hedge in turn, starting at the last one:
for hedge_name in hedges[::-1]:
hedge_func = self.translate_hedge(hedge_name)
mf_vals = hedge_func(mf_vals)
# All the hedges processed, so add this as a new mf to the variable:
fvar[mf_name] = mf_vals
return fvar[mf_name]
def _finalise_rules(self, rbname, rulelist, options):
'''
Prefix the rule labels by the ruleblock name (if any).
Propagate any ruleblock AND/OR option-values to individual rules.
Ignoring any ACCU option here, since skfuzzy does this at the
variable level & could have same variable in different rule-blocks.
'''
and_key = options.get('AND', None)
or_key = options.get('OR', None)
fam = self.translate_and_or(and_key, or_key)
for rule in rulelist:
if rbname:
self.set_rule_label(rule, '{}.{}'.format(rbname, rule.label))
rule.and_func = fam.and_func
rule.or_func = fam.or_func
return rulelist
def read_fcl_file(self, filename):
'''
Read the given FCL file and parse it.
Returns the parser object, to facilitate create-and-call.
'''
self.lex.reset_lineno(filename)
self.flag_error_on_redefine()
with codecs.open(filename, 'r',
encoding='utf-8', errors='ignore') as fileh:
try:
self.lex.input(fileh.read())
self.function_block()
return self
except ParsingError as parsing_error:
raise parsing_error
except Exception as other_error:
# Show all errors as parser errors so we get line,col ref:
self._report_error(str(other_error), 'internal error')
# ########################################## #
# ### FCL grammar definition starts here ### #
# ########################################## #
# All of these parsing routines correspond to a grammar non-terminal,
# all can be called wiht a string (and will parse that string)
# and (nearly) all return an corresponding fuzzy object.
# ################################# #
# 1. Overall FCL program structure: #
# ################################# #
def function_block(self, input_string=None):
'''
This is the grammar's start symbol.
function_block_declaration ::=
'FUNCTION_BLOCK' function_block_name
{fb_io_var_declarations}
{fuzzify_block}
{defuzzify_block}
{rule_block}
{option_block}
'END_FUNCTION_BLOCK'
Actually, I take these contents in any order.
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('FUNCTION_BLOCK')
self.fb_name = self.lex.recognise_if_there('IDENTIFIER')
while self.lex.peek_not(['END_FUNCTION_BLOCK']):
if self.lex.peek_some(['VAR_INPUT', 'VAR_OUTPUT']):
self.var_decls()
elif self.lex.peek('FUZZIFY'):
self.fuzzify_block()
elif self.lex.peek('DEFUZZIFY'):
self.defuzzify_block()
elif self.lex.peek('RULEBLOCK'):
self.rule_block()
elif self.lex.peek('OPTION'):
self.option_block()
else:
self._report_error('Unknown element in function block')
self.lex.recognise('END_FUNCTION_BLOCK')
return None
def var_decls(self, input_string=None):
'''
fb_io_var_declarations ::=
'VAR_INPUT' {IDENTIFIER ':' IDENTIFIER ';'} 'END_VAR'
| 'VAR_OUTPUT' {IDENTIFIER ':' IDENTIFIER ';'} 'END_VAR'
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise_some(['VAR_INPUT', 'VAR_OUTPUT'])
decls = []
while self.lex.peek_not(['END_VAR']):
vname = self.lex.recognise('IDENTIFIER')
self.lex.recognise('COLON')
vtype = self.lex.recognise('IDENTIFIER')
self.lex.recognise_if_there('SEMICOLON')
decls.append((vname, vtype))
self.lex.recognise('END_VAR')
return decls
def option_block(self, input_string=None):
'''
option_block ::= 'OPTION' any-old-stuff 'END_OPTION'
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('OPTION')
while self.lex.peek_not(['END_OPTION']):
self.lex.recognise_anything() # Chuck away any contents...
self.lex.recognise('END_OPTION')
return None # Just for emphasis
# ################### #
# 2. Fuzzy variables: #
# ################### #
def _option_def(self, keyword):
'''
Options in variable or rule-block definitions:
an_option ::= keyword ':' IDENTIFIER ';'
'''
key = self.lex.recognise(keyword)
self.lex.recognise('COLON')
value = self.lex.recognise('IDENTIFIER')
self.lex.recognise_if_there('SEMICOLON')
return {key: value}
def fuzzify_block(self, input_string=None):
'''
fuzzify_block ::=
'FUZZIFY' variable_name
{linguistic_term}
[range]
'END_FUZZIFY'
The range can occur at beginning or end (or anywhere in between).
Don't add the terms until you have the range.
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('FUZZIFY')
varname = self.lex.recognise('IDENTIFIER')
termlist = []
universe = ()
while self.lex.peek_not(['END_FUZZIFY']):
if self.lex.peek('TERM'):
termlist.append(self.term_def())
elif self.lex.peek('RANGE'):
universe = self.range_def()
else:
self._report_error('Unknown element in fuzzify block')
self.lex.recognise('END_FUZZIFY')
if len(universe) == 0:
self._report_error('No universe for variable "{}"'
.format(varname), 'range error')
fuzzyvar = self._finalise_ante_var(universe, varname)
self._finalise_terms(fuzzyvar, termlist)
return fuzzyvar
def defuzzify_block(self, input_string=None):
'''
defuzzify_block ::=
'DEFUZZIFY' variable_name
{linguistic_term}
'ACCU' ':' accumulation_method ';'
'METHOD' ':' defuzzification_method ';'
default_value
[range]
'END_FUZZIFY'
defuzzification_method ::= IDENTIFIER
accumulation_method ::= IDENTIFIER
default_value ::= 'DEFAULT' ':=' numeric_literal | 'NC' ';'
I'm not fussy about the order of the block contents, and I accept
any identifier as a defuzz/accu method, and worry about it later.
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('DEFUZZIFY')
varname = self.lex.recognise('IDENTIFIER')
options = {}
termlist = []
universe = ()
while self.lex.peek_not(['END_DEFUZZIFY']):
toktype = self.lex.peek_type()
if toktype == 'TERM':
termlist.append(self.term_def())
elif toktype == 'RANGE':
universe = self.range_def()
elif toktype in ['METHOD', 'ACCU']:
options.update(self._option_def(toktype))
elif self.lex.recognise_if_there('DEFAULT'):
self.lex.recognise_some(['ASSIGN', 'COLON'])
if self.lex.recognise_if_there('NC'):
default_val = 'NC'
if self.lex.recognise_if_there('NAN'):
default_val = 'NAN'
else:
default_val = self.number()
self.lex.recognise_if_there('SEMICOLON')
options['DEFAULT'] = default_val
else:
self._report_error('Unknown element in defuzzify block')
self.lex.recognise('END_DEFUZZIFY')
if len(universe) == 0:
self._report_error('No universe for variable "{}"'
.format(varname), 'range error')
fuzzyvar = self._finalise_cons_var(universe, varname, options)
self._finalise_terms(fuzzyvar, termlist)
return fuzzyvar
def range_def(self, input_string=None):
'''
range ::= 'RANGE ':=' '(' numeric_literal '..' numeric_literal ')'
[WITH numeric_literal]
';'
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('RANGE')
self.lex.recognise('ASSIGN')
self.lex.recognise('LPAREN')
rmin = self.number() # originally ident_or_number()
self.lex.recognise('DOTDOT')
rmax = self.number()
self.lex.recognise('RPAREN')
numpoints = None
if self.lex.recognise_if_there('WITH'):
numpoints = self.number()
self.lex.recognise_if_there('SEMICOLON')
return self._calc_universe(rmin, rmax, numpoints)
# ###################################### #
# 3. Fuzzy terms (membership functions): #
# ###################################### #
def term_def(self, input_string=None):
'''
linguistic_term ::= term_header membership_function ';'
'''
self.lex.maybe_set_input(input_string)
name = self.term_header()
body = self.mf()
self.lex.recognise_if_there('SEMICOLON')
if body[0] == 'MF': # No universe defined yet
body[0] = name
return body
else: # Have a universe so make a term:
return fuzzterm.Term(name, body)
def term_header(self, input_string=None):
'''
term_header ::= 'TERM' term_name ':='
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('TERM')
name = self.lex.recognise('IDENTIFIER')
self.lex.recognise('ASSIGN')
return str(name)
def mf(self, input_string=None, universe=[]):
'''
membership_function ::= singleton | points | funcall
singleton ::= numeric_literal
funcall ::= 'IDENTIFIER' {'IDENTIFIER'}
'''
self.lex.maybe_set_input(input_string)
if self.lex.peek('LPAREN'):
fname, params = 'pointlist', self.point_list()
elif self.lex.peek('IDENTIFIER'):
fname = self.lex.recognise('IDENTIFIER')
# Possible list of parameter values now follows:
params = []
while self.lex.peek_some(['INT_CONST', 'FLOAT_CONST']):
params.append(self.number())
else: # Must be a singleton value
fname, params = 'singleton', [self.number()]
# Make a term if we have a universe:
if len(universe) > 0:
mf_def = self._make_mf(universe, fname, params)
else: # No universe defined yet, return items for the moment:
mf_def = ['MF', fname, params]
return mf_def
def point_list(self, input_string=None):
'''
points ::= {'(' numeric_literal ',' numeric_literal ')'}
The original allowed an ident for first point; not sure why.
'''
self.lex.maybe_set_input(input_string)
plist = []
while self.lex.recognise_if_there('LPAREN'):
x_val = self.number()
self.lex.recognise('COMMA')
y_val = self.number()
self.lex.recognise('RPAREN')
plist.append((x_val, y_val))
return plist
# ####################### #
# ### 4. Fuzzy rules: ### #
# ####################### #
def rule_block(self, input_string=None):
'''
rule_block ::=
'RULEBLOCK' [rule_block_name]
'AND' ':' operator_definition ';'
'OR' ':' operator_definition ';'
'ACT' ':' activation_method ';'
'ACCU' ':' accumulation_method ';'
{rule}
'END_RULEBLOCK'
operator_definition ::= IDENTIFER
activation_method ::= IDENTIFER
accumulation_method ::= IDENTIFER
I'm not fussy about the order of the block contents,
and I've made its name optional.
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('RULEBLOCK')
rbname = self.lex.recognise_if_there('IDENTIFIER')
rules = []
options = {}
while self.lex.peek_not(['END_RULEBLOCK']):
toktype = self.lex.peek_type()
if toktype == 'RULE':
rules.append(self.rule_def())
elif toktype in ['AND', 'OR', 'ACT', 'ACCU']:
options.update(self._option_def(toktype))
else:
self._report_error('Unknown element in rule block')
self.lex.recognise('END_RULEBLOCK')
return self._finalise_rules(rbname, rules, options)
def rule_def(self, input_string=None):
'''
rule ::= rule_header rule 'SEMICOLON'
'''
self.lex.maybe_set_input(input_string)
name = self.rule_header()
body = self.rule()
self.lex.recognise_if_there('SEMICOLON')
self.set_rule_label(body, name)
return body
def rule_header(self, input_string=None):
'''
rule_header ::= 'RULE' integer_literal ':'
I allow an identifier (or a number) as a rule name.
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('RULE')
name = self.ident_or_number()
self.lex.recognise('COLON')
return str(name)
def rule(self, input_string=None):
'''
rule ::= 'IF' antecedent 'THEN' consequent [WITH weighting_factor]
weighting_factor ::= variable | numeric_literal
'''
self.lex.maybe_set_input(input_string)
self.lex.recognise('IF')
ant = self.antecedent()
self.lex.recognise('THEN')
con = self.consequent()
# Recognise a weighting_factor if there is one:
if self.lex.recognise_if_there('WITH'):
weight = self.ident_or_number()
con = [fuzzterm.WeightedTerm(c, weight) for c in con]
return self.add_rule(ctrl.Rule(ant, con))
def antecedent(self, input_string=None):
'''
condition ::= clause {('AND' | 'OR') clause}
I need to do enforce precedence, so this is actually:
condition ::= _condition_and {'OR' _condition_and}
'''
self.lex.maybe_set_input(input_string)
left = self._antecedent_and()
while self.lex.recognise_if_there('OR'):
right = self._antecedent_and()
left = fuzzterm.TermAggregate(left, right, 'or')
return left
def _antecedent_and(self):
'''
condition_and ::= clause {('COMMA' | 'AND') clause}
Assuming 'COMMA' is just another way of saying 'AND'
'''
left = self.clause(parent_rule=self.antecedent)
while self.lex.peek_some(['COMMA', 'AND']):
self.lex.recognise_some(['COMMA', 'AND'])
right = self.clause(parent_rule=self.antecedent)
left = fuzzterm.TermAggregate(left, right, 'and')
return left
def consequent(self, input_string=None):
'''
condition ::= clause {'AND' clause}
Return a list of these.
'''
self.lex.maybe_set_input(input_string)
clist = [self.clause(parent_rule=self.consequent)]
while self.lex.peek_some(['COMMA', 'AND']):
self.lex.recognise_some(['COMMA', 'AND'])
clist.append(self.clause(parent_rule=self.consequent))
return clist
def clause(self, input_string=None, parent_rule=None):
'''
clause ::=
| 'NOT' condition()
| '(' condition() ')' # Allow extra parentheses
| atomic_clause
The syntax has been loosened to permit more flexible expressions;
These are the same: 'NOT v IS t', 'v IS NOT t', 'NOT(v IS t')
'''
# Note that the parent (caller) might be antecedent or consequent
# We pass it as a parameter so we can call it for sub-clauses.
self.lex.maybe_set_input(input_string)
if self.lex.recognise_if_there('NOT'):
subclause = self.clause(parent_rule=parent_rule)
return fuzzterm.TermAggregate(subclause, None, 'not')
elif self.lex.recognise_if_there('LPAREN'):
subclause = parent_rule() if parent_rule else self.clause()
self.lex.recognise('RPAREN')
return subclause
else:
in_consequent = (parent_rule == self.consequent)
return self.atomic_clause(in_consequent=in_consequent)
def atomic_clause(self, input_string=None, in_consequent=False):
'''
atomic_clause ::=
| variable_name # Not doing this!
| variable_name 'IS' {hedge} term_name
The optional hedges are: any identifier or 'NOT'.
'''
varname = self.lex.recognise('IDENTIFIER')
hedges = []
self.lex.recognise('IS')
while self.lex.peek_some(['IDENTIFIER', 'NOT']):
hedges.append(self.lex.recognise_some(['IDENTIFIER', 'NOT']))
# Actually, the last one was the member function name:
membfun = hedges.pop()
fvar = self.get_var_defn(varname)
this_clause = fvar[membfun]
# Special case when the only hedge is 'not':
if len(hedges) == 1 and hedges[0] == 'NOT':
this_clause = fuzzterm.TermAggregate(this_clause, None, 'not')
# Otherwise apply the hedge functions, if there are any:
elif len(hedges) > 0:
this_clause = self._add_hedges(fvar, hedges, membfun)
return this_clause
def ident_or_number(self, input_string=None):
'''
ident_or_number ::= identifier | integer_literal | real_literal
'''
self.lex.maybe_set_input(input_string)
if self.lex.peek('IDENTIFIER'):
return self.lex.recognise('IDENTIFIER')
if self.lex.peek('INT_CONST'):
return int(self.lex.recognise('INT_CONST'))
if self.lex.peek('FLOAT_CONST'):
return float(self.lex.recognise('FLOAT_CONST'))
self._report_error('expected ident/num')
def number(self, input_string=None):
'''
numeric_literal ::= integer_literal | real_literal
'''
self.lex.maybe_set_input(input_string)
if self.lex.peek('INT_CONST'):
return int(self.lex.recognise('INT_CONST'))
if self.lex.peek('FLOAT_CONST'):
return float(self.lex.recognise('FLOAT_CONST'))
self._report_error('expected numeric literal')
# ######################################### #
# ### FCL grammar definition ends here ### #
# ######################################### #
_FCL_SUFFIX = '.fcl'
def parse_dir(parser, rootdir, want_output=False):
'''
Scan all the .fcl files in rootdir and its subdirs.
Print any errors and the number of files parsed.
'''
files_tot, files_err = 0, 0
for rootpath, _, files in os.walk(rootdir):
for filename in files:
if filename.endswith(_FCL_SUFFIX):
filepath = os.path.join(rootpath, filename)
print('===', filepath)
try:
files_tot += 1
parser.clear()
parser.read_fcl_file(filepath)
if want_output:
print(parser)
except Exception as exc:
files_err += 1
print(exc)
print('Parsed %d files (%d had errors).' % (files_tot, files_err))
if __name__ == '__main__':
_parser = FCLParser()
if len(sys.argv) == 1: # No args, scan all examples
parse_dir(_parser, 'Examples')
else: # Parse the given files:
for fcl_filename in sys.argv[1:]:
_parser.read_fcl_file(fcl_filename)
print(_parser)

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'''
A scanner for the Fuzzy Control Language (FCL).
This is written using PLY (Python Lex-Yacc: http://www.dabeaz.com/ply)
I'm working from the *draft* IEC 61131-7 standard..
References: https://en.wikipedia.org/wiki/Fuzzy_Control_Language
@author: james.power@mu.ie, Created on Tue Aug 14 09:58:10 2018
'''
import sys
import os
import codecs
import ply.lex as lex # So, you need to install PLY to run this.
class FCLLexer(object):
'''
A scanner for the FCL language.
Call scan_text() on a string, scan_file with a filename.
'''
def __init__(self, strict=False):
'''Set up the lexer, ready to accept some input'''
# Load reserved words: default is upper case
self.reserved = {a.upper(): a.upper() for a in FCLLexer.reserved_words}
if not strict: # Allow lower-case reserved words too:
self.reserved.update({a.lower(): a.upper()
for a in FCLLexer.reserved_words})
# Get PLY to do its magic and build the lexer:
self.lexer = lex.lex(module=self)
# Initialise context information:
self.line_start = 1 # char position of most recent line-start
self.error_count = 0 # no. of errors seen in this file
self.reset_lineno()
self.next_token = None
def lexical_error(self, tok, msg):
'''Report an error, print the position and next token.'''
print('>>> {} {}'.format(self.get_pos(tok), msg))
self.error_count += 1
def reset_lineno(self, filename=None):
''' Resets the internal line-number counter of the lexer. '''
self.lexer.lineno = 1
self.line_start = 1 # char position of most recent line-start
self.filename = filename
self.error_count = 0
self.clear_lookahead()
def get_pos(self, token):
'''Return a string with the token's position: filename, line, column'''
pstr = ''
if self.filename:
pstr = self.filename
if token:
colno = 1+(token.lexpos - self.line_start)
pstr += '[%d,%d]' % (token.lineno, colno)
else:
pstr += '[EOF]'
return pstr
def input(self, text):
'''Load the given text into the scanner, and prepare to scan'''
self.lexer.input(text)
def clear_lookahead(self):
'''Throw away the current lookahead token (e.g. after an error)'''
self.next_token = None
def token(self):
'''Read the next word from the input and return its token'''
tok = self.next_token if self.next_token else self.lexer.token()
self.next_token = None
return tok
def lookahead(self):
'''Have a look at the next token, but don't consume it'''
if not self.next_token:
self.next_token = self.lexer.token()
return self.next_token
def scan_text(self, data, filename=None, silent=False):
'''
Run the lexer with the given string as input.
Can specify the filename (it's only used for error messages).
If not silent then print each token as it is scanned.
'''
self.reset_lineno(filename)
self.lexer.input(data)
for tok in self.lexer:
if not silent:
print(tok)
def scan_file(self, filename, silent=False):
'''Run the lexer with the contents of filename as input.'''
with codecs.open(filename, 'r',
encoding='utf-8', errors='ignore') as fileh:
self.scan_text(fileh.read(), filename, silent)
# #################################### #
# ### FCL LEXICAL RULES START HERE ### #
# #################################### #
states = (
('incomment', 'exclusive'),
)
reserved_words = '''
fuzzify defuzzify ruleblock function_block
end_fuzzify end_defuzzify end_ruleblock end_function_block
var_input var_output var end_var option end_option
accu act default method range term nan nc
rule if then with and or not is
lock enabled
'''.split()
tokens = '''
INT_CONST FLOAT_CONST IDENTIFIER
COMMA DOTDOT SEMICOLON COLON ASSIGN
LPAREN RPAREN
'''.split() + [a.upper() for a in reserved_words]
# Token patterns:
t_COMMA = r','
t_DOTDOT = r'\.\.'
t_SEMICOLON = r';'
t_COLON = r':'
t_ASSIGN = r':='
t_LPAREN = r'\('
t_RPAREN = r'\)'
def t_ANY_newline(self, t):
r'\n+'
t.lexer.lineno += t.value.count("\n")
self.line_start = t.lexpos
def t_OPEN_COMMENT(self, t):
r'/\*'
t.lexer.begin('incomment')
def t_incomment_CLOSE_COMMENT(self, t):
r'\*/'
t.lexer.begin('INITIAL')
def t_incomment_ignore_stuff(self, t):
r'[^\*\n]'
t_incomment_ignore = ' \t'
def t_incomment_error(self, t):
self.lexical_error(t, 'Discarding "{}"'.format(t.value[:3]))
t.lexer.skip(1)
# Note that newline below is enabled for any state (including incomment)
# Floats: borrowed and adapted from pycparser/c_lexer.py
exponent_part = r"""([eE][-+]?[0-9]+)"""
fractional_constant = r"""([0-9]*\.[0-9]+)|([0-9]+\.)"""
floating_constant = '[-+]?((((' + fractional_constant + ')' \
+ exponent_part + '?)'\
+ '|([0-9]+' + exponent_part + '))[FfLl]?)'
@lex.TOKEN(floating_constant)
def t_FLOAT_CONST(self, t):
try:
t.value = float(t.value)
except ValueError:
self.lexical_error(t, 'Float value "{}" is not valid'
.format(t.value))
t.value = 0.0
return t
def t_INT_CONST(self, t):
r'[-+]?\d+'
try:
t.value = int(t.value)
except ValueError:
self.lexical_error(t, 'Integer value "{}" is too large'
.format(t.value))
t.value = 0
return t
def t_IDENTIFIER(self, t):
r'[a-zA-Z_][a-zA-Z_0-9\-]*'
t.type = self.reserved.get(t.value, 'IDENTIFIER')
return t
# Ignore whitespace:
t_ignore = ' \t'
# Ignore single-line comments (C++/Java or Python style):
t_ignore_PY_COMMENT = r'\#.*'
t_ignore_CPP_COMMENT = r'//.*'
def t_error(self, t):
'''Only get here if the current char was unrecognised'''
self.lexical_error(t, 'Illegal character "{}"'.format(t.value[0]))
t.lexer.skip(1)
# ################################## #
# ### FCL LEXICAL RULES END HERE ### #
# ################################## #
class BufferedFCLLexer(FCLLexer):
'''
A wrapper for the FCLLexer to support look-ahead for parsing.
Mainly a bunch of routines of the form: "what token is next?"
The peek_* functions look at the next token but don't consume it.
The recognise_* functions consume a token, maybe throw an error.
'''
def __init__(self, error_handler):
'''
Bind the scanner's error-handling function to the one given.
'''
FCLLexer.__init__(self)
self.error_handler = error_handler
def lexical_error(self, tok, msg):
'''
Raise a lexical error at the given token position.
Redirect handling this to the supplied error handler.
We supply the pos, so we don't call token() (again) to get it.
'''
self.error_handler(msg, 'lexical error', self.get_pos(tok))
def maybe_set_input(self, input_string):
'''
Set the input to be the given string, if there is a given string.
'''
if input_string:
self.reset_lineno()
self.input(input_string)
def peek_type(self):
'''
Return the type of the next token, or None if EOF
'''
next_tok = self.lookahead()
if next_tok:
return next_tok.type
return None
def peek(self, toktype):
'''
Return true iff the next token has type toktype.
Does not consume the token.
'''
return self.peek_some([toktype])
def peek_some(self, toktypes):
'''
Return true iff the next token has one of the types toktypes.
Does not consume token. Returns False if next token is EOF.
i.e. *check* if a toktype token is next.
'''
next_tok = self.lookahead()
if next_tok and next_tok.type in toktypes:
return next_tok
return None
def peek_not(self, toktypes):
'''
Return true iff the next token has a type other than toktypes.
Does not consume token. N.B. returns False if next token is EOF.
i.e. *check* that the next token isn't of type toktype.
Used mostly in loops, so that's why we return False on EOF.
'''
next_tok = self.lookahead()
if next_tok and next_tok.type not in toktypes:
return True
return False
def recognise(self, toktype):
'''
If the next token is of the type toktype, then read it
and return its value. Else signal a syntax error.
'''
return self.recognise_some([toktype])
def recognise_some(self, toktypes):
'''
If the next token has one of the types toktypes, then read it
and return its value. Else signal a syntax error.
i.e. *demand* that a toktype token is next.
'''
next_tok = self.lookahead()
if next_tok and next_tok.type in toktypes:
return self.token().value
self.error_handler('expected {}'.format(toktypes))
def recognise_if_there(self, toktype):
'''
If the next token has one of the types toktypes, then read it
and return its value. Else return False (don't signal an error).
i.e. *optionally* recognise a toktype token, if it is next.
'''
next_tok = self.lookahead()
if next_tok and next_tok.type == toktype:
return self.token().value
return None
def recognise_anything(self):
'''Read the next token, whatever it is.'''
return self.token()
_FCL_SUFFIX = '.fcl'
def scan_dir(lexer, rootdir, silent=False):
'''
Scan all the .fcl files in root and its subdirs.
Default is to print out each token as it is recognised.
'''
files_tot, files_err = 0, 0
for rootpath, _, files in os.walk(rootdir):
for filename in files:
if filename.endswith(_FCL_SUFFIX):
filepath = os.path.join(rootpath, filename)
print('===', filepath)
files_tot += 1
lexer.scan_file(filepath, silent)
if lexer.error_count > 0:
files_err += 1
print('Scanned %d files (%d had errors).' % (files_tot, files_err))
if __name__ == '__main__':
_LEXER = FCLLexer()
if len(sys.argv) == 1: # No args, scan all examples
scan_dir(_LEXER, 'Examples')
else: # Parse the given files:
for fcl_filename in sys.argv[1:]:
_LEXER.scan_file(fcl_filename)

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# -*- coding: utf-8 -*-
'''
This maps the names of IEEE and FCL options to their implementation.
For the IEEE names I'm working from the XML standard (IEEE 1855-2016).
For the FCL names I'm following fuzzylite/src/imex/FclImporter.cpp
I only record FCL names if they're different from the IEEE ones.
@author: james.power@mu.ie Created on Wed Aug 22 11:59:59 2018
'''
from collections import OrderedDict
import numpy as np
import skfuzzy.membership as fuzzmf
import skfuzzy.control.fuzzyvariable as fuzzvar
import skfuzzy.control as ctrl
import skfuzzy.control.term as fuzzterm
import extramf
import hedges
import tnorms
# ############################
# ### Membership functions ###
# ############################
# Return skfuzzy version, or one of the extras:
_IEEE_MF = { # IEEE name: (fuzz-mf, split-parameters?)
'triangular': (fuzzmf.trimf, False),
'rightlinear': (extramf.rightlinearmf, True),
'leftlinear': (extramf.leftlinearmf, True),
'pi': (fuzzmf.pimf, True),
'gaussian': (fuzzmf.gaussmf, True),
'rightgaussian': (extramf.rightgaussmf, True),
'leftgaussian': (extramf.leftgaussmf, True),
'trapezoid': (fuzzmf.trapmf, False),
's': (fuzzmf.smf, True),
'z': (fuzzmf.zmf, True),
'rectangular': (extramf.rectanglemf, True),
'singleton': (extramf.singletonmf, True),
'pointset': (extramf.pointsetmf, False),
}
# jFuzzyLogic likes these names:
_JFUZZYLOGIC_MF = {
'trian': (fuzzmf.trimf, False),
'trape': (fuzzmf.trapmf, False),
'gauss': (fuzzmf.gaussmf, True),
'gauss2': (fuzzmf.gauss2mf, True),
'gbell': (fuzzmf.gbellmf, True),
'sigm': (extramf.jfl_sigmf, True),
}
# These are some other MFs I found, mostly from fuzzylite
_FCL_MF = { # FCL name: (fuzz-mf, split-parameters?)
'bell': (extramf.fl_bellmf, True),
'concave': (extramf.concavemf, True),
'cosine': (extramf.cosinemf, True),
'gaussianproduct': (extramf.gaussprod, True),
'pishape': (fuzzmf.pimf, True),
'pointlist': (extramf.pointsetmf, False),
'ramp': (extramf.rampmf, True),
'rectangle': (extramf.rectanglemf, True),
'sigmoid': (fuzzmf.sigmf, True),
'sigmoiddifference': (fuzzmf.dsigmf, True),
'sigmoidproduct': (fuzzmf.psigmf, True),
'spike': (extramf.spikemf, True),
'sshape': (fuzzmf.smf, True),
'triangle': (fuzzmf.trimf, False),
'zshape': (fuzzmf.zmf, True),
}
# ################################
# ### Defuzzification methods: ###
# ################################
# return a string that skfuzzy.defuzzify.defuzz() can be called with.
_IEEE_DEFUZZ = {
'cog': 'centroid',
'coa': 'bisector',
'lm': 'som',
'rm': 'lom',
'mom': 'mom'
}
_FCL_DEFUZZ = {
'mm': 'mom',
'cogs': 'centroid',
# 'cogs': WeightedAverage, not implemented
# 'cogss': WeightedSum, not implemented
}
# #####################################
# ### Aggregation (AND/OR) methods: ###
# #####################################
# Note that these all return a FuzzyAggregationMethods object
# that is, you get both and/or when you lookup either one of them.
_IEEE_AND = {
'min': tnorms.MIN_MAX,
'prod': tnorms.PRODUCT_SUM,
'bdif': tnorms.BOUNDED,
'drp': tnorms.DRASTIC,
'eprod': tnorms.EINSTEIN,
'hprod': tnorms.HAMACHER,
'nilmin': tnorms.NILPOTENT,
}
_IEEE_OR = {
'max': tnorms.MIN_MAX,
'probor': tnorms.PRODUCT_SUM,
'bsum': tnorms.BOUNDED,
'drs': tnorms.DRASTIC,
'esum': tnorms.EINSTEIN,
'hsum': tnorms.HAMACHER,
'nilmax': tnorms.NILPOTENT,
}
_FCL_AND = {
'dprod': tnorms.DRASTIC,
'nmin': tnorms.NILPOTENT,
}
_FCL_OR = {
'asum': tnorms.PRODUCT_SUM, # 'algebraic sum'
'dsum': tnorms.DRASTIC,
# 'nsum' is not implemented
'nmax': tnorms.NILPOTENT,
}
_JFUZZYLOGIC_AND = {
'dmin': tnorms.DRASTIC,
'hamacher': tnorms.HAMACHER,
'nipmin': tnorms.NILPOTENT,
}
_JFUZZYLOGIC_OR = {
'asum': tnorms.PRODUCT_SUM, # 'algebraic sum'
'dmax': tnorms.DRASTIC,
'einstein': tnorms.EINSTEIN,
'nipmax': tnorms.NILPOTENT,
}
# ######################################
# ### Class to map names to objects: ###
# ######################################
class NameMapper(object):
'''
Just three dicts, mapping names to: mfs, defuzz methods and norms.
These are loaded up with the IEEE and FCL names
'''
def __init__(self):
'''
Initialise lists of known mfs, defuzz methods and and/or methods.
Can load in names from IEEE XML standard as well as FCL.
'''
self.known_mfs = {} # Membership functions
self.defuzz_methods = {} # Defuzzification methods
self.and_names = {} # And function (to be applied in rules)
self.or_names = {} # Or function (to be applied in rules)
self.hedge_names = {} # Hedge functions that can be used in rules
def load_ieee_names(self):
'''Load in the names used by the IEEE (XML) standard'''
self.known_mfs.update(_IEEE_MF)
self.defuzz_methods.update(_IEEE_DEFUZZ)
self.and_names.update(_IEEE_AND)
self.or_names.update(_IEEE_OR)
self.hedge_names.update(hedges._IEEE_HEDGES)
def load_fcl_names_too(self):
'''
Load in the names used by the IEC 1131-7 (FCL) draft standard
Note: we assume you've already loaded in the IEEE names.
'''
self.known_mfs.update(_FCL_MF)
self.defuzz_methods.update(_FCL_DEFUZZ)
self.and_names.update(_FCL_AND)
self.or_names.update(_FCL_OR)
def load_jfl_names(self):
self.known_mfs.update(_JFUZZYLOGIC_MF)
self.and_names.update(_JFUZZYLOGIC_AND)
self.or_names.update(_JFUZZYLOGIC_OR)
def _report_error(self, msg, kind, pos=None):
'''Simple error reporter (so override me)'''
assert False, '{}: {}'.format(kind, msg)
def _unsupported(self, msg):
'''Raise an 'unsupported feature' error at the current position'''
self._report_error(msg, 'unsupported feature')
def translate_mf(self, mf_name):
'''Translate a member-function name to an actual function'''
if mf_name.lower() in self.known_mfs:
return self.known_mfs[mf_name.lower()]
else:
self._unsupported('membership function "{}"'.format(mf_name))
def translate_defuzz(self, df_name):
'''Translate a given defuzz method to its skfuzzy name'''
if df_name.lower() in self.defuzz_methods:
return self.defuzz_methods[df_name.lower()]
else:
self._unsupported('defuzzify method "{}"'.format(df_name))
def translate_accu(self, accu_name):
'''
Translate a given accumulation method to its skfuzzy name.
Use skfuzzy for max/prod, otherwise select a co-norm.
'''
# First check for teh built-ins:
if accu_name.lower() == 'max':
return ctrl.accumulation_max
elif accu_name.lower() == 'prod':
return ctrl.accumulation_prod
elif accu_name.lower() in self.or_names:
return self.or_names[accu_name.lower()].or_func
else:
self._unsupported('accumulation method "{}"'.format(accu_name))
def translate_hedge(self, hedge_name):
'''
Find the named hedge function, and return the function itself.
'''
if hedge_name.lower() in self.hedge_names:
return self.hedge_names[hedge_name.lower()]
else:
self._unsupported('hedge function "{}"'.format(hedge_name))
def translate_and_or(self, and_name, or_name):
'''
Get the and/or function corresponding to the given names.
If only one specified, the other will be its dual method.
If both are specified, then take both, even if not dual.
Return a FuzzyAggregationMethods object with both functions.
'''
# First check that both names, if specified, are valid:
if and_name and and_name.lower() not in self.and_names:
self._unsupported('and method "{}"'.format(and_name))
if or_name and or_name.lower() not in self.or_names:
self._unsupported('and method "{}"'.format(or_name))
# Set up the default (is actually min/max):
fam = fuzzterm.FuzzyAggregationMethods()
# Now see if one/both have been specified
if and_name and or_name: # Set both separately:
fam.and_func = self.and_names[and_name.lower()].and_func
fam.or_func = self.or_names[or_name.lower()].or_func
elif and_name:
fam = self.and_names[and_name.lower()]
elif or_name:
fam = self.or_names[or_name.lower()]
return fam
# #######################################
# ### Symbol Table for use by parser: ###
# #######################################
class SymbolTable(object):
'''
A very simple symbol table with a list of variables and rules.
The interface mirros some methods of skfuzzy.control.ControlSystem
'''
def __init__(self, varlist=None):
'''Set up an empty symbol table; optionally supply list of variables'''
self.fb_name = None # Name of function block (if any in file)
self.variables = OrderedDict() # Map variable label to FuzzyVariable
self.all_rules = OrderedDict() # Map rule label to Rule object
self.error_on_redefine = False
if varlist:
self.add_vars(varlist)
def flag_error_on_redefine(self):
'''
Signal an error if var or rule is redefined.
Probably want to set this for files, but not for interactive use.
'''
self.error_on_redefine = True
def clear(self):
''' Empty all items in the symbol table'''
self.fb_name = None
self.variables.clear()
self.all_rules.clear()
def _report_error(self, msg, kind, pos=None):
'''Simple error reporter (so override me)'''
assert False, '{}: {}'.format(kind, msg)
def add_var(self, fvar):
'''
Add a variables to the set of those known to us.
Will potentially overwrite any previous variables with this name.
'''
assert isinstance(fvar, fuzzvar.FuzzyVariable),\
'{} should be a variable'.format(fvar)
if self.error_on_redefine and fvar.label in self.variables:
self._report_error('variable "{}"'.format(fvar.label),
'redefinition error')
self.variables[fvar.label] = fvar
def add_vars(self, varlist):
'''
Add these variables to the set of those known to us.
Will overwrite any previous variables with these names.
'''
for fvar in varlist:
self.add_var(fvar)
def get_var_defn(self, varname):
'''
Gives the variable definition for this name; error if not there.
'''
if varname not in self.variables:
self._report_error('Variable "{}" not found'.format(varname),
'scope error')
return self.variables[varname]
def is_input_var(self, varname):
'''Return true iff this varaible has been decared as input/fuzzy'''
return varname in self.variables and \
isinstance(self.variables[varname], ctrl.Antecedent)
def is_output_var(self, varname):
'''Return true iff this varaible has been decared as output/defuzzy'''
return varname in self.variables and \
isinstance(self.variables[varname], ctrl.Consequent)
def add_term_to_var(self, fvar, fterm):
if self.error_on_redefine and fterm.label in fvar.terms:
self._report_error('term "{}" of variable "{}"'
.format(fterm.label, fvar.label),
'redefinition error')
fvar[fterm.label] = fterm
@property
def antecedents(self):
"""Generator which yields Antecedents in the system."""
for node in self.variables.values():
if isinstance(node, ctrl.Antecedent):
yield node
@property
def consequents(self):
"""Generator which yields Consequents in the system."""
for node in self.variables.values():
if isinstance(node, ctrl.Consequent):
yield node
@property
def fuzzy_variables(self):
'''Return an iterator over all the variable objects'''
return self.variables.values()
@property
def rules(self):
'''Return an iterator over all the rule objects'''
return self.all_rules.values()
def add_rule(self, rule):
'''
Add this rule to the list of those known to us.
Will potentially overwrite any previous rule with the same label.
'''
assert isinstance(rule, ctrl.Rule),\
'{} should be a rule object'.format(rule)
if self.error_on_redefine and rule.label in self.all_rules:
self._report_error('rule "{}"'.format(rule.label),
'redefinition error')
self.all_rules[rule.label] = rule
return rule
def set_rule_label(self, rule, new_label):
'''
Changing the rule label has consequences for our dict,
so use this method rather than setting it directly.
Will potentially overwrite any previous rule with the same label.
'''
# Remove the old-labelled version, if there is one:
self.all_rules.pop(rule.label, None)
rule.label = new_label
self.add_rule(rule)
def __getitem__(self, key):
'''
Allows the parser to be accessed as a dict;
the key should be a variable or rule name,
returns the definition corresponding to that name (or error).
'''
if key in self.variables:
return self.variables[key]
elif key in self.all_rules:
return self.all_rules[key]
else:
self._report_error('"{}" is not a known variable or rule name'
.format(key), 'scope error')
def __str__(self):
pstr = ''
if self.fb_name:
pstr += 'Function-Block "{}"\n'.format(self.fb_name)
for var in self.fuzzy_variables:
lo, hi = np.min(var.universe), np.max(var.universe)
pstr += '{}, range := ({} .. {})\n'.format(var, lo, hi)
pstr += '{}terms: {}\n'.format(' '*12, [t for t in var.terms])
for rule in self.rules:
pstr += 'Rule {}: {}\n'.format(rule.label, rule)
return pstr

