From 7940bee468eaf12c08dc2b814ae32380a46a23aa Mon Sep 17 00:00:00 2001 From: s460941 Date: Thu, 4 Jun 2020 19:21:01 +0200 Subject: [PATCH] init --- .gitignore | 104 + .idea/LSR2.iml | 11 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 + .idea/workspace.xml | 75 + Examples/README.md | 64 + Examples/fuzzylite-mamdani/AllTerms.fcl | 90 + Examples/fuzzylite-mamdani/AllTerms.fld | 1025 ++++++ Examples/fuzzylite-mamdani/LICENSE | 674 ++++ Examples/fuzzylite-mamdani/Laundry.fcl | 58 + Examples/fuzzylite-mamdani/Laundry.fld | 1025 ++++++ .../fuzzylite-mamdani/ObstacleAvoidance.fcl | 34 + .../fuzzylite-mamdani/ObstacleAvoidance.fld | 1025 ++++++ Examples/fuzzylite-mamdani/SimpleDimmer.fcl | 37 + Examples/fuzzylite-mamdani/SimpleDimmer.fld | 1025 ++++++ .../fuzzylite-mamdani/SimpleDimmerChained.fcl | 51 + .../fuzzylite-mamdani/SimpleDimmerChained.fld | 1025 ++++++ Examples/jFuzzyLogic/LICENSE | 165 + .../jFuzzyLogic/QoS/LarsenQoSFewRules.fcl | 55 + .../jFuzzyLogic/QoS/LarsenQoSFewRules.fld | 126 + .../jFuzzyLogic/QoS/LarsenQoSManyRules.fcl | 83 + .../jFuzzyLogic/QoS/LarsenQoSManyRules.fld | 126 + .../jFuzzyLogic/QoS/MamdaniQoSFewRules.fcl | 55 + .../jFuzzyLogic/QoS/MamdaniQoSFewRules.fld | 126 + .../jFuzzyLogic/QoS/MamdaniQoSManyRules.fcl | 83 + .../jFuzzyLogic/QoS/MamdaniQoSManyRules.fld | 126 + .../jFuzzyLogic/QoS/SingletonQoSFewRules.fcl | 55 + .../jFuzzyLogic/QoS/SingletonQoSFewRules.fld | 126 + .../jFuzzyLogic/QoS/SingletonQoSManyRules.fcl | 83 + .../jFuzzyLogic/QoS/SingletonQoSManyRules.fld | 126 + Examples/jFuzzyLogic/bathTub.fcl | 42 + Examples/jFuzzyLogic/bathTub.fld | 6 + Examples/jFuzzyLogic/block.fcl | 51 + Examples/jFuzzyLogic/block.fld | 26 + Examples/jFuzzyLogic/ip2.fcl | 59 + Examples/jFuzzyLogic/ip2.fld | 26 + Examples/jFuzzyLogic/qualify.fcl | 124 + Examples/jFuzzyLogic/qualify.fld | 3126 +++++++++++++++++ Examples/jFuzzyLogic/qualify_optimized.fcl | 98 + Examples/jFuzzyLogic/qualify_optimized.fld | 3126 +++++++++++++++++ Examples/jFuzzyLogic/qurat.fcl | 62 + Examples/jFuzzyLogic/qurat.fld | 26 + Examples/jFuzzyLogic/robot.fcl | 135 + Examples/jFuzzyLogic/robot.fld | 626 ++++ Examples/jFuzzyLogic/robot_optimized.fcl | 121 + Examples/jFuzzyLogic/robot_optimized.fld | 626 ++++ Examples/jFuzzyLogic/s2.fcl | 65 + Examples/jFuzzyLogic/s2.fld | 26 + Examples/jFuzzyLogic/testDeMorgan_1.fcl | 42 + Examples/jFuzzyLogic/testDeMorgan_1.fld | 26 + Examples/jFuzzyLogic/testDeMorgan_2.fcl | 42 + Examples/jFuzzyLogic/testDeMorgan_2.fld | 26 + Examples/jFuzzyLogic/testDeMorgan_3.fcl | 42 + Examples/jFuzzyLogic/testDeMorgan_3.fld | 26 + Examples/jFuzzyLogic/testDeMorgan_4.fcl | 42 + Examples/jFuzzyLogic/testDeMorgan_4.fld | 26 + Examples/jFuzzyLogic/testDeMorgan_5.fcl | 42 + Examples/jFuzzyLogic/testDeMorgan_5.fld | 26 + Examples/jFuzzyLogic/testDeMorgan_6.fcl | 42 + Examples/jFuzzyLogic/testDeMorgan_6.fld | 26 + Examples/jFuzzyLogic/tipper.fcl | 56 + Examples/jFuzzyLogic/tipper.fld | 26 + Examples/jFuzzyLogic/tipper_with.fcl | 56 + Examples/jFuzzyLogic/tipper_with.fld | 26 + Examples/jFuzzyLogic/trust/LarsenFewRules.fcl | 61 + Examples/jFuzzyLogic/trust/LarsenFewRules.fld | 126 + .../jFuzzyLogic/trust/LarsenManyRules.fcl | 86 + .../jFuzzyLogic/trust/LarsenManyRules.fld | 126 + .../jFuzzyLogic/trust/MamdaniFewRules.fcl | 58 + .../jFuzzyLogic/trust/MamdaniFewRules.fld | 126 + .../jFuzzyLogic/trust/MamdaniManyRules.fcl | 85 + .../jFuzzyLogic/trust/MamdaniManyRules.fld | 126 + .../jFuzzyLogic/trust/SingletonFewRules.fcl | 57 + .../jFuzzyLogic/trust/SingletonFewRules.fld | 126 + .../jFuzzyLogic/trust/SingletonManyRules.fcl | 86 + .../jFuzzyLogic/trust/SingletonManyRules.fld | 126 + Examples/jFuzzyLogic/z.fcl | 35 + Examples/jFuzzyLogic/z.fld | 6 + README.md | 242 ++ extramf.py | 205 ++ fcl_parser.py | 674 ++++ fcl_scanner.py | 337 ++ fcl_symbols.py | 418 +++ hedges.py | 134 + main.py | 44 + matches_list.csv | 294 ++ simulate.py | 443 +++ teams_list.csv | 519 +++ tests/multiple.fcl | 68 + tests/test_controlsystem.py | 424 +++ tests/test_fcl_parser.py | 484 +++ tests/tipper.fcl | 50 + tnorms.py | 240 ++ worker.fcl | 58 + 95 files changed, 22483 insertions(+) create mode 100644 .gitignore create mode 100644 .idea/LSR2.iml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/workspace.xml create mode 100644 Examples/README.md create mode 100644 Examples/fuzzylite-mamdani/AllTerms.fcl create mode 100644 Examples/fuzzylite-mamdani/AllTerms.fld create mode 100644 Examples/fuzzylite-mamdani/LICENSE create mode 100644 Examples/fuzzylite-mamdani/Laundry.fcl create mode 100644 Examples/fuzzylite-mamdani/Laundry.fld create mode 100644 Examples/fuzzylite-mamdani/ObstacleAvoidance.fcl create mode 100644 Examples/fuzzylite-mamdani/ObstacleAvoidance.fld create mode 100644 Examples/fuzzylite-mamdani/SimpleDimmer.fcl create mode 100644 Examples/fuzzylite-mamdani/SimpleDimmer.fld create mode 100644 Examples/fuzzylite-mamdani/SimpleDimmerChained.fcl create mode 100644 Examples/fuzzylite-mamdani/SimpleDimmerChained.fld create mode 100644 Examples/jFuzzyLogic/LICENSE create mode 100644 Examples/jFuzzyLogic/QoS/LarsenQoSFewRules.fcl create mode 100644 Examples/jFuzzyLogic/QoS/LarsenQoSFewRules.fld create mode 100644 Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fcl create mode 100644 Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fld create mode 100644 Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fcl create mode 100644 Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fld create mode 100644 Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fcl create mode 100644 Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fld create mode 100644 Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fcl create mode 100644 Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fld create mode 100644 Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fcl create mode 100644 Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fld create mode 100644 Examples/jFuzzyLogic/bathTub.fcl create mode 100644 Examples/jFuzzyLogic/bathTub.fld create mode 100644 Examples/jFuzzyLogic/block.fcl create mode 100644 Examples/jFuzzyLogic/block.fld create mode 100644 Examples/jFuzzyLogic/ip2.fcl create mode 100644 Examples/jFuzzyLogic/ip2.fld create mode 100644 Examples/jFuzzyLogic/qualify.fcl create mode 100644 Examples/jFuzzyLogic/qualify.fld create mode 100644 Examples/jFuzzyLogic/qualify_optimized.fcl create mode 100644 Examples/jFuzzyLogic/qualify_optimized.fld create mode 100644 Examples/jFuzzyLogic/qurat.fcl create mode 100644 Examples/jFuzzyLogic/qurat.fld create mode 100644 Examples/jFuzzyLogic/robot.fcl create mode 100644 Examples/jFuzzyLogic/robot.fld create mode 100644 Examples/jFuzzyLogic/robot_optimized.fcl create mode 100644 Examples/jFuzzyLogic/robot_optimized.fld create mode 100644 Examples/jFuzzyLogic/s2.fcl create mode 100644 Examples/jFuzzyLogic/s2.fld