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# -*- coding: utf-8 -*-
"""
Hedge functions, based on the definitions in the IEEE standard.
Each function here maps a mf to a new mf.
I'm following Annex A in IEEE 1855-2016, definintions A.1-A.13.
@author: james.power@mu.ie Created on Wed Aug 1 14:14:46 2018
"""
# Each hedge takes a membership function and modifies it,
# returning a 'hedged' membership function of the same size.
import numpy as np
import skfuzzy.membership as skmemb
import extramf
def above(mf):
''' A.1: above(mf)=0 if x<x_max, 1-mf if x>= x_max; cf below'''
max_pos = np.argmax(mf)
new_mf = 1 - mf
new_mf[:max_pos] = 0
return new_mf
def any_of(mf):
'''A.2: any(mf) = 1.. ('any' is a Python built-in)'''
return np.ones_like(mf)
def below(mf):
'''A.3: below(mf) = 0 if x>x_max, 1-mf(x) if x<x_max; cf above'''
max_pos = np.argmax(mf)
new_mf = 1 - mf
new_mf[max_pos:] = 0
return new_mf
def extremely(mf):
'''A.4: extremely(mf) = mf ** 3'''
return mf ** 3
def intensify(mf):
'''A.5: 2*mf^2 if mf<0.5, 1-2*(1-mf)^2 otherwise; cf seldom '''
new_mf = np.zeros_like(mf)
under, over = np.nonzero(mf <= 0.5), np.nonzero(mf > 0.5)
new_mf[under] = 2 * (mf[under] ** 2)
new_mf[over] = 1 - (2 * ((1 - mf[over]) ** 2))
return new_mf
def more_or_less(mf):
'''A.6: more_or_less(mf) = mf ^ 3'''
return mf ** (1/3)
def norm(mf):
'''A.7: norm divides by maximum'''
return mf / mf.max()
def is_not(mf):
'''A.8: not(mf) = 1-mf ('not' is a Python keyword)'''
return 1 - mf
def plus(mf):
'''A.9: more_or_less(mf) = mf ^ (5/4)'''
return mf ** (5/4)
def seldom(mf):
'''
A.10: (mf/2)^(1/2) if mf<=0.5, and 1-((1-mf)/2)^(1/2) otherwise;
cf intensify
'''
new_mf = np.zeros_like(mf)
under, over = np.nonzero(mf <= 0.5), np.nonzero(mf > 0.5)
new_mf[under] = np.sqrt(mf[under] / 2)
new_mf[over] = 1 - np.sqrt((1 - mf[over]) / 2)
return new_mf
def slightly(mf):
'''A.11: Defined as: intensify [ norm (plus S AND not very S) ]'''
def min_and(x, y):
''' Let's implement AND as the elementwise min of two arrays'''
return np.minimum.reduce([x, y])
return intensify(norm(min_and(plus(mf), is_not(very(mf)))))
def somewhat(mf):
'''A.12: somewhat(mf) = mf ^ (1/2)'''
return mf ** (1/2)
def very(mf):
'''A.13: very(mf) = mf ^ 2'''
return mf ** 2
# List of all hedges, maps name to function
_IEEE_HEDGES = {
'above': above,
'any': any_of,
'below': below,
'extremely': extremely,
'intensify': intensify,
'more_or_less': more_or_less,
'norm': norm,
'not': is_not,
'plus': plus,
'seldom': seldom,
'slightly': slightly,
'somewhat': somewhat,
'very': very,
}
def test_all_hedges(y):
results = []
hedgenames = sorted(all_hedges.keys()) # Want them in alphabetical order
for name in hedgenames:
func = all_hedges[name]
results.append(func(y))
return (hedgenames, results)
if __name__ == '__main__':
x = np.arange(0, 100)
y = skmemb.gaussmf(x, 50, 15)
(titles, data) = test_all_hedges(y)
extramf.visualise_all(x, [y]+data, ['original (gaussian)']+titles)

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import os
from fcl_parser import FCLParser
import skfuzzy.control as ctrl
import pandas as pd
teams = pd.read_csv('teams_list.csv')
matches = pd.read_csv('matches_list.csv')
not_in_df = ['Miedź Legnica','Zagłębie Sosnowiec']
matches_input = []
for i in range(25,150):
team1 = matches['Team1'][i][:-1]
team2 = matches['Team2'][i][1:-1]
gameweek = matches['Gameweek'][i]
if((team1 not in not_in_df) and (team2 not in not_in_df)):
form_1 = teams.loc[ (teams['Team'] == team1) & (teams['Gameweek'] == gameweek)].Form.values[0]
temp_1 = teams.loc[ (teams['Team'] == team1) & (teams['Gameweek'] == gameweek)].Points.values[0]
form_2 = teams.loc[ (teams['Team'] == team2) & (teams['Gameweek'] == gameweek)].Form.values[0]
temp_2 = teams.loc[ (teams['Team'] == team2) & (teams['Gameweek'] == gameweek)].Points.values[0]
points = temp_1 - temp_2
temp = teams.loc[ (teams['Team'] == team1) & (teams['Gameweek'] == gameweek)].Result.values[0]
ground_truth = 0
if(temp == 'W'):
ground_truth = -1
elif(temp == 'D'):
ground_truth = 0
else:
ground_truth = 1
matches_input.append([form_1, form_2, points, ground_truth])
p = FCLParser() # Create the parser
p.read_fcl_file('worker.fcl')
cs1 = ctrl.ControlSystem(p.rules)
module = ctrl.ControlSystemSimulation(cs1)
for i in range(50,75):
module.input['form1'] = matches_input[i][0]
module.input['form2'] = matches_input[i][1]
module.input['points'] = matches_input[i][2]
module.compute()
print(str(module.output['result']) +": " + str(matches_input[i][3]))

294
matches_list.csv Normal file
View File

@ -0,0 +1,294 @@
,Match,Date,Gameweek,Team1,Team2
76,Miedź Legnica - Pogoń Szczecin ,2018-07-20,1,Miedź Legnica , Pogoń Szczecin
78,Zagłębie Sosnowiec - Piast Gliwice ,2018-07-23,1,Zagłębie Sosnowiec , Piast Gliwice
82,Wisła Płock - Lech Poznań ,2018-07-22,1,Wisła Płock , Lech Poznań
74,Górnik Zabrze - Korona Kielce ,2018-07-22,1,Górnik Zabrze , Korona Kielce
88,Jagiellonia Białystok - Lechia Gdańsk ,2018-07-20,1,Jagiellonia Białystok , Lechia Gdańsk
76,Wisła Kraków - Arka Gdynia ,2018-07-21,1,Wisła Kraków , Arka Gdynia
90,Legia Warszawa - Zagłębie Lubin ,2018-07-21,1,Legia Warszawa , Zagłębie Lubin
80,Śląsk Wrocław - Cracovia Kraków ,2018-07-21,1,Śląsk Wrocław , Cracovia Kraków
74,Wisła Kraków - Miedź Legnica ,2018-07-27,2,Wisła Kraków , Miedź Legnica
86,Lechia Gdańsk - Śląsk Wrocław ,2018-07-27,2,Lechia Gdańsk , Śląsk Wrocław
72,Korona Kielce - Legia Warszawa ,2018-07-28,2,Korona Kielce , Legia Warszawa
72,Zagłębie Lubin - Zagłębie Sosnowiec ,2018-07-28,2,Zagłębie Lubin , Zagłębie Sosnowiec
74,Arka Gdynia - Jagiellonia Białystok ,2018-07-29,2,Arka Gdynia , Jagiellonia Białystok
88,Lech Poznań - Cracovia Kraków ,2018-07-29,2,Lech Poznań , Cracovia Kraków
80,Górnik Zabrze - Wisła Płock ,2018-07-29,2,Górnik Zabrze , Wisła Płock
76,Pogoń Szczecin - Piast Gliwice ,2018-07-30,2,Pogoń Szczecin , Piast Gliwice
72,Cracovia Kraków - Arka Gdynia ,2018-08-06,3,Cracovia Kraków , Arka Gdynia
76,Miedź Legnica - Górnik Zabrze ,2018-08-05,3,Miedź Legnica , Górnik Zabrze
84,Śląsk Wrocław - Lech Poznań ,2018-08-05,3,Śląsk Wrocław , Lech Poznań
70,Wisła Płock - Korona Kielce ,2018-08-03,3,Wisła Płock , Korona Kielce
84,Legia Warszawa - Lechia Gdańsk ,2018-08-04,3,Legia Warszawa , Lechia Gdańsk
74,Piast Gliwice - Zagłębie Lubin ,2018-08-04,3,Piast Gliwice , Zagłębie Lubin
72,Zagłębie Sosnowiec - Pogoń Szczecin ,2018-08-03,3,Zagłębie Sosnowiec , Pogoń Szczecin
86,Jagiellonia Białystok - Wisła Kraków ,2018-08-05,3,Jagiellonia Białystok , Wisła Kraków
82,Zagłębie Lubin - Jagiellonia Białystok ,2018-08-12,4,Zagłębie Lubin , Jagiellonia Białystok
74,Korona Kielce - Śląsk Wrocław ,2018-08-13,4,Korona Kielce , Śląsk Wrocław
80,Lech Poznań - Zagłębie Sosnowiec ,2018-08-12,4,Lech Poznań , Zagłębie Sosnowiec
82,Lechia Gdańsk - Miedź Legnica ,2018-08-10,4,Lechia Gdańsk , Miedź Legnica
74,Arka Gdynia - Górnik Zabrze ,2018-08-11,4,Arka Gdynia , Górnik Zabrze
74,Pogoń Szczecin - Cracovia Kraków ,2018-08-11,4,Pogoń Szczecin , Cracovia Kraków
76,Wisła Kraków - Wisła Płock ,2018-08-10,4,Wisła Kraków , Wisła Płock
72,Piast Gliwice - Legia Warszawa ,2018-08-12,4,Piast Gliwice , Legia Warszawa
72,Cracovia Kraków - Zagłębie Lubin ,2018-08-17,5,Cracovia Kraków , Zagłębie Lubin
66,Miedź Legnica - Korona Kielce ,2018-08-17,5,Miedź Legnica , Korona Kielce
68,Wisła Płock - Arka Gdynia ,2018-08-18,5,Wisła Płock , Arka Gdynia
72,Górnik Zabrze - Lechia Gdańsk ,2018-08-18,5,Górnik Zabrze , Lechia Gdańsk
70,Jagiellonia Białystok - Piast Gliwice ,2018-08-19,5,Jagiellonia Białystok , Piast Gliwice
68,Lech Poznań - Wisła Kraków ,2018-08-19,5,Lech Poznań , Wisła Kraków
74,Legia Warszawa - Zagłębie Sosnowiec ,2018-08-19,5,Legia Warszawa , Zagłębie Sosnowiec
72,Śląsk Wrocław - Pogoń Szczecin ,2018-08-20,5,Śląsk Wrocław , Pogoń Szczecin
76,Jagiellonia Białystok - Miedź Legnica ,2018-08-26,6,Jagiellonia Białystok , Miedź Legnica
70,Zagłębie Sosnowiec - Śląsk Wrocław ,2018-08-27,6,Zagłębie Sosnowiec , Śląsk Wrocław
72,Legia Warszawa - Wisła Płock ,2018-08-26,6,Legia Warszawa , Wisła Płock
68,Piast Gliwice - Cracovia Kraków ,2018-08-24,6,Piast Gliwice , Cracovia Kraków
66,Pogoń Szczecin - Lechia Gdańsk ,2018-08-25,6,Pogoń Szczecin , Lechia Gdańsk
64,Zagłębie Lubin - Lech Poznań ,2018-08-26,6,Zagłębie Lubin , Lech Poznań
64,Korona Kielce - Arka Gdynia ,2018-08-24,6,Korona Kielce , Arka Gdynia
70,Wisła Kraków - Górnik Zabrze ,2018-08-25,6,Wisła Kraków , Górnik Zabrze
76,Lechia Gdańsk - Korona Kielce ,2018-08-31,7,Lechia Gdańsk , Korona Kielce
68,Górnik Zabrze - Pogoń Szczecin ,2018-08-31,7,Górnik Zabrze , Pogoń Szczecin
64,Śląsk Wrocław - Wisła Kraków ,2018-09-01,7,Śląsk Wrocław , Wisła Kraków
72,Lech Poznań - Piast Gliwice ,2018-09-01,7,Lech Poznań , Piast Gliwice
64,Arka Gdynia - Zagłębie Sosnowiec ,2018-09-01,7,Arka Gdynia , Zagłębie Sosnowiec
68,Cracovia Kraków - Legia Warszawa ,2018-09-02,7,Cracovia Kraków , Legia Warszawa
62,Miedź Legnica - Zagłębie Lubin ,2018-09-02,7,Miedź Legnica , Zagłębie Lubin
74,Wisła Płock - Jagiellonia Białystok ,2018-09-02,7,Wisła Płock , Jagiellonia Białystok
66,Zagłębie Sosnowiec - Górnik Zabrze ,2018-09-17,8,Zagłębie Sosnowiec , Górnik Zabrze
62,Pogoń Szczecin - Korona Kielce ,2018-09-16,8,Pogoń Szczecin , Korona Kielce
70,Legia Warszawa - Lech Poznań ,2018-09-16,8,Legia Warszawa , Lech Poznań
62,Wisła Kraków - Lechia Gdańsk ,2018-09-15,8,Wisła Kraków , Lechia Gdańsk
66,Zagłębie Lubin - Śląsk Wrocław ,2018-09-14,8,Zagłębie Lubin , Śląsk Wrocław
72,Jagiellonia Białystok - Cracovia Kraków ,2018-09-14,8,Jagiellonia Białystok , Cracovia Kraków
64,Piast Gliwice - Arka Gdynia ,2018-09-15,8,Piast Gliwice , Arka Gdynia
68,Wisła Płock - Miedź Legnica ,2018-09-15,8,Wisła Płock , Miedź Legnica
60,Pogoń Szczecin - Wisła Kraków ,2018-09-21,9,Pogoń Szczecin , Wisła Kraków
68,Arka Gdynia - Lech Poznań ,2018-09-21,9,Arka Gdynia , Lech Poznań
64,Cracovia Kraków - Wisła Płock ,2018-09-22,9,Cracovia Kraków , Wisła Płock
58,Lechia Gdańsk - Zagłębie Lubin ,2018-09-22,9,Lechia Gdańsk , Zagłębie Lubin
66,Miedź Legnica - Legia Warszawa ,2018-09-22,9,Miedź Legnica , Legia Warszawa
64,Śląsk Wrocław - Piast Gliwice ,2018-09-23,9,Śląsk Wrocław , Piast Gliwice
64,Górnik Zabrze - Jagiellonia Białystok ,2018-09-23,9,Górnik Zabrze , Jagiellonia Białystok
58,Korona Kielce - Zagłębie Sosnowiec ,2018-09-24,9,Korona Kielce , Zagłębie Sosnowiec
64,Lech Poznań - Miedź Legnica ,2018-09-30,10,Lech Poznań , Miedź Legnica
60,Jagiellonia Białystok - Śląsk Wrocław ,2018-10-01,10,Jagiellonia Białystok , Śląsk Wrocław
60,Zagłębie Sosnowiec - Cracovia Kraków ,2018-09-30,10,Zagłębie Sosnowiec , Cracovia Kraków
56,Wisła Kraków - Korona Kielce ,2018-09-29,10,Wisła Kraków , Korona Kielce
58,Piast Gliwice - Górnik Zabrze ,2018-09-29,10,Piast Gliwice , Górnik Zabrze
56,Zagłębie Lubin - Pogoń Szczecin ,2018-09-29,10,Zagłębie Lubin , Pogoń Szczecin
58,Legia Warszawa - Arka Gdynia ,2018-09-28,10,Legia Warszawa , Arka Gdynia
68,Wisła Płock - Lechia Gdańsk ,2018-09-28,10,Wisła Płock , Lechia Gdańsk
60,Górnik Zabrze - Lech Poznań ,2018-10-05,11,Górnik Zabrze , Lech Poznań
56,Miedź Legnica - Piast Gliwice ,2018-10-05,11,Miedź Legnica , Piast Gliwice
62,Pogoń Szczecin - Wisła Płock ,2018-10-06,11,Pogoń Szczecin , Wisła Płock
58,Śląsk Wrocław - Legia Warszawa ,2018-10-06,11,Śląsk Wrocław , Legia Warszawa
66,Lechia Gdańsk - Zagłębie Sosnowiec ,2018-10-06,11,Lechia Gdańsk , Zagłębie Sosnowiec
64,Korona Kielce - Jagiellonia Białystok ,2018-10-07,11,Korona Kielce , Jagiellonia Białystok
54,Arka Gdynia - Zagłębie Lubin ,2018-10-07,11,Arka Gdynia , Zagłębie Lubin
54,Cracovia Kraków - Wisła Kraków ,2018-10-07,11,Cracovia Kraków , Wisła Kraków
56,Cracovia Kraków - Górnik Zabrze ,2018-10-22,12,Cracovia Kraków , Górnik Zabrze
52,Legia Warszawa - Wisła Kraków ,2018-10-21,12,Legia Warszawa , Wisła Kraków
52,Zagłębie Lubin - Wisła Płock ,2018-10-21,12,Zagłębie Lubin , Wisła Płock
64,Piast Gliwice - Lechia Gdańsk ,2018-10-19,12,Piast Gliwice , Lechia Gdańsk
52,Lech Poznań - Korona Kielce ,2018-10-20,12,Lech Poznań , Korona Kielce
52,Jagiellonia Białystok - Pogoń Szczecin ,2018-10-19,12,Jagiellonia Białystok , Pogoń Szczecin
54,Śląsk Wrocław - Arka Gdynia ,2018-10-20,12,Śląsk Wrocław , Arka Gdynia
50,Wisła Kraków - Zagłębie Sosnowiec ,2018-10-29,13,Wisła Kraków , Zagłębie Sosnowiec
54,Korona Kielce - Cracovia Kraków ,2018-10-28,13,Korona Kielce , Cracovia Kraków
56,Górnik Zabrze - Zagłębie Lubin ,2018-10-28,13,Górnik Zabrze , Zagłębie Lubin
58,Wisła Płock - Piast Gliwice ,2018-10-27,13,Wisła Płock , Piast Gliwice
62,Lechia Gdańsk - Arka Gdynia ,2018-10-27,13,Lechia Gdańsk , Arka Gdynia
54,Miedź Legnica - Śląsk Wrocław ,2018-10-26,13,Miedź Legnica , Śląsk Wrocław
56,Jagiellonia Białystok - Legia Warszawa ,2018-10-26,13,Jagiellonia Białystok , Legia Warszawa
50,Pogoń Szczecin - Lech Poznań ,2018-10-27,13,Pogoń Szczecin , Lech Poznań
48,Piast Gliwice - Wisła Kraków ,2018-11-02,14,Piast Gliwice , Wisła Kraków
50,Arka Gdynia - Pogoń Szczecin ,2018-11-02,14,Arka Gdynia , Pogoń Szczecin
50,Cracovia Kraków - Miedź Legnica ,2018-11-03,14,Cracovia Kraków , Miedź Legnica
52,Legia Warszawa - Górnik Zabrze ,2018-11-03,14,Legia Warszawa , Górnik Zabrze
50,Śląsk Wrocław - Wisła Płock ,2018-11-03,14,Śląsk Wrocław , Wisła Płock
60,Lech Poznań - Lechia Gdańsk ,2018-11-04,14,Lech Poznań , Lechia Gdańsk
48,Zagłębie Lubin - Korona Kielce ,2018-11-04,14,Zagłębie Lubin , Korona Kielce
56,Zagłębie Sosnowiec - Jagiellonia Białystok ,2018-11-05,14,Zagłębie Sosnowiec , Jagiellonia Białystok
48,Miedź Legnica - Arka Gdynia ,2018-11-11,15,Miedź Legnica , Arka Gdynia
54,Jagiellonia Białystok - Lech Poznań ,2018-11-11,15,Jagiellonia Białystok , Lech Poznań
46,Korona Kielce - Piast Gliwice ,2018-11-11,15,Korona Kielce , Piast Gliwice
48,Lechia Gdańsk - Cracovia Kraków ,2018-11-10,15,Lechia Gdańsk , Cracovia Kraków
46,Wisła Kraków - Zagłębie Lubin ,2018-11-10,15,Wisła Kraków , Zagłębie Lubin
54,Wisła Płock - Zagłębie Sosnowiec ,2018-11-10,15,Wisła Płock , Zagłębie Sosnowiec
50,Górnik Zabrze - Śląsk Wrocław ,2018-11-09,15,Górnik Zabrze , Śląsk Wrocław
50,Pogoń Szczecin - Legia Warszawa ,2018-11-09,15,Pogoń Szczecin , Legia Warszawa
44,Piast Gliwice - Zagłębie Sosnowiec ,2018-11-23,16,Piast Gliwice , Zagłębie Sosnowiec
48,Korona Kielce - Górnik Zabrze ,2018-11-24,16,Korona Kielce , Górnik Zabrze
46,Cracovia Kraków - Śląsk Wrocław ,2018-11-24,16,Cracovia Kraków , Śląsk Wrocław
50,Lech Poznań - Wisła Płock ,2018-11-24,16,Lech Poznań , Wisła Płock
52,Lechia Gdańsk - Jagiellonia Białystok ,2018-11-25,16,Lechia Gdańsk , Jagiellonia Białystok
44,Pogoń Szczecin - Miedź Legnica ,2018-11-25,16,Pogoń Szczecin , Miedź Legnica
48,Zagłębie Lubin - Legia Warszawa ,2018-11-25,16,Zagłębie Lubin , Legia Warszawa
44,Arka Gdynia - Wisła Kraków ,2018-11-26,16,Arka Gdynia , Wisła Kraków
42,Piast Gliwice - Pogoń Szczecin ,2018-12-03,17,Piast Gliwice , Pogoń Szczecin
42,Zagłębie Sosnowiec - Zagłębie Lubin ,2018-12-02,17,Zagłębie Sosnowiec , Zagłębie Lubin
48,Cracovia Kraków - Lech Poznań ,2018-12-02,17,Cracovia Kraków , Lech Poznań
42,Miedź Legnica - Wisła Kraków ,2018-12-01,17,Miedź Legnica , Wisła Kraków
44,Jagiellonia Białystok - Arka Gdynia ,2018-11-30,17,Jagiellonia Białystok , Arka Gdynia
44,Śląsk Wrocław - Lechia Gdańsk ,2018-11-30,17,Śląsk Wrocław , Lechia Gdańsk
46,Wisła Płock - Górnik Zabrze ,2018-12-01,17,Wisła Płock , Górnik Zabrze
46,Legia Warszawa - Korona Kielce ,2018-12-01,17,Legia Warszawa , Korona Kielce
46,Lech Poznań - Śląsk Wrocław ,2018-12-07,18,Lech Poznań , Śląsk Wrocław
40,Zagłębie Lubin - Piast Gliwice ,2018-12-07,18,Zagłębie Lubin , Piast Gliwice
46,Wisła Kraków - Jagiellonia Białystok ,2018-12-08,18,Wisła Kraków , Jagiellonia Białystok
46,Korona Kielce - Wisła Płock ,2018-12-08,18,Korona Kielce , Wisła Płock
40,Pogoń Szczecin - Zagłębie Sosnowiec ,2018-12-08,18,Pogoń Szczecin , Zagłębie Sosnowiec
42,Górnik Zabrze - Miedź Legnica ,2018-12-09,18,Górnik Zabrze , Miedź Legnica
42,Lechia Gdańsk - Legia Warszawa ,2018-12-09,18,Lechia Gdańsk , Legia Warszawa
40,Arka Gdynia - Cracovia Kraków ,2018-12-10,18,Arka Gdynia , Cracovia Kraków
40,Cracovia Kraków - Pogoń Szczecin ,2018-12-15,19,Cracovia Kraków , Pogoń Szczecin
44,Jagiellonia Białystok - Zagłębie Lubin ,2018-12-14,19,Jagiellonia Białystok , Zagłębie Lubin
44,Wisła Płock - Wisła Kraków ,2018-12-14,19,Wisła Płock , Wisła Kraków
38,Legia Warszawa - Piast Gliwice ,2018-12-15,19,Legia Warszawa , Piast Gliwice
44,Zagłębie Sosnowiec - Lech Poznań ,2018-12-16,19,Zagłębie Sosnowiec , Lech Poznań
38,Śląsk Wrocław - Korona Kielce ,2018-12-16,19,Śląsk Wrocław , Korona Kielce
46,Miedź Legnica - Lechia Gdańsk ,2018-12-17,19,Miedź Legnica , Lechia Gdańsk
40,Górnik Zabrze - Arka Gdynia ,2018-12-15,19,Górnik Zabrze , Arka Gdynia
36,Pogoń Szczecin - Śląsk Wrocław ,2018-12-20,20,Pogoń Szczecin , Śląsk Wrocław
38,Zagłębie Sosnowiec - Legia Warszawa ,2018-12-20,20,Zagłębie Sosnowiec , Legia Warszawa
36,Arka Gdynia - Wisła Płock ,2018-12-21,20,Arka Gdynia , Wisła Płock
42,Piast Gliwice - Jagiellonia Białystok ,2018-12-21,20,Piast Gliwice , Jagiellonia Białystok
42,Wisła Kraków - Lech Poznań ,2018-12-21,20,Wisła Kraków , Lech Poznań
38,Lechia Gdańsk - Górnik Zabrze ,2018-12-22,20,Lechia Gdańsk , Górnik Zabrze
36,Korona Kielce - Miedź Legnica ,2018-12-22,20,Korona Kielce , Miedź Legnica
38,Zagłębie Lubin - Cracovia Kraków ,2018-12-22,20,Zagłębie Lubin , Cracovia Kraków
34,Górnik Zabrze - Wisła Kraków ,2019-02-11,21,Górnik Zabrze , Wisła Kraków
36,Wisła Płock - Legia Warszawa ,2019-02-10,21,Wisła Płock , Legia Warszawa
34,Arka Gdynia - Korona Kielce ,2019-02-10,21,Arka Gdynia , Korona Kielce
34,Śląsk Wrocław - Zagłębie Sosnowiec ,2019-02-09,21,Śląsk Wrocław , Zagłębie Sosnowiec
40,Lechia Gdańsk - Pogoń Szczecin ,2019-02-09,21,Lechia Gdańsk , Pogoń Szczecin
34,Cracovia Kraków - Piast Gliwice ,2019-02-09,21,Cracovia Kraków , Piast Gliwice
34,Lech Poznań - Zagłębie Lubin ,2019-02-08,21,Lech Poznań , Zagłębie Lubin
40,Miedź Legnica - Jagiellonia Białystok ,2019-02-08,21,Miedź Legnica , Jagiellonia Białystok
32,Piast Gliwice - Lech Poznań ,2019-02-15,22,Piast Gliwice , Lech Poznań
34,Pogoń Szczecin - Górnik Zabrze ,2019-02-15,22,Pogoń Szczecin , Górnik Zabrze
32,Zagłębie Sosnowiec - Arka Gdynia ,2019-02-16,22,Zagłębie Sosnowiec , Arka Gdynia
38,Jagiellonia Białystok - Wisła Płock ,2019-02-16,22,Jagiellonia Białystok , Wisła Płock
38,Korona Kielce - Lechia Gdańsk ,2019-02-16,22,Korona Kielce , Lechia Gdańsk
32,Legia Warszawa - Cracovia Kraków ,2019-02-17,22,Legia Warszawa , Cracovia Kraków
32,Zagłębie Lubin - Miedź Legnica ,2019-02-17,22,Zagłębie Lubin , Miedź Legnica
32,Wisła Kraków - Śląsk Wrocław ,2019-02-18,22,Wisła Kraków , Śląsk Wrocław
30,Cracovia Kraków - Jagiellonia Białystok ,2019-02-24,23,Cracovia Kraków , Jagiellonia Białystok
32,Miedź Legnica - Wisła Płock ,2019-02-24,23,Miedź Legnica , Wisła Płock
32,Górnik Zabrze - Zagłębie Sosnowiec ,2019-02-23,23,Górnik Zabrze , Zagłębie Sosnowiec
30,Śląsk Wrocław - Zagłębie Lubin ,2019-02-25,23,Śląsk Wrocław , Zagłębie Lubin
36,Lechia Gdańsk - Wisła Kraków ,2019-02-23,23,Lechia Gdańsk , Wisła Kraków
30,Korona Kielce - Pogoń Szczecin ,2019-02-22,23,Korona Kielce , Pogoń Szczecin
30,Arka Gdynia - Piast Gliwice ,2019-02-22,23,Arka Gdynia , Piast Gliwice
30,Lech Poznań - Legia Warszawa ,2019-02-23,23,Lech Poznań , Legia Warszawa
28,Piast Gliwice - Śląsk Wrocław ,2019-03-01,24,Piast Gliwice , Śląsk Wrocław
30,Legia Warszawa - Miedź Legnica ,2019-03-01,24,Legia Warszawa , Miedź Legnica
28,Jagiellonia Białystok - Górnik Zabrze ,2019-03-02,24,Jagiellonia Białystok , Górnik Zabrze
30,Wisła Płock - Cracovia Kraków ,2019-03-02,24,Wisła Płock , Cracovia Kraków
28,Zagłębie Sosnowiec - Korona Kielce ,2019-03-02,24,Zagłębie Sosnowiec , Korona Kielce
28,Lech Poznań - Arka Gdynia ,2019-03-03,24,Lech Poznań , Arka Gdynia
28,Wisła Kraków - Pogoń Szczecin ,2019-03-03,24,Wisła Kraków , Pogoń Szczecin
28,Zagłębie Lubin - Lechia Gdańsk ,2019-03-04,24,Zagłębie Lubin , Lechia Gdańsk
26,Miedź Legnica - Lech Poznań ,2019-03-10,25,Miedź Legnica , Lech Poznań
26,Pogoń Szczecin - Zagłębie Lubin ,2019-03-10,25,Pogoń Szczecin , Zagłębie Lubin
26,Cracovia Kraków - Zagłębie Sosnowiec ,2019-03-09,25,Cracovia Kraków , Zagłębie Sosnowiec
30,Lechia Gdańsk - Wisła Płock ,2019-03-11,25,Lechia Gdańsk , Wisła Płock
26,Arka Gdynia - Legia Warszawa ,2019-03-09,25,Arka Gdynia , Legia Warszawa
32,Śląsk Wrocław - Jagiellonia Białystok ,2019-03-08,25,Śląsk Wrocław , Jagiellonia Białystok
26,Górnik Zabrze - Piast Gliwice ,2019-03-08,25,Górnik Zabrze , Piast Gliwice
26,Korona Kielce - Wisła Kraków ,2019-03-09,25,Korona Kielce , Wisła Kraków
24,Zagłębie Lubin - Arka Gdynia ,2019-03-17,26,Zagłębie Lubin , Arka Gdynia
24,Piast Gliwice - Miedź Legnica ,2019-03-17,26,Piast Gliwice , Miedź Legnica
28,Jagiellonia Białystok - Korona Kielce ,2019-03-16,26,Jagiellonia Białystok , Korona Kielce
24,Wisła Kraków - Cracovia Kraków ,2019-03-17,26,Wisła Kraków , Cracovia Kraków
28,Zagłębie Sosnowiec - Lechia Gdańsk ,2019-03-16,26,Zagłębie Sosnowiec , Lechia Gdańsk
24,Lech Poznań - Górnik Zabrze ,2019-03-15,26,Lech Poznań , Górnik Zabrze
24,Wisła Płock - Pogoń Szczecin ,2019-03-15,26,Wisła Płock , Pogoń Szczecin
24,Legia Warszawa - Śląsk Wrocław ,2019-03-16,26,Legia Warszawa , Śląsk Wrocław
26,Lechia Gdańsk - Piast Gliwice ,2019-03-29,27,Lechia Gdańsk , Piast Gliwice
24,Wisła Płock - Zagłębie Lubin ,2019-03-29,27,Wisła Płock , Zagłębie Lubin
22,Górnik Zabrze - Cracovia Kraków ,2019-03-29,27,Górnik Zabrze , Cracovia Kraków
22,Korona Kielce - Lech Poznań ,2019-03-30,27,Korona Kielce , Lech Poznań
22,Arka Gdynia - Śląsk Wrocław ,2019-03-30,27,Arka Gdynia , Śląsk Wrocław
22,Wisła Kraków - Legia Warszawa ,2019-03-31,27,Wisła Kraków , Legia Warszawa
22,Pogoń Szczecin - Jagiellonia Białystok ,2019-03-31,27,Pogoń Szczecin , Jagiellonia Białystok
20,Zagłębie Sosnowiec - Wisła Kraków ,2019-04-03,28,Zagłębie Sosnowiec , Wisła Kraków
20,Lech Poznań - Pogoń Szczecin ,2019-04-03,28,Lech Poznań , Pogoń Szczecin
20,Legia Warszawa - Jagiellonia Białystok ,2019-04-03,28,Legia Warszawa , Jagiellonia Białystok
22,Piast Gliwice - Wisła Płock ,2019-04-03,28,Piast Gliwice , Wisła Płock
20,Zagłębie Lubin - Górnik Zabrze ,2019-04-02,28,Zagłębie Lubin , Górnik Zabrze
20,Śląsk Wrocław - Miedź Legnica ,2019-04-02,28,Śląsk Wrocław , Miedź Legnica
24,Arka Gdynia - Lechia Gdańsk ,2019-04-02,28,Arka Gdynia , Lechia Gdańsk
20,Cracovia Kraków - Korona Kielce ,2019-04-02,28,Cracovia Kraków , Korona Kielce
18,Korona Kielce - Zagłębie Lubin ,2019-04-05,29,Korona Kielce , Zagłębie Lubin
18,Miedź Legnica - Cracovia Kraków ,2019-04-05,29,Miedź Legnica , Cracovia Kraków
18,Wisła Kraków - Piast Gliwice ,2019-04-06,29,Wisła Kraków , Piast Gliwice
18,Lechia Gdańsk - Lech Poznań ,2019-04-06,29,Lechia Gdańsk , Lech Poznań
22,Jagiellonia Białystok - Zagłębie Sosnowiec ,2019-04-06,29,Jagiellonia Białystok , Zagłębie Sosnowiec
18,Górnik Zabrze - Legia Warszawa ,2019-04-07,29,Górnik Zabrze , Legia Warszawa
18,Pogoń Szczecin - Arka Gdynia ,2019-04-07,29,Pogoń Szczecin , Arka Gdynia
18,Wisła Płock - Śląsk Wrocław ,2019-04-08,29,Wisła Płock , Śląsk Wrocław
16,Arka Gdynia - Miedź Legnica ,2019-04-13,30,Arka Gdynia , Miedź Legnica
18,Zagłębie Sosnowiec - Wisła Płock ,2019-04-13,30,Zagłębie Sosnowiec , Wisła Płock
16,Lech Poznań - Jagiellonia Białystok ,2019-04-13,30,Lech Poznań , Jagiellonia Białystok
16,Legia Warszawa - Pogoń Szczecin ,2019-04-13,30,Legia Warszawa , Pogoń Szczecin
16,Piast Gliwice - Korona Kielce ,2019-04-13,30,Piast Gliwice , Korona Kielce
16,Cracovia Kraków - Lechia Gdańsk ,2019-04-13,30,Cracovia Kraków , Lechia Gdańsk
16,Zagłębie Lubin - Wisła Kraków ,2019-04-13,30,Zagłębie Lubin , Wisła Kraków
16,Śląsk Wrocław - Górnik Zabrze ,2019-04-13,30,Śląsk Wrocław , Górnik Zabrze
14,Korona Kielce - Śląsk Wrocław ,2019-04-22,31,Korona Kielce , Śląsk Wrocław
16,Wisła Kraków - Wisła Płock ,2019-04-22,31,Wisła Kraków , Wisła Płock
14,Górnik Zabrze - Arka Gdynia ,2019-04-22,31,Górnik Zabrze , Arka Gdynia
14,Legia Warszawa - Cracovia Kraków ,2019-04-20,31,Legia Warszawa , Cracovia Kraków
14,Jagiellonia Białystok - Lech Poznań ,2019-04-20,31,Jagiellonia Białystok , Lech Poznań
16,Lechia Gdańsk - Piast Gliwice ,2019-04-20,31,Lechia Gdańsk , Piast Gliwice
14,Zagłębie Lubin - Pogoń Szczecin ,2019-04-20,31,Zagłębie Lubin , Pogoń Szczecin
12,Piast Gliwice - Zagłębie Lubin ,2019-04-23,32,Piast Gliwice , Zagłębie Lubin
12,Lech Poznań - Legia Warszawa ,2019-04-24,32,Lech Poznań , Legia Warszawa
14,Pogoń Szczecin - Lechia Gdańsk ,2019-04-24,32,Pogoń Szczecin , Lechia Gdańsk
12,Arka Gdynia - Miedź Legnica ,2019-04-25,32,Arka Gdynia , Miedź Legnica
12,Zagłębie Sosnowiec - Wisła Kraków ,2019-04-25,32,Zagłębie Sosnowiec , Wisła Kraków
12,Śląsk Wrocław - Górnik Zabrze ,2019-04-25,32,Śląsk Wrocław , Górnik Zabrze
12,Wisła Płock - Korona Kielce ,2019-04-25,32,Wisła Płock , Korona Kielce
14,Cracovia Kraków - Jagiellonia Białystok ,2019-04-23,32,Cracovia Kraków , Jagiellonia Białystok
12,Miedź Legnica - Wisła Płock ,2019-04-28,33,Miedź Legnica , Wisła Płock
10,Górnik Zabrze - Zagłębie Sosnowiec ,2019-04-29,33,Górnik Zabrze , Zagłębie Sosnowiec
10,Korona Kielce - Arka Gdynia ,2019-04-28,33,Korona Kielce , Arka Gdynia
10,Lechia Gdańsk - Legia Warszawa ,2019-04-27,33,Lechia Gdańsk , Legia Warszawa
10,Pogoń Szczecin - Lech Poznań ,2019-04-27,33,Pogoń Szczecin , Lech Poznań
10,Zagłębie Lubin - Jagiellonia Białystok ,2019-04-26,33,Zagłębie Lubin , Jagiellonia Białystok
10,Piast Gliwice - Cracovia Kraków ,2019-04-26,33,Piast Gliwice , Cracovia Kraków
10,Wisła Kraków - Śląsk Wrocław ,2019-04-28,33,Wisła Kraków , Śląsk Wrocław
8,Górnik Zabrze - Wisła Kraków ,2019-05-03,34,Górnik Zabrze , Wisła Kraków
10,Wisła Płock - Arka Gdynia ,2019-05-03,34,Wisła Płock , Arka Gdynia
8,Legia Warszawa - Piast Gliwice ,2019-05-04,34,Legia Warszawa , Piast Gliwice
8,Lech Poznań - Zagłębie Lubin ,2019-05-04,34,Lech Poznań , Zagłębie Lubin
8,Korona Kielce - Miedź Legnica ,2019-05-04,34,Korona Kielce , Miedź Legnica
8,Cracovia Kraków - Lechia Gdańsk ,2019-05-05,34,Cracovia Kraków , Lechia Gdańsk
8,Zagłębie Sosnowiec - Śląsk Wrocław ,2019-05-05,34,Zagłębie Sosnowiec , Śląsk Wrocław
8,Jagiellonia Białystok - Pogoń Szczecin ,2019-05-06,34,Jagiellonia Białystok , Pogoń Szczecin
6,Piast Gliwice - Jagiellonia Białystok ,2019-05-12,35,Piast Gliwice , Jagiellonia Białystok
6,Legia Warszawa - Pogoń Szczecin ,2019-05-12,35,Legia Warszawa , Pogoń Szczecin
6,Miedź Legnica - Górnik Zabrze ,2019-05-11,35,Miedź Legnica , Górnik Zabrze
6,Lechia Gdańsk - Zagłębie Lubin ,2019-05-12,35,Lechia Gdańsk , Zagłębie Lubin
6,Śląsk Wrocław - Wisła Płock ,2019-05-11,35,Śląsk Wrocław , Wisła Płock
6,Wisła Kraków - Korona Kielce ,2019-05-10,35,Wisła Kraków , Korona Kielce
6,Arka Gdynia - Zagłębie Sosnowiec ,2019-05-10,35,Arka Gdynia , Zagłębie Sosnowiec
6,Cracovia Kraków - Lech Poznań ,2019-05-11,35,Cracovia Kraków , Lech Poznań
4,Lech Poznań - Lechia Gdańsk ,2019-05-15,36,Lech Poznań , Lechia Gdańsk
4,Pogoń Szczecin - Piast Gliwice ,2019-05-15,36,Pogoń Szczecin , Piast Gliwice
4,Jagiellonia Białystok - Legia Warszawa ,2019-05-15,36,Jagiellonia Białystok , Legia Warszawa
4,Zagłębie Lubin - Cracovia Kraków ,2019-05-15,36,Zagłębie Lubin , Cracovia Kraków
6,Górnik Zabrze - Wisła Płock ,2019-05-14,36,Górnik Zabrze , Wisła Płock
4,Zagłębie Sosnowiec - Korona Kielce ,2019-05-14,36,Zagłębie Sosnowiec , Korona Kielce
4,Arka Gdynia - Wisła Kraków ,2019-05-13,36,Arka Gdynia , Wisła Kraków
4,Miedź Legnica - Śląsk Wrocław ,2019-05-14,36,Miedź Legnica , Śląsk Wrocław
2,Korona Kielce - Górnik Zabrze ,2019-05-18,37,Korona Kielce , Górnik Zabrze
2,Wisła Kraków - Miedź Legnica ,2019-05-18,37,Wisła Kraków , Miedź Legnica
2,Śląsk Wrocław - Arka Gdynia ,2019-05-18,37,Śląsk Wrocław , Arka Gdynia
4,Wisła Płock - Zagłębie Sosnowiec ,2019-05-18,37,Wisła Płock , Zagłębie Sosnowiec
2,Legia Warszawa - Zagłębie Lubin ,2019-05-19,37,Legia Warszawa , Zagłębie Lubin
2,Piast Gliwice - Lech Poznań ,2019-05-19,37,Piast Gliwice , Lech Poznań
2,Cracovia Kraków - Pogoń Szczecin ,2019-05-19,37,Cracovia Kraków , Pogoń Szczecin
2,Lechia Gdańsk - Jagiellonia Białystok ,2019-05-19,37,Lechia Gdańsk , Jagiellonia Białystok
1 Match Date Gameweek Team1 Team2
2 76 Miedź Legnica - Pogoń Szczecin 2018-07-20 1 Miedź Legnica Pogoń Szczecin
3 78 Zagłębie Sosnowiec - Piast Gliwice 2018-07-23 1 Zagłębie Sosnowiec Piast Gliwice
4 82 Wisła Płock - Lech Poznań 2018-07-22 1 Wisła Płock Lech Poznań
5 74 Górnik Zabrze - Korona Kielce 2018-07-22 1 Górnik Zabrze Korona Kielce
6 88 Jagiellonia Białystok - Lechia Gdańsk 2018-07-20 1 Jagiellonia Białystok Lechia Gdańsk
7 76 Wisła Kraków - Arka Gdynia 2018-07-21 1 Wisła Kraków Arka Gdynia
8 90 Legia Warszawa - Zagłębie Lubin 2018-07-21 1 Legia Warszawa Zagłębie Lubin
9 80 Śląsk Wrocław - Cracovia Kraków 2018-07-21 1 Śląsk Wrocław Cracovia Kraków
10 74 Wisła Kraków - Miedź Legnica 2018-07-27 2 Wisła Kraków Miedź Legnica
11 86 Lechia Gdańsk - Śląsk Wrocław 2018-07-27 2 Lechia Gdańsk Śląsk Wrocław
12 72 Korona Kielce - Legia Warszawa 2018-07-28 2 Korona Kielce Legia Warszawa
13 72 Zagłębie Lubin - Zagłębie Sosnowiec 2018-07-28 2 Zagłębie Lubin Zagłębie Sosnowiec
14 74 Arka Gdynia - Jagiellonia Białystok 2018-07-29 2 Arka Gdynia Jagiellonia Białystok
15 88 Lech Poznań - Cracovia Kraków 2018-07-29 2 Lech Poznań Cracovia Kraków
16 80 Górnik Zabrze - Wisła Płock 2018-07-29 2 Górnik Zabrze Wisła Płock
17 76 Pogoń Szczecin - Piast Gliwice 2018-07-30 2 Pogoń Szczecin Piast Gliwice
18 72 Cracovia Kraków - Arka Gdynia 2018-08-06 3 Cracovia Kraków Arka Gdynia
19 76 Miedź Legnica - Górnik Zabrze 2018-08-05 3 Miedź Legnica Górnik Zabrze
20 84 Śląsk Wrocław - Lech Poznań 2018-08-05 3 Śląsk Wrocław Lech Poznań
21 70 Wisła Płock - Korona Kielce 2018-08-03 3 Wisła Płock Korona Kielce
22 84 Legia Warszawa - Lechia Gdańsk 2018-08-04 3 Legia Warszawa Lechia Gdańsk
23 74 Piast Gliwice - Zagłębie Lubin 2018-08-04 3 Piast Gliwice Zagłębie Lubin
24 72 Zagłębie Sosnowiec - Pogoń Szczecin 2018-08-03 3 Zagłębie Sosnowiec Pogoń Szczecin
25 86 Jagiellonia Białystok - Wisła Kraków 2018-08-05 3 Jagiellonia Białystok Wisła Kraków
26 82 Zagłębie Lubin - Jagiellonia Białystok 2018-08-12 4 Zagłębie Lubin Jagiellonia Białystok
27 74 Korona Kielce - Śląsk Wrocław 2018-08-13 4 Korona Kielce Śląsk Wrocław
28 80 Lech Poznań - Zagłębie Sosnowiec 2018-08-12 4 Lech Poznań Zagłębie Sosnowiec
29 82 Lechia Gdańsk - Miedź Legnica 2018-08-10 4 Lechia Gdańsk Miedź Legnica
30 74 Arka Gdynia - Górnik Zabrze 2018-08-11 4 Arka Gdynia Górnik Zabrze
31 74 Pogoń Szczecin - Cracovia Kraków 2018-08-11 4 Pogoń Szczecin Cracovia Kraków
32 76 Wisła Kraków - Wisła Płock 2018-08-10 4 Wisła Kraków Wisła Płock
33 72 Piast Gliwice - Legia Warszawa 2018-08-12 4 Piast Gliwice Legia Warszawa
34 72 Cracovia Kraków - Zagłębie Lubin 2018-08-17 5 Cracovia Kraków Zagłębie Lubin
35 66 Miedź Legnica - Korona Kielce 2018-08-17 5 Miedź Legnica Korona Kielce
36 68 Wisła Płock - Arka Gdynia 2018-08-18 5 Wisła Płock Arka Gdynia
37 72 Górnik Zabrze - Lechia Gdańsk 2018-08-18 5 Górnik Zabrze Lechia Gdańsk
38 70 Jagiellonia Białystok - Piast Gliwice 2018-08-19 5 Jagiellonia Białystok Piast Gliwice
39 68 Lech Poznań - Wisła Kraków 2018-08-19 5 Lech Poznań Wisła Kraków
40 74 Legia Warszawa - Zagłębie Sosnowiec 2018-08-19 5 Legia Warszawa Zagłębie Sosnowiec
41 72 Śląsk Wrocław - Pogoń Szczecin 2018-08-20 5 Śląsk Wrocław Pogoń Szczecin
42 76 Jagiellonia Białystok - Miedź Legnica 2018-08-26 6 Jagiellonia Białystok Miedź Legnica
43 70 Zagłębie Sosnowiec - Śląsk Wrocław 2018-08-27 6 Zagłębie Sosnowiec Śląsk Wrocław
44 72 Legia Warszawa - Wisła Płock 2018-08-26 6 Legia Warszawa Wisła Płock
45 68 Piast Gliwice - Cracovia Kraków 2018-08-24 6 Piast Gliwice Cracovia Kraków
46 66 Pogoń Szczecin - Lechia Gdańsk 2018-08-25 6 Pogoń Szczecin Lechia Gdańsk
47 64 Zagłębie Lubin - Lech Poznań 2018-08-26 6 Zagłębie Lubin Lech Poznań
48 64 Korona Kielce - Arka Gdynia 2018-08-24 6 Korona Kielce Arka Gdynia
49 70 Wisła Kraków - Górnik Zabrze 2018-08-25 6 Wisła Kraków Górnik Zabrze
50 76 Lechia Gdańsk - Korona Kielce 2018-08-31 7 Lechia Gdańsk Korona Kielce
51 68 Górnik Zabrze - Pogoń Szczecin 2018-08-31 7 Górnik Zabrze Pogoń Szczecin
52 64 Śląsk Wrocław - Wisła Kraków 2018-09-01 7 Śląsk Wrocław Wisła Kraków
53 72 Lech Poznań - Piast Gliwice 2018-09-01 7 Lech Poznań Piast Gliwice
54 64 Arka Gdynia - Zagłębie Sosnowiec 2018-09-01 7 Arka Gdynia Zagłębie Sosnowiec
55 68 Cracovia Kraków - Legia Warszawa 2018-09-02 7 Cracovia Kraków Legia Warszawa
56 62 Miedź Legnica - Zagłębie Lubin 2018-09-02 7 Miedź Legnica Zagłębie Lubin
57 74 Wisła Płock - Jagiellonia Białystok 2018-09-02 7 Wisła Płock Jagiellonia Białystok
58 66 Zagłębie Sosnowiec - Górnik Zabrze 2018-09-17 8 Zagłębie Sosnowiec Górnik Zabrze
59 62 Pogoń Szczecin - Korona Kielce 2018-09-16 8 Pogoń Szczecin Korona Kielce
60 70 Legia Warszawa - Lech Poznań 2018-09-16 8 Legia Warszawa Lech Poznań
61 62 Wisła Kraków - Lechia Gdańsk 2018-09-15 8 Wisła Kraków Lechia Gdańsk
62 66 Zagłębie Lubin - Śląsk Wrocław 2018-09-14 8 Zagłębie Lubin Śląsk Wrocław
63 72 Jagiellonia Białystok - Cracovia Kraków 2018-09-14 8 Jagiellonia Białystok Cracovia Kraków
64 64 Piast Gliwice - Arka Gdynia 2018-09-15 8 Piast Gliwice Arka Gdynia
65 68 Wisła Płock - Miedź Legnica 2018-09-15 8 Wisła Płock Miedź Legnica
66 60 Pogoń Szczecin - Wisła Kraków 2018-09-21 9 Pogoń Szczecin Wisła Kraków
67 68 Arka Gdynia - Lech Poznań 2018-09-21 9 Arka Gdynia Lech Poznań
68 64 Cracovia Kraków - Wisła Płock 2018-09-22 9 Cracovia Kraków Wisła Płock
69 58 Lechia Gdańsk - Zagłębie Lubin 2018-09-22 9 Lechia Gdańsk Zagłębie Lubin
70 66 Miedź Legnica - Legia Warszawa 2018-09-22 9 Miedź Legnica Legia Warszawa
71 64 Śląsk Wrocław - Piast Gliwice 2018-09-23 9 Śląsk Wrocław Piast Gliwice
72 64 Górnik Zabrze - Jagiellonia Białystok 2018-09-23 9 Górnik Zabrze Jagiellonia Białystok
73 58 Korona Kielce - Zagłębie Sosnowiec 2018-09-24 9 Korona Kielce Zagłębie Sosnowiec
74 64 Lech Poznań - Miedź Legnica 2018-09-30 10 Lech Poznań Miedź Legnica
75 60 Jagiellonia Białystok - Śląsk Wrocław 2018-10-01 10 Jagiellonia Białystok Śląsk Wrocław
76 60 Zagłębie Sosnowiec - Cracovia Kraków 2018-09-30 10 Zagłębie Sosnowiec Cracovia Kraków
77 56 Wisła Kraków - Korona Kielce 2018-09-29 10 Wisła Kraków Korona Kielce
78 58 Piast Gliwice - Górnik Zabrze 2018-09-29 10 Piast Gliwice Górnik Zabrze
79 56 Zagłębie Lubin - Pogoń Szczecin 2018-09-29 10 Zagłębie Lubin Pogoń Szczecin
80 58 Legia Warszawa - Arka Gdynia 2018-09-28 10 Legia Warszawa Arka Gdynia
81 68 Wisła Płock - Lechia Gdańsk 2018-09-28 10 Wisła Płock Lechia Gdańsk
82 60 Górnik Zabrze - Lech Poznań 2018-10-05 11 Górnik Zabrze Lech Poznań
83 56 Miedź Legnica - Piast Gliwice 2018-10-05 11 Miedź Legnica Piast Gliwice
84 62 Pogoń Szczecin - Wisła Płock 2018-10-06 11 Pogoń Szczecin Wisła Płock
85 58 Śląsk Wrocław - Legia Warszawa 2018-10-06 11 Śląsk Wrocław Legia Warszawa
86 66 Lechia Gdańsk - Zagłębie Sosnowiec 2018-10-06 11 Lechia Gdańsk Zagłębie Sosnowiec
87 64 Korona Kielce - Jagiellonia Białystok 2018-10-07 11 Korona Kielce Jagiellonia Białystok
88 54 Arka Gdynia - Zagłębie Lubin 2018-10-07 11 Arka Gdynia Zagłębie Lubin
89 54 Cracovia Kraków - Wisła Kraków 2018-10-07 11 Cracovia Kraków Wisła Kraków
90 56 Cracovia Kraków - Górnik Zabrze 2018-10-22 12 Cracovia Kraków Górnik Zabrze
91 52 Legia Warszawa - Wisła Kraków 2018-10-21 12 Legia Warszawa Wisła Kraków
92 52 Zagłębie Lubin - Wisła Płock 2018-10-21 12 Zagłębie Lubin Wisła Płock
93 64 Piast Gliwice - Lechia Gdańsk 2018-10-19 12 Piast Gliwice Lechia Gdańsk
94 52 Lech Poznań - Korona Kielce 2018-10-20 12 Lech Poznań Korona Kielce
95 52 Jagiellonia Białystok - Pogoń Szczecin 2018-10-19 12 Jagiellonia Białystok Pogoń Szczecin
96 54 Śląsk Wrocław - Arka Gdynia 2018-10-20 12 Śląsk Wrocław Arka Gdynia
97 50 Wisła Kraków - Zagłębie Sosnowiec 2018-10-29 13 Wisła Kraków Zagłębie Sosnowiec
98 54 Korona Kielce - Cracovia Kraków 2018-10-28 13 Korona Kielce Cracovia Kraków
99 56 Górnik Zabrze - Zagłębie Lubin 2018-10-28 13 Górnik Zabrze Zagłębie Lubin
100 58 Wisła Płock - Piast Gliwice 2018-10-27 13 Wisła Płock Piast Gliwice
101 62 Lechia Gdańsk - Arka Gdynia 2018-10-27 13 Lechia Gdańsk Arka Gdynia
102 54 Miedź Legnica - Śląsk Wrocław 2018-10-26 13 Miedź Legnica Śląsk Wrocław
103 56 Jagiellonia Białystok - Legia Warszawa 2018-10-26 13 Jagiellonia Białystok Legia Warszawa
104 50 Pogoń Szczecin - Lech Poznań 2018-10-27 13 Pogoń Szczecin Lech Poznań
105 48 Piast Gliwice - Wisła Kraków 2018-11-02 14 Piast Gliwice Wisła Kraków
106 50 Arka Gdynia - Pogoń Szczecin 2018-11-02 14 Arka Gdynia Pogoń Szczecin
107 50 Cracovia Kraków - Miedź Legnica 2018-11-03 14 Cracovia Kraków Miedź Legnica
108 52 Legia Warszawa - Górnik Zabrze 2018-11-03 14 Legia Warszawa Górnik Zabrze
109 50 Śląsk Wrocław - Wisła Płock 2018-11-03 14 Śląsk Wrocław Wisła Płock
110 60 Lech Poznań - Lechia Gdańsk 2018-11-04 14 Lech Poznań Lechia Gdańsk
111 48 Zagłębie Lubin - Korona Kielce 2018-11-04 14 Zagłębie Lubin Korona Kielce
112 56 Zagłębie Sosnowiec - Jagiellonia Białystok 2018-11-05 14 Zagłębie Sosnowiec Jagiellonia Białystok
113 48 Miedź Legnica - Arka Gdynia 2018-11-11 15 Miedź Legnica Arka Gdynia
114 54 Jagiellonia Białystok - Lech Poznań 2018-11-11 15 Jagiellonia Białystok Lech Poznań
115 46 Korona Kielce - Piast Gliwice 2018-11-11 15 Korona Kielce Piast Gliwice
116 48 Lechia Gdańsk - Cracovia Kraków 2018-11-10 15 Lechia Gdańsk Cracovia Kraków
117 46 Wisła Kraków - Zagłębie Lubin 2018-11-10 15 Wisła Kraków Zagłębie Lubin
118 54 Wisła Płock - Zagłębie Sosnowiec 2018-11-10 15 Wisła Płock Zagłębie Sosnowiec
119 50 Górnik Zabrze - Śląsk Wrocław 2018-11-09 15 Górnik Zabrze Śląsk Wrocław
120 50 Pogoń Szczecin - Legia Warszawa 2018-11-09 15 Pogoń Szczecin Legia Warszawa
121 44 Piast Gliwice - Zagłębie Sosnowiec 2018-11-23 16 Piast Gliwice Zagłębie Sosnowiec
122 48 Korona Kielce - Górnik Zabrze 2018-11-24 16 Korona Kielce Górnik Zabrze
123 46 Cracovia Kraków - Śląsk Wrocław 2018-11-24 16 Cracovia Kraków Śląsk Wrocław
124 50 Lech Poznań - Wisła Płock 2018-11-24 16 Lech Poznań Wisła Płock
125 52 Lechia Gdańsk - Jagiellonia Białystok 2018-11-25 16 Lechia Gdańsk Jagiellonia Białystok
126 44 Pogoń Szczecin - Miedź Legnica 2018-11-25 16 Pogoń Szczecin Miedź Legnica
127 48 Zagłębie Lubin - Legia Warszawa 2018-11-25 16 Zagłębie Lubin Legia Warszawa
128 44 Arka Gdynia - Wisła Kraków 2018-11-26 16 Arka Gdynia Wisła Kraków
129 42 Piast Gliwice - Pogoń Szczecin 2018-12-03 17 Piast Gliwice Pogoń Szczecin
130 42 Zagłębie Sosnowiec - Zagłębie Lubin 2018-12-02 17 Zagłębie Sosnowiec Zagłębie Lubin
131 48 Cracovia Kraków - Lech Poznań 2018-12-02 17 Cracovia Kraków Lech Poznań
132 42 Miedź Legnica - Wisła Kraków 2018-12-01 17 Miedź Legnica Wisła Kraków
133 44 Jagiellonia Białystok - Arka Gdynia 2018-11-30 17 Jagiellonia Białystok Arka Gdynia
134 44 Śląsk Wrocław - Lechia Gdańsk 2018-11-30 17 Śląsk Wrocław Lechia Gdańsk
135 46 Wisła Płock - Górnik Zabrze 2018-12-01 17 Wisła Płock Górnik Zabrze
136 46 Legia Warszawa - Korona Kielce 2018-12-01 17 Legia Warszawa Korona Kielce
137 46 Lech Poznań - Śląsk Wrocław 2018-12-07 18 Lech Poznań Śląsk Wrocław
138 40 Zagłębie Lubin - Piast Gliwice 2018-12-07 18 Zagłębie Lubin Piast Gliwice
139 46 Wisła Kraków - Jagiellonia Białystok 2018-12-08 18 Wisła Kraków Jagiellonia Białystok
140 46 Korona Kielce - Wisła Płock 2018-12-08 18 Korona Kielce Wisła Płock
141 40 Pogoń Szczecin - Zagłębie Sosnowiec 2018-12-08 18 Pogoń Szczecin Zagłębie Sosnowiec
142 42 Górnik Zabrze - Miedź Legnica 2018-12-09 18 Górnik Zabrze Miedź Legnica
143 42 Lechia Gdańsk - Legia Warszawa 2018-12-09 18 Lechia Gdańsk Legia Warszawa
144 40 Arka Gdynia - Cracovia Kraków 2018-12-10 18 Arka Gdynia Cracovia Kraków
145 40 Cracovia Kraków - Pogoń Szczecin 2018-12-15 19 Cracovia Kraków Pogoń Szczecin
146 44 Jagiellonia Białystok - Zagłębie Lubin 2018-12-14 19 Jagiellonia Białystok Zagłębie Lubin
147 44 Wisła Płock - Wisła Kraków 2018-12-14 19 Wisła Płock Wisła Kraków
148 38 Legia Warszawa - Piast Gliwice 2018-12-15 19 Legia Warszawa Piast Gliwice
149 44 Zagłębie Sosnowiec - Lech Poznań 2018-12-16 19 Zagłębie Sosnowiec Lech Poznań
150 38 Śląsk Wrocław - Korona Kielce 2018-12-16 19 Śląsk Wrocław Korona Kielce
151 46 Miedź Legnica - Lechia Gdańsk 2018-12-17 19 Miedź Legnica Lechia Gdańsk
152 40 Górnik Zabrze - Arka Gdynia 2018-12-15 19 Górnik Zabrze Arka Gdynia
153 36 Pogoń Szczecin - Śląsk Wrocław 2018-12-20 20 Pogoń Szczecin Śląsk Wrocław
154 38 Zagłębie Sosnowiec - Legia Warszawa 2018-12-20 20 Zagłębie Sosnowiec Legia Warszawa
155 36 Arka Gdynia - Wisła Płock 2018-12-21 20 Arka Gdynia Wisła Płock
156 42 Piast Gliwice - Jagiellonia Białystok 2018-12-21 20 Piast Gliwice Jagiellonia Białystok
157 42 Wisła Kraków - Lech Poznań 2018-12-21 20 Wisła Kraków Lech Poznań
158 38 Lechia Gdańsk - Górnik Zabrze 2018-12-22 20 Lechia Gdańsk Górnik Zabrze
159 36 Korona Kielce - Miedź Legnica 2018-12-22 20 Korona Kielce Miedź Legnica
160 38 Zagłębie Lubin - Cracovia Kraków 2018-12-22 20 Zagłębie Lubin Cracovia Kraków
161 34 Górnik Zabrze - Wisła Kraków 2019-02-11 21 Górnik Zabrze Wisła Kraków
162 36 Wisła Płock - Legia Warszawa 2019-02-10 21 Wisła Płock Legia Warszawa
163 34 Arka Gdynia - Korona Kielce 2019-02-10 21 Arka Gdynia Korona Kielce
164 34 Śląsk Wrocław - Zagłębie Sosnowiec 2019-02-09 21 Śląsk Wrocław Zagłębie Sosnowiec
165 40 Lechia Gdańsk - Pogoń Szczecin 2019-02-09 21 Lechia Gdańsk Pogoń Szczecin
166 34 Cracovia Kraków - Piast Gliwice 2019-02-09 21 Cracovia Kraków Piast Gliwice
167 34 Lech Poznań - Zagłębie Lubin 2019-02-08 21 Lech Poznań Zagłębie Lubin
168 40 Miedź Legnica - Jagiellonia Białystok 2019-02-08 21 Miedź Legnica Jagiellonia Białystok
169 32 Piast Gliwice - Lech Poznań 2019-02-15 22 Piast Gliwice Lech Poznań
170 34 Pogoń Szczecin - Górnik Zabrze 2019-02-15 22 Pogoń Szczecin Górnik Zabrze
171 32 Zagłębie Sosnowiec - Arka Gdynia 2019-02-16 22 Zagłębie Sosnowiec Arka Gdynia
172 38 Jagiellonia Białystok - Wisła Płock 2019-02-16 22 Jagiellonia Białystok Wisła Płock
173 38 Korona Kielce - Lechia Gdańsk 2019-02-16 22 Korona Kielce Lechia Gdańsk
174 32 Legia Warszawa - Cracovia Kraków 2019-02-17 22 Legia Warszawa Cracovia Kraków
175 32 Zagłębie Lubin - Miedź Legnica 2019-02-17 22 Zagłębie Lubin Miedź Legnica
176 32 Wisła Kraków - Śląsk Wrocław 2019-02-18 22 Wisła Kraków Śląsk Wrocław
177 30 Cracovia Kraków - Jagiellonia Białystok 2019-02-24 23 Cracovia Kraków Jagiellonia Białystok
178 32 Miedź Legnica - Wisła Płock 2019-02-24 23 Miedź Legnica Wisła Płock
179 32 Górnik Zabrze - Zagłębie Sosnowiec 2019-02-23 23 Górnik Zabrze Zagłębie Sosnowiec
180 30 Śląsk Wrocław - Zagłębie Lubin 2019-02-25 23 Śląsk Wrocław Zagłębie Lubin
181 36 Lechia Gdańsk - Wisła Kraków 2019-02-23 23 Lechia Gdańsk Wisła Kraków
182 30 Korona Kielce - Pogoń Szczecin 2019-02-22 23 Korona Kielce Pogoń Szczecin
183 30 Arka Gdynia - Piast Gliwice 2019-02-22 23 Arka Gdynia Piast Gliwice
184 30 Lech Poznań - Legia Warszawa 2019-02-23 23 Lech Poznań Legia Warszawa
185 28 Piast Gliwice - Śląsk Wrocław 2019-03-01 24 Piast Gliwice Śląsk Wrocław
186 30 Legia Warszawa - Miedź Legnica 2019-03-01 24 Legia Warszawa Miedź Legnica
187 28 Jagiellonia Białystok - Górnik Zabrze 2019-03-02 24 Jagiellonia Białystok Górnik Zabrze
188 30 Wisła Płock - Cracovia Kraków 2019-03-02 24 Wisła Płock Cracovia Kraków
189 28 Zagłębie Sosnowiec - Korona Kielce 2019-03-02 24 Zagłębie Sosnowiec Korona Kielce
190 28 Lech Poznań - Arka Gdynia 2019-03-03 24 Lech Poznań Arka Gdynia
191 28 Wisła Kraków - Pogoń Szczecin 2019-03-03 24 Wisła Kraków Pogoń Szczecin
192 28 Zagłębie Lubin - Lechia Gdańsk 2019-03-04 24 Zagłębie Lubin Lechia Gdańsk
193 26 Miedź Legnica - Lech Poznań 2019-03-10 25 Miedź Legnica Lech Poznań
194 26 Pogoń Szczecin - Zagłębie Lubin 2019-03-10 25 Pogoń Szczecin Zagłębie Lubin
195 26 Cracovia Kraków - Zagłębie Sosnowiec 2019-03-09 25 Cracovia Kraków Zagłębie Sosnowiec
196 30 Lechia Gdańsk - Wisła Płock 2019-03-11 25 Lechia Gdańsk Wisła Płock
197 26 Arka Gdynia - Legia Warszawa 2019-03-09 25 Arka Gdynia Legia Warszawa
198 32 Śląsk Wrocław - Jagiellonia Białystok 2019-03-08 25 Śląsk Wrocław Jagiellonia Białystok
199 26 Górnik Zabrze - Piast Gliwice 2019-03-08 25 Górnik Zabrze Piast Gliwice
200 26 Korona Kielce - Wisła Kraków 2019-03-09 25 Korona Kielce Wisła Kraków
201 24 Zagłębie Lubin - Arka Gdynia 2019-03-17 26 Zagłębie Lubin Arka Gdynia
202 24 Piast Gliwice - Miedź Legnica 2019-03-17 26 Piast Gliwice Miedź Legnica
203 28 Jagiellonia Białystok - Korona Kielce 2019-03-16 26 Jagiellonia Białystok Korona Kielce
204 24 Wisła Kraków - Cracovia Kraków 2019-03-17 26 Wisła Kraków Cracovia Kraków
205 28 Zagłębie Sosnowiec - Lechia Gdańsk 2019-03-16 26 Zagłębie Sosnowiec Lechia Gdańsk
206 24 Lech Poznań - Górnik Zabrze 2019-03-15 26 Lech Poznań Górnik Zabrze
207 24 Wisła Płock - Pogoń Szczecin 2019-03-15 26 Wisła Płock Pogoń Szczecin
208 24 Legia Warszawa - Śląsk Wrocław 2019-03-16 26 Legia Warszawa Śląsk Wrocław
209 26 Lechia Gdańsk - Piast Gliwice 2019-03-29 27 Lechia Gdańsk Piast Gliwice
210 24 Wisła Płock - Zagłębie Lubin 2019-03-29 27 Wisła Płock Zagłębie Lubin
211 22 Górnik Zabrze - Cracovia Kraków 2019-03-29 27 Górnik Zabrze Cracovia Kraków
212 22 Korona Kielce - Lech Poznań 2019-03-30 27 Korona Kielce Lech Poznań
213 22 Arka Gdynia - Śląsk Wrocław 2019-03-30 27 Arka Gdynia Śląsk Wrocław
214 22 Wisła Kraków - Legia Warszawa 2019-03-31 27 Wisła Kraków Legia Warszawa
215 22 Pogoń Szczecin - Jagiellonia Białystok 2019-03-31 27 Pogoń Szczecin Jagiellonia Białystok
216 20 Zagłębie Sosnowiec - Wisła Kraków 2019-04-03 28 Zagłębie Sosnowiec Wisła Kraków
217 20 Lech Poznań - Pogoń Szczecin 2019-04-03 28 Lech Poznań Pogoń Szczecin
218 20 Legia Warszawa - Jagiellonia Białystok 2019-04-03 28 Legia Warszawa Jagiellonia Białystok
219 22 Piast Gliwice - Wisła Płock 2019-04-03 28 Piast Gliwice Wisła Płock
220 20 Zagłębie Lubin - Górnik Zabrze 2019-04-02 28 Zagłębie Lubin Górnik Zabrze
221 20 Śląsk Wrocław - Miedź Legnica 2019-04-02 28 Śląsk Wrocław Miedź Legnica
222 24 Arka Gdynia - Lechia Gdańsk 2019-04-02 28 Arka Gdynia Lechia Gdańsk
223 20 Cracovia Kraków - Korona Kielce 2019-04-02 28 Cracovia Kraków Korona Kielce
224 18 Korona Kielce - Zagłębie Lubin 2019-04-05 29 Korona Kielce Zagłębie Lubin
225 18 Miedź Legnica - Cracovia Kraków 2019-04-05 29 Miedź Legnica Cracovia Kraków
226 18 Wisła Kraków - Piast Gliwice 2019-04-06 29 Wisła Kraków Piast Gliwice
227 18 Lechia Gdańsk - Lech Poznań 2019-04-06 29 Lechia Gdańsk Lech Poznań
228 22 Jagiellonia Białystok - Zagłębie Sosnowiec 2019-04-06 29 Jagiellonia Białystok Zagłębie Sosnowiec
229 18 Górnik Zabrze - Legia Warszawa 2019-04-07 29 Górnik Zabrze Legia Warszawa
230 18 Pogoń Szczecin - Arka Gdynia 2019-04-07 29 Pogoń Szczecin Arka Gdynia
231 18 Wisła Płock - Śląsk Wrocław 2019-04-08 29 Wisła Płock Śląsk Wrocław
232 16 Arka Gdynia - Miedź Legnica 2019-04-13 30 Arka Gdynia Miedź Legnica
233 18 Zagłębie Sosnowiec - Wisła Płock 2019-04-13 30 Zagłębie Sosnowiec Wisła Płock
234 16 Lech Poznań - Jagiellonia Białystok 2019-04-13 30 Lech Poznań Jagiellonia Białystok
235 16 Legia Warszawa - Pogoń Szczecin 2019-04-13 30 Legia Warszawa Pogoń Szczecin
236 16 Piast Gliwice - Korona Kielce 2019-04-13 30 Piast Gliwice Korona Kielce
237 16 Cracovia Kraków - Lechia Gdańsk 2019-04-13 30 Cracovia Kraków Lechia Gdańsk
238 16 Zagłębie Lubin - Wisła Kraków 2019-04-13 30 Zagłębie Lubin Wisła Kraków
239 16 Śląsk Wrocław - Górnik Zabrze 2019-04-13 30 Śląsk Wrocław Górnik Zabrze
240 14 Korona Kielce - Śląsk Wrocław 2019-04-22 31 Korona Kielce Śląsk Wrocław
241 16 Wisła Kraków - Wisła Płock 2019-04-22 31 Wisła Kraków Wisła Płock
242 14 Górnik Zabrze - Arka Gdynia 2019-04-22 31 Górnik Zabrze Arka Gdynia
243 14 Legia Warszawa - Cracovia Kraków 2019-04-20 31 Legia Warszawa Cracovia Kraków
244 14 Jagiellonia Białystok - Lech Poznań 2019-04-20 31 Jagiellonia Białystok Lech Poznań
245 16 Lechia Gdańsk - Piast Gliwice 2019-04-20 31 Lechia Gdańsk Piast Gliwice
246 14 Zagłębie Lubin - Pogoń Szczecin 2019-04-20 31 Zagłębie Lubin Pogoń Szczecin
247 12 Piast Gliwice - Zagłębie Lubin 2019-04-23 32 Piast Gliwice Zagłębie Lubin
248 12 Lech Poznań - Legia Warszawa 2019-04-24 32 Lech Poznań Legia Warszawa
249 14 Pogoń Szczecin - Lechia Gdańsk 2019-04-24 32 Pogoń Szczecin Lechia Gdańsk
250 12 Arka Gdynia - Miedź Legnica 2019-04-25 32 Arka Gdynia Miedź Legnica
251 12 Zagłębie Sosnowiec - Wisła Kraków 2019-04-25 32 Zagłębie Sosnowiec Wisła Kraków
252 12 Śląsk Wrocław - Górnik Zabrze 2019-04-25 32 Śląsk Wrocław Górnik Zabrze
253 12 Wisła Płock - Korona Kielce 2019-04-25 32 Wisła Płock Korona Kielce
254 14 Cracovia Kraków - Jagiellonia Białystok 2019-04-23 32 Cracovia Kraków Jagiellonia Białystok
255 12 Miedź Legnica - Wisła Płock 2019-04-28 33 Miedź Legnica Wisła Płock
256 10 Górnik Zabrze - Zagłębie Sosnowiec 2019-04-29 33 Górnik Zabrze Zagłębie Sosnowiec
257 10 Korona Kielce - Arka Gdynia 2019-04-28 33 Korona Kielce Arka Gdynia
258 10 Lechia Gdańsk - Legia Warszawa 2019-04-27 33 Lechia Gdańsk Legia Warszawa
259 10 Pogoń Szczecin - Lech Poznań 2019-04-27 33 Pogoń Szczecin Lech Poznań
260 10 Zagłębie Lubin - Jagiellonia Białystok 2019-04-26 33 Zagłębie Lubin Jagiellonia Białystok
261 10 Piast Gliwice - Cracovia Kraków 2019-04-26 33 Piast Gliwice Cracovia Kraków
262 10 Wisła Kraków - Śląsk Wrocław 2019-04-28 33 Wisła Kraków Śląsk Wrocław
263 8 Górnik Zabrze - Wisła Kraków 2019-05-03 34 Górnik Zabrze Wisła Kraków
264 10 Wisła Płock - Arka Gdynia 2019-05-03 34 Wisła Płock Arka Gdynia
265 8 Legia Warszawa - Piast Gliwice 2019-05-04 34 Legia Warszawa Piast Gliwice
266 8 Lech Poznań - Zagłębie Lubin 2019-05-04 34 Lech Poznań Zagłębie Lubin
267 8 Korona Kielce - Miedź Legnica 2019-05-04 34 Korona Kielce Miedź Legnica
268 8 Cracovia Kraków - Lechia Gdańsk 2019-05-05 34 Cracovia Kraków Lechia Gdańsk
269 8 Zagłębie Sosnowiec - Śląsk Wrocław 2019-05-05 34 Zagłębie Sosnowiec Śląsk Wrocław
270 8 Jagiellonia Białystok - Pogoń Szczecin 2019-05-06 34 Jagiellonia Białystok Pogoń Szczecin
271 6 Piast Gliwice - Jagiellonia Białystok 2019-05-12 35 Piast Gliwice Jagiellonia Białystok
272 6 Legia Warszawa - Pogoń Szczecin 2019-05-12 35 Legia Warszawa Pogoń Szczecin
273 6 Miedź Legnica - Górnik Zabrze 2019-05-11 35 Miedź Legnica Górnik Zabrze
274 6 Lechia Gdańsk - Zagłębie Lubin 2019-05-12 35 Lechia Gdańsk Zagłębie Lubin
275 6 Śląsk Wrocław - Wisła Płock 2019-05-11 35 Śląsk Wrocław Wisła Płock
276 6 Wisła Kraków - Korona Kielce 2019-05-10 35 Wisła Kraków Korona Kielce
277 6 Arka Gdynia - Zagłębie Sosnowiec 2019-05-10 35 Arka Gdynia Zagłębie Sosnowiec
278 6 Cracovia Kraków - Lech Poznań 2019-05-11 35 Cracovia Kraków Lech Poznań
279 4 Lech Poznań - Lechia Gdańsk 2019-05-15 36 Lech Poznań Lechia Gdańsk
280 4 Pogoń Szczecin - Piast Gliwice 2019-05-15 36 Pogoń Szczecin Piast Gliwice
281 4 Jagiellonia Białystok - Legia Warszawa 2019-05-15 36 Jagiellonia Białystok Legia Warszawa
282 4 Zagłębie Lubin - Cracovia Kraków 2019-05-15 36 Zagłębie Lubin Cracovia Kraków
283 6 Górnik Zabrze - Wisła Płock 2019-05-14 36 Górnik Zabrze Wisła Płock
284 4 Zagłębie Sosnowiec - Korona Kielce 2019-05-14 36 Zagłębie Sosnowiec Korona Kielce
285 4 Arka Gdynia - Wisła Kraków 2019-05-13 36 Arka Gdynia Wisła Kraków
286 4 Miedź Legnica - Śląsk Wrocław 2019-05-14 36 Miedź Legnica Śląsk Wrocław
287 2 Korona Kielce - Górnik Zabrze 2019-05-18 37 Korona Kielce Górnik Zabrze
288 2 Wisła Kraków - Miedź Legnica 2019-05-18 37 Wisła Kraków Miedź Legnica
289 2 Śląsk Wrocław - Arka Gdynia 2019-05-18 37 Śląsk Wrocław Arka Gdynia
290 4 Wisła Płock - Zagłębie Sosnowiec 2019-05-18 37 Wisła Płock Zagłębie Sosnowiec
291 2 Legia Warszawa - Zagłębie Lubin 2019-05-19 37 Legia Warszawa Zagłębie Lubin
292 2 Piast Gliwice - Lech Poznań 2019-05-19 37 Piast Gliwice Lech Poznań
293 2 Cracovia Kraków - Pogoń Szczecin 2019-05-19 37 Cracovia Kraków Pogoń Szczecin
294 2 Lechia Gdańsk - Jagiellonia Białystok 2019-05-19 37 Lechia Gdańsk Jagiellonia Białystok