create mode 100644 Examples/jFuzzyLogic/testDeMorgan_1.fcl create mode 100644 Examples/jFuzzyLogic/testDeMorgan_1.fld create mode 100644 Examples/jFuzzyLogic/testDeMorgan_2.fcl create mode 100644 Examples/jFuzzyLogic/testDeMorgan_2.fld create mode 100644 Examples/jFuzzyLogic/testDeMorgan_3.fcl create mode 100644 Examples/jFuzzyLogic/testDeMorgan_3.fld create mode 100644 Examples/jFuzzyLogic/testDeMorgan_4.fcl create mode 100644 Examples/jFuzzyLogic/testDeMorgan_4.fld create mode 100644 Examples/jFuzzyLogic/testDeMorgan_5.fcl create mode 100644 Examples/jFuzzyLogic/testDeMorgan_5.fld create mode 100644 Examples/jFuzzyLogic/testDeMorgan_6.fcl create mode 100644 Examples/jFuzzyLogic/testDeMorgan_6.fld create mode 100644 Examples/jFuzzyLogic/tipper.fcl create mode 100644 Examples/jFuzzyLogic/tipper.fld create mode 100644 Examples/jFuzzyLogic/tipper_with.fcl create mode 100644 Examples/jFuzzyLogic/tipper_with.fld create mode 100644 Examples/jFuzzyLogic/trust/LarsenFewRules.fcl create mode 100644 Examples/jFuzzyLogic/trust/LarsenFewRules.fld create mode 100644 Examples/jFuzzyLogic/trust/LarsenManyRules.fcl create mode 100644 Examples/jFuzzyLogic/trust/LarsenManyRules.fld create mode 100644 Examples/jFuzzyLogic/trust/MamdaniFewRules.fcl create mode 100644 Examples/jFuzzyLogic/trust/MamdaniFewRules.fld create mode 100644 Examples/jFuzzyLogic/trust/MamdaniManyRules.fcl create mode 100644 Examples/jFuzzyLogic/trust/MamdaniManyRules.fld create mode 100644 Examples/jFuzzyLogic/trust/SingletonFewRules.fcl create mode 100644 Examples/jFuzzyLogic/trust/SingletonFewRules.fld create mode 100644 Examples/jFuzzyLogic/trust/SingletonManyRules.fcl create mode 100644 Examples/jFuzzyLogic/trust/SingletonManyRules.fld create mode 100644 Examples/jFuzzyLogic/z.fcl create mode 100644 Examples/jFuzzyLogic/z.fld create mode 100644 README.md create mode 100644 extramf.py create mode 100644 fcl_parser.py create mode 100644 fcl_scanner.py create mode 100644 fcl_symbols.py create mode 100644 hedges.py create mode 100644 main.py create mode 100644 matches_list.csv create mode 100644 simulate.py create mode 100644 teams_list.csv create mode 100644 tests/multiple.fcl create mode 100644 tests/test_controlsystem.py create mode 100644 tests/test_fcl_parser.py create mode 100644 tests/tipper.fcl create mode 100644 tnorms.py create mode 100644 worker.fcl diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..894a44c --- /dev/null +++ b/.gitignore @@ -0,0 +1,104 @@ +# 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/ diff --git a/.idea/LSR2.iml b/.idea/LSR2.iml new file mode 100644 index 0000000..6711606 --- /dev/null +++ b/.idea/LSR2.iml @@ -0,0 +1,11 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..65531ca --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..67a6573 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 0000000..21a1a14 --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,75 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1591290249678 + + + + \ No newline at end of file diff --git a/Examples/README.md b/Examples/README.md new file mode 100644 index 0000000..6c20409 --- /dev/null +++ b/Examples/README.md @@ -0,0 +1,64 @@ +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. \ No newline at end of file diff --git a/Examples/fuzzylite-mamdani/AllTerms.fcl b/Examples/fuzzylite-mamdani/AllTerms.fcl new file mode 100644 index 0000000..0c56fd6 --- /dev/null +++ b/Examples/fuzzylite-mamdani/AllTerms.fcl @@ -0,0 +1,90 @@ +//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 diff --git a/Examples/fuzzylite-mamdani/AllTerms.fld b/Examples/fuzzylite-mamdani/AllTerms.fld new file mode 100644 index 0000000..b765a5b --- /dev/null +++ b/Examples/fuzzylite-mamdani/AllTerms.fld @@ -0,0 +1,1025 @@ +AllInputTerms AllOutputTerms +0.000000000 4.599895277 +0.006353861 4.599895277 +0.012707722 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But first, please read +. diff --git a/Examples/fuzzylite-mamdani/Laundry.fcl b/Examples/fuzzylite-mamdani/Laundry.fcl new file mode 100644 index 0000000..9a34532 --- /dev/null +++ b/Examples/fuzzylite-mamdani/Laundry.fcl @@ -0,0 +1,58 @@ +//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 diff --git a/Examples/fuzzylite-mamdani/Laundry.fld b/Examples/fuzzylite-mamdani/Laundry.fld new file mode 100644 index 0000000..a8f27dd --- /dev/null +++ b/Examples/fuzzylite-mamdani/Laundry.fld @@ -0,0 +1,1025 @@ +Load Dirt Detergent Cycle +0.000000000 0.000000000 39.920000000 5.000000000 +0.000000000 0.193548387 39.920000000 5.000000000 +0.000000000 0.387096774 39.920000000 5.000000000 +0.000000000 0.580645161 39.920000000 5.000000000 +0.000000000 0.774193548 39.920000000 5.000000000 +0.000000000 0.967741935 39.920000000 5.000000000 +0.000000000 1.161290323 39.680000000 5.160000000 +0.000000000 1.354838710 39.280000000 5.360000000 +0.000000000 1.548387097 38.880000000 5.540000000 +0.000000000 1.741935484 38.480000000 5.740000000 +0.000000000 1.935483871 38.160000000 5.940000000 +0.000000000 2.129032258 37.600000000 6.200000000 +0.000000000 2.322580645 37.040000000 6.480000000 +0.000000000 2.516129032 36.400000000 6.780000000 +0.000000000 2.709677419 35.920000000 7.060000000 +0.000000000 2.903225806 35.280000000 7.360000000 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a/Examples/fuzzylite-mamdani/SimpleDimmer.fcl b/Examples/fuzzylite-mamdani/SimpleDimmer.fcl new file mode 100644 index 0000000..563c153 --- /dev/null +++ b/Examples/fuzzylite-mamdani/SimpleDimmer.fcl @@ -0,0 +1,37 @@ +//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 diff --git a/Examples/fuzzylite-mamdani/SimpleDimmer.fld b/Examples/fuzzylite-mamdani/SimpleDimmer.fld new file mode 100644 index 0000000..bd4dc51 --- /dev/null +++ b/Examples/fuzzylite-mamdani/SimpleDimmer.fld @@ -0,0 +1,1025 @@ +Ambient Power +0.000000000 nan +0.000977517 0.750000000 +0.001955034 0.750000000 +0.002932551 0.750000000 +0.003910068 0.750000000 +0.004887586 0.750000000 +0.005865103 0.750000000 +0.006842620 0.750000000 +0.007820137 0.750000000 +0.008797654 0.750000000 +0.009775171 0.750000000 +0.010752688 0.750000000 +0.011730205 0.750000000 +0.012707722 0.750000000 +0.013685239 0.750000000 +0.014662757 0.750000000 +0.015640274 0.750000000 +0.016617791 0.750000000 +0.017595308 0.750000000 +0.018572825 0.750000000 +0.019550342 0.750000000 +0.020527859 0.750000000 +0.021505376 0.750000000 +0.022482893 0.750000000 +0.023460411 0.750000000 +0.024437928 0.750000000 +0.025415445 0.750000000 +0.026392962 0.750000000 +0.027370479 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file mode 100644 index 0000000..29bb4ea --- /dev/null +++ b/Examples/fuzzylite-mamdani/SimpleDimmerChained.fcl @@ -0,0 +1,51 @@ +//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 diff --git a/Examples/fuzzylite-mamdani/SimpleDimmerChained.fld b/Examples/fuzzylite-mamdani/SimpleDimmerChained.fld