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# -*- coding: utf-8 -*-
'''
Read and run an FLC file over some given data, taken from an FLD file.
This can be used used to test one system against another,
e.g. generate data using jFuzzyLogic and test it using skfuzzy.
Typically we generate random inputs and compare outputs and rules.
@author: james.power@mu.ie Created on Tue Aug 7 15:06:34 2018
'''
from __future__ import print_function
import sys
import os.path
import codecs
from datetime import datetime
from collections import OrderedDict
import numpy as np
from skfuzzy import control as ctrl
from skfuzzy.control import ControlSystemSimulation
from skfuzzy.control.controlsystem import CrispValueCalculator
from fcl_parser import FCLParser
_COMMENT_CHAR = '#'
_FCL_SUFFIX = '.fcl'
_FLD_SUFFIX = '.fld'
_DEFAULT_PERCENT_ACCURACY = 2 # Percentage error that's OK in outputs
_DEC_PLACES = 2 # number of decimal places to print
# This function was robbed from controlsystem.py, and tidied up
def _print_simulator_state(testnum, simulator):
"""
Print info about the inner workings of a ControlSystemSimulation.
"""
# if next(simulator.ctrl.consequents).output[simulator] is None:
# raise ValueError("Call compute method first.")
print('-'*70)
print('* Run', testnum, ': Antecedents')
for var in simulator.ctrl.antecedents:
print(" * {0} = {1}".format(var, var.input[simulator]))
for term in var.terms.values():
print(" - {0}: {1}"
.format(term.label, term.membership_value[simulator]))
print("")
print('* Run', testnum, ': Rules ')
sorted_rules = sorted(simulator.ctrl.rules, key=lambda r: r.label)
rule_number = {}
for rn, rule in enumerate(sorted_rules):
rule_number[rule] = "RULE #%d" % rn
print(" * RULE %s (#%d): %s" % (rule.label, rn, rule))
print(" = Aggregation (IF-clause):")
for term in rule.antecedent_terms:
print(" Input: {0} = {1}"
.format(term.full_label, term.membership_value[simulator]))
print(" Total: {0} = {1}"
.format(rule.antecedent, rule.aggregate_firing[simulator]))
print(" = Activation (THEN-clause):")
for conseq in rule.consequent:
print(" {0} : {1}"
.format(conseq, conseq.activation[simulator]))
print("")
print('* Run', testnum, ': Intermediaries and Consequents ')
for conseq in simulator.ctrl.consequents:
cvc = CrispValueCalculator(conseq, simulator)
try:
print(" * {0} = {1}".format(conseq, cvc.defuzz()))
except Exception as exc:
print('\t- {}'.format(exc))
# If you want to drill into the output mfs, print these:
ups_universe, output_mf, cut_mfs = cvc.find_memberships()
# print(ups_universe, output_mf, cut_mfs)
for term in conseq.terms.values():
print(" - %s:" % term.label)
for cut_rule, cut_value in term.cuts[simulator].items():
print(" {0} : {1}".format(cut_rule, cut_value))
accu = "Accumulate using %s" % conseq.accumulation_method.__name__
print(" ({0} : {1})"
.format(accu, term.membership_value[simulator]))
print("")
def _print_memberships(var):
'''
Tabulate the values in each of the membership functions for a variable.
For each universe value, print the corresponding mf value.
'''
print('-', 'Variable', var)
# Print the names of the terms:
print('{:8}'.format(''), ['{:>8}'.format(v.label)
for v in var.terms.values()])
# Then print each unverse value and the corresponding term values:
for i, x in enumerate(var.universe):
print('{:8.3}'.format(x),
['{:8.3}'.format(v.mf[i]) for v in var.terms.values()])
class TestData(object):
'''
Just a container for var/rule names and corresponding test data.
A list of names, and then an array with one column per name.
'''
def __init__(self, names, num_tests):
'''One row per test case, one col per name, initialise to zero'''
self.names = list(names) # Order is important here!
self.value = np.zeros((num_tests, len(self.names)))
self.message = {} # Hold error messages (if any)
@property
def num_tests(self):
'''The number of test cases is the number of rows'''
return self.value.shape[0]
class SimulationHarness(object):
'''
A class to handle reading FLD files and running simulations.
'''
def __init__(self, verbose=False):
# N.B. the following are stored in lists since the order is important
self.antecedents = OrderedDict() # Maps names to variable objects
self.consequents = OrderedDict() # Maps names to variable objects
self.all_rules = OrderedDict() # Maps names to rule objects
self.control_system = None
self.percent_accuracy = _DEFAULT_PERCENT_ACCURACY
self.verbose = verbose
def set_verbose(self):
'''Will set flag to print detailed simulation results'''
self.verbose = True
def make_fld_filename(self, fclfile):
'''
How to get the FLD file corresponding to a FCL file.
At the moment this just looks in the same directory,
but you may wish to change this.
'''
return fclfile.replace(_FCL_SUFFIX, _FLD_SUFFIX)
def read_fcl_file(self, fclfile):
'''Read an FCL file and initialise the variable/rule lists.'''
assert os.path.isfile(fclfile),\
'Can\'t find specified FCL file "{}"'.format(fclfile)
parser = FCLParser().read_fcl_file(fclfile)
if self.verbose:
print(parser)
self.antecedents = {var.label: var for var in parser.antecedents}
self.consequents = {var.label: var for var in parser.consequents}
self.all_rules = OrderedDict(parser.all_rules)
self.control_system = ctrl.ControlSystem(self.all_rules.values())
def simulate_one(self, input_dict):
'''
A utility routine to run a simluation with a given set of data.
Supply the data as a dict of var-name:value pairs.
Handy for testing; not used elsewhere here.
'''
simulator = ControlSystemSimulation(self.control_system)
for k, v in input_dict.items():
simulator.input[k] = v
simulator.compute()
print('-'*70)
_print_simulator_state(simulator)
@staticmethod
def _get_fs(simulator, rule, weighted=True):
'''
Return the fire-strength for a rule (after a simulation run).
With no weighting this is the accumulation for the rule,
with weighting it's the activation (we pick the first consequent)
'''
if not weighted: # want the fire strength before weighting
return rule.aggregate_firing[simulator]
else: # want the activation i.e. *after* weighting
first_conseq = rule.consequent[0] # Pick the first one
# I'm assuming activation is the same for other consequents.
return first_conseq.activation[simulator]
def simulate(self, input_data):
'''
Supply the inputs, run the system, collect the outputs,
return the results (outputs, rules), once row for each test.
'''
simulator = ControlSystemSimulation(self.control_system)
num_tests = input_data.num_tests
output_data = TestData(self.consequents.keys(), num_tests)
rule_data = TestData(self.all_rules.keys(), num_tests)
if self.verbose:
print('-'*70)
for var in (list(self.antecedents.values()) +
list(self.consequents.values())):
_print_memberships(var)
print('-'*70)
# For each test case (row of input values):
for row in range(num_tests):
# Load up the inputs and run:
for j, vname in enumerate(input_data.names):
simulator.input[vname] = input_data.value[row][j]
try:
simulator.compute()
if self.verbose:
_print_simulator_state(row, simulator)
except Exception as exc:
if self.verbose:
_print_simulator_state(row, simulator)
output_data.message[row] = '\t- {}'.format(exc)
continue
# Collect the output values:
for j, vname in enumerate(output_data.names):
output_data.value[row][j] = simulator.output[vname]
# Collect the rule fire-strengths:
for rule in simulator.ctrl.rules:
if rule.label in rule_data.names: # and it should be
col = rule_data.names.index(rule.label)
rule_data.value[row][col] = self._get_fs(simulator, rule)
return output_data, rule_data
def read_fld_file(self, fldfile):
'''
Read an FLD file, which has space-separated data values.
Ensure order of variable-names is synched to what we're expecting.
Return three arrays, one each for: inputs, outputs, rules.
Each row in an array corresponds to one test case.
'''
assert os.path.isfile(fldfile),\
'Can\'t find data file "{}"'.format(fldfile)
with codecs.open(fldfile, 'r') as fileh:
# First line is the variable names:
varnames = fileh.readline().strip().split()
# Remaining lines are the space-separated values
data = np.loadtxt(fileh)
# One row per test, no. of columns is the number of variables:
num_tests = data.shape[0]
assert len(varnames) == data.shape[1],\
'Got {} data values for {} variables {}'\
.format(data.shape[1], len(varnames), varnames)
# Let's check that the variables were the ones we were expecting:
input_data = TestData(self.antecedents.keys(), num_tests)
output_data = TestData(self.consequents.keys(), num_tests)
rule_data = TestData(self.all_rules.keys(), num_tests)
wanted_vars = input_data.names + output_data.names
fld_has_rules = len(varnames) > len(wanted_vars)
if fld_has_rules:
wanted_vars += rule_data.names
s_want, s_have = set(wanted_vars), set(varnames)
missing = s_want - s_have
assert len(missing) == 0,\
'{} is missing data for variables {}'.format(fldfile, missing)
extra = s_have - s_want
assert len(extra) == 0,\
'{} has data for unknown variables {}'.format(fldfile, extra)
# Now synch the order of variables to be the one we want
wanted_order = [varnames.index(v) for v in wanted_vars]
data = data[:, wanted_order] # numpy trickery for rearranging columns
# Finally, split the array into (input, output) and return it
inum = len(input_data.names)
onum = inum + len(output_data.names)
if fld_has_rules:
input_data.value, output_data.value, rule_data.value \
= np.hsplit(data, (inum, onum))
else: # ... second arg to hsplit must be a tuple:
input_data.value, output_data.value \
= np.hsplit(data, (inum, ))
rule_data = None
return input_data, output_data, rule_data
def gen_sample_inputs(self, num_tests):
'''
Generate a set of random values for the input variables.
'''
input_data = TestData(self.antecedents.keys(), num_tests)
# Generate the data one variable (row) at a time:
for i, var_name in enumerate(input_data.names):
var = self.antecedents[var_name]
lo, hi = np.min(var.universe), np.max(var.universe)
input_data.value[:, i] = np.random.uniform(lo, hi, num_tests)
return input_data
@staticmethod
def _calc_error(want, got):
'''
Calculate the absolute percentage error, rounded to 1%
'''
# We're working in whole-percentage values:
if want != 0:
error = np.round(100*(got-want) / want, 0)
else:
error = np.round(got, 0)
return np.abs(error)
def _check_outputs(self, row, output_want, output_got):
'''
Check each output value for this test case;
return true iff they all were within the desired accuracy.
'''
print('Output variables:')
if row in output_got.message:
print('Error running test case no. {}:'.format(row))
print(output_got.message[row])
all_outputs_ok = True
for col, vname in enumerate(output_want.names):
want = output_want.value[row][col]
got = output_got.value[row][col]
error_perc = self._calc_error(want, got)
if error_perc < self.percent_accuracy:
msg = 'CORRECT'
else:
all_outputs_ok = False
msg = 'FAIL (wanted {1:.{0}f})'.format(_DEC_PLACES, want)
print(' {1}={2:.{0}f} {3}, ERROR={4:03.0f}%'
.format(_DEC_PLACES, vname, got, msg, error_perc))
return all_outputs_ok
def _check_rule_fs(self, row, rule_want, rule_got):
'''
Check the fire-strength for each rule for this test case;
return true iff they all were within the desired accuracy.
'''
print('Rule fire-strengths for test case {}:'.format(row))
rules_failed = 0
for col, rname in enumerate(rule_got.names):
got = rule_got.value[row][col]
rstr = ' RULE {1} = {2:.{0}f}'.format(_DEC_PLACES, rname, got)
# If we have desired rule fire-strenghts, then compare:
if rule_want:
want = rule_want.value[row][col]
# Ruel fire strengths are between 0 and 1 anyway:
error_perc = self._calc_error(want, got)
if error_perc/100.0 < self.percent_accuracy:
rstr += ' (CORRECT)'
else:
rules_failed += 1
rstr += ' (wanted {1:.{0}f})'.format(_DEC_PLACES, got)
print(rstr)
return rules_failed == 0
def simulate_and_check(self, input_data, output_want, rule_want=None):
'''
Run the system with the given input data;
then check the results against the given outputs.
We are given target ouput values and (maybe) rule fire-strengths
'''
output_got, rule_got = self.simulate(input_data)
failed_cases = 0
# Each row is a test case:
for row in range(input_data.num_tests):
print('-' * 70)
print('Run', row, end=': ')
for col, vname in enumerate(input_data.names):
print('{1}={2:.{0}f}'.format(_DEC_PLACES, vname,
input_data.value[row][col]), end=' ')
print()
# Check outputs, rules:
out_fail = not self._check_outputs(row, output_want, output_got)
rule_fail = not self._check_rule_fs(row, rule_want, rule_got)
if out_fail or rule_fail:
failed_cases += 1
print('-' * 70)
print('Failed {} of {} test cases (within {}%)'
.format(failed_cases, input_data.num_tests,
self.percent_accuracy))
def simulate_from_file(self, fclfile, data_filename=None):
'''
Read and test the given FCL file with the given data file.
Can find a corresponding data filename if non given.
'''
self.read_fcl_file(fclfile)
if not data_filename:
data_filename = self.make_fld_filename(fclfile)
input_data, output_data, rule_data = self.read_fld_file(data_filename)
print('=' * 70)
print('=', fclfile, 'on', datetime.now().strftime("%d %b %Y at %H:%M"))
print('=' * 70)
self.simulate_and_check(input_data, output_data, rule_data)
def simulate_to_file(self, fclfile, num_tests, rules_too=False):
'''
Simulate the system (generating some sample inputs),
and then write the results to a file in the given dir.
'''
self.read_fcl_file(fclfile)
# Get an array of input values:
input_data = self.gen_sample_inputs(num_tests)
# And generate corresponding results for outputs and rules:
output_data, rule_data = self.simulate(input_data)
# Last, write these to a file:
data_filename = self.make_fld_filename(fclfile)
file_name_comment = 'Generated using {} on {}'\
.format(fclfile, datetime.now())
all_names = input_data.names + output_data.names
all_vals = (input_data.value, output_data.value)
if rules_too:
all_names += rule_data.names
all_vals = (input_data.value, output_data.value, rule_data.value)
with codecs.open(data_filename, 'w') as fileh:
fileh.write(' '.join(all_names) + '\n')
np.savetxt(fileh,
np.concatenate(all_vals, axis=1),
comments=_COMMENT_CHAR, header=file_name_comment)
def simulate_from_dir(self, datadir):
'''
Read and test all the FCL files in root and its subdirs.
Uses the data from the corresponding FLD file.
'''
for dirpath, _, files in os.walk(datadir):
for filename in files:
if not filename.endswith(_FCL_SUFFIX):
continue
filepath = os.path.join(dirpath, filename)
print('===', filepath)
self.simulate_from_file(filepath)
def simulate_to_dir(self, fclrootdir, num_tests):
'''
Read and test all the .fcl files in root and its subdirs.
Generate random data for num_tests test cases for the inputs,
and these values, along with the outputs are written to a file.
One data file is written for each input fcl file.
'''
for dirpath, _, files in os.walk(fclrootdir):
for filename in files:
if not filename.endswith(_FCL_SUFFIX):
continue
filepath = os.path.join(dirpath, filename)
print('===', filepath)
self.simulate_to_file(filepath, num_tests)
if __name__ == '__main__':
harness = SimulationHarness(True)
if len(sys.argv) == 1: # No args, test all examples
harness.simulate_from_dir('Examples')
else: # Test with the given FCL files:
for fcl_filename in sys.argv[1:]:
harness.simulate_from_file(fcl_filename)

519
teams_list.csv Normal file
View File

@ -0,0 +1,519 @@
,Team,Goals,Conceded goals,Gameweek,Opponent,Result,Points,Form
88,Lechia Gdańsk,1.0,0.0,1,Jagiellonia Białystok,W,0,0.0
86,Lechia Gdańsk,1.0,1.0,2,Śląsk Wrocław,D,3,1.0
84,Lechia Gdańsk,0.0,0.0,3,Legia Warszawa,D,4,1.5
82,Lechia Gdańsk,2.0,0.0,4,Miedź Legnica,W,5,2.0
80,Lechia Gdańsk,2.0,0.0,5,Górnik Zabrze,W,8,3.0
78,Lechia Gdańsk,3.0,2.0,6,Pogoń Szczecin,W,11,4.0
76,Lechia Gdańsk,2.0,0.0,7,Korona Kielce,W,14,4.0
74,Lechia Gdańsk,2.0,5.0,8,Wisła Kraków,L,17,4.5
72,Lechia Gdańsk,3.0,3.0,9,Zagłębie Lubin,D,17,4.0
68,Lechia Gdańsk,0.0,1.0,10,Wisła Płock,L,18,3.5
66,Lechia Gdańsk,4.0,1.0,11,Zagłębie Sosnowiec,W,18,2.5
64,Lechia Gdańsk,1.0,1.0,12,Piast Gliwice,D,21,2.5
62,Lechia Gdańsk,2.0,1.0,13,Arka Gdynia,W,22,2.0
60,Lechia Gdańsk,1.0,0.0,14,Lech Poznań,W,25,3.0
56,Lechia Gdańsk,1.0,0.0,15,Cracovia Kraków,W,28,3.5
54,Lechia Gdańsk,3.0,2.0,16,Jagiellonia Białystok,W,31,4.5
52,Lechia Gdańsk,2.0,0.0,17,Śląsk Wrocław,W,34,4.5
48,Lechia Gdańsk,0.0,0.0,18,Legia Warszawa,D,37,5.0
46,Lechia Gdańsk,0.0,0.0,19,Miedź Legnica,D,38,4.5
44,Lechia Gdańsk,4.0,0.0,20,Górnik Zabrze,W,39,4.0
40,Lechia Gdańsk,2.0,1.0,21,Pogoń Szczecin,W,42,4.0
38,Lechia Gdańsk,0.0,0.0,22,Korona Kielce,D,45,4.0
36,Lechia Gdańsk,1.0,0.0,23,Wisła Kraków,W,46,3.5
32,Lechia Gdańsk,1.0,2.0,24,Zagłębie Lubin,L,49,4.0
30,Lechia Gdańsk,1.0,1.0,25,Wisła Płock,D,49,3.5
28,Lechia Gdańsk,1.0,0.0,26,Zagłębie Sosnowiec,W,50,3.0
26,Lechia Gdańsk,2.0,0.0,27,Piast Gliwice,W,53,3.0
24,Lechia Gdańsk,0.0,0.0,28,Arka Gdynia,D,56,3.5
22,Lechia Gdańsk,1.0,0.0,29,Lech Poznań,W,57,3.0
18,Lechia Gdańsk,2.0,4.0,30,Cracovia Kraków,L,60,4.0
16,Lechia Gdańsk,0.0,2.0,31,Piast Gliwice,L,60,3.5
14,Lechia Gdańsk,4.0,3.0,32,Pogoń Szczecin,W,60,2.5
12,Lechia Gdańsk,1.0,3.0,33,Legia Warszawa,L,63,2.5
8,Lechia Gdańsk,0.0,2.0,34,Cracovia Kraków,L,63,2.0
6,Lechia Gdańsk,1.0,1.0,35,Zagłębie Lubin,D,63,1.0
4,Lechia Gdańsk,1.0,2.0,36,Lech Poznań,L,64,1.5
2,Lechia Gdańsk,2.0,0.0,37,Jagiellonia Białystok,W,64,1.5
92,Lech Poznań,2.0,1.0,1,Wisła Płock,W,0,0.0
88,Lech Poznań,2.0,0.0,2,Cracovia Kraków,W,3,1.0
84,Lech Poznań,1.0,0.0,3,Śląsk Wrocław,W,6,2.0
80,Lech Poznań,4.0,0.0,4,Zagłębie Sosnowiec,W,9,3.0
76,Lech Poznań,2.0,5.0,5,Wisła Kraków,L,12,4.0
74,Lech Poznań,1.0,2.0,6,Zagłębie Lubin,L,12,4.0
72,Lech Poznań,1.0,1.0,7,Piast Gliwice,D,12,3.0
70,Lech Poznań,0.0,1.0,8,Legia Warszawa,L,13,2.5
68,Lech Poznań,0.0,1.0,9,Arka Gdynia,L,13,1.5
64,Lech Poznań,2.0,1.0,10,Miedź Legnica,W,13,0.5
62,Lech Poznań,2.0,2.0,11,Górnik Zabrze,D,16,1.5
60,Lech Poznań,2.0,1.0,12,Korona Kielce,W,17,2.0
58,Lech Poznań,0.0,3.0,13,Pogoń Szczecin,L,20,2.5
54,Lech Poznań,0.0,1.0,14,Lechia Gdańsk,L,20,2.5
52,Lech Poznań,2.0,2.0,15,Jagiellonia Białystok,D,20,2.5
50,Lech Poznań,2.0,1.0,16,Wisła Płock,W,21,2.0
48,Lech Poznań,0.0,1.0,17,Cracovia Kraków,L,24,2.5
46,Lech Poznań,2.0,0.0,18,Śląsk Wrocław,W,24,1.5
44,Lech Poznań,6.0,0.0,19,Zagłębie Sosnowiec,W,27,2.5
42,Lech Poznań,1.0,0.0,20,Wisła Kraków,W,30,3.5
34,Lech Poznań,1.0,2.0,21,Zagłębie Lubin,L,33,4.0
32,Lech Poznań,0.0,4.0,22,Piast Gliwice,L,33,3.0
30,Lech Poznań,2.0,0.0,23,Legia Warszawa,W,33,3.0
28,Lech Poznań,1.0,0.0,24,Arka Gdynia,W,36,3.0
26,Lech Poznań,2.0,3.0,25,Miedź Legnica,L,39,3.0
24,Lech Poznań,0.0,3.0,26,Górnik Zabrze,L,39,2.0
22,Lech Poznań,0.0,0.0,27,Korona Kielce,D,39,2.0
20,Lech Poznań,3.0,2.0,28,Pogoń Szczecin,W,40,2.5
18,Lech Poznań,0.0,1.0,29,Lechia Gdańsk,L,43,2.5
16,Lech Poznań,0.0,2.0,30,Jagiellonia Białystok,L,43,1.5
14,Lech Poznań,3.0,3.0,31,Jagiellonia Białystok,D,43,1.5
12,Lech Poznań,1.0,0.0,32,Legia Warszawa,W,44,2.0
10,Lech Poznań,1.0,1.0,33,Pogoń Szczecin,D,47,2.5
8,Lech Poznań,1.0,1.0,34,Zagłębie Lubin,D,48,2.0
6,Lech Poznań,0.0,1.0,35,Cracovia Kraków,L,49,2.5
4,Lech Poznań,2.0,1.0,36,Lechia Gdańsk,W,49,2.5
2,Lech Poznań,0.0,1.0,37,Piast Gliwice,L,52,3.0
94,Jagiellonia Białystok,0.0,1.0,1,Lechia Gdańsk,L,0,0.0
90,Jagiellonia Białystok,2.0,0.0,2,Arka Gdynia,W,0,0.0
86,Jagiellonia Białystok,1.0,0.0,3,Wisła Kraków,W,3,1.0
82,Jagiellonia Białystok,2.0,0.0,4,Zagłębie Lubin,W,6,2.0
78,Jagiellonia Białystok,2.0,1.0,5,Piast Gliwice,W,9,3.0
76,Jagiellonia Białystok,2.0,3.0,6,Miedź Legnica,L,12,4.0
74,Jagiellonia Białystok,1.0,1.0,7,Wisła Płock,D,12,4.0
72,Jagiellonia Białystok,3.0,1.0,8,Cracovia Kraków,W,13,3.5
70,Jagiellonia Białystok,3.0,1.0,9,Górnik Zabrze,W,16,3.5
66,Jagiellonia Białystok,0.0,4.0,10,Śląsk Wrocław,L,19,3.5
64,Jagiellonia Białystok,1.0,1.0,11,Korona Kielce,D,19,2.5
62,Jagiellonia Białystok,2.0,1.0,12,Pogoń Szczecin,W,20,3.0
60,Jagiellonia Białystok,1.0,1.0,13,Legia Warszawa,D,23,3.5
56,Jagiellonia Białystok,4.0,1.0,14,Zagłębie Sosnowiec,W,24,3.0
54,Jagiellonia Białystok,2.0,2.0,15,Lech Poznań,D,27,3.0
52,Jagiellonia Białystok,2.0,3.0,16,Lechia Gdańsk,L,28,3.5
50,Jagiellonia Białystok,3.0,1.0,17,Arka Gdynia,W,28,3.0
46,Jagiellonia Białystok,2.0,2.0,18,Wisła Kraków,D,31,3.0
44,Jagiellonia Białystok,0.0,4.0,19,Zagłębie Lubin,L,32,3.0
42,Jagiellonia Białystok,1.0,1.0,20,Piast Gliwice,D,32,2.0
40,Jagiellonia Białystok,3.0,0.0,21,Miedź Legnica,W,33,2.0
38,Jagiellonia Białystok,1.0,0.0,22,Wisła Płock,W,36,3.0
36,Jagiellonia Białystok,0.0,1.0,23,Cracovia Kraków,L,39,3.0
34,Jagiellonia Białystok,2.0,2.0,24,Górnik Zabrze,D,39,2.5
32,Jagiellonia Białystok,0.0,2.0,25,Śląsk Wrocław,L,40,3.0
28,Jagiellonia Białystok,1.0,3.0,26,Korona Kielce,L,40,2.5
26,Jagiellonia Białystok,0.0,0.0,27,Pogoń Szczecin,D,40,1.5
24,Jagiellonia Białystok,0.0,3.0,28,Legia Warszawa,L,41,1.0
22,Jagiellonia Białystok,2.0,1.0,29,Zagłębie Sosnowiec,W,41,1.0
18,Jagiellonia Białystok,2.0,0.0,30,Lech Poznań,W,44,1.5
16,Jagiellonia Białystok,3.0,3.0,31,Lech Poznań,D,47,2.5
14,Jagiellonia Białystok,1.0,0.0,32,Cracovia Kraków,W,48,3.0
12,Jagiellonia Białystok,0.0,2.0,33,Zagłębie Lubin,L,51,3.5
8,Jagiellonia Białystok,4.0,2.0,34,Pogoń Szczecin,W,51,3.5
6,Jagiellonia Białystok,1.0,2.0,35,Piast Gliwice,L,54,3.5
4,Jagiellonia Białystok,1.0,0.0,36,Legia Warszawa,W,54,2.5
2,Jagiellonia Białystok,0.0,2.0,37,Lechia Gdańsk,L,57,3.0
80,Cracovia Kraków,1.0,3.0,1,Śląsk Wrocław,L,0,0.0
78,Cracovia Kraków,0.0,2.0,2,Lech Poznań,L,0,0.0
76,Cracovia Kraków,0.0,0.0,3,Arka Gdynia,D,0,0.0
74,Cracovia Kraków,1.0,1.0,4,Pogoń Szczecin,D,1,0.5
72,Cracovia Kraków,0.0,1.0,5,Zagłębie Lubin,L,2,1.0
70,Cracovia Kraków,1.0,3.0,6,Piast Gliwice,L,2,1.0
68,Cracovia Kraków,0.0,0.0,7,Legia Warszawa,D,2,1.0
66,Cracovia Kraków,1.0,3.0,8,Jagiellonia Białystok,L,3,1.5
64,Cracovia Kraków,3.0,1.0,9,Wisła Płock,W,3,1.0
60,Cracovia Kraków,1.0,1.0,10,Zagłębie Sosnowiec,D,6,1.5
58,Cracovia Kraków,0.0,2.0,11,Wisła Kraków,L,7,2.0
56,Cracovia Kraków,2.0,0.0,12,Górnik Zabrze,W,7,2.0
54,Cracovia Kraków,1.0,0.0,13,Korona Kielce,W,10,2.5
50,Cracovia Kraków,0.0,0.0,14,Miedź Legnica,D,13,3.5
48,Cracovia Kraków,0.0,1.0,15,Lechia Gdańsk,L,14,3.0
46,Cracovia Kraków,1.0,1.0,16,Śląsk Wrocław,D,14,2.5
44,Cracovia Kraków,1.0,0.0,17,Lech Poznań,W,15,3.0
42,Cracovia Kraków,3.0,0.0,18,Arka Gdynia,W,18,3.0
40,Cracovia Kraków,2.0,1.0,19,Pogoń Szczecin,W,21,3.0
38,Cracovia Kraków,2.0,1.0,20,Zagłębie Lubin,W,24,3.5
34,Cracovia Kraków,2.0,1.0,21,Piast Gliwice,W,27,4.5
32,Cracovia Kraków,2.0,0.0,22,Legia Warszawa,W,30,5.0
30,Cracovia Kraków,1.0,0.0,23,Jagiellonia Białystok,W,33,5.0
28,Cracovia Kraków,2.0,3.0,24,Wisła Płock,L,36,5.0
26,Cracovia Kraków,2.0,1.0,25,Zagłębie Sosnowiec,W,36,4.0
24,Cracovia Kraków,2.0,3.0,26,Wisła Kraków,L,39,4.0
22,Cracovia Kraków,1.0,0.0,27,Górnik Zabrze,W,39,3.0
20,Cracovia Kraków,2.0,1.0,28,Korona Kielce,W,42,3.0
18,Cracovia Kraków,1.0,2.0,29,Miedź Legnica,L,45,3.0
16,Cracovia Kraków,4.0,2.0,30,Lechia Gdańsk,W,45,3.0
14,Cracovia Kraków,0.0,1.0,31,Legia Warszawa,L,48,3.0
12,Cracovia Kraków,0.0,1.0,32,Jagiellonia Białystok,L,48,3.0
10,Cracovia Kraków,1.0,3.0,33,Piast Gliwice,L,48,2.0
8,Cracovia Kraków,2.0,0.0,34,Lechia Gdańsk,W,48,1.0
6,Cracovia Kraków,1.0,0.0,35,Lech Poznań,W,51,2.0
4,Cracovia Kraków,2.0,1.0,36,Zagłębie Lubin,W,54,2.0
2,Cracovia Kraków,0.0,3.0,37,Pogoń Szczecin,L,57,3.0
76,Pogoń Szczecin,0.0,1.0,1,Miedź Legnica,L,0,0.0
74,Pogoń Szczecin,0.0,2.0,2,Piast Gliwice,L,0,0.0
72,Pogoń Szczecin,0.0,3.0,3,Zagłębie Sosnowiec,L,0,0.0
70,Pogoń Szczecin,1.0,1.0,4,Cracovia Kraków,D,0,0.0
68,Pogoń Szczecin,0.0,0.0,5,Śląsk Wrocław,D,1,0.5
66,Pogoń Szczecin,2.0,3.0,6,Lechia Gdańsk,L,2,1.0
64,Pogoń Szczecin,1.0,1.0,7,Górnik Zabrze,D,2,1.0
62,Pogoń Szczecin,1.0,1.0,8,Korona Kielce,D,3,1.5
60,Pogoń Szczecin,2.0,1.0,9,Wisła Kraków,W,4,2.0
56,Pogoń Szczecin,2.0,0.0,10,Zagłębie Lubin,W,7,2.5
54,Pogoń Szczecin,4.0,0.0,11,Wisła Płock,W,10,3.0
52,Pogoń Szczecin,1.0,2.0,12,Jagiellonia Białystok,L,13,4.0
50,Pogoń Szczecin,3.0,0.0,13,Lech Poznań,W,13,3.5
48,Pogoń Szczecin,3.0,2.0,14,Arka Gdynia,W,16,4.0
46,Pogoń Szczecin,2.0,1.0,15,Legia Warszawa,W,19,4.0
44,Pogoń Szczecin,2.0,0.0,16,Miedź Legnica,W,22,4.0
42,Pogoń Szczecin,0.0,3.0,17,Piast Gliwice,L,25,4.0
40,Pogoń Szczecin,1.0,0.0,18,Zagłębie Sosnowiec,W,25,4.0
38,Pogoń Szczecin,1.0,2.0,19,Cracovia Kraków,L,28,4.0
36,Pogoń Szczecin,2.0,1.0,20,Śląsk Wrocław,W,28,3.0
34,Pogoń Szczecin,1.0,2.0,21,Lechia Gdańsk,L,31,3.0
32,Pogoń Szczecin,3.0,1.0,22,Górnik Zabrze,W,31,2.0
30,Pogoń Szczecin,1.0,1.0,23,Korona Kielce,D,34,3.0
28,Pogoń Szczecin,3.0,2.0,24,Wisła Kraków,W,35,2.5
26,Pogoń Szczecin,0.0,3.0,25,Zagłębie Lubin,L,38,3.5
24,Pogoń Szczecin,2.0,0.0,26,Wisła Płock,W,38,2.5
22,Pogoń Szczecin,0.0,0.0,27,Jagiellonia Białystok,D,41,3.5
20,Pogoń Szczecin,2.0,3.0,28,Lech Poznań,L,42,3.0
18,Pogoń Szczecin,3.0,3.0,29,Arka Gdynia,D,42,2.5
16,Pogoń Szczecin,1.0,3.0,30,Legia Warszawa,L,43,2.0
14,Pogoń Szczecin,3.0,2.0,31,Zagłębie Lubin,W,43,2.0
12,Pogoń Szczecin,3.0,4.0,32,Lechia Gdańsk,L,46,2.0
10,Pogoń Szczecin,1.0,1.0,33,Lech Poznań,D,46,1.5
8,Pogoń Szczecin,2.0,4.0,34,Jagiellonia Białystok,L,47,2.0
6,Pogoń Szczecin,1.0,1.0,35,Legia Warszawa,D,47,1.5
4,Pogoń Szczecin,0.0,0.0,36,Piast Gliwice,D,48,2.0
2,Pogoń Szczecin,3.0,0.0,37,Cracovia Kraków,W,49,1.5
78,Piast Gliwice,2.0,1.0,1,Zagłębie Sosnowiec,W,0,0.0
76,Piast Gliwice,2.0,0.0,2,Pogoń Szczecin,W,3,1.0
74,Piast Gliwice,2.0,1.0,3,Zagłębie Lubin,W,6,2.0
72,Piast Gliwice,1.0,3.0,4,Legia Warszawa,L,9,3.0
70,Piast Gliwice,1.0,2.0,5,Jagiellonia Białystok,L,9,3.0
68,Piast Gliwice,3.0,1.0,6,Cracovia Kraków,W,9,3.0
66,Piast Gliwice,1.0,1.0,7,Lech Poznań,D,12,3.0
64,Piast Gliwice,1.0,0.0,8,Arka Gdynia,W,13,2.5
62,Piast Gliwice,1.0,4.0,9,Śląsk Wrocław,L,16,2.5
58,Piast Gliwice,1.0,0.0,10,Górnik Zabrze,W,16,2.5
56,Piast Gliwice,2.0,2.0,11,Miedź Legnica,D,19,3.5
54,Piast Gliwice,1.0,1.0,12,Lechia Gdańsk,D,20,3.0
52,Piast Gliwice,1.0,1.0,13,Wisła Płock,D,21,3.0
48,Piast Gliwice,2.0,0.0,14,Wisła Kraków,W,22,2.5
46,Piast Gliwice,0.0,1.0,15,Korona Kielce,L,25,3.5
44,Piast Gliwice,0.0,0.0,16,Zagłębie Sosnowiec,D,25,2.5
42,Piast Gliwice,3.0,0.0,17,Pogoń Szczecin,W,26,2.5
40,Piast Gliwice,2.0,2.0,18,Zagłębie Lubin,D,29,3.0
38,Piast Gliwice,0.0,2.0,19,Legia Warszawa,L,30,3.0
36,Piast Gliwice,1.0,1.0,20,Jagiellonia Białystok,D,30,2.0
34,Piast Gliwice,1.0,2.0,21,Cracovia Kraków,L,31,2.5
32,Piast Gliwice,4.0,0.0,22,Lech Poznań,W,31,2.0
30,Piast Gliwice,2.0,1.0,23,Arka Gdynia,W,34,2.0
28,Piast Gliwice,2.0,0.0,24,Śląsk Wrocław,W,37,2.5
26,Piast Gliwice,2.0,0.0,25,Górnik Zabrze,W,40,3.5
24,Piast Gliwice,2.0,1.0,26,Miedź Legnica,W,43,4.0
22,Piast Gliwice,0.0,2.0,27,Lechia Gdańsk,L,46,5.0
20,Piast Gliwice,1.0,0.0,28,Wisła Płock,W,46,4.0
18,Piast Gliwice,2.0,2.0,29,Wisła Kraków,D,49,4.0
16,Piast Gliwice,4.0,0.0,30,Korona Kielce,W,50,3.5
14,Piast Gliwice,2.0,0.0,31,Lechia Gdańsk,W,53,3.5
12,Piast Gliwice,1.0,0.0,32,Zagłębie Lubin,W,56,3.5
10,Piast Gliwice,3.0,1.0,33,Cracovia Kraków,W,59,4.5
8,Piast Gliwice,1.0,0.0,34,Legia Warszawa,W,62,4.5
6,Piast Gliwice,2.0,1.0,35,Jagiellonia Białystok,W,65,5.0
4,Piast Gliwice,0.0,0.0,36,Pogoń Szczecin,D,68,5.0
2,Piast Gliwice,1.0,0.0,37,Lech Poznań,W,69,4.5
74,Zagłębie Lubin,3.0,1.0,1,Legia Warszawa,W,0,0.0
72,Zagłębie Lubin,2.0,1.0,2,Zagłębie Sosnowiec,W,3,1.0
70,Zagłębie Lubin,1.0,2.0,3,Piast Gliwice,L,6,2.0
68,Zagłębie Lubin,0.0,2.0,4,Jagiellonia Białystok,L,6,2.0
66,Zagłębie Lubin,1.0,0.0,5,Cracovia Kraków,W,6,2.0
64,Zagłębie Lubin,2.0,1.0,6,Lech Poznań,W,9,3.0
62,Zagłębie Lubin,0.0,2.0,7,Miedź Legnica,L,12,3.0
60,Zagłębie Lubin,4.0,0.0,8,Śląsk Wrocław,W,12,2.0
58,Zagłębie Lubin,3.0,3.0,9,Lechia Gdańsk,D,15,3.0
56,Zagłębie Lubin,0.0,2.0,10,Pogoń Szczecin,L,16,3.5
54,Zagłębie Lubin,1.0,3.0,11,Arka Gdynia,L,16,2.5
52,Zagłębie Lubin,3.0,3.0,12,Wisła Płock,D,16,1.5
50,Zagłębie Lubin,0.0,2.0,13,Górnik Zabrze,L,17,2.0
48,Zagłębie Lubin,0.0,1.0,14,Korona Kielce,L,17,1.0
46,Zagłębie Lubin,2.0,3.0,15,Wisła Kraków,L,17,0.5
44,Zagłębie Lubin,0.0,1.0,16,Legia Warszawa,L,17,0.5
42,Zagłębie Lubin,2.0,1.0,17,Zagłębie Sosnowiec,W,17,0.5
40,Zagłębie Lubin,2.0,2.0,18,Piast Gliwice,D,20,1.0
38,Zagłębie Lubin,4.0,0.0,19,Jagiellonia Białystok,W,21,1.5
36,Zagłębie Lubin,1.0,2.0,20,Cracovia Kraków,L,24,2.5
34,Zagłębie Lubin,2.0,1.0,21,Lech Poznań,W,24,2.5
32,Zagłębie Lubin,3.0,0.0,22,Miedź Legnica,W,27,3.5
30,Zagłębie Lubin,0.0,2.0,23,Śląsk Wrocław,L,30,3.5
28,Zagłębie Lubin,2.0,1.0,24,Lechia Gdańsk,W,30,3.0
26,Zagłębie Lubin,3.0,0.0,25,Pogoń Szczecin,W,33,3.0
24,Zagłębie Lubin,0.0,0.0,26,Arka Gdynia,D,36,4.0
22,Zagłębie Lubin,1.0,0.0,27,Wisła Płock,W,37,3.5
20,Zagłębie Lubin,1.0,1.0,28,Górnik Zabrze,D,40,3.5
18,Zagłębie Lubin,2.0,0.0,29,Korona Kielce,W,41,4.0
16,Zagłębie Lubin,3.0,1.0,30,Wisła Kraków,W,44,4.0
14,Zagłębie Lubin,2.0,3.0,31,Pogoń Szczecin,L,47,4.0
12,Zagłębie Lubin,0.0,1.0,32,Piast Gliwice,L,47,3.5
10,Zagłębie Lubin,2.0,0.0,33,Jagiellonia Białystok,W,47,2.5
8,Zagłębie Lubin,1.0,1.0,34,Lech Poznań,D,50,3.0
6,Zagłębie Lubin,1.0,1.0,35,Lechia Gdańsk,D,51,2.5
4,Zagłębie Lubin,1.0,2.0,36,Cracovia Kraków,L,52,2.0
2,Zagłębie Lubin,2.0,2.0,37,Legia Warszawa,D,52,2.0
90,Legia Warszawa,1.0,3.0,1,Zagłębie Lubin,L,0,0.0
86,Legia Warszawa,2.0,1.0,2,Korona Kielce,W,0,0.0
82,Legia Warszawa,0.0,0.0,3,Lechia Gdańsk,D,3,1.0
78,Legia Warszawa,3.0,1.0,4,Piast Gliwice,W,4,1.5
74,Legia Warszawa,2.0,1.0,5,Zagłębie Sosnowiec,W,7,2.5
72,Legia Warszawa,1.0,4.0,6,Wisła Płock,L,10,3.5
70,Legia Warszawa,0.0,0.0,7,Cracovia Kraków,D,10,3.5
68,Legia Warszawa,1.0,0.0,8,Lech Poznań,W,11,3.0
66,Legia Warszawa,4.0,1.0,9,Miedź Legnica,W,14,3.5
62,Legia Warszawa,1.0,1.0,10,Arka Gdynia,D,17,3.5
60,Legia Warszawa,1.0,0.0,11,Śląsk Wrocław,W,18,3.0
58,Legia Warszawa,3.0,3.0,12,Wisła Kraków,D,21,4.0
56,Legia Warszawa,1.0,1.0,13,Jagiellonia Białystok,D,22,4.0
52,Legia Warszawa,4.0,0.0,14,Górnik Zabrze,W,23,3.5
50,Legia Warszawa,1.0,2.0,15,Pogoń Szczecin,L,26,3.5
48,Legia Warszawa,1.0,0.0,16,Zagłębie Lubin,W,26,3.0
46,Legia Warszawa,3.0,0.0,17,Korona Kielce,W,29,3.0
42,Legia Warszawa,0.0,0.0,18,Lechia Gdańsk,D,32,3.5
40,Legia Warszawa,2.0,0.0,19,Piast Gliwice,W,33,3.5
38,Legia Warszawa,3.0,2.0,20,Zagłębie Sosnowiec,W,36,3.5
36,Legia Warszawa,1.0,0.0,21,Wisła Płock,W,39,4.5
34,Legia Warszawa,0.0,2.0,22,Cracovia Kraków,L,42,4.5
32,Legia Warszawa,0.0,2.0,23,Lech Poznań,L,42,3.5
30,Legia Warszawa,2.0,0.0,24,Miedź Legnica,W,42,3.0
28,Legia Warszawa,2.0,1.0,25,Arka Gdynia,W,45,3.0
24,Legia Warszawa,1.0,0.0,26,Śląsk Wrocław,W,48,3.0
22,Legia Warszawa,0.0,4.0,27,Wisła Kraków,L,51,3.0
20,Legia Warszawa,3.0,0.0,28,Jagiellonia Białystok,W,51,3.0
18,Legia Warszawa,2.0,1.0,29,Górnik Zabrze,W,54,4.0
16,Legia Warszawa,3.0,1.0,30,Pogoń Szczecin,W,57,4.0
14,Legia Warszawa,1.0,0.0,31,Cracovia Kraków,W,60,4.0
12,Legia Warszawa,0.0,1.0,32,Lech Poznań,L,63,4.0
10,Legia Warszawa,3.0,1.0,33,Lechia Gdańsk,W,63,4.0
8,Legia Warszawa,0.0,1.0,34,Piast Gliwice,L,66,4.0
6,Legia Warszawa,1.0,1.0,35,Pogoń Szczecin,D,66,3.0
4,Legia Warszawa,0.0,1.0,36,Jagiellonia Białystok,L,67,2.5
2,Legia Warszawa,2.0,2.0,37,Zagłębie Lubin,D,67,1.5
84,Górnik Zabrze,1.0,1.0,1,Korona Kielce,D,0,0.0
80,Górnik Zabrze,1.0,1.0,2,Wisła Płock,D,1,0.5
76,Górnik Zabrze,3.0,1.0,3,Miedź Legnica,W,2,1.0
74,Górnik Zabrze,1.0,1.0,4,Arka Gdynia,D,5,2.0
72,Górnik Zabrze,0.0,2.0,5,Lechia Gdańsk,L,6,2.5
70,Górnik Zabrze,0.0,3.0,6,Wisła Kraków,L,6,2.5
68,Górnik Zabrze,1.0,1.0,7,Pogoń Szczecin,D,6,2.0
66,Górnik Zabrze,1.0,1.0,8,Zagłębie Sosnowiec,D,7,2.0
64,Górnik Zabrze,1.0,3.0,9,Jagiellonia Białystok,L,8,1.5
62,Górnik Zabrze,0.0,1.0,10,Piast Gliwice,L,8,1.0
60,Górnik Zabrze,2.0,2.0,11,Lech Poznań,D,8,1.0
58,Górnik Zabrze,0.0,2.0,12,Cracovia Kraków,L,9,1.5
56,Górnik Zabrze,2.0,0.0,13,Zagłębie Lubin,W,9,1.0
52,Górnik Zabrze,0.0,4.0,14,Legia Warszawa,L,12,1.5
50,Górnik Zabrze,2.0,2.0,15,Śląsk Wrocław,D,12,1.5
48,Górnik Zabrze,2.0,4.0,16,Korona Kielce,L,13,2.0
46,Górnik Zabrze,4.0,0.0,17,Wisła Płock,W,13,1.5
42,Górnik Zabrze,1.0,3.0,18,Miedź Legnica,L,16,2.5
40,Górnik Zabrze,1.0,1.0,19,Arka Gdynia,D,16,1.5
38,Górnik Zabrze,0.0,4.0,20,Lechia Gdańsk,L,17,2.0
36,Górnik Zabrze,2.0,0.0,21,Wisła Kraków,W,17,1.5
34,Górnik Zabrze,1.0,3.0,22,Pogoń Szczecin,L,20,2.5
32,Górnik Zabrze,2.0,1.0,23,Zagłębie Sosnowiec,W,20,1.5
28,Górnik Zabrze,2.0,2.0,24,Jagiellonia Białystok,D,23,2.5
26,Górnik Zabrze,0.0,2.0,25,Piast Gliwice,L,24,2.5
24,Górnik Zabrze,3.0,0.0,26,Lech Poznań,W,24,2.5
22,Górnik Zabrze,0.0,1.0,27,Cracovia Kraków,L,27,2.5
20,Górnik Zabrze,1.0,1.0,28,Zagłębie Lubin,D,27,2.5
18,Górnik Zabrze,1.0,2.0,29,Legia Warszawa,L,28,2.0
16,Górnik Zabrze,1.0,0.0,30,Śląsk Wrocław,W,28,1.5
14,Górnik Zabrze,1.0,0.0,31,Arka Gdynia,W,31,2.5
12,Górnik Zabrze,2.0,1.0,32,Śląsk Wrocław,W,34,2.5
10,Górnik Zabrze,4.0,0.0,33,Zagłębie Sosnowiec,W,37,3.5
8,Górnik Zabrze,1.0,2.0,34,Wisła Kraków,L,40,4.0
6,Górnik Zabrze,1.0,0.0,35,Miedź Legnica,W,40,4.0
4,Górnik Zabrze,0.0,1.0,36,Wisła Płock,L,43,4.0
2,Górnik Zabrze,3.0,0.0,37,Korona Kielce,W,43,3.0
80,Śląsk Wrocław,3.0,1.0,1,Cracovia Kraków,W,0,0.0
78,Śląsk Wrocław,1.0,1.0,2,Lechia Gdańsk,D,3,1.0
76,Śląsk Wrocław,0.0,1.0,3,Lech Poznań,L,4,1.5
74,Śląsk Wrocław,1.0,2.0,4,Korona Kielce,L,4,1.5
72,Śląsk Wrocław,0.0,0.0,5,Pogoń Szczecin,D,4,1.5
70,Śląsk Wrocław,3.0,3.0,6,Zagłębie Sosnowiec,D,5,2.0
68,Śląsk Wrocław,0.0,1.0,7,Wisła Kraków,L,6,1.5
66,Śląsk Wrocław,0.0,4.0,8,Zagłębie Lubin,L,6,1.0
64,Śląsk Wrocław,4.0,1.0,9,Piast Gliwice,W,6,1.0
60,Śląsk Wrocław,4.0,0.0,10,Jagiellonia Białystok,W,9,2.0
58,Śląsk Wrocław,0.0,1.0,11,Legia Warszawa,L,12,2.5
56,Śląsk Wrocław,1.0,2.0,12,Arka Gdynia,L,12,2.0
54,Śląsk Wrocław,5.0,0.0,13,Miedź Legnica,W,12,2.0
50,Śląsk Wrocław,0.0,3.0,14,Wisła Płock,L,15,3.0
48,Śląsk Wrocław,2.0,2.0,15,Górnik Zabrze,D,15,2.0
46,Śląsk Wrocław,1.0,1.0,16,Cracovia Kraków,D,16,1.5
44,Śląsk Wrocław,0.0,2.0,17,Lechia Gdańsk,L,17,2.0
40,Śląsk Wrocław,0.0,2.0,18,Lech Poznań,L,17,2.0
38,Śląsk Wrocław,1.0,1.0,19,Korona Kielce,D,17,1.0
36,Śląsk Wrocław,1.0,2.0,20,Pogoń Szczecin,L,18,1.5
34,Śląsk Wrocław,2.0,0.0,21,Zagłębie Sosnowiec,W,18,1.0
32,Śląsk Wrocław,0.0,1.0,22,Wisła Kraków,L,21,1.5
30,Śląsk Wrocław,2.0,0.0,23,Zagłębie Lubin,W,21,1.5
28,Śląsk Wrocław,0.0,2.0,24,Piast Gliwice,L,24,2.5
26,Śląsk Wrocław,2.0,0.0,25,Jagiellonia Białystok,W,24,2.0
24,Śląsk Wrocław,0.0,1.0,26,Legia Warszawa,L,27,3.0
22,Śląsk Wrocław,2.0,0.0,27,Arka Gdynia,W,27,2.0
20,Śląsk Wrocław,0.0,0.0,28,Miedź Legnica,D,30,3.0
18,Śląsk Wrocław,0.0,2.0,29,Wisła Płock,L,31,2.5
16,Śląsk Wrocław,0.0,1.0,30,Górnik Zabrze,L,31,2.5
14,Śląsk Wrocław,0.0,2.0,31,Korona Kielce,L,31,1.5
12,Śląsk Wrocław,1.0,2.0,32,Górnik Zabrze,L,31,1.5
10,Śląsk Wrocław,1.0,1.0,33,Wisła Kraków,D,31,0.5
8,Śląsk Wrocław,4.0,2.0,34,Zagłębie Sosnowiec,W,32,0.5
6,Śląsk Wrocław,2.0,1.0,35,Wisła Płock,W,35,1.5
4,Śląsk Wrocław,2.0,0.0,36,Miedź Legnica,W,38,2.5
2,Śląsk Wrocław,4.0,0.0,37,Arka Gdynia,W,41,3.5
82,Wisła Płock,1.0,2.0,1,Lech Poznań,L,0,0.0
80,Wisła Płock,1.0,1.0,2,Górnik Zabrze,D,0,0.0
78,Wisła Płock,1.0,2.0,3,Korona Kielce,L,1,0.5
76,Wisła Płock,1.0,1.0,4,Wisła Kraków,D,1,0.5
74,Wisła Płock,1.0,3.0,5,Arka Gdynia,L,2,1.0
72,Wisła Płock,4.0,1.0,6,Legia Warszawa,W,2,1.0
70,Wisła Płock,1.0,1.0,7,Jagiellonia Białystok,D,5,2.0
68,Wisła Płock,2.0,2.0,8,Miedź Legnica,D,6,2.0
66,Wisła Płock,1.0,3.0,9,Cracovia Kraków,L,7,2.5
64,Wisła Płock,1.0,0.0,10,Lechia Gdańsk,W,7,2.0
62,Wisła Płock,0.0,4.0,11,Pogoń Szczecin,L,10,3.0
60,Wisła Płock,3.0,3.0,12,Zagłębie Lubin,D,10,2.0
58,Wisła Płock,1.0,1.0,13,Piast Gliwice,D,11,2.0
56,Wisła Płock,3.0,0.0,14,Śląsk Wrocław,W,12,2.0
54,Wisła Płock,2.0,0.0,15,Zagłębie Sosnowiec,W,15,3.0
52,Wisła Płock,1.0,2.0,16,Lech Poznań,L,18,3.0
50,Wisła Płock,0.0,4.0,17,Górnik Zabrze,L,18,3.0
46,Wisła Płock,2.0,2.0,18,Korona Kielce,D,18,2.5
44,Wisła Płock,1.0,2.0,19,Wisła Kraków,L,19,2.5
42,Wisła Płock,3.0,3.0,20,Arka Gdynia,D,19,1.5
36,Wisła Płock,0.0,1.0,21,Legia Warszawa,L,20,1.0
34,Wisła Płock,0.0,1.0,22,Jagiellonia Białystok,L,20,1.0
32,Wisła Płock,1.0,2.0,23,Miedź Legnica,L,20,1.0
30,Wisła Płock,3.0,2.0,24,Cracovia Kraków,W,20,0.5
28,Wisła Płock,1.0,1.0,25,Lechia Gdańsk,D,23,1.5
26,Wisła Płock,0.0,2.0,26,Pogoń Szczecin,L,24,1.5
24,Wisła Płock,0.0,1.0,27,Zagłębie Lubin,L,24,1.5
22,Wisła Płock,0.0,1.0,28,Piast Gliwice,L,24,1.5
20,Wisła Płock,2.0,0.0,29,Śląsk Wrocław,W,24,1.5
18,Wisła Płock,3.0,1.0,30,Zagłębie Sosnowiec,W,27,1.5
16,Wisła Płock,3.0,2.0,31,Wisła Kraków,W,30,2.0
14,Wisła Płock,2.0,1.0,32,Korona Kielce,W,33,3.0
12,Wisła Płock,2.0,3.0,33,Miedź Legnica,L,36,4.0
10,Wisła Płock,1.0,1.0,34,Arka Gdynia,D,36,4.0
8,Wisła Płock,1.0,2.0,35,Śląsk Wrocław,L,37,3.5
6,Wisła Płock,1.0,0.0,36,Górnik Zabrze,W,37,2.5
4,Wisła Płock,0.0,0.0,37,Zagłębie Sosnowiec,D,40,2.5
76,Arka Gdynia,0.0,0.0,1,Wisła Kraków,D,0,0.0
74,Arka Gdynia,0.0,2.0,2,Jagiellonia Białystok,L,1,0.5
72,Arka Gdynia,0.0,0.0,3,Cracovia Kraków,D,1,0.5
70,Arka Gdynia,1.0,1.0,4,Górnik Zabrze,D,2,1.0
68,Arka Gdynia,3.0,1.0,5,Wisła Płock,W,3,1.5
66,Arka Gdynia,1.0,2.0,6,Korona Kielce,L,6,2.5
64,Arka Gdynia,2.0,2.0,7,Zagłębie Sosnowiec,D,6,2.0
62,Arka Gdynia,0.0,1.0,8,Piast Gliwice,L,7,2.5
60,Arka Gdynia,1.0,0.0,9,Lech Poznań,W,7,2.0
58,Arka Gdynia,1.0,1.0,10,Legia Warszawa,D,10,2.5
56,Arka Gdynia,3.0,1.0,11,Zagłębie Lubin,W,11,2.0
54,Arka Gdynia,2.0,1.0,12,Śląsk Wrocław,W,14,3.0
52,Arka Gdynia,1.0,2.0,13,Lechia Gdańsk,L,17,3.5
50,Arka Gdynia,2.0,3.0,14,Pogoń Szczecin,L,17,3.5
48,Arka Gdynia,4.0,0.0,15,Miedź Legnica,W,17,2.5
46,Arka Gdynia,4.0,1.0,16,Wisła Kraków,W,20,3.0
44,Arka Gdynia,1.0,3.0,17,Jagiellonia Białystok,L,23,3.0
40,Arka Gdynia,0.0,3.0,18,Cracovia Kraków,L,23,2.0
38,Arka Gdynia,1.0,1.0,19,Górnik Zabrze,D,23,2.0
36,Arka Gdynia,3.0,3.0,20,Wisła Płock,D,24,2.5
34,Arka Gdynia,1.0,2.0,21,Korona Kielce,L,25,2.0
32,Arka Gdynia,2.0,3.0,22,Zagłębie Sosnowiec,L,25,1.0
30,Arka Gdynia,1.0,2.0,23,Piast Gliwice,L,25,1.0
28,Arka Gdynia,0.0,1.0,24,Lech Poznań,L,25,1.0
26,Arka Gdynia,1.0,2.0,25,Legia Warszawa,L,25,0.5
24,Arka Gdynia,0.0,0.0,26,Zagłębie Lubin,D,25,0.0
22,Arka Gdynia,0.0,2.0,27,Śląsk Wrocław,L,26,0.5
20,Arka Gdynia,0.0,0.0,28,Lechia Gdańsk,D,26,0.5
18,Arka Gdynia,3.0,3.0,29,Pogoń Szczecin,D,27,1.0
16,Arka Gdynia,1.0,1.0,30,Miedź Legnica,D,28,1.5
14,Arka Gdynia,0.0,1.0,31,Górnik Zabrze,L,29,2.0
12,Arka Gdynia,2.0,0.0,32,Miedź Legnica,W,29,1.5
10,Arka Gdynia,2.0,0.0,33,Korona Kielce,W,32,2.5
8,Arka Gdynia,1.0,1.0,34,Wisła Płock,D,35,3.0
6,Arka Gdynia,2.0,0.0,35,Zagłębie Sosnowiec,W,36,3.0
4,Arka Gdynia,3.0,1.0,36,Wisła Kraków,W,39,3.5
2,Arka Gdynia,0.0,4.0,37,Śląsk Wrocław,L,42,4.5
76,Wisła Kraków,0.0,0.0,1,Arka Gdynia,D,0,0.0
74,Wisła Kraków,2.0,1.0,2,Miedź Legnica,W,1,0.5
72,Wisła Kraków,0.0,1.0,3,Jagiellonia Białystok,L,4,1.5
70,Wisła Kraków,1.0,1.0,4,Wisła Płock,D,4,1.5
68,Wisła Kraków,5.0,2.0,5,Lech Poznań,W,5,2.0
66,Wisła Kraków,3.0,0.0,6,Górnik Zabrze,W,8,3.0
64,Wisła Kraków,1.0,0.0,7,Śląsk Wrocław,W,11,3.5
62,Wisła Kraków,5.0,2.0,8,Lechia Gdańsk,W,14,3.5
60,Wisła Kraków,1.0,2.0,9,Pogoń Szczecin,L,17,4.5
56,Wisła Kraków,0.0,1.0,10,Korona Kielce,L,17,4.0
54,Wisła Kraków,2.0,0.0,11,Cracovia Kraków,W,17,3.0
52,Wisła Kraków,3.0,3.0,12,Legia Warszawa,D,20,3.0
50,Wisła Kraków,2.0,2.0,13,Zagłębie Sosnowiec,D,21,2.5
48,Wisła Kraków,0.0,2.0,14,Piast Gliwice,L,22,2.0
46,Wisła Kraków,3.0,2.0,15,Zagłębie Lubin,W,22,2.0
44,Wisła Kraków,1.0,4.0,16,Arka Gdynia,L,25,3.0
42,Wisła Kraków,0.0,2.0,17,Miedź Legnica,L,25,2.0
40,Wisła Kraków,2.0,2.0,18,Jagiellonia Białystok,D,25,1.5
38,Wisła Kraków,2.0,1.0,19,Wisła Płock,W,26,1.5
36,Wisła Kraków,0.0,1.0,20,Lech Poznań,L,29,2.5
34,Wisła Kraków,0.0,2.0,21,Górnik Zabrze,L,29,1.5
32,Wisła Kraków,1.0,0.0,22,Śląsk Wrocław,W,29,1.5
30,Wisła Kraków,0.0,1.0,23,Lechia Gdańsk,L,32,2.5
28,Wisła Kraków,2.0,3.0,24,Pogoń Szczecin,L,32,2.0
26,Wisła Kraków,6.0,2.0,25,Korona Kielce,W,32,1.0
24,Wisła Kraków,3.0,2.0,26,Cracovia Kraków,W,35,2.0
22,Wisła Kraków,4.0,0.0,27,Legia Warszawa,W,38,3.0
20,Wisła Kraków,3.0,4.0,28,Zagłębie Sosnowiec,L,41,3.0
18,Wisła Kraków,2.0,2.0,29,Piast Gliwice,D,41,3.0
16,Wisła Kraków,1.0,3.0,30,Zagłębie Lubin,L,42,3.5
14,Wisła Kraków,2.0,3.0,31,Wisła Płock,L,42,2.5
12,Wisła Kraków,1.0,2.0,32,Zagłębie Sosnowiec,L,42,1.5
10,Wisła Kraków,1.0,1.0,33,Śląsk Wrocław,D,42,0.5
8,Wisła Kraków,2.0,1.0,34,Górnik Zabrze,W,43,1.0
6,Wisła Kraków,1.0,0.0,35,Korona Kielce,W,46,1.5
4,Wisła Kraków,1.0,3.0,36,Arka Gdynia,L,49,2.5
2,Wisła Kraków,4.0,5.0,37,Miedź Legnica,L,49,2.5
74,Korona Kielce,1.0,1.0,1,Górnik Zabrze,D,0,0.0
72,Korona Kielce,1.0,2.0,2,Legia Warszawa,L,1,0.5
70,Korona Kielce,2.0,1.0,3,Wisła Płock,W,1,0.5
68,Korona Kielce,2.0,1.0,4,Śląsk Wrocław,W,4,1.5
66,Korona Kielce,1.0,1.0,5,Miedź Legnica,D,7,2.5
64,Korona Kielce,2.0,1.0,6,Arka Gdynia,W,8,3.0
62,Korona Kielce,0.0,2.0,7,Lechia Gdańsk,L,11,3.5
60,Korona Kielce,1.0,1.0,8,Pogoń Szczecin,D,11,3.5
58,Korona Kielce,3.0,1.0,9,Zagłębie Sosnowiec,W,12,3.0
56,Korona Kielce,1.0,0.0,10,Wisła Kraków,W,15,3.0
54,Korona Kielce,1.0,1.0,11,Jagiellonia Białystok,D,18,3.5
52,Korona Kielce,1.0,2.0,12,Lech Poznań,L,19,3.0
50,Korona Kielce,0.0,1.0,13,Cracovia Kraków,L,19,3.0
48,Korona Kielce,1.0,0.0,14,Zagłębie Lubin,W,19,2.5
46,Korona Kielce,1.0,0.0,15,Piast Gliwice,W,22,2.5
44,Korona Kielce,4.0,2.0,16,Górnik Zabrze,W,25,2.5
42,Korona Kielce,0.0,3.0,17,Legia Warszawa,L,28,3.0
40,Korona Kielce,2.0,2.0,18,Wisła Płock,D,28,3.0
38,Korona Kielce,1.0,1.0,19,Śląsk Wrocław,D,29,3.5
36,Korona Kielce,0.0,0.0,20,Miedź Legnica,D,30,3.0
34,Korona Kielce,2.0,1.0,21,Arka Gdynia,W,31,2.5
32,Korona Kielce,0.0,0.0,22,Lechia Gdańsk,D,34,2.5
30,Korona Kielce,1.0,1.0,23,Pogoń Szczecin,D,35,3.0
28,Korona Kielce,1.0,4.0,24,Zagłębie Sosnowiec,L,36,3.0
26,Korona Kielce,2.0,6.0,25,Wisła Kraków,L,36,2.5
24,Korona Kielce,3.0,1.0,26,Jagiellonia Białystok,W,36,2.0
22,Korona Kielce,0.0,0.0,27,Lech Poznań,D,39,2.0
20,Korona Kielce,1.0,2.0,28,Cracovia Kraków,L,40,2.0
18,Korona Kielce,0.0,2.0,29,Zagłębie Lubin,L,40,1.5
16,Korona Kielce,0.0,4.0,30,Piast Gliwice,L,40,1.5
14,Korona Kielce,2.0,0.0,31,Śląsk Wrocław,W,40,1.5
12,Korona Kielce,1.0,2.0,32,Wisła Płock,L,43,1.5
10,Korona Kielce,0.0,2.0,33,Arka Gdynia,L,43,1.0
8,Korona Kielce,0.0,0.0,34,Miedź Legnica,D,43,1.0
6,Korona Kielce,0.0,1.0,35,Wisła Kraków,L,44,1.5
4,Korona Kielce,4.0,2.0,36,Zagłębie Sosnowiec,W,44,1.5
2,Korona Kielce,0.0,3.0,37,Górnik Zabrze,L,47,1.5
1 Team Goals Conceded goals Gameweek Opponent Result Points Form
2 88 Lechia Gdańsk 1.0 0.0 1 Jagiellonia Białystok W 0 0.0
3 86 Lechia Gdańsk 1.0 1.0 2 Śląsk Wrocław D 3 1.0
4 84 Lechia Gdańsk 0.0 0.0 3 Legia Warszawa D 4 1.5
5 82 Lechia Gdańsk 2.0 0.0 4 Miedź Legnica W 5 2.0
6 80 Lechia Gdańsk 2.0 0.0 5 Górnik Zabrze W 8 3.0
7 78 Lechia Gdańsk 3.0 2.0 6 Pogoń Szczecin W 11 4.0
8 76 Lechia Gdańsk 2.0 0.0 7 Korona Kielce W 14 4.0
9 74 Lechia Gdańsk 2.0 5.0 8 Wisła Kraków L 17 4.5
10 72 Lechia Gdańsk 3.0 3.0 9 Zagłębie Lubin D 17 4.0
11 68 Lechia Gdańsk 0.0 1.0 10 Wisła Płock L 18 3.5
12 66 Lechia Gdańsk 4.0 1.0 11 Zagłębie Sosnowiec W 18 2.5
13 64 Lechia Gdańsk 1.0 1.0 12 Piast Gliwice D 21 2.5
14 62 Lechia Gdańsk 2.0 1.0 13 Arka Gdynia W 22 2.0
15 60 Lechia Gdańsk 1.0 0.0 14 Lech Poznań W 25 3.0
16 56 Lechia Gdańsk 1.0 0.0 15 Cracovia Kraków W 28 3.5
17 54 Lechia Gdańsk 3.0 2.0 16 Jagiellonia Białystok W 31 4.5
18 52 Lechia Gdańsk 2.0 0.0 17 Śląsk Wrocław W 34 4.5