new file mode 100644 index 0000000..843c017 --- /dev/null +++ b/Examples/fuzzylite-mamdani/SimpleDimmerChained.fld @@ -0,0 +1,1025 @@ +Ambient Power InversePower +0.000000000 nan nan +0.000977517 0.750000000 0.221671502 +0.001955034 0.750000000 0.220306607 +0.002932551 0.750000000 0.219272108 +0.003910068 0.750000000 0.218372593 +0.004887586 0.750000000 0.217595038 +0.005865103 0.750000000 0.216891736 +0.006842620 0.750000000 0.216242049 +0.007820137 0.750000000 0.215635499 +0.008797654 0.750000000 0.215066121 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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 diff --git a/Examples/jFuzzyLogic/QoS/LarsenQoSFewRules.fld b/Examples/jFuzzyLogic/QoS/LarsenQoSFewRules.fld new file mode 100644 index 0000000..efccb97 --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/LarsenQoSFewRules.fld @@ -0,0 +1,126 @@ +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 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a/Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fcl b/Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fcl new file mode 100644 index 0000000..08c4e0e --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fcl @@ -0,0 +1,83 @@ +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 diff --git a/Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fld b/Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fld new file mode 100644 index 0000000..3d3287a --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/LarsenQoSManyRules.fld @@ -0,0 +1,126 @@ +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 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2.2285730391707147E-17 4.3661800689911194E-8 2.8964811369387465E-11 1.854681152821655E-17 1.2754076295260396E-9 diff --git a/Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fcl b/Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fcl new file mode 100644 index 0000000..3afe927 --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fcl @@ -0,0 +1,55 @@ +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 diff --git a/Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fld b/Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fld new file mode 100644 index 0000000..ef0ad79 --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/MamdaniQoSFewRules.fld @@ -0,0 +1,126 @@ +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 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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 diff --git a/Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fcl b/Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fcl new file mode 100644 index 0000000..7374b4f --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fcl @@ -0,0 +1,83 @@ +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 diff --git a/Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fld b/Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fld new file mode 100644 index 0000000..c7df6dd --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/MamdaniQoSManyRules.fld @@ -0,0 +1,126 @@ +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 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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 diff --git a/Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fcl b/Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fcl new file mode 100644 index 0000000..785c1d7 --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fcl @@ -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 diff --git a/Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fld b/Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fld new file mode 100644 index 0000000..3e20239 --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/SingletonQoSFewRules.fld @@ -0,0 +1,126 @@ +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 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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 diff --git a/Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fcl b/Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fcl new file mode 100644 index 0000000..311804e --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fcl @@ -0,0 +1,83 @@ +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 diff --git a/Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fld b/Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fld new file mode 100644 index 0000000..21111b5 --- /dev/null +++ b/Examples/jFuzzyLogic/QoS/SingletonQoSManyRules.fld @@ -0,0 +1,126 @@ +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 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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 diff --git a/Examples/jFuzzyLogic/bathTub.fcl b/Examples/jFuzzyLogic/bathTub.fcl new file mode 100644 index 0000000..e2a136f --- /dev/null +++ b/Examples/jFuzzyLogic/bathTub.fcl @@ -0,0 +1,42 @@ +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 diff --git a/Examples/jFuzzyLogic/bathTub.fld b/Examples/jFuzzyLogic/bathTub.fld new file mode 100644 index 0000000..8570c1d --- /dev/null +++ b/Examples/jFuzzyLogic/bathTub.fld @@ -0,0 +1,6 @@ +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 diff --git a/Examples/jFuzzyLogic/block.fcl b/Examples/jFuzzyLogic/block.fcl new file mode 100644 index 0000000..393464a --- /dev/null +++ b/Examples/jFuzzyLogic/block.fcl @@ -0,0 +1,51 @@ +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 diff --git a/Examples/jFuzzyLogic/block.fld b/Examples/jFuzzyLogic/block.fld new file mode 100644 index 0000000..21d0049 --- /dev/null +++ b/Examples/jFuzzyLogic/block.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/ip2.fcl b/Examples/jFuzzyLogic/ip2.fcl new file mode 100644 index 0000000..55b5ded --- /dev/null +++ b/Examples/jFuzzyLogic/ip2.fcl @@ -0,0 +1,59 @@ +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 diff --git a/Examples/jFuzzyLogic/ip2.fld b/Examples/jFuzzyLogic/ip2.fld new file mode 100644 index 0000000..803f058 --- /dev/null +++ b/Examples/jFuzzyLogic/ip2.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/qualify.fcl b/Examples/jFuzzyLogic/qualify.fcl new file mode 100644 index 0000000..172163d --- /dev/null +++ b/Examples/jFuzzyLogic/qualify.fcl @@ -0,0 +1,124 @@ +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 diff --git a/Examples/jFuzzyLogic/qualify.fld b/Examples/jFuzzyLogic/qualify.fld new file mode 100644 index 0000000..e59ad4b --- /dev/null +++ b/Examples/jFuzzyLogic/qualify.fld @@ -0,0 +1,3126 @@ +scoring occupation_type city scoring_partner sel credLimMul qualify 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 +0.0 0.0 0.0 -100.0 -100.0 -0.999995991983968 1.0000000000000029 0.0 0.0 0.0 0.0 1.0 1.0530617357553813E-20 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 -100.0 1920.0 0.9717937848853463 2.5814077922077967 0.0 0.0 0.0 0.0 1.0 6.240255430523462E-12 