19 48 Lechia Gdańsk 0.0 0.0 18 Legia Warszawa D 37 5.0
20 46 Lechia Gdańsk 0.0 0.0 19 Miedź Legnica D 38 4.5
21 44 Lechia Gdańsk 4.0 0.0 20 Górnik Zabrze W 39 4.0
22 40 Lechia Gdańsk 2.0 1.0 21 Pogoń Szczecin W 42 4.0
23 38 Lechia Gdańsk 0.0 0.0 22 Korona Kielce D 45 4.0
24 36 Lechia Gdańsk 1.0 0.0 23 Wisła Kraków W 46 3.5
25 32 Lechia Gdańsk 1.0 2.0 24 Zagłębie Lubin L 49 4.0
26 30 Lechia Gdańsk 1.0 1.0 25 Wisła Płock D 49 3.5
27 28 Lechia Gdańsk 1.0 0.0 26 Zagłębie Sosnowiec W 50 3.0
28 26 Lechia Gdańsk 2.0 0.0 27 Piast Gliwice W 53 3.0
29 24 Lechia Gdańsk 0.0 0.0 28 Arka Gdynia D 56 3.5
30 22 Lechia Gdańsk 1.0 0.0 29 Lech Poznań W 57 3.0
31 18 Lechia Gdańsk 2.0 4.0 30 Cracovia Kraków L 60 4.0
32 16 Lechia Gdańsk 0.0 2.0 31 Piast Gliwice L 60 3.5
33 14 Lechia Gdańsk 4.0 3.0 32 Pogoń Szczecin W 60 2.5
34 12 Lechia Gdańsk 1.0 3.0 33 Legia Warszawa L 63 2.5
35 8 Lechia Gdańsk 0.0 2.0 34 Cracovia Kraków L 63 2.0
36 6 Lechia Gdańsk 1.0 1.0 35 Zagłębie Lubin D 63 1.0
37 4 Lechia Gdańsk 1.0 2.0 36 Lech Poznań L 64 1.5
38 2 Lechia Gdańsk 2.0 0.0 37 Jagiellonia Białystok W 64 1.5
39 92 Lech Poznań 2.0 1.0 1 Wisła Płock W 0 0.0
40 88 Lech Poznań 2.0 0.0 2 Cracovia Kraków W 3 1.0
41 84 Lech Poznań 1.0 0.0 3 Śląsk Wrocław W 6 2.0
42 80 Lech Poznań 4.0 0.0 4 Zagłębie Sosnowiec W 9 3.0
43 76 Lech Poznań 2.0 5.0 5 Wisła Kraków L 12 4.0
44 74 Lech Poznań 1.0 2.0 6 Zagłębie Lubin L 12 4.0
45 72 Lech Poznań 1.0 1.0 7 Piast Gliwice D 12 3.0
46 70 Lech Poznań 0.0 1.0 8 Legia Warszawa L 13 2.5
47 68 Lech Poznań 0.0 1.0 9 Arka Gdynia L 13 1.5
48 64 Lech Poznań 2.0 1.0 10 Miedź Legnica W 13 0.5
49 62 Lech Poznań 2.0 2.0 11 Górnik Zabrze D 16 1.5
50 60 Lech Poznań 2.0 1.0 12 Korona Kielce W 17 2.0
51 58 Lech Poznań 0.0 3.0 13 Pogoń Szczecin L 20 2.5
52 54 Lech Poznań 0.0 1.0 14 Lechia Gdańsk L 20 2.5
53 52 Lech Poznań 2.0 2.0 15 Jagiellonia Białystok D 20 2.5
54 50 Lech Poznań 2.0 1.0 16 Wisła Płock W 21 2.0
55 48 Lech Poznań 0.0 1.0 17 Cracovia Kraków L 24 2.5
56 46 Lech Poznań 2.0 0.0 18 Śląsk Wrocław W 24 1.5
57 44 Lech Poznań 6.0 0.0 19 Zagłębie Sosnowiec W 27 2.5
58 42 Lech Poznań 1.0 0.0 20 Wisła Kraków W 30 3.5
59 34 Lech Poznań 1.0 2.0 21 Zagłębie Lubin L 33 4.0
60 32 Lech Poznań 0.0 4.0 22 Piast Gliwice L 33 3.0
61 30 Lech Poznań 2.0 0.0 23 Legia Warszawa W 33 3.0
62 28 Lech Poznań 1.0 0.0 24 Arka Gdynia W 36 3.0
63 26 Lech Poznań 2.0 3.0 25 Miedź Legnica L 39 3.0
64 24 Lech Poznań 0.0 3.0 26 Górnik Zabrze L 39 2.0
65 22 Lech Poznań 0.0 0.0 27 Korona Kielce D 39 2.0
66 20 Lech Poznań 3.0 2.0 28 Pogoń Szczecin W 40 2.5
67 18 Lech Poznań 0.0 1.0 29 Lechia Gdańsk L 43 2.5
68 16 Lech Poznań 0.0 2.0 30 Jagiellonia Białystok L 43 1.5
69 14 Lech Poznań 3.0 3.0 31 Jagiellonia Białystok D 43 1.5
70 12 Lech Poznań 1.0 0.0 32 Legia Warszawa W 44 2.0
71 10 Lech Poznań 1.0 1.0 33 Pogoń Szczecin D 47 2.5
72 8 Lech Poznań 1.0 1.0 34 Zagłębie Lubin D 48 2.0
73 6 Lech Poznań 0.0 1.0 35 Cracovia Kraków L 49 2.5
74 4 Lech Poznań 2.0 1.0 36 Lechia Gdańsk W 49 2.5
75 2 Lech Poznań 0.0 1.0 37 Piast Gliwice L 52 3.0
76 94 Jagiellonia Białystok 0.0 1.0 1 Lechia Gdańsk L 0 0.0
77 90 Jagiellonia Białystok 2.0 0.0 2 Arka Gdynia W 0 0.0
78 86 Jagiellonia Białystok 1.0 0.0 3 Wisła Kraków W 3 1.0
79 82 Jagiellonia Białystok 2.0 0.0 4 Zagłębie Lubin W 6 2.0
80 78 Jagiellonia Białystok 2.0 1.0 5 Piast Gliwice W 9 3.0
81 76 Jagiellonia Białystok 2.0 3.0 6 Miedź Legnica L 12 4.0
82 74 Jagiellonia Białystok 1.0 1.0 7 Wisła Płock D 12 4.0
83 72 Jagiellonia Białystok 3.0 1.0 8 Cracovia Kraków W 13 3.5
84 70 Jagiellonia Białystok 3.0 1.0 9 Górnik Zabrze W 16 3.5
85 66 Jagiellonia Białystok 0.0 4.0 10 Śląsk Wrocław L 19 3.5
86 64 Jagiellonia Białystok 1.0 1.0 11 Korona Kielce D 19 2.5
87 62 Jagiellonia Białystok 2.0 1.0 12 Pogoń Szczecin W 20 3.0
88 60 Jagiellonia Białystok 1.0 1.0 13 Legia Warszawa D 23 3.5
89 56 Jagiellonia Białystok 4.0 1.0 14 Zagłębie Sosnowiec W 24 3.0
90 54 Jagiellonia Białystok 2.0 2.0 15 Lech Poznań D 27 3.0
91 52 Jagiellonia Białystok 2.0 3.0 16 Lechia Gdańsk L 28 3.5
92 50 Jagiellonia Białystok 3.0 1.0 17 Arka Gdynia W 28 3.0
93 46 Jagiellonia Białystok 2.0 2.0 18 Wisła Kraków D 31 3.0
94 44 Jagiellonia Białystok 0.0 4.0 19 Zagłębie Lubin L 32 3.0
95 42 Jagiellonia Białystok 1.0 1.0 20 Piast Gliwice D 32 2.0
96 40 Jagiellonia Białystok 3.0 0.0 21 Miedź Legnica W 33 2.0
97 38 Jagiellonia Białystok 1.0 0.0 22 Wisła Płock W 36 3.0
98 36 Jagiellonia Białystok 0.0 1.0 23 Cracovia Kraków L 39 3.0
99 34 Jagiellonia Białystok 2.0 2.0 24 Górnik Zabrze D 39 2.5
100 32 Jagiellonia Białystok 0.0 2.0 25 Śląsk Wrocław L 40 3.0
101 28 Jagiellonia Białystok 1.0 3.0 26 Korona Kielce L 40 2.5
102 26 Jagiellonia Białystok 0.0 0.0 27 Pogoń Szczecin D 40 1.5
103 24 Jagiellonia Białystok 0.0 3.0 28 Legia Warszawa L 41 1.0
104 22 Jagiellonia Białystok 2.0 1.0 29 Zagłębie Sosnowiec W 41 1.0
105 18 Jagiellonia Białystok 2.0 0.0 30 Lech Poznań W 44 1.5
106 16 Jagiellonia Białystok 3.0 3.0 31 Lech Poznań D 47 2.5
107 14 Jagiellonia Białystok 1.0 0.0 32 Cracovia Kraków W 48 3.0
108 12 Jagiellonia Białystok 0.0 2.0 33 Zagłębie Lubin L 51 3.5
109 8 Jagiellonia Białystok 4.0 2.0 34 Pogoń Szczecin W 51 3.5
110 6 Jagiellonia Białystok 1.0 2.0 35 Piast Gliwice L 54 3.5
111 4 Jagiellonia Białystok 1.0 0.0 36 Legia Warszawa W 54 2.5
112 2 Jagiellonia Białystok 0.0 2.0 37 Lechia Gdańsk L 57 3.0
113 80 Cracovia Kraków 1.0 3.0 1 Śląsk Wrocław L 0 0.0
114 78 Cracovia Kraków 0.0 2.0 2 Lech Poznań L 0 0.0
115 76 Cracovia Kraków 0.0 0.0 3 Arka Gdynia D 0 0.0
116 74 Cracovia Kraków 1.0 1.0 4 Pogoń Szczecin D 1 0.5
117 72 Cracovia Kraków 0.0 1.0 5 Zagłębie Lubin L 2 1.0
118 70 Cracovia Kraków 1.0 3.0 6 Piast Gliwice L 2 1.0
119 68 Cracovia Kraków 0.0 0.0 7 Legia Warszawa D 2 1.0
120 66 Cracovia Kraków 1.0 3.0 8 Jagiellonia Białystok L 3 1.5
121 64 Cracovia Kraków 3.0 1.0 9 Wisła Płock W 3 1.0
122 60 Cracovia Kraków 1.0 1.0 10 Zagłębie Sosnowiec D 6 1.5
123 58 Cracovia Kraków 0.0 2.0 11 Wisła Kraków L 7 2.0
124 56 Cracovia Kraków 2.0 0.0 12 Górnik Zabrze W 7 2.0
125 54 Cracovia Kraków 1.0 0.0 13 Korona Kielce W 10 2.5
126 50 Cracovia Kraków 0.0 0.0 14 Miedź Legnica D 13 3.5
127 48 Cracovia Kraków 0.0 1.0 15 Lechia Gdańsk L 14 3.0
128 46 Cracovia Kraków 1.0 1.0 16 Śląsk Wrocław D 14 2.5
129 44 Cracovia Kraków 1.0 0.0 17 Lech Poznań W 15 3.0
130 42 Cracovia Kraków 3.0 0.0 18 Arka Gdynia W 18 3.0
131 40 Cracovia Kraków 2.0 1.0 19 Pogoń Szczecin W 21 3.0
132 38 Cracovia Kraków 2.0 1.0 20 Zagłębie Lubin W 24 3.5
133 34 Cracovia Kraków 2.0 1.0 21 Piast Gliwice W 27 4.5
134 32 Cracovia Kraków 2.0 0.0 22 Legia Warszawa W 30 5.0
135 30 Cracovia Kraków 1.0 0.0 23 Jagiellonia Białystok W 33 5.0
136 28 Cracovia Kraków 2.0 3.0 24 Wisła Płock L 36 5.0
137 26 Cracovia Kraków 2.0 1.0 25 Zagłębie Sosnowiec W 36 4.0
138 24 Cracovia Kraków 2.0 3.0 26 Wisła Kraków L 39 4.0
139 22 Cracovia Kraków 1.0 0.0 27 Górnik Zabrze W 39 3.0
140 20 Cracovia Kraków 2.0 1.0 28 Korona Kielce W 42 3.0
141 18 Cracovia Kraków 1.0 2.0 29 Miedź Legnica L 45 3.0
142 16 Cracovia Kraków 4.0 2.0 30 Lechia Gdańsk W 45 3.0
143 14 Cracovia Kraków 0.0 1.0 31 Legia Warszawa L 48 3.0
144 12 Cracovia Kraków 0.0 1.0 32 Jagiellonia Białystok L 48 3.0
145 10 Cracovia Kraków 1.0 3.0 33 Piast Gliwice L 48 2.0
146 8 Cracovia Kraków 2.0 0.0 34 Lechia Gdańsk W 48 1.0
147 6 Cracovia Kraków 1.0 0.0 35 Lech Poznań W 51 2.0
148 4 Cracovia Kraków 2.0 1.0 36 Zagłębie Lubin W 54 2.0
149 2 Cracovia Kraków 0.0 3.0 37 Pogoń Szczecin L 57 3.0
150 76 Pogoń Szczecin 0.0 1.0 1 Miedź Legnica L 0 0.0
151 74 Pogoń Szczecin 0.0 2.0 2 Piast Gliwice L 0 0.0
152 72 Pogoń Szczecin 0.0 3.0 3 Zagłębie Sosnowiec L 0 0.0
153 70 Pogoń Szczecin 1.0 1.0 4 Cracovia Kraków D 0 0.0
154 68 Pogoń Szczecin 0.0 0.0 5 Śląsk Wrocław D 1 0.5
155 66 Pogoń Szczecin 2.0 3.0 6 Lechia Gdańsk L 2 1.0
156 64 Pogoń Szczecin 1.0 1.0 7 Górnik Zabrze D 2 1.0
157 62 Pogoń Szczecin 1.0 1.0 8 Korona Kielce D 3 1.5
158 60 Pogoń Szczecin 2.0 1.0 9 Wisła Kraków W 4 2.0
159 56 Pogoń Szczecin 2.0 0.0 10 Zagłębie Lubin W 7 2.5
160 54 Pogoń Szczecin 4.0 0.0 11 Wisła Płock W 10 3.0
161 52 Pogoń Szczecin 1.0 2.0 12 Jagiellonia Białystok L 13 4.0
162 50 Pogoń Szczecin 3.0 0.0 13 Lech Poznań W 13 3.5
163 48 Pogoń Szczecin 3.0 2.0 14 Arka Gdynia W 16 4.0
164 46 Pogoń Szczecin 2.0 1.0 15 Legia Warszawa W 19 4.0
165 44 Pogoń Szczecin 2.0 0.0 16 Miedź Legnica W 22 4.0
166 42 Pogoń Szczecin 0.0 3.0 17 Piast Gliwice L 25 4.0
167 40 Pogoń Szczecin 1.0 0.0 18 Zagłębie Sosnowiec W 25 4.0
168 38 Pogoń Szczecin 1.0 2.0 19 Cracovia Kraków L 28 4.0
169 36 Pogoń Szczecin 2.0 1.0 20 Śląsk Wrocław W 28 3.0
170 34 Pogoń Szczecin 1.0 2.0 21 Lechia Gdańsk L 31 3.0
171 32 Pogoń Szczecin 3.0 1.0 22 Górnik Zabrze W 31 2.0
172 30 Pogoń Szczecin 1.0 1.0 23 Korona Kielce D 34 3.0
173 28 Pogoń Szczecin 3.0 2.0 24 Wisła Kraków W 35 2.5
174 26 Pogoń Szczecin 0.0 3.0 25 Zagłębie Lubin L 38 3.5
175 24 Pogoń Szczecin 2.0 0.0 26 Wisła Płock W 38 2.5
176 22 Pogoń Szczecin 0.0 0.0 27 Jagiellonia Białystok D 41 3.5
177 20 Pogoń Szczecin 2.0 3.0 28 Lech Poznań L 42 3.0
178 18 Pogoń Szczecin 3.0 3.0 29 Arka Gdynia D 42 2.5
179 16 Pogoń Szczecin 1.0 3.0 30 Legia Warszawa L 43 2.0
180 14 Pogoń Szczecin 3.0 2.0 31 Zagłębie Lubin W 43 2.0
181 12 Pogoń Szczecin 3.0 4.0 32 Lechia Gdańsk L 46 2.0
182 10 Pogoń Szczecin 1.0 1.0 33 Lech Poznań D 46 1.5
183 8 Pogoń Szczecin 2.0 4.0 34 Jagiellonia Białystok L 47 2.0
184 6 Pogoń Szczecin 1.0 1.0 35 Legia Warszawa D 47 1.5
185 4 Pogoń Szczecin 0.0 0.0 36 Piast Gliwice D 48 2.0
186 2 Pogoń Szczecin 3.0 0.0 37 Cracovia Kraków W 49 1.5
187 78 Piast Gliwice 2.0 1.0 1 Zagłębie Sosnowiec W 0 0.0
188 76 Piast Gliwice 2.0 0.0 2 Pogoń Szczecin W 3 1.0
189 74 Piast Gliwice 2.0 1.0 3 Zagłębie Lubin W 6 2.0
190 72 Piast Gliwice 1.0 3.0 4 Legia Warszawa L 9 3.0
191 70 Piast Gliwice 1.0 2.0 5 Jagiellonia Białystok L 9 3.0
192 68 Piast Gliwice 3.0 1.0 6 Cracovia Kraków W 9 3.0
193 66 Piast Gliwice 1.0 1.0 7 Lech Poznań D 12 3.0
194 64 Piast Gliwice 1.0 0.0 8 Arka Gdynia W 13 2.5
195 62 Piast Gliwice 1.0 4.0 9 Śląsk Wrocław L 16 2.5
196 58 Piast Gliwice 1.0 0.0 10 Górnik Zabrze W 16 2.5
197 56 Piast Gliwice 2.0 2.0 11 Miedź Legnica D 19 3.5
198 54 Piast Gliwice 1.0 1.0 12 Lechia Gdańsk D 20 3.0
199 52 Piast Gliwice 1.0 1.0 13 Wisła Płock D 21 3.0
200 48 Piast Gliwice 2.0 0.0 14 Wisła Kraków W 22 2.5
201 46 Piast Gliwice 0.0 1.0 15 Korona Kielce L 25 3.5
202 44 Piast Gliwice 0.0 0.0 16 Zagłębie Sosnowiec D 25 2.5
203 42 Piast Gliwice 3.0 0.0 17 Pogoń Szczecin W 26 2.5
204 40 Piast Gliwice 2.0 2.0 18 Zagłębie Lubin D 29 3.0
205 38 Piast Gliwice 0.0 2.0 19 Legia Warszawa L 30 3.0
206 36 Piast Gliwice 1.0 1.0 20 Jagiellonia Białystok D 30 2.0
207 34 Piast Gliwice 1.0 2.0 21 Cracovia Kraków L 31 2.5
208 32 Piast Gliwice 4.0 0.0 22 Lech Poznań W 31 2.0
209 30 Piast Gliwice 2.0 1.0 23 Arka Gdynia W 34 2.0
210 28 Piast Gliwice 2.0 0.0 24 Śląsk Wrocław W 37 2.5
211 26 Piast Gliwice 2.0 0.0 25 Górnik Zabrze W 40 3.5
212 24 Piast Gliwice 2.0 1.0 26 Miedź Legnica W 43 4.0
213 22 Piast Gliwice 0.0 2.0 27 Lechia Gdańsk L 46 5.0
214 20 Piast Gliwice 1.0 0.0 28 Wisła Płock W 46 4.0
215 18 Piast Gliwice 2.0 2.0 29 Wisła Kraków D 49 4.0
216 16 Piast Gliwice 4.0 0.0 30 Korona Kielce W 50 3.5
217 14 Piast Gliwice 2.0 0.0 31 Lechia Gdańsk W 53 3.5
218 12 Piast Gliwice 1.0 0.0 32 Zagłębie Lubin W 56 3.5
219 10 Piast Gliwice 3.0 1.0 33 Cracovia Kraków W 59 4.5
220 8 Piast Gliwice 1.0 0.0 34 Legia Warszawa W 62 4.5
221 6 Piast Gliwice 2.0 1.0 35 Jagiellonia Białystok W 65 5.0
222 4 Piast Gliwice 0.0 0.0 36 Pogoń Szczecin D 68 5.0
223 2 Piast Gliwice 1.0 0.0 37 Lech Poznań W 69 4.5
224 74 Zagłębie Lubin 3.0 1.0 1 Legia Warszawa W 0 0.0
225 72 Zagłębie Lubin 2.0 1.0 2 Zagłębie Sosnowiec W 3 1.0
226 70 Zagłębie Lubin 1.0 2.0 3 Piast Gliwice L 6 2.0
227 68 Zagłębie Lubin 0.0 2.0 4 Jagiellonia Białystok L 6 2.0
228 66 Zagłębie Lubin 1.0 0.0 5 Cracovia Kraków W 6 2.0
229 64 Zagłębie Lubin 2.0 1.0 6 Lech Poznań W 9 3.0
230 62 Zagłębie Lubin 0.0 2.0 7 Miedź Legnica L 12 3.0
231 60 Zagłębie Lubin 4.0 0.0 8 Śląsk Wrocław W 12 2.0
232 58 Zagłębie Lubin 3.0 3.0 9 Lechia Gdańsk D 15 3.0
233 56 Zagłębie Lubin 0.0 2.0 10 Pogoń Szczecin L 16 3.5
234 54 Zagłębie Lubin 1.0 3.0 11 Arka Gdynia L 16 2.5
235 52 Zagłębie Lubin 3.0 3.0 12 Wisła Płock D 16 1.5
236 50 Zagłębie Lubin 0.0 2.0 13 Górnik Zabrze L 17 2.0
237 48 Zagłębie Lubin 0.0 1.0 14 Korona Kielce L 17 1.0
238 46 Zagłębie Lubin 2.0 3.0 15 Wisła Kraków L 17 0.5
239 44 Zagłębie Lubin 0.0 1.0 16 Legia Warszawa L 17 0.5
240 42 Zagłębie Lubin 2.0 1.0 17 Zagłębie Sosnowiec W 17 0.5
241 40 Zagłębie Lubin 2.0 2.0 18 Piast Gliwice D 20 1.0
242 38 Zagłębie Lubin 4.0 0.0 19 Jagiellonia Białystok W 21 1.5
243 36 Zagłębie Lubin 1.0 2.0 20 Cracovia Kraków L 24 2.5
244 34 Zagłębie Lubin 2.0 1.0 21 Lech Poznań W 24 2.5
245 32 Zagłębie Lubin 3.0 0.0 22 Miedź Legnica W 27 3.5
246 30 Zagłębie Lubin 0.0 2.0 23 Śląsk Wrocław L 30 3.5
247 28 Zagłębie Lubin 2.0 1.0 24 Lechia Gdańsk W 30 3.0
248 26 Zagłębie Lubin 3.0 0.0 25 Pogoń Szczecin W 33 3.0
249 24 Zagłębie Lubin 0.0 0.0 26 Arka Gdynia D 36 4.0
250 22 Zagłębie Lubin 1.0 0.0 27 Wisła Płock W 37 3.5
251 20 Zagłębie Lubin 1.0 1.0 28 Górnik Zabrze D 40 3.5
252 18 Zagłębie Lubin 2.0 0.0 29 Korona Kielce W 41 4.0
253 16 Zagłębie Lubin 3.0 1.0 30 Wisła Kraków W 44 4.0
254 14 Zagłębie Lubin 2.0 3.0 31 Pogoń Szczecin L 47 4.0
255 12 Zagłębie Lubin 0.0 1.0 32 Piast Gliwice L 47 3.5
256 10 Zagłębie Lubin 2.0 0.0 33 Jagiellonia Białystok W 47 2.5
257 8 Zagłębie Lubin 1.0 1.0 34 Lech Poznań D 50 3.0
258 6 Zagłębie Lubin 1.0 1.0 35 Lechia Gdańsk D 51 2.5
259 4 Zagłębie Lubin 1.0 2.0 36 Cracovia Kraków L 52 2.0
260 2 Zagłębie Lubin 2.0 2.0 37 Legia Warszawa D 52 2.0
261 90 Legia Warszawa 1.0 3.0 1 Zagłębie Lubin L 0 0.0
262 86 Legia Warszawa 2.0 1.0 2 Korona Kielce W 0 0.0
263 82 Legia Warszawa 0.0 0.0 3 Lechia Gdańsk D 3 1.0
264 78 Legia Warszawa 3.0 1.0 4 Piast Gliwice W 4 1.5
265 74 Legia Warszawa 2.0 1.0 5 Zagłębie Sosnowiec W 7 2.5
266 72 Legia Warszawa 1.0 4.0 6 Wisła Płock L 10 3.5
267 70 Legia Warszawa 0.0 0.0 7 Cracovia Kraków D 10 3.5
268 68 Legia Warszawa 1.0 0.0 8 Lech Poznań W 11 3.0
269 66 Legia Warszawa 4.0 1.0 9 Miedź Legnica W 14 3.5
270 62 Legia Warszawa 1.0 1.0 10 Arka Gdynia D 17 3.5
271 60 Legia Warszawa 1.0 0.0 11 Śląsk Wrocław W 18 3.0
272 58 Legia Warszawa 3.0 3.0 12 Wisła Kraków D 21 4.0
273 56 Legia Warszawa 1.0 1.0 13 Jagiellonia Białystok D 22 4.0
274 52 Legia Warszawa 4.0 0.0 14 Górnik Zabrze W 23 3.5
275 50 Legia Warszawa 1.0 2.0 15 Pogoń Szczecin L 26 3.5
276 48 Legia Warszawa 1.0 0.0 16 Zagłębie Lubin W 26 3.0
277 46 Legia Warszawa 3.0 0.0 17 Korona Kielce W 29 3.0
278 42 Legia Warszawa 0.0 0.0 18 Lechia Gdańsk D 32 3.5
279 40 Legia Warszawa 2.0 0.0 19 Piast Gliwice W 33 3.5
280 38 Legia Warszawa 3.0 2.0 20 Zagłębie Sosnowiec W 36 3.5
281 36 Legia Warszawa 1.0 0.0 21 Wisła Płock W 39 4.5
282 34 Legia Warszawa 0.0 2.0 22 Cracovia Kraków L 42 4.5
283 32 Legia Warszawa 0.0 2.0 23 Lech Poznań L 42 3.5
284 30 Legia Warszawa 2.0 0.0 24 Miedź Legnica W 42 3.0
285 28 Legia Warszawa 2.0 1.0 25 Arka Gdynia W 45 3.0
286 24 Legia Warszawa 1.0 0.0 26 Śląsk Wrocław W 48 3.0
287 22 Legia Warszawa 0.0 4.0 27 Wisła Kraków L 51 3.0
288 20 Legia Warszawa 3.0 0.0 28 Jagiellonia Białystok W 51 3.0
289 18 Legia Warszawa 2.0 1.0 29 Górnik Zabrze W 54 4.0
290 16 Legia Warszawa 3.0 1.0 30 Pogoń Szczecin W 57 4.0
291 14 Legia Warszawa 1.0 0.0 31 Cracovia Kraków W 60 4.0
292 12 Legia Warszawa 0.0 1.0 32 Lech Poznań L 63 4.0
293 10 Legia Warszawa 3.0 1.0 33 Lechia Gdańsk W 63 4.0
294 8 Legia Warszawa 0.0 1.0 34 Piast Gliwice L 66 4.0
295 6 Legia Warszawa 1.0 1.0 35 Pogoń Szczecin D 66 3.0
296 4 Legia Warszawa 0.0 1.0 36 Jagiellonia Białystok L 67 2.5
297 2 Legia Warszawa 2.0 2.0 37 Zagłębie Lubin D 67 1.5
298 84 Górnik Zabrze 1.0 1.0 1 Korona Kielce D 0 0.0
299 80 Górnik Zabrze 1.0 1.0 2 Wisła Płock D 1 0.5
300 76 Górnik Zabrze 3.0 1.0 3 Miedź Legnica W 2 1.0
301 74 Górnik Zabrze 1.0 1.0 4 Arka Gdynia D 5 2.0
302 72 Górnik Zabrze 0.0 2.0 5 Lechia Gdańsk L 6 2.5
303 70 Górnik Zabrze 0.0 3.0 6 Wisła Kraków L 6 2.5
304 68 Górnik Zabrze 1.0 1.0 7 Pogoń Szczecin D 6 2.0
305 66 Górnik Zabrze 1.0 1.0 8 Zagłębie Sosnowiec D 7 2.0
306 64 Górnik Zabrze 1.0 3.0 9 Jagiellonia Białystok L 8 1.5
307 62 Górnik Zabrze 0.0 1.0 10 Piast Gliwice L 8 1.0
308 60 Górnik Zabrze 2.0 2.0 11 Lech Poznań D 8 1.0
309 58 Górnik Zabrze 0.0 2.0 12 Cracovia Kraków L 9 1.5
310 56 Górnik Zabrze 2.0 0.0 13 Zagłębie Lubin W 9 1.0
311 52 Górnik Zabrze 0.0 4.0 14 Legia Warszawa L 12 1.5
312 50 Górnik Zabrze 2.0 2.0 15 Śląsk Wrocław D 12 1.5
313 48 Górnik Zabrze 2.0 4.0 16 Korona Kielce L 13 2.0
314 46 Górnik Zabrze 4.0 0.0 17 Wisła Płock W 13 1.5
315 42 Górnik Zabrze 1.0 3.0 18 Miedź Legnica L 16 2.5
316 40 Górnik Zabrze 1.0 1.0 19 Arka Gdynia D 16 1.5
317 38 Górnik Zabrze 0.0 4.0 20 Lechia Gdańsk L 17 2.0
318 36 Górnik Zabrze 2.0 0.0 21 Wisła Kraków W 17 1.5
319 34 Górnik Zabrze 1.0 3.0 22 Pogoń Szczecin L 20 2.5
320 32 Górnik Zabrze 2.0 1.0 23 Zagłębie Sosnowiec W 20 1.5
321 28 Górnik Zabrze 2.0 2.0 24 Jagiellonia Białystok D 23 2.5
322 26 Górnik Zabrze 0.0 2.0 25 Piast Gliwice L 24 2.5
323 24 Górnik Zabrze 3.0 0.0 26 Lech Poznań W 24 2.5
324 22 Górnik Zabrze 0.0 1.0 27 Cracovia Kraków L 27 2.5
325 20 Górnik Zabrze 1.0 1.0 28 Zagłębie Lubin D 27 2.5
326 18 Górnik Zabrze 1.0 2.0 29 Legia Warszawa L 28 2.0
327 16 Górnik Zabrze 1.0 0.0 30 Śląsk Wrocław W 28 1.5
328 14 Górnik Zabrze 1.0 0.0 31 Arka Gdynia W 31 2.5
329 12 Górnik Zabrze 2.0 1.0 32 Śląsk Wrocław W 34 2.5
330 10 Górnik Zabrze 4.0 0.0 33 Zagłębie Sosnowiec W 37 3.5
331 8 Górnik Zabrze 1.0 2.0 34 Wisła Kraków L 40 4.0
332 6 Górnik Zabrze 1.0 0.0 35 Miedź Legnica W 40 4.0
333 4 Górnik Zabrze 0.0 1.0 36 Wisła Płock L 43 4.0
334 2 Górnik Zabrze 3.0 0.0 37 Korona Kielce W 43 3.0
335 80 Śląsk Wrocław 3.0 1.0 1 Cracovia Kraków W 0 0.0
336 78 Śląsk Wrocław 1.0 1.0 2 Lechia Gdańsk D 3 1.0
337 76 Śląsk Wrocław 0.0 1.0 3 Lech Poznań L 4 1.5
338 74 Śląsk Wrocław 1.0 2.0 4 Korona Kielce L 4 1.5
339 72 Śląsk Wrocław 0.0 0.0 5 Pogoń Szczecin D 4 1.5
340 70 Śląsk Wrocław 3.0 3.0 6 Zagłębie Sosnowiec D 5 2.0
341 68 Śląsk Wrocław 0.0 1.0 7 Wisła Kraków L 6 1.5
342 66 Śląsk Wrocław 0.0 4.0 8 Zagłębie Lubin L 6 1.0
343 64 Śląsk Wrocław 4.0 1.0 9 Piast Gliwice W 6 1.0
344 60 Śląsk Wrocław 4.0 0.0 10 Jagiellonia Białystok W 9 2.0
345 58 Śląsk Wrocław 0.0 1.0 11 Legia Warszawa L 12 2.5
346 56 Śląsk Wrocław 1.0 2.0 12 Arka Gdynia L 12 2.0
347 54 Śląsk Wrocław 5.0 0.0 13 Miedź Legnica W 12 2.0
348 50 Śląsk Wrocław 0.0 3.0 14 Wisła Płock L 15 3.0
349 48 Śląsk Wrocław 2.0 2.0 15 Górnik Zabrze D 15 2.0
350 46 Śląsk Wrocław 1.0 1.0 16 Cracovia Kraków D 16 1.5
351 44 Śląsk Wrocław 0.0 2.0 17 Lechia Gdańsk L 17 2.0
352 40 Śląsk Wrocław 0.0 2.0 18 Lech Poznań L 17 2.0
353 38 Śląsk Wrocław 1.0 1.0 19 Korona Kielce D 17 1.0
354 36 Śląsk Wrocław 1.0 2.0 20 Pogoń Szczecin L 18 1.5
355 34 Śląsk Wrocław 2.0 0.0 21 Zagłębie Sosnowiec W 18 1.0
356 32 Śląsk Wrocław 0.0 1.0 22 Wisła Kraków L 21 1.5
357 30 Śląsk Wrocław 2.0 0.0 23 Zagłębie Lubin W 21 1.5
358 28 Śląsk Wrocław 0.0 2.0 24 Piast Gliwice L 24 2.5
359 26 Śląsk Wrocław 2.0 0.0 25 Jagiellonia Białystok W 24 2.0
360 24 Śląsk Wrocław 0.0 1.0 26 Legia Warszawa L 27 3.0
361 22 Śląsk Wrocław 2.0 0.0 27 Arka Gdynia W 27 2.0
362 20 Śląsk Wrocław 0.0 0.0 28 Miedź Legnica D 30 3.0
363 18 Śląsk Wrocław 0.0 2.0 29 Wisła Płock L 31 2.5
364 16 Śląsk Wrocław 0.0 1.0 30 Górnik Zabrze L 31 2.5
365 14 Śląsk Wrocław 0.0 2.0 31 Korona Kielce L 31 1.5
366 12 Śląsk Wrocław 1.0 2.0 32 Górnik Zabrze L 31 1.5
367 10 Śląsk Wrocław 1.0 1.0 33 Wisła Kraków D 31 0.5
368 8 Śląsk Wrocław 4.0 2.0 34 Zagłębie Sosnowiec W 32 0.5
369 6 Śląsk Wrocław 2.0 1.0 35 Wisła Płock W 35 1.5
370 4 Śląsk Wrocław 2.0 0.0 36 Miedź Legnica W 38 2.5
371 2 Śląsk Wrocław 4.0 0.0 37 Arka Gdynia W 41 3.5
372 82 Wisła Płock 1.0 2.0 1 Lech Poznań L 0 0.0
373 80 Wisła Płock 1.0 1.0 2 Górnik Zabrze D 0 0.0
374 78 Wisła Płock 1.0 2.0 3 Korona Kielce L 1 0.5
375 76 Wisła Płock 1.0 1.0 4 Wisła Kraków D 1 0.5
376 74 Wisła Płock 1.0 3.0 5 Arka Gdynia L 2 1.0
377 72 Wisła Płock 4.0 1.0 6 Legia Warszawa W 2 1.0
378 70 Wisła Płock 1.0 1.0 7 Jagiellonia Białystok D 5 2.0
379 68 Wisła Płock 2.0 2.0 8 Miedź Legnica D 6 2.0
380 66 Wisła Płock 1.0 3.0 9 Cracovia Kraków L 7 2.5
381 64 Wisła Płock 1.0 0.0 10 Lechia Gdańsk W 7 2.0
382 62 Wisła Płock 0.0 4.0 11 Pogoń Szczecin L 10 3.0
383 60 Wisła Płock 3.0 3.0 12 Zagłębie Lubin D 10 2.0
384 58 Wisła Płock 1.0 1.0 13 Piast Gliwice D 11 2.0
385 56 Wisła Płock 3.0 0.0 14 Śląsk Wrocław W 12 2.0
386 54 Wisła Płock 2.0 0.0 15 Zagłębie Sosnowiec W 15 3.0
387 52 Wisła Płock 1.0 2.0 16 Lech Poznań L 18 3.0
388 50 Wisła Płock 0.0 4.0 17 Górnik Zabrze L 18 3.0
389 46 Wisła Płock 2.0 2.0 18 Korona Kielce D 18 2.5
390 44 Wisła Płock 1.0 2.0 19 Wisła Kraków L 19 2.5
391 42 Wisła Płock 3.0 3.0 20 Arka Gdynia D 19 1.5
392 36 Wisła Płock 0.0 1.0 21 Legia Warszawa L 20 1.0
393 34 Wisła Płock 0.0 1.0 22 Jagiellonia Białystok L 20 1.0
394 32 Wisła Płock 1.0 2.0 23 Miedź Legnica L 20 1.0
395 30 Wisła Płock 3.0 2.0 24 Cracovia Kraków W 20 0.5
396 28 Wisła Płock 1.0 1.0 25 Lechia Gdańsk D 23 1.5
397 26 Wisła Płock 0.0 2.0 26 Pogoń Szczecin L 24 1.5
398 24 Wisła Płock 0.0 1.0 27 Zagłębie Lubin L 24 1.5
399 22 Wisła Płock 0.0 1.0 28 Piast Gliwice L 24 1.5
400 20 Wisła Płock 2.0 0.0 29 Śląsk Wrocław W 24 1.5
401 18 Wisła Płock 3.0 1.0 30 Zagłębie Sosnowiec W 27 1.5
402 16 Wisła Płock 3.0 2.0 31 Wisła Kraków W 30 2.0
403 14 Wisła Płock 2.0 1.0 32 Korona Kielce W 33 3.0
404 12 Wisła Płock 2.0 3.0 33 Miedź Legnica L 36 4.0
405 10 Wisła Płock 1.0 1.0 34 Arka Gdynia D 36 4.0
406 8 Wisła Płock 1.0 2.0 35 Śląsk Wrocław L 37 3.5
407 6 Wisła Płock 1.0 0.0 36 Górnik Zabrze W 37 2.5
408 4 Wisła Płock 0.0 0.0 37 Zagłębie Sosnowiec D 40 2.5
409 76 Arka Gdynia 0.0 0.0 1 Wisła Kraków D 0 0.0
410 74 Arka Gdynia 0.0 2.0 2 Jagiellonia Białystok L 1 0.5
411 72 Arka Gdynia 0.0 0.0 3 Cracovia Kraków D 1 0.5
412 70 Arka Gdynia 1.0 1.0 4 Górnik Zabrze D 2 1.0
413 68 Arka Gdynia 3.0 1.0 5 Wisła Płock W 3 1.5
414 66 Arka Gdynia 1.0 2.0 6 Korona Kielce L 6 2.5
415 64 Arka Gdynia 2.0 2.0 7 Zagłębie Sosnowiec D 6 2.0
416 62 Arka Gdynia 0.0 1.0 8 Piast Gliwice L 7 2.5
417 60 Arka Gdynia 1.0 0.0 9 Lech Poznań W 7 2.0
418 58 Arka Gdynia 1.0 1.0 10 Legia Warszawa D 10 2.5
419 56 Arka Gdynia 3.0 1.0 11 Zagłębie Lubin W 11 2.0
420 54 Arka Gdynia 2.0 1.0 12 Śląsk Wrocław W 14 3.0
421 52 Arka Gdynia 1.0 2.0 13 Lechia Gdańsk L 17 3.5
422 50 Arka Gdynia 2.0 3.0 14 Pogoń Szczecin L 17 3.5
423 48 Arka Gdynia 4.0 0.0 15 Miedź Legnica W 17 2.5
424 46 Arka Gdynia 4.0 1.0 16 Wisła Kraków W 20 3.0
425 44 Arka Gdynia 1.0 3.0 17 Jagiellonia Białystok L 23 3.0
426 40 Arka Gdynia 0.0 3.0 18 Cracovia Kraków L 23 2.0
427 38 Arka Gdynia 1.0 1.0 19 Górnik Zabrze D 23 2.0
428 36 Arka Gdynia 3.0 3.0 20 Wisła Płock D 24 2.5
429 34 Arka Gdynia 1.0 2.0 21 Korona Kielce L 25 2.0
430 32 Arka Gdynia 2.0 3.0 22 Zagłębie Sosnowiec L 25 1.0
431 30 Arka Gdynia 1.0 2.0 23 Piast Gliwice L 25 1.0
432 28 Arka Gdynia 0.0 1.0 24 Lech Poznań L 25 1.0
433 26 Arka Gdynia 1.0 2.0 25 Legia Warszawa L 25 0.5
434 24 Arka Gdynia 0.0 0.0 26 Zagłębie Lubin D 25 0.0
435 22 Arka Gdynia 0.0 2.0 27 Śląsk Wrocław L 26 0.5
436 20 Arka Gdynia 0.0 0.0 28 Lechia Gdańsk D 26 0.5
437 18 Arka Gdynia 3.0 3.0 29 Pogoń Szczecin D 27 1.0
438 16 Arka Gdynia 1.0 1.0 30 Miedź Legnica D 28 1.5
439 14 Arka Gdynia 0.0 1.0 31 Górnik Zabrze L 29 2.0
440 12 Arka Gdynia 2.0 0.0 32 Miedź Legnica W 29 1.5
441 10 Arka Gdynia 2.0 0.0 33 Korona Kielce W 32 2.5
442 8 Arka Gdynia 1.0 1.0 34 Wisła Płock D 35 3.0
443 6 Arka Gdynia 2.0 0.0 35 Zagłębie Sosnowiec W 36 3.0
444 4 Arka Gdynia 3.0 1.0 36 Wisła Kraków W 39 3.5
445 2 Arka Gdynia 0.0 4.0 37 Śląsk Wrocław L 42 4.5
446 76 Wisła Kraków 0.0 0.0 1 Arka Gdynia D 0 0.0
447 74 Wisła Kraków 2.0 1.0 2 Miedź Legnica W 1 0.5
448 72 Wisła Kraków 0.0 1.0 3 Jagiellonia Białystok L 4 1.5
449 70 Wisła Kraków 1.0 1.0 4 Wisła Płock D 4 1.5
450 68 Wisła Kraków 5.0 2.0 5 Lech Poznań W 5 2.0
451 66 Wisła Kraków 3.0 0.0 6 Górnik Zabrze W 8 3.0
452 64 Wisła Kraków 1.0 0.0 7 Śląsk Wrocław W 11 3.5
453 62 Wisła Kraków 5.0 2.0 8 Lechia Gdańsk W 14 3.5
454 60 Wisła Kraków 1.0 2.0 9 Pogoń Szczecin L 17 4.5
455 56 Wisła Kraków 0.0 1.0 10 Korona Kielce L 17 4.0
456 54 Wisła Kraków 2.0 0.0 11 Cracovia Kraków W 17 3.0
457 52 Wisła Kraków 3.0 3.0 12 Legia Warszawa D 20 3.0
458 50 Wisła Kraków 2.0 2.0 13 Zagłębie Sosnowiec D 21 2.5
459 48 Wisła Kraków 0.0 2.0 14 Piast Gliwice L 22 2.0
460 46 Wisła Kraków 3.0 2.0 15 Zagłębie Lubin W 22 2.0
461 44 Wisła Kraków 1.0 4.0 16 Arka Gdynia L 25 3.0
462 42 Wisła Kraków 0.0 2.0 17 Miedź Legnica L 25 2.0
463 40 Wisła Kraków 2.0 2.0 18 Jagiellonia Białystok D 25 1.5
464 38 Wisła Kraków 2.0 1.0 19 Wisła Płock W 26 1.5
465 36 Wisła Kraków 0.0 1.0 20 Lech Poznań L 29 2.5
466 34 Wisła Kraków 0.0 2.0 21 Górnik Zabrze L 29 1.5
467 32 Wisła Kraków 1.0 0.0 22 Śląsk Wrocław W 29 1.5
468 30 Wisła Kraków 0.0 1.0 23 Lechia Gdańsk L 32 2.5
469 28 Wisła Kraków 2.0 3.0 24 Pogoń Szczecin L 32 2.0
470 26 Wisła Kraków 6.0 2.0 25 Korona Kielce W 32 1.0
471 24 Wisła Kraków 3.0 2.0 26 Cracovia Kraków W 35 2.0
472 22 Wisła Kraków 4.0 0.0 27 Legia Warszawa W 38 3.0
473 20 Wisła Kraków 3.0 4.0 28 Zagłębie Sosnowiec L 41 3.0
474 18 Wisła Kraków 2.0 2.0 29 Piast Gliwice D 41 3.0
475 16 Wisła Kraków 1.0 3.0 30 Zagłębie Lubin L 42 3.5
476 14 Wisła Kraków 2.0 3.0 31 Wisła Płock L 42 2.5
477 12 Wisła Kraków 1.0 2.0 32 Zagłębie Sosnowiec L 42 1.5
478 10 Wisła Kraków 1.0 1.0 33 Śląsk Wrocław D 42 0.5
479 8 Wisła Kraków 2.0 1.0 34 Górnik Zabrze W 43 1.0
480 6 Wisła Kraków 1.0 0.0 35 Korona Kielce W 46 1.5
481 4 Wisła Kraków 1.0 3.0 36 Arka Gdynia L 49 2.5
482 2 Wisła Kraków 4.0 5.0 37 Miedź Legnica L 49 2.5
483 74 Korona Kielce 1.0 1.0 1 Górnik Zabrze D 0 0.0
484 72 Korona Kielce 1.0 2.0 2 Legia Warszawa L 1 0.5
485 70 Korona Kielce 2.0 1.0 3 Wisła Płock W 1 0.5
486 68 Korona Kielce 2.0 1.0 4 Śląsk Wrocław W 4 1.5
487 66 Korona Kielce 1.0 1.0 5 Miedź Legnica D 7 2.5
488 64 Korona Kielce 2.0 1.0 6 Arka Gdynia W 8 3.0
489 62 Korona Kielce 0.0 2.0 7 Lechia Gdańsk L 11 3.5
490 60 Korona Kielce 1.0 1.0 8 Pogoń Szczecin D 11 3.5
491 58 Korona Kielce 3.0 1.0 9 Zagłębie Sosnowiec W 12 3.0
492 56 Korona Kielce 1.0 0.0 10 Wisła Kraków W 15 3.0
493 54 Korona Kielce 1.0 1.0 11 Jagiellonia Białystok D 18 3.5
494 52 Korona Kielce 1.0 2.0 12 Lech Poznań L 19 3.0
495 50 Korona Kielce 0.0 1.0 13 Cracovia Kraków L 19 3.0
496 48 Korona Kielce 1.0 0.0 14 Zagłębie Lubin W 19 2.5
497 46 Korona Kielce 1.0 0.0 15 Piast Gliwice W 22 2.5
498 44 Korona Kielce 4.0 2.0 16 Górnik Zabrze W 25 2.5
499 42 Korona Kielce 0.0 3.0 17 Legia Warszawa L 28 3.0
500 40 Korona Kielce 2.0 2.0 18 Wisła Płock D 28 3.0
501 38 Korona Kielce 1.0 1.0 19 Śląsk Wrocław D 29 3.5
502 36 Korona Kielce 0.0 0.0 20 Miedź Legnica D 30 3.0
503 34 Korona Kielce 2.0 1.0 21 Arka Gdynia W 31 2.5
504 32 Korona Kielce 0.0 0.0 22 Lechia Gdańsk D 34 2.5
505 30 Korona Kielce 1.0 1.0 23 Pogoń Szczecin D 35 3.0
506 28 Korona Kielce 1.0 4.0 24 Zagłębie Sosnowiec L 36 3.0
507 26 Korona Kielce 2.0 6.0 25 Wisła Kraków L 36 2.5
508 24 Korona Kielce 3.0 1.0 26 Jagiellonia Białystok W 36 2.0
509 22 Korona Kielce 0.0 0.0 27 Lech Poznań D 39 2.0
510 20 Korona Kielce 1.0 2.0 28 Cracovia Kraków L 40 2.0
511 18 Korona Kielce 0.0 2.0 29 Zagłębie Lubin L 40 1.5
512 16 Korona Kielce 0.0 4.0 30 Piast Gliwice L 40 1.5
513 14 Korona Kielce 2.0 0.0 31 Śląsk Wrocław W 40 1.5
514 12 Korona Kielce 1.0 2.0 32 Wisła Płock L 43 1.5
515 10 Korona Kielce 0.0 2.0 33 Arka Gdynia L 43 1.0
516 8 Korona Kielce 0.0 0.0 34 Miedź Legnica D 43 1.0
517 6 Korona Kielce 0.0 1.0 35 Wisła Kraków L 44 1.5
518 4 Korona Kielce 4.0 2.0 36 Zagłębie Sosnowiec W 44 1.5
519 2 Korona Kielce 0.0 3.0 37 Górnik Zabrze L 47 1.5

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// This is a simple example to test rules with the same consequent terms.
// A system with rules 1, 2 and 3 below should be the same
// as a system with the single rule 123.
// An entire system (variables + rules) lives in a function-block:
FUNCTION_BLOCK test_multiple_rules_same_consequent_term
// Input variables are (x1, x2), output is y
FUZZIFY x1
RANGE := (0 .. 2.1) WITH 0.01 // Hacked the FCL syntax a bit here
TERM label0 := Triangle 0.2 0.2 0.6
TERM label1 := Triangle 0.2 0.6 1.0
TERM label2 := Triangle 0.6 1.0 1.4
TERM label3 := Triangle 1.0 1.4 1.8
TERM label4 := Triangle 1.4 1.8 1.8
END_FUZZIFY
FUZZIFY x2
RANGE := (0 .. 2.1) WITH 0.01
TERM label0 := Triangle 0.0 0.0 0.45
TERM label1 := Triangle 0.0 0.45 0.9
TERM label2 := Triangle 0.45 0.9 1.35
TERM label3 := Triangle 0.9 1.35 1.8
TERM label4 := Triangle 1.35 1.8 1.8
END_FUZZIFY
DEFUZZIFY y
RANGE := (0 .. 2.1) WITH 0.01
TERM label0 := Triangle 0.3 0.3 0.725
TERM label1 := Triangle 0.3 0.725 1.15
TERM label2 := Triangle 0.725 1.15 1.575
TERM label3 := Triangle 1.15 1.575 2.0
TERM label4 := Triangle 1.575 2.0 2.0
// You could set other options here, e.g. defuzzification method.
END_DEFUZZIFY
// Rule-blocks are sets of rules, with an optional name.
// These are mostly useful for global options (e.g. accumulation etc.)
// but here I just use blocks to break up the rules into separate lists.
RULEBLOCK // Name of rule-block is optional
RULE 1: IF x1 is label0 AND x2 is label2 THEN y is label0
RULE 2: IF x1 is label1 AND x2 is label0 THEN y is label0
RULE 3: IF x1 is label1 AND x2 is label2 THEN y is label0
END_RULEBLOCK
// If you give a block name it will be prefixed to the rule names.
RULEBLOCK extra // ... so these rule names will be prefixed with "extra."
RULE 123:
IF x1 is label0 AND x2 is label2
OR x1 is label1 AND x2 is label0
OR x1 is label1 AND x2 is label2
THEN y is label0
END_RULEBLOCK
RULEBLOCK // Can have as many rule-blocks as you like
RULE 4: IF x1 is label2 AND x2 is label1 THEN y is label2
RULE 5: IF x1 is label2 AND x2 is label3 THEN y is label3
RULE 6: IF x1 is label4 AND x2 is label4 THEN y is label4
END_RULEBLOCK
END_FUNCTION_BLOCK

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from __future__ import division
import sys
import numpy as np
import numpy.testing as tst
import networkx
import nose
import skfuzzy as fuzz
import skfuzzy.control as ctrl
def test_tipping_problem():
# The full tipping problem uses many of these methods
food = ctrl.Antecedent(np.linspace(0, 10, 11), 'quality')
service = ctrl.Antecedent(np.linspace(0, 10, 11), 'service')
tip = ctrl.Consequent(np.linspace(0, 25, 26), 'tip')
food.automf(3)
service.automf(3)
# Manual membership function definition
tip['bad'] = fuzz.trimf(tip.universe, [0, 0, 13])
tip['middling'] = fuzz.trimf(tip.universe, [0, 13, 25])
tip['lots'] = fuzz.trimf(tip.universe, [13, 25, 25])
# Define fuzzy rules
rule1 = ctrl.Rule(food['poor'] | service['poor'], tip['bad'])
rule2 = ctrl.Rule(service['average'], tip['middling'])
rule3 = ctrl.Rule(service['good'] | food['good'], tip['lots'])
# The control system - defined both possible ways
tipping = ctrl.ControlSystem([rule1, rule2, rule3])
tipping2 = ctrl.ControlSystem(rule1)
tipping2.addrule(rule2)
tipping2.addrule(rule3)
tip_sim = ctrl.ControlSystemSimulation(tipping)
tip_sim2 = ctrl.ControlSystemSimulation(tipping2)
# Inputs added both possible ways
inputs = {'quality': 6.5, 'service': 9.8}
for key, value in inputs.items():
tip_sim.input[key] = value
tip_sim2.inputs(inputs)
# Compute the system
tip_sim.compute()
tip_sim2.compute()
# Ensure both methods of defining rules yield the same results
for val0, val1 in zip(tip_sim.output.values(),
tip_sim2.output.values()):
tst.assert_allclose(val0, val1)
# Verify against manual computation
tst.assert_allclose(tip_sim.output['tip'], 19.8578, atol=1e-2, rtol=1e-2)
def setup_rule_order():
global a, b, c, d
a = ctrl.Antecedent(np.linspace(0, 10, 11), 'a')
b = ctrl.Antecedent(np.linspace(0, 10, 11), 'b')
c = ctrl.Antecedent(np.linspace(0, 10, 11), 'c')
d = ctrl.Antecedent(np.linspace(0, 10, 11), 'd')
for v in (a, b, c, d):
v.automf(3)
@tst.decorators.skipif(float(networkx.__version__) >= 2.0)
@nose.with_setup(setup_rule_order)
def test_rule_order():
# Make sure rules are exposed in the order needed to solve them
# correctly
global a, b, c, d
r1 = ctrl.Rule(a['average'] | a['poor'], c['poor'], label='r1')
r2 = ctrl.Rule(c['poor'] | b['poor'], c['good'], label='r2')
r3 = ctrl.Rule(c['good'] | a['good'], d['good'], label='r3')
ctrl_sys = ctrl.ControlSystem([r1, r2, r3])
resolved = [r for r in ctrl_sys.rules]
assert resolved == [r1, r2, r3], "Order given was: {0}, expected {1}".format(
resolved, [r1.label, r2.label, r3.label])
# The assert_raises decorator does not work in Python 2.6
@tst.decorators.skipif(
(sys.version_info < (2, 7)) or (float(networkx.__version__) >= 2.0))
@nose.with_setup(setup_rule_order)
def test_unresolvable_rule_order():
# Make sure we don't get suck in an infinite loop when the user
# gives an unresolvable rule order
global a, b, c, d
r1 = ctrl.Rule(a['average'] | a['poor'], c['poor'], label='r1')
r2 = ctrl.Rule(c['poor'] | b['poor'], c['poor'], label='r2')
r3 = ctrl.Rule(c['good'] | a['good'], d['good'], label='r3')
ex_msg = "Unable to resolve rule execution order"
with tst.assert_raises(RuntimeError, expected_regexp=ex_msg):
ctrl_sys = ctrl.ControlSystem([r1, r2, r3])
list(ctrl_sys.rules)
@nose.with_setup(setup_rule_order)
def test_bad_rules():
not_rules = ['me', 192238, 42, dict()]
tst.assert_raises(ValueError, ctrl.ControlSystem, not_rules)
testsystem = ctrl.ControlSystem()
tst.assert_raises(ValueError, testsystem.addrule, a)
def test_multiple_rules_same_consequent_term():
# 2 input variables, 1 output variable and 7 instances.
x1_inputs = [0.6, 0.2, 0.4, 0.7, 1, 1.2, 1.8]
x2_inputs = [0.9, 1, 0.8, 0, 1.2, 0.6, 1.8]
dom = np.arange(0, 2.1, 0.01)
x1 = ctrl.Antecedent(dom, "x1")
x1['label0'] = fuzz.trimf(x1.universe, (0.2, 0.2, 0.6))
x1['label1'] = fuzz.trimf(x1.universe, (0.2, 0.6, 1.0))
x1['label2'] = fuzz.trimf(x1.universe, (0.6, 1.0, 1.4))
x1['label3'] = fuzz.trimf(x1.universe, (1.0, 1.4, 1.8))
x1['label4'] = fuzz.trimf(x1.universe, (1.4, 1.8, 1.8))
x2 = ctrl.Antecedent(dom, "x2")
x2['label0'] = fuzz.trimf(x2.universe, (0.0, 0.0, 0.45))
x2['label1'] = fuzz.trimf(x2.universe, (0.0, 0.45, 0.9))
x2['label2'] = fuzz.trimf(x2.universe, (0.45, 0.9, 1.35))
x2['label3'] = fuzz.trimf(x2.universe, (0.9, 1.35, 1.8))
x2['label4'] = fuzz.trimf(x2.universe, (1.35, 1.8, 1.8))
y = ctrl.Consequent(dom, "y")
y['label0'] = fuzz.trimf(y.universe, (0.3, 0.3, 0.725))
y['label1'] = fuzz.trimf(y.universe, (0.3, 0.725, 1.15))
y['label2'] = fuzz.trimf(y.universe, (0.725, 1.15, 1.575))
y['label3'] = fuzz.trimf(y.universe, (1.15, 1.575, 2.0))
y['label4'] = fuzz.trimf(y.universe, (1.575, 2.0, 2.0))
r1 = ctrl.Rule(x1['label0'] & x2['label2'], y['label0'])
r2 = ctrl.Rule(x1['label1'] & x2['label0'], y['label0'])
r3 = ctrl.Rule(x1['label1'] & x2['label2'], y['label0'])
# Equivalent to above 3 rules
r123 = ctrl.Rule((x1['label0'] & x2['label2']) |
(x1['label1'] & x2['label0']) |
(x1['label1'] & x2['label2']), y['label0'])
r4 = ctrl.Rule(x1['label2'] & x2['label1'], y['label2'])
r5 = ctrl.Rule(x1['label2'] & x2['label3'], y['label3'])
r6 = ctrl.Rule(x1['label4'] & x2['label4'], y['label4'])
# Build a system with three rules targeting the same Consequent Term,
# and then an equivalent system with those three rules combined into one.
cs0 = ctrl.ControlSystem([r1, r2, r3, r4, r5, r6])
cs1 = ctrl.ControlSystem([r123, r4, r5, r6])
expected_results = [0.438372093023,
0.443962536855,
0.461436409933,
0.445290345769,
1.575,
1.15,
1.86162790698]
# Ensure the results are equivalent within error
for inst, expected in zip(range(7), expected_results):
sim0 = ctrl.ControlSystemSimulation(cs0)
sim1 = ctrl.ControlSystemSimulation(cs1)
sim0.input["x1"] = x1_inputs[inst]
sim0.input["x2"] = x2_inputs[inst]
sim1.input["x1"] = x1_inputs[inst]
sim1.input["x2"] = x2_inputs[inst]
sim0.compute()
sim1.compute()
tst.assert_allclose(sim0.output['y'], sim1.output['y'])
tst.assert_allclose(expected, sim0.output['y'], atol=1e-4, rtol=1e-4)
def test_complex_system():
# A much more complex system, run multiple times & with array inputs
universe = np.linspace(-2, 2, 5)
error = ctrl.Antecedent(universe, 'error')
delta = ctrl.Antecedent(universe, 'delta')
output = ctrl.Consequent(universe, 'output')
names = ['nb', 'ns', 'ze', 'ps', 'pb']
error.automf(names=names)
delta.automf(names=names)
output.automf(names=names)
# The rulebase:
# rule 1: IF e = ZE AND delta = ZE THEN output = ZE
# rule 2: IF e = ZE AND delta = SP THEN output = SN
# rule 3: IF e = SN AND delta = SN THEN output = LP
# rule 4: IF e = LP OR delta = LP THEN output = LN
rule0 = ctrl.Rule(antecedent=((error['nb'] & delta['nb']) | # This combination, or...
(error['ns'] & delta['nb']) |
(error['nb'] & delta['ns'])),
consequent=output['nb'], label='rule nb')
rule1 = ctrl.Rule(antecedent=((error['nb'] & delta['ze']) |
(error['nb'] & delta['ps']) |
(error['ns'] & delta['ns']) |
(error['ns'] & delta['ze']) |
(error['ze'] & delta['ns']) |
(error['ze'] & delta['nb']) |
(error['ps'] & delta['nb'])),
consequent=output['ns'], label='rule ns')
rule2 = ctrl.Rule(antecedent=((error['nb'] & delta['pb']) |
(error['ns'] & delta['ps']) |
(error['ze'] & delta['ze']) |
(error['ps'] & delta['ns']) |
(error['pb'] & delta['nb'])),
consequent=output['ze'], label='rule ze')
rule3 = ctrl.Rule(antecedent=((error['ns'] & delta['pb']) |
(error['ze'] & delta['pb']) |
(error['ze'] & delta['ps']) |
(error['ps'] & delta['ps']) |
(error['ps'] & delta['ze']) |
(error['pb'] & delta['ze']) |
(error['pb'] & delta['ns'])),
consequent=output['ps'], label='rule ps')
rule4 = ctrl.Rule(antecedent=((error['ps'] & delta['pb']) |
(error['pb'] & delta['pb']) |
(error['pb'] & delta['ps'])),
consequent=output['pb'], label='rule pb')
system = ctrl.ControlSystem(rules=[rule0, rule1, rule2, rule3, rule4])
sim = ctrl.ControlSystemSimulation(system, cache=False)
x, y = np.meshgrid(np.linspace(-2, 2, 21), np.linspace(-2, 2, 21))
z0 = np.zeros_like(x)
z1 = np.zeros_like(x)
# The original, slow way - one set of values at a time
for i in range(21):
for j in range(21):
sim.input['error'] = x[i, j]
sim.input['delta'] = y[i, j]
sim.compute()
z0[i, j] = sim.output['output']
sim.reset()
# The new way - array inputs
sim.input['error'] = x
sim.input['delta'] = y
sim.compute()
z1 = sim.output['output']
# Ensure results align
np.testing.assert_allclose(z0, z1)
# Big expected array
expected = \
np.array([[ -1.66666667e+00, -1.65555556e+00, -1.62857143e+00,
-1.62857143e+00, -1.65555556e+00, -1.66666667e+00,
-1.34414414e+00, -1.18294574e+00, -1.10000000e+00,
-1.05641026e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -7.37704918e-01, -5.72916667e-01,
-4.27083333e-01, -2.62295082e-01, -2.77555756e-17],
[ -1.65555556e+00, -1.34414414e+00, -1.29494949e+00,
-1.29494949e+00, -1.34414414e+00, -1.34414414e+00,
-1.34414414e+00, -1.18294574e+00, -1.10000000e+00,
-1.05641026e+00, -1.00000000e+00, -7.37704918e-01,
-7.13580247e-01, -7.13580247e-01, -7.37704918e-01,
-7.37704918e-01, -4.36619718e-01, -2.91555556e-01,
-1.56140351e-01, 1.96914557e-16, 2.62295082e-01],
[ -1.62857143e+00, -1.29494949e+00, -1.18294574e+00,
-1.18294574e+00, -1.18294574e+00, -1.18294574e+00,
-1.18294574e+00, -1.18294574e+00, -1.10000000e+00,
-1.05333333e+00, -1.00000000e+00, -7.13580247e-01,
-5.72916667e-01, -5.72916667e-01, -5.72916667e-01,
-5.72916667e-01, -2.91555556e-01, -1.26984127e-01,
6.45478503e-17, 1.56140351e-01, 4.27083333e-01],
[ -1.62857143e+00, -1.29494949e+00, -1.18294574e+00,
-1.10000000e+00, -1.10000000e+00, -1.10000000e+00,
-1.10000000e+00, -1.10000000e+00, -1.10000000e+00,
-1.05333333e+00, -1.00000000e+00, -7.13580247e-01,
-5.72916667e-01, -4.27083333e-01, -4.27083333e-01,
-4.27083333e-01, -1.56140351e-01, 2.42054439e-16,
1.26984127e-01, 2.91555556e-01, 5.72916667e-01],
[ -1.65555556e+00, -1.34414414e+00, -1.18294574e+00,
-1.10000000e+00, -1.05641026e+00, -1.05641026e+00,
-1.05641026e+00, -1.05333333e+00, -1.05333333e+00,
-1.05641026e+00, -1.00000000e+00, -7.37704918e-01,
-5.72916667e-01, -4.27083333e-01, -2.62295082e-01,
-2.62295082e-01, 2.29733650e-16, 1.56140351e-01,
2.91555556e-01, 4.36619718e-01, 7.37704918e-01],
[ -1.66666667e+00, -1.34414414e+00, -1.18294574e+00,
-1.10000000e+00, -1.05641026e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -7.37704918e-01,
-5.72916667e-01, -4.27083333e-01, -2.62295082e-01,
-2.77555756e-17, 2.62295082e-01, 4.27083333e-01,
5.72916667e-01, 7.37704918e-01, 1.00000000e+00],
[ -1.34414414e+00, -1.34414414e+00, -1.18294574e+00,
-1.10000000e+00, -1.05641026e+00, -1.00000000e+00,
-7.37704918e-01, -7.13580247e-01, -7.13580247e-01,
-7.37704918e-01, -7.37704918e-01, -4.36619718e-01,
-2.91555556e-01, -1.56140351e-01, 4.17271323e-16,
2.62295082e-01, 2.62295082e-01, 4.27083333e-01,
5.72916667e-01, 7.37704918e-01, 1.00000000e+00],
[ -1.18294574e+00, -1.18294574e+00, -1.18294574e+00,
-1.10000000e+00, -1.05333333e+00, -1.00000000e+00,
-7.13580247e-01, -5.72916667e-01, -5.72916667e-01,
-5.72916667e-01, -5.72916667e-01, -2.91555556e-01,
-1.26984127e-01, 2.09780513e-16, 1.56140351e-01,
4.27083333e-01, 4.27083333e-01, 4.27083333e-01,
5.72916667e-01, 7.13580247e-01, 1.00000000e+00],
[ -1.10000000e+00, -1.10000000e+00, -1.10000000e+00,
-1.10000000e+00, -1.05333333e+00, -1.00000000e+00,
-7.13580247e-01, -5.72916667e-01, -4.27083333e-01,
-4.27083333e-01, -4.27083333e-01, -1.56140351e-01,
2.42054439e-16, 1.26984127e-01, 2.91555556e-01,
5.72916667e-01, 5.72916667e-01, 5.72916667e-01,
5.72916667e-01, 7.13580247e-01, 1.00000000e+00],
[ -1.05641026e+00, -1.05641026e+00, -1.05333333e+00,
-1.05333333e+00, -1.05641026e+00, -1.00000000e+00,
-7.37704918e-01, -5.72916667e-01, -4.27083333e-01,
-2.62295082e-01, -2.62295082e-01, 2.29733650e-16,
1.56140351e-01, 2.91555556e-01, 4.36619718e-01,
7.37704918e-01, 7.37704918e-01, 7.13580247e-01,
7.13580247e-01, 7.37704918e-01, 1.00000000e+00],
[ -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-7.37704918e-01, -5.72916667e-01, -4.27083333e-01,
-2.62295082e-01, -2.77555756e-17, 2.62295082e-01,
4.27083333e-01, 5.72916667e-01, 7.37704918e-01,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
[ -1.00000000e+00, -7.37704918e-01, -7.13580247e-01,
-7.13580247e-01, -7.37704918e-01, -7.37704918e-01,
-4.36619718e-01, -2.91555556e-01, -1.56140351e-01,
2.29733650e-16, 2.62295082e-01, 2.62295082e-01,
4.27083333e-01, 5.72916667e-01, 7.37704918e-01,
1.00000000e+00, 1.05641026e+00, 1.05333333e+00,
1.05333333e+00, 1.05641026e+00, 1.05641026e+00],
[ -1.00000000e+00, -7.13580247e-01, -5.72916667e-01,
-5.72916667e-01, -5.72916667e-01, -5.72916667e-01,
-2.91555556e-01, -1.26984127e-01, 2.42054439e-16,
1.56140351e-01, 4.27083333e-01, 4.27083333e-01,
4.27083333e-01, 5.72916667e-01, 7.13580247e-01,
1.00000000e+00, 1.05333333e+00, 1.10000000e+00,
1.10000000e+00, 1.10000000e+00, 1.10000000e+00],
[ -1.00000000e+00, -7.13580247e-01, -5.72916667e-01,
-4.27083333e-01, -4.27083333e-01, -4.27083333e-01,
-1.56140351e-01, 2.09780513e-16, 1.26984127e-01,
2.91555556e-01, 5.72916667e-01, 5.72916667e-01,
5.72916667e-01, 5.72916667e-01, 7.13580247e-01,
1.00000000e+00, 1.05333333e+00, 1.10000000e+00,
1.18294574e+00, 1.18294574e+00, 1.18294574e+00],
[ -1.00000000e+00, -7.37704918e-01, -5.72916667e-01,
-4.27083333e-01, -2.62295082e-01, -2.62295082e-01,
4.17271323e-16, 1.56140351e-01, 2.91555556e-01,
4.36619718e-01, 7.37704918e-01, 7.37704918e-01,
7.13580247e-01, 7.13580247e-01, 7.37704918e-01,
1.00000000e+00, 1.05641026e+00, 1.10000000e+00,
1.18294574e+00, 1.34414414e+00, 1.34414414e+00],
[ -1.00000000e+00, -7.37704918e-01, -5.72916667e-01,
-4.27083333e-01, -2.62295082e-01, -2.77555756e-17,
2.62295082e-01, 4.27083333e-01, 5.72916667e-01,
7.37704918e-01, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.05641026e+00, 1.10000000e+00,
1.18294574e+00, 1.34414414e+00, 1.66666667e+00],
[ -7.37704918e-01, -4.36619718e-01, -2.91555556e-01,
-1.56140351e-01, 2.29733650e-16, 2.62295082e-01,
2.62295082e-01, 4.27083333e-01, 5.72916667e-01,
7.37704918e-01, 1.00000000e+00, 1.05641026e+00,
1.05333333e+00, 1.05333333e+00, 1.05641026e+00,
1.05641026e+00, 1.05641026e+00, 1.10000000e+00,
1.18294574e+00, 1.34414414e+00, 1.65555556e+00],
[ -5.72916667e-01, -2.91555556e-01, -1.26984127e-01,
2.42054439e-16, 1.56140351e-01, 4.27083333e-01,
4.27083333e-01, 4.27083333e-01, 5.72916667e-01,
7.13580247e-01, 1.00000000e+00, 1.05333333e+00,
1.10000000e+00, 1.10000000e+00, 1.10000000e+00,
1.10000000e+00, 1.10000000e+00, 1.10000000e+00,
1.18294574e+00, 1.29494949e+00, 1.62857143e+00],
[ -4.27083333e-01, -1.56140351e-01, 6.45478503e-17,
1.26984127e-01, 2.91555556e-01, 5.72916667e-01,
5.72916667e-01, 5.72916667e-01, 5.72916667e-01,
7.13580247e-01, 1.00000000e+00, 1.05333333e+00,
1.10000000e+00, 1.18294574e+00, 1.18294574e+00,
1.18294574e+00, 1.18294574e+00, 1.18294574e+00,
1.18294574e+00, 1.29494949e+00, 1.62857143e+00],
[ -2.62295082e-01, 1.96914557e-16, 1.56140351e-01,
2.91555556e-01, 4.36619718e-01, 7.37704918e-01,
7.37704918e-01, 7.13580247e-01, 7.13580247e-01,
7.37704918e-01, 1.00000000e+00, 1.05641026e+00,
1.10000000e+00, 1.18294574e+00, 1.34414414e+00,
1.34414414e+00, 1.34414414e+00, 1.29494949e+00,
1.29494949e+00, 1.34414414e+00, 1.65555556e+00],
[ -2.77555756e-17, 2.62295082e-01, 4.27083333e-01,
5.72916667e-01, 7.37704918e-01, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.05641026e+00,
1.10000000e+00, 1.18294574e+00, 1.34414414e+00,
1.66666667e+00, 1.65555556e+00, 1.62857143e+00,
1.62857143e+00, 1.65555556e+00, 1.66666667e+00]])
# Ensure results are within expected limits
np.testing.assert_allclose(z1, expected)
if __name__ == '__main__':
tst.run_module_suite()

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# -*- coding: utf-8 -*-
'''
This started as a copy of skfuzzy/control/tests/test_controlsystem.py
I've hacked it a bit to partially use the FCL parser.
@hacker: james.power@mu.ie Created on Tue Aug 21 10:11:04 2018
'''
from __future__ import division
import os
import numpy as np
import numpy.testing as tst
import nose
import skfuzzy.control as ctrl
from fcl_parser import FCLParser
def test_tipping_problem():
'''
Define the variables as usual,
but use FCL for some membership functions and for all the rules.
'''
# First we set up the variables in the usual way:
food = ctrl.Antecedent(np.linspace(0, 10, 11), 'quality')
service = ctrl.Antecedent(np.linspace(0, 10, 11), 'service')
tip = ctrl.Consequent(np.linspace(0, 25, 26), 'tip')
# Auto-generate the membership functions for the inputs:
food.automf(3)
service.automf(3)
# Define a FCL parser-object:
p = FCLParser()
# Use FCL to define membership functions for the output:
tip['bad'] = p.mf('Triangle 0 0 13', tip.universe)
tip['middling'] = p.mf('Triangle 0 13 25', tip.universe)
tip['lots'] = p.mf('Triangle 13 25 25', tip.universe)
# We need to tell the parser about the variables before we parse the rules:
p.add_vars([food, service, tip])
# Now use FCL to define three rules:
rule1 = p.rule('IF quality IS poor OR service IS poor THEN tip IS bad')
rule2 = p.rule('IF service is average THEN tip is middling')
rule3 = p.rule('if service is good or quality is good then tip is lots')
# To get the control system, just add the rules (from the parser):
tipping = ctrl.ControlSystem(p.rules)
# From here on it's just the same as the original:
tipping2 = ctrl.ControlSystem(rule1)
tipping2.addrule(rule2)
tipping2.addrule(rule3)
tip_sim = ctrl.ControlSystemSimulation(tipping)
tip_sim2 = ctrl.ControlSystemSimulation(tipping2)
# Inputs added both possible ways
inputs = {'quality': 6.5, 'service': 9.8}
for key, value in inputs.items():
tip_sim.input[key] = value
tip_sim2.inputs(inputs)
# Compute the system
tip_sim.compute()
tip_sim2.compute()
# Ensure both methods of defining rules yield the same results
for val0, val1 in zip(tip_sim.output.values(),
tip_sim2.output.values()):
tst.assert_allclose(val0, val1)
# Verify against manual computation
tst.assert_allclose(tip_sim.output['tip'], 19.8578, atol=1e-2, rtol=1e-2)
def setup_rule_order():
''' We can define variables in FCL and add terms afterwards: '''
global _parser # Make this global so we can access vars elsewhere
_parser = FCLParser()
# Use the parser to define the variables and their universes
_parser.function_block('''
FUNCTION_BLOCK
// Define variables, but not terms for the moment:
FUZZIFY a RANGE := (0 .. 11) WITH 1 END_FUZZIFY
FUZZIFY b RANGE := (0 .. 11) WITH 1 END_FUZZIFY
FUZZIFY c RANGE := (0 .. 11) WITH 1 END_FUZZIFY
FUZZIFY d RANGE := (0 .. 11) WITH 1 END_FUZZIFY
// No rules at all; that's OK.
END_FUNCTION_BLOCK
''')
# The use skfuzzy to define the membership functions:
for v in _parser.fuzzy_variables:
v.automf(3)
@nose.with_setup(setup_rule_order)
def test_bad_rules():
'''Can access variables by using parser as a dict'''
not_rules = ['me', 192238, 42, dict()]
tst.assert_raises(ValueError, ctrl.ControlSystem, not_rules)
testsystem = ctrl.ControlSystem()
tst.assert_raises(ValueError, testsystem.addrule, _parser['a'])
def test_multiple_rules_same_consequent_term():
'''
Here we define the variables fully in FCL,
and use a rule-block for the set of rules.
'''
x1_inputs = [0.6, 0.2, 0.4, 0.7, 1, 1.2, 1.8]
x2_inputs = [0.9, 1, 0.8, 0, 1.2, 0.6, 1.8]
p = FCLParser()
p.fuzzify_block('''
FUZZIFY x1
RANGE := (0 .. 2.1) WITH 0.01 // Hacked the FCL syntax here
TERM label0 := Triangle 0.2 0.2 0.6
TERM label1 := Triangle 0.2 0.6 1.0
TERM label2 := Triangle 0.6 1.0 1.4
TERM label3 := Triangle 1.0 1.4 1.8
TERM label4 := Triangle 1.4 1.8 1.8
END_FUZZIFY
''')
p.fuzzify_block('''
FUZZIFY x2
RANGE := (0 .. 2.1) WITH 0.01
TERM label0 := Triangle 0.0 0.0 0.45
TERM label1 := Triangle 0.0 0.45 0.9
TERM label2 := Triangle 0.45 0.9 1.35
TERM label3 := Triangle 0.9 1.35 1.8
TERM label4 := Triangle 1.35 1.8 1.8
END_FUZZIFY
''')
p.defuzzify_block('''
DEFUZZIFY y
RANGE := (0 .. 2.1) WITH 0.01
TERM label0 := Triangle 0.3 0.3 0.725
TERM label1 := Triangle 0.3 0.725 1.15
TERM label2 := Triangle 0.725 1.15 1.575
TERM label3 := Triangle 1.15 1.575 2.0
TERM label4 := Triangle 1.575 2.0 2.0
END_DEFUZZIFY
''')
first_three = p.rule_block('''
RULEBLOCK // Name of rule-block is optional
RULE 1: IF x1 is label0 AND x2 is label2 THEN y is label0
RULE 2: IF x1 is label1 AND x2 is label0 THEN y is label0
RULE 3: IF x1 is label1 AND x2 is label2 THEN y is label0
END_RULEBLOCK
''')
# Equivalent to above 3 rules
r123 = p.rule('''
IF x1 is label0 AND x2 is label2
OR x1 is label1 AND x2 is label0
OR x1 is label1 AND x2 is label2
THEN y is label0
''')
last_three = p.rule_block('''
RULEBLOCK // Name of rule-block is optional
RULE 4: IF x1 is label2 AND x2 is label1 THEN y is label2
RULE 5: IF x1 is label2 AND x2 is label3 THEN y is label3
RULE 6: IF x1 is label4 AND x2 is label4 THEN y is label4
END_RULEBLOCK
''')
# Build a system with three rules targeting the same Consequent Term,
# and then an equivalent system with those three rules combined into one.
cs0 = ctrl.ControlSystem(first_three + last_three)
cs1 = ctrl.ControlSystem([r123] + last_three)
expected_results = [0.438372093023,
0.443962536855,
0.461436409933,
0.445290345769,
1.575,
1.15,
1.86162790698]
# Ensure the results are equivalent within error
for inst, expected in zip(range(7), expected_results):
sim0 = ctrl.ControlSystemSimulation(cs0)
sim1 = ctrl.ControlSystemSimulation(cs1)
sim0.input["x1"] = x1_inputs[inst]
sim0.input["x2"] = x2_inputs[inst]
sim1.input["x1"] = x1_inputs[inst]
sim1.input["x2"] = x2_inputs[inst]
sim0.compute()
sim1.compute()
tst.assert_allclose(sim0.output['y'], sim1.output['y'])
tst.assert_allclose(expected, sim0.output['y'], atol=1e-4, rtol=1e-4)
def test_multiple_rules_same_consequent_term_file():
'''
Here we define the whole system in an FCL file, and read it in...
'''
x1_inputs = [0.6, 0.2, 0.4, 0.7, 1, 1.2, 1.8]
x2_inputs = [0.9, 1, 0.8, 0, 1.2, 0.6, 1.8]
p = FCLParser()
# FCL input file is in the same directory as this script:
infile = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'multiple.fcl')
p.read_fcl_file(infile)
# Build a system with three rules targeting the same Consequent Term,
# and then an equivalent system with those three rules combined into one.
separate = [r for r in p.rules if not r.label.startswith('extra')]
cs0 = ctrl.ControlSystem(separate) # 6 rules, as before
cs1 = ctrl.ControlSystem(p.rules) # Throw in all 7 rules
expected_results = [0.438372093023,
0.443962536855,
0.461436409933,
0.445290345769,
1.575,
1.15,
1.86162790698]
# Ensure the results are equivalent within error
for inst, expected in zip(range(7), expected_results):
sim0 = ctrl.ControlSystemSimulation(cs0)
sim1 = ctrl.ControlSystemSimulation(cs1)
sim0.input["x1"] = x1_inputs[inst]
sim0.input["x2"] = x2_inputs[inst]
sim1.input["x1"] = x1_inputs[inst]
sim1.input["x2"] = x2_inputs[inst]
sim0.compute()
sim1.compute()
tst.assert_allclose(sim0.output['y'], sim1.output['y'])
tst.assert_allclose(expected, sim0.output['y'], atol=1e-4, rtol=1e-4)
def test_complex_system():
'''In this example we parse a whole rule-back in one go'''
universe = np.linspace(-2, 2, 5)
vars = [ctrl.Antecedent(universe, 'error'),
ctrl.Antecedent(universe, 'delta'),
ctrl.Consequent(universe, 'output')]
for var in vars:
var.automf(names=['nb', 'ns', 'ze', 'ps', 'pb'])
# Define a FCL parser-object, tell it about the variables:
p = FCLParser(vars)
# Now supply all the rules as a ruleblock
rulebase = p.rule_block('''
RULEBLOCK ComplexSystem // Name of rule-block is optional
RULE rule_nb:
IF error is nb and delta is nb
or error is ns and delta is nb
or error is nb and delta is ns
THEN output is nb
RULE rule_ns:
IF error is nb and delta is ze or error is nb and delta is ps
or error is ns and delta is ns or error is ns and delta is ze
or error is ze and delta is ns or error is ze and delta is nb
or error is ps and delta is nb
THEN output is ns
RULE rule_ze:
IF error is nb and delta is pb
or error is ns and delta is ps
or error is ze and delta is ze
or error is ps and delta is ns
or error is pb and delta is nb
THEN output is ze
RULE rule_ps:
IF error is ns and delta is pb
or error is ze and delta is pb or error is ze and delta is ps
or error is ps and delta is ps or error is ps and delta is ze
or error is pb and delta is ze or error is pb and delta is ns
THEN output is ps
RULE rule_pb:
IF error is ps and delta is pb
or error is pb and delta is pb or error is pb and delta is ps
THEN output is pb
END_RULEBLOCK
''')
# Same as before from here on...
system = ctrl.ControlSystem(rules=rulebase)
sim = ctrl.ControlSystemSimulation(system, cache=False)
x, y = np.meshgrid(np.linspace(-2, 2, 21), np.linspace(-2, 2, 21))
z0 = np.zeros_like(x)
z1 = np.zeros_like(x)
# The original, slow way - one set of values at a time
for i in range(21):
for j in range(21):
sim.input['error'] = x[i, j]
sim.input['delta'] = y[i, j]
sim.compute()
z0[i, j] = sim.output['output']
sim.reset()
# The new way - array inputs
sim.input['error'] = x
sim.input['delta'] = y
sim.compute()
z1 = sim.output['output']
# Ensure results align
np.testing.assert_allclose(z0, z1)
# Big expected array
expected = \
np.array([[-1.66666667e+00, -1.65555556e+00, -1.62857143e+00,
-1.62857143e+00, -1.65555556e+00, -1.66666667e+00,
-1.34414414e+00, -1.18294574e+00, -1.10000000e+00,
-1.05641026e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -7.37704918e-01, -5.72916667e-01,
-4.27083333e-01, -2.62295082e-01, -2.77555756e-17],
[-1.65555556e+00, -1.34414414e+00, -1.29494949e+00,
-1.29494949e+00, -1.34414414e+00, -1.34414414e+00,
-1.34414414e+00, -1.18294574e+00, -1.10000000e+00,
-1.05641026e+00, -1.00000000e+00, -7.37704918e-01,
-7.13580247e-01, -7.13580247e-01, -7.37704918e-01,
-7.37704918e-01, -4.36619718e-01, -2.91555556e-01,
-1.56140351e-01, 1.96914557e-16, 2.62295082e-01],
[-1.62857143e+00, -1.29494949e+00, -1.18294574e+00,
-1.18294574e+00, -1.18294574e+00, -1.18294574e+00,
-1.18294574e+00, -1.18294574e+00, -1.10000000e+00,
-1.05333333e+00, -1.00000000e+00, -7.13580247e-01,
-5.72916667e-01, -5.72916667e-01, -5.72916667e-01,
-5.72916667e-01, -2.91555556e-01, -1.26984127e-01,
6.45478503e-17, 1.56140351e-01, 4.27083333e-01],
[-1.62857143e+00, -1.29494949e+00, -1.18294574e+00,
-1.10000000e+00, -1.10000000e+00, -1.10000000e+00,
-1.10000000e+00, -1.10000000e+00, -1.10000000e+00,
-1.05333333e+00, -1.00000000e+00, -7.13580247e-01,
-5.72916667e-01, -4.27083333e-01, -4.27083333e-01,
-4.27083333e-01, -1.56140351e-01, 2.42054439e-16,
1.26984127e-01, 2.91555556e-01, 5.72916667e-01],
[-1.65555556e+00, -1.34414414e+00, -1.18294574e+00,
-1.10000000e+00, -1.05641026e+00, -1.05641026e+00,
-1.05641026e+00, -1.05333333e+00, -1.05333333e+00,
-1.05641026e+00, -1.00000000e+00, -7.37704918e-01,
-5.72916667e-01, -4.27083333e-01, -2.62295082e-01,
-2.62295082e-01, 2.29733650e-16, 1.56140351e-01,
2.91555556e-01, 4.36619718e-01, 7.37704918e-01],
[-1.66666667e+00, -1.34414414e+00, -1.18294574e+00,
-1.10000000e+00, -1.05641026e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -7.37704918e-01,
-5.72916667e-01, -4.27083333e-01, -2.62295082e-01,
-2.77555756e-17, 2.62295082e-01, 4.27083333e-01,
5.72916667e-01, 7.37704918e-01, 1.00000000e+00],
[-1.34414414e+00, -1.34414414e+00, -1.18294574e+00,
-1.10000000e+00, -1.05641026e+00, -1.00000000e+00,
-7.37704918e-01, -7.13580247e-01, -7.13580247e-01,
-7.37704918e-01, -7.37704918e-01, -4.36619718e-01,
-2.91555556e-01, -1.56140351e-01, 4.17271323e-16,
2.62295082e-01, 2.62295082e-01, 4.27083333e-01,
5.72916667e-01, 7.37704918e-01, 1.00000000e+00],
[-1.18294574e+00, -1.18294574e+00, -1.18294574e+00,
-1.10000000e+00, -1.05333333e+00, -1.00000000e+00,
-7.13580247e-01, -5.72916667e-01, -5.72916667e-01,
-5.72916667e-01, -5.72916667e-01, -2.91555556e-01,
-1.26984127e-01, 2.09780513e-16, 1.56140351e-01,
4.27083333e-01, 4.27083333e-01, 4.27083333e-01,
5.72916667e-01, 7.13580247e-01, 1.00000000e+00],
[-1.10000000e+00, -1.10000000e+00, -1.10000000e+00,
-1.10000000e+00, -1.05333333e+00, -1.00000000e+00,
-7.13580247e-01, -5.72916667e-01, -4.27083333e-01,
-4.27083333e-01, -4.27083333e-01, -1.56140351e-01,
2.42054439e-16, 1.26984127e-01, 2.91555556e-01,
5.72916667e-01, 5.72916667e-01, 5.72916667e-01,
5.72916667e-01, 7.13580247e-01, 1.00000000e+00],
[-1.05641026e+00, -1.05641026e+00, -1.05333333e+00,
-1.05333333e+00, -1.05641026e+00, -1.00000000e+00,
-7.37704918e-01, -5.72916667e-01, -4.27083333e-01,
-2.62295082e-01, -2.62295082e-01, 2.29733650e-16,
1.56140351e-01, 2.91555556e-01, 4.36619718e-01,
7.37704918e-01, 7.37704918e-01, 7.13580247e-01,
7.13580247e-01, 7.37704918e-01, 1.00000000e+00],
[-1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-1.00000000e+00, -1.00000000e+00, -1.00000000e+00,
-7.37704918e-01, -5.72916667e-01, -4.27083333e-01,
-2.62295082e-01, -2.77555756e-17, 2.62295082e-01,
4.27083333e-01, 5.72916667e-01, 7.37704918e-01,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
[-1.00000000e+00, -7.37704918e-01, -7.13580247e-01,
-7.13580247e-01, -7.37704918e-01, -7.37704918e-01,
-4.36619718e-01, -2.91555556e-01, -1.56140351e-01,
2.29733650e-16, 2.62295082e-01, 2.62295082e-01,
4.27083333e-01, 5.72916667e-01, 7.37704918e-01,
1.00000000e+00, 1.05641026e+00, 1.05333333e+00,
1.05333333e+00, 1.05641026e+00, 1.05641026e+00],
[-1.00000000e+00, -7.13580247e-01, -5.72916667e-01,
-5.72916667e-01, -5.72916667e-01, -5.72916667e-01,
-2.91555556e-01, -1.26984127e-01, 2.42054439e-16,
1.56140351e-01, 4.27083333e-01, 4.27083333e-01,
4.27083333e-01, 5.72916667e-01, 7.13580247e-01,
1.00000000e+00, 1.05333333e+00, 1.10000000e+00,
1.10000000e+00, 1.10000000e+00, 1.10000000e+00],
[-1.00000000e+00, -7.13580247e-01, -5.72916667e-01,
-4.27083333e-01, -4.27083333e-01, -4.27083333e-01,
-1.56140351e-01, 2.09780513e-16, 1.26984127e-01,
2.91555556e-01, 5.72916667e-01, 5.72916667e-01,
5.72916667e-01, 5.72916667e-01, 7.13580247e-01,
1.00000000e+00, 1.05333333e+00, 1.10000000e+00,
1.18294574e+00, 1.18294574e+00, 1.18294574e+00],
[-1.00000000e+00, -7.37704918e-01, -5.72916667e-01,
-4.27083333e-01, -2.62295082e-01, -2.62295082e-01,
4.17271323e-16, 1.56140351e-01, 2.91555556e-01,
4.36619718e-01, 7.37704918e-01, 7.37704918e-01,
7.13580247e-01, 7.13580247e-01, 7.37704918e-01,
1.00000000e+00, 1.05641026e+00, 1.10000000e+00,
1.18294574e+00, 1.34414414e+00, 1.34414414e+00],
[-1.00000000e+00, -7.37704918e-01, -5.72916667e-01,
-4.27083333e-01, -2.62295082e-01, -2.77555756e-17,
2.62295082e-01, 4.27083333e-01, 5.72916667e-01,
7.37704918e-01, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.05641026e+00, 1.10000000e+00,
1.18294574e+00, 1.34414414e+00, 1.66666667e+00],
[-7.37704918e-01, -4.36619718e-01, -2.91555556e-01,
-1.56140351e-01, 2.29733650e-16, 2.62295082e-01,
2.62295082e-01, 4.27083333e-01, 5.72916667e-01,
7.37704918e-01, 1.00000000e+00, 1.05641026e+00,
1.05333333e+00, 1.05333333e+00, 1.05641026e+00,
1.05641026e+00, 1.05641026e+00, 1.10000000e+00,
1.18294574e+00, 1.34414414e+00, 1.65555556e+00],
[-5.72916667e-01, -2.91555556e-01, -1.26984127e-01,
2.42054439e-16, 1.56140351e-01, 4.27083333e-01,
4.27083333e-01, 4.27083333e-01, 5.72916667e-01,
7.13580247e-01, 1.00000000e+00, 1.05333333e+00,
1.10000000e+00, 1.10000000e+00, 1.10000000e+00,
1.10000000e+00, 1.10000000e+00, 1.10000000e+00,
1.18294574e+00, 1.29494949e+00, 1.62857143e+00],
[-4.27083333e-01, -1.56140351e-01, 6.45478503e-17,
1.26984127e-01, 2.91555556e-01, 5.72916667e-01,
5.72916667e-01, 5.72916667e-01, 5.72916667e-01,
7.13580247e-01, 1.00000000e+00, 1.05333333e+00,
1.10000000e+00, 1.18294574e+00, 1.18294574e+00,
1.18294574e+00, 1.18294574e+00, 1.18294574e+00,
1.18294574e+00, 1.29494949e+00, 1.62857143e+00],
[-2.62295082e-01, 1.96914557e-16, 1.56140351e-01,
2.91555556e-01, 4.36619718e-01, 7.37704918e-01,
7.37704918e-01, 7.13580247e-01, 7.13580247e-01,
7.37704918e-01, 1.00000000e+00, 1.05641026e+00,
1.10000000e+00, 1.18294574e+00, 1.34414414e+00,
1.34414414e+00, 1.34414414e+00, 1.29494949e+00,
1.29494949e+00, 1.34414414e+00, 1.65555556e+00],
[-2.77555756e-17, 2.62295082e-01, 4.27083333e-01,
5.72916667e-01, 7.37704918e-01, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.05641026e+00,
1.10000000e+00, 1.18294574e+00, 1.34414414e+00,
1.66666667e+00, 1.65555556e+00, 1.62857143e+00,
1.62857143e+00, 1.65555556e+00, 1.66666667e+00]]) # nopep8
# Ensure results are within expected limits
np.testing.assert_allclose(z1, expected)
if __name__ == '__main__':
tst.run_module_suite()

50
tests/tipper.fcl Normal file
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@ -0,0 +1,50 @@
// Example taken from the fuzzylite distribution,
// Covered by the GNU General Public License (GPL) 3.0.
// see: https://www.fuzzylite.com/cpp/
//Code automatically generated with fuzzylite 6.0.
FUNCTION_BLOCK tipper
VAR_INPUT
service: REAL;
food: REAL;
END_VAR
VAR_OUTPUT
tip: REAL;
END_VAR
FUZZIFY service
RANGE := (0.000 .. 10.000);
TERM poor := Gaussian 0.000 1.500;
TERM good := Gaussian 5.000 1.500;
TERM excellent := Gaussian 10.000 1.500;
END_FUZZIFY
FUZZIFY food
RANGE := (0.000 .. 10.000);
TERM rancid := Trapezoid 0.000 0.000 1.000 3.000;
TERM delicious := Trapezoid 7.000 9.000 10.000 10.000;
END_FUZZIFY
DEFUZZIFY tip
RANGE := (0.000 .. 30.000);
TERM cheap := Triangle 0.000 5.000 10.000;
TERM average := Triangle 10.000 15.000 20.000;
TERM generous := Triangle 20.000 25.000 30.000;
METHOD : COG;
ACCU : MAX;
DEFAULT := nan;
END_DEFUZZIFY
RULEBLOCK
AND : MIN;
OR : MAX;
ACT : MIN;
RULE 1 : if service is poor or food is rancid then tip is cheap
RULE 2 : if service is good then tip is average
RULE 3 : if service is excellent or food is delicious then tip is generous
END_RULEBLOCK
END_FUNCTION_BLOCK

240
tnorms.py Normal file
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# -*- coding: utf-8 -*-
'''
Norms and co-norms based on the definitions in the IEEE standard.
Each function here returns a FuzzyAggregationMethods object,
which is really just a pair of functions: (and_func, or_func).
I'm following Annex A in IEEE 1855-2016, definintions A.14-A.27.
@author: james.power@mu.ie Created on Fri Aug 10 12:48:30 2018
'''
import numpy as np
from skfuzzy.control.term import FuzzyAggregationMethods
''' Reminder of what's in term.py:
class FuzzyAggregationMethods(object):
def __init__(self, and_func=np.fmin, or_func=np.fmax):
self.and_func = and_func
self.or_func = or_func
'''
def _fam_vectorise(and_func, or_func):
'''
Vectorise an and/or function pair; return a FuzzyAggregationMethods.
Make sure the function names are propoagated to the resulting object.
'''
fam = FuzzyAggregationMethods(np.vectorize(and_func),
np.vectorize(or_func))
fam.and_func.__name__ = and_func.__name__
fam.or_func.__name__ = or_func.__name__
return fam
# This is the default:
# A.14: minimum t-norm, A.21: maximum t-conorm
MIN_MAX = FuzzyAggregationMethods(np.fmin, np.fmax)
def ps_prod_and(a, b):
'''A.15: product t-norm'''
return a*b
def ps_sum_or(a, b):
'''A.21: Probabilistic sum t-conorm'''
return a+b - (a*b)
PRODUCT_SUM = FuzzyAggregationMethods(ps_prod_and, ps_sum_or)
def bounded_and(a, b):
'A.16: bounded difference t-norm'
return np.fmax(0, a+b - 1)
def bounded_or(a, b):
'A.22: bounded sum t-conorm'
return np.fmin(1, a+b)
BOUNDED = FuzzyAggregationMethods(bounded_and, bounded_or)
'''This is just a synonym for bounded'''
LUCASIEWICZ = BOUNDED
def drastic_and(a, b):
'A.17: drastic product t-norm'
if a == 1:
return b
elif b == 1:
return a
else:
return 0
def drastic_or(a, b):
'A.23: drastic sum t-conorm'
if a == 0:
return b
elif b == 0:
return a
else:
return 1
DRASTIC = _fam_vectorise(drastic_and, drastic_or)
def einstein_and(a, b):
'A.18: Einstein product t-norm'
return (a*b) / (2 - (a+b - a*b))
def einstein_or(a, b):
'A.24: Einstein sum t-conorm'
return (a+b) / (1 + a*b)
EINSTEIN = FuzzyAggregationMethods(einstein_and, einstein_or)
# Denoninator is wrong in A.19 whihc says: ((a+b) / ((a+b) - a*b))
def hamacher_and(a, b):
'''A.19: Hamacher product t-norm; added divide-by-zero check'''
if a == b == 0:
return 0.0
else:
return (a*b) / ((a+b) - a*b)
def hamacher_or(a, b):
'A.25: Hamacher sum t-conorm; added divide-by-zero check'
if a == b == 1:
return 1.0
else:
return (a+b - 2*a*b) / (1 - a*b)
HAMACHER = _fam_vectorise(hamacher_and, hamacher_or)
def nilpotent_and(a, b):
'A.20: Nilpotent minum t-norm'
if a+b > 1:
return np.fmin(a, b)
else:
return 0.0
def nilpotent_or(a, b):
'A.26: Nilpotent maximum t-conorm'
if a+b < 1:
return np.fmax(a, b)
else:
return 1.0
NILPOTENT = _fam_vectorise(nilpotent_and, nilpotent_or)
# ################# ###
# ### Test routines ###
# ################# ###
def check_classic(fam):
'''
Test that norm and co-norm work like classic and/or for 0, 1 inputs.
'''
a = np.array([0, 0, 1, 1])
b = np.array([0, 1, 0, 1])
try:
# First check that the product works like the AND function:
expected = np.logical_and(a, b)
fprod = fam.and_func(a, b)
if not (fprod == expected).all():
print('Failed check_classic', fam.and_func.__name__, '\n',
'Inputs:', (a, b), '\n',
'Product =', fprod)
# Now check that the sum works like the OR function:
expected = np.logical_or(a, b)
fsum = fam.or_func(a, b)
if not (fsum == expected).all():
print('Failed check_classic', fam.or_func.__name__, '\n',
'Inputs:', (a, b), '\n',
'Sum =', fsum)
# A divide-by-zero error is also a problem:
except ZeroDivisionError as e:
print('check_classic',
fam.and_func.__name__, fam.or_func.__name__, '\n',
'Inputs:', (a, b), '\n',
'Divide by zero')
def check_duality(fam):
'''
Run some tests to make sure that the norm and co-norm are duals.
That is, they obey de Morgan's law: (a and b) = not((not a) or (not b))
'''
# Test a range of values between 0.0 and 1.0 inclusive:
a = np.arange(0.0, 1.1, 0.1)
b = np.arange(0.0, 1.1, 0.1)
try:
prod = fam.and_func(a, b)
# Need to round, since e.g. 1-0.7 is not 0.3 otherwise:
dual_sum = 1 - fam.or_func(np.round(1-a, 1), np.round(1-b, 1))
if not np.isclose(prod, dual_sum).all():
print('Failed check_duality', fam.and_func.__name__, '\n',
'Inputs:', (a, b), '\n',
'Product =', prod, '\n',
'Dual Sum =', dual_sum)
except ZeroDivisionError as e:
print('Failed check_duality',
fam.and_func.__name__, fam.or_func.__name__, '\n',
'Inputs:', (a, b), '\n',
'Divide by zero')
_all_norms = {
'Min/Max': MIN_MAX,
'Prod/Sum': PRODUCT_SUM,
'Bounded': BOUNDED,
'Drastic': DRASTIC,
'Einstein': EINSTEIN,
'Hamacher': HAMACHER,
'Nilpotent': NILPOTENT,
}
import matplotlib.pyplot as plt
import skfuzzy.membership as skmemb
def visualise_all(x, y1, y2, all_norms=_all_norms):
'''Plot the norm and conorm for the given sample inputs'''
ncols = 3
fig, axes = plt.subplots(nrows=len(all_norms), ncols=ncols, figsize=(8, 9))
fig.tight_layout()
fig.subplots_adjust(bottom=-.25)
for row, name in enumerate(_all_norms.keys()):
for col in range(ncols): # so all have the same (0,1) y-axis
axes[row][col].set_ylim([-0.05, 1.05])
axes[row][0].set_title('Sample inputs')
axes[row][0].plot(x, y1)
axes[row][0].plot(x, y2)
axes[row][1].set_title(name + ' norm')
axes[row][1].plot(x, _all_norms[name].and_func(y1, y2))
axes[row][2].set_title(name + ' co-norm')
axes[row][2].plot(x, _all_norms[name].or_func(y1, y2))
if __name__ == '__main__':
for fam in _all_norms.values():
check_classic(fam)
check_duality(fam)
sample_x = np.arange(0, 100)
sample_y1 = skmemb.trapmf(sample_x, [15, 30, 55, 75])
sample_y2 = skmemb.trapmf(sample_x, [25, 45, 70, 85])
visualise_all(sample_x, sample_y1, sample_y2)

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FUNCTION_BLOCK worker
VAR_INPUT
form1: REAL;
form2: REAL;
points: REAL;
END_VAR
VAR_OUTPUT
result: REAL;
END_VAR
FUZZIFY form1
RANGE := (0.000 .. 5.000);
TERM bad := (1,1)(3,0);
TERM avg := (1,0)(2,1)(3,1)(4,0);
TERM great := (3,0)(5,1);
END_FUZZIFY
FUZZIFY form2
RANGE := (0.000 .. 5.000);
TERM bad := (1,1)(3,0);
TERM avg := (1,0)(2,1)(3,1)(4,0);
TERM great := (3,0)(5,1);
END_FUZZIFY
FUZZIFY points
RANGE := (-30.000 .. 30.000);
TERM low := (-20,1)(-5,0);
TERM avg := (-10,0)(-3,1)(3,1)(10,0);
TERM high := (10,0)(20,1);
END_FUZZIFY
DEFUZZIFY result
RANGE := (-1.000 .. 1.000);
TERM home := (-1,1) (0,0);
TERM draw := (-0.5,0) (0,1) (0.5,1);
TERM away := (0,0) (1,1);
METHOD : COG;
// ACCU : MAX;
DEFAULT := 0
END_DEFUZZIFY
RULEBLOCK
AND : MIN;
ACCU : MAX;
ACT : MIN;
RULE 1 : if form1 is great and form2 is bad then result is home
RULE 2 : if form1 is bad and form2 is great then result is away
RULE 3 : if form1 is avg and form2 is avg then result is draw
RULE 4 : if form1 is great and form2 is great and points is low then result is draw
RULE 5 : if form1 is great and form2 is avg and points is avg then result is away
RULE 6 : if form1 is avg and form2 is avg and points is high then result is home
RULE 7 : if form1 is avg and form2 is avg and points is low then result is away
RULE 8 : if points is avg then result is away
END_RULEBLOCK
END_FUNCTION_BLOCK