0.8857142857142857 1.0 0.11428571428571432 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 -100.0 3940.0 1.0129300137804726 2.500000000000006 0.0 0.0 0.0 0.0 1.0 1.0 0.53 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 -100.0 5960.0 0.4999880159041644 2.499999998288685 0.0 0.0 0.0 0.0 1.0 0.9999995436475715 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 -100.0 7980.0 0.4999880159041644 2.500000000000006 0.0 0.0 0.0 0.0 1.0 0.9999999999999993 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 120.0 -100.0 -0.7049193086629225 1.3015861111111253 0.0 0.0 0.0 0.0 1.0 1.0530617357553813E-20 0.0 0.0 0.0 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +0.0 0.0 0.0 120.0 1920.0 0.9481370807845155 2.5672630511910906 0.0 0.0 0.0 0.0 1.0 6.240255430523462E-12 0.8857142857142857 1.0 0.11428571428571432 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +0.0 0.0 0.0 120.0 3940.0 0.9559391995993537 2.4821421875000174 0.0 0.0 0.0 0.0 1.0 1.0 0.53 0.0 0.0 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +0.0 0.0 0.0 120.0 5960.0 0.49998716705526425 2.482142185984784 0.0 0.0 0.0 0.0 1.0 0.9999995436475715 0.0 0.0 0.0 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +0.0 0.0 0.0 120.0 7980.0 0.49998716705526425 2.4821421875000174 0.0 0.0 0.0 0.0 1.0 0.9999999999999993 0.0 0.0 0.0 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +0.0 0.0 0.0 340.0 -100.0 -0.5122453113582078 1.4894138470964762 0.0 0.0 0.0 0.0 1.0 1.0530617357553813E-20 0.0 0.0 0.0 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +0.0 0.0 0.0 340.0 1920.0 0.9124078431462704 2.4995454795674283 0.0 0.0 0.0 0.0 1.0 6.240255430523462E-12 0.8857142857142857 1.0 0.11428571428571432 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +0.0 0.0 0.0 340.0 3940.0 0.9116823795411357 2.414560596475939 0.0 0.0 0.0 0.0 1.0 1.0 0.53 0.0 0.0 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +0.0 0.0 0.0 340.0 5960.0 0.4999887717651503 2.4145605951428615 0.0 0.0 0.0 0.0 1.0 0.9999995436475715 0.0 0.0 0.0 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +0.0 0.0 0.0 340.0 7980.0 0.4999887717651503 2.414560596475939 0.0 0.0 0.0 0.0 1.0 0.9999999999999993 0.0 0.0 0.0 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +0.0 0.0 0.0 560.0 -100.0 -0.5014835956340478 1.4987253301251806 0.0 0.0 0.0 0.0 1.0 1.0530617357553813E-20 0.0 0.0 0.0 0.0 1.0 0.0 0.9333333333333333 0.0 +0.0 0.0 0.0 560.0 1920.0 0.9102101066707782 2.4946259844259755 0.0 0.0 0.0 0.0 1.0 6.240255430523462E-12 0.8857142857142857 1.0 0.11428571428571432 0.0 1.0 0.0 0.9333333333333333 0.0 +0.0 0.0 0.0 560.0 3940.0 0.9090144871384124 2.409753965507829 0.0 0.0 0.0 0.0 1.0 1.0 0.53 0.0 0.0 0.0 1.0 0.0 0.9333333333333333 0.0 +0.0 0.0 0.0 560.0 5960.0 0.4999889516479702 2.4097539641862133 0.0 0.0 0.0 0.0 1.0 0.9999995436475715 0.0 0.0 0.0 0.0 1.0 0.0 0.9333333333333333 0.0 +0.0 0.0 0.0 560.0 7980.0 0.4999889516479702 2.409753965507829 0.0 0.0 0.0 0.0 1.0 0.9999999999999993 0.0 0.0 0.0 0.0 1.0 0.0 0.9333333333333333 0.0 +0.0 0.0 0.0 780.0 -100.0 -0.999995991983968 1.0000000000000029 0.0 0.0 0.0 0.0 1.0 1.0530617357553813E-20 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 780.0 1920.0 0.9717937848853463 2.5814077922077967 0.0 0.0 0.0 0.0 1.0 6.240255430523462E-12 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0.0 1.0 0.53 0.0 0.0 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +800.0 16.0 8000.0 120.0 5960.0 1.2077605151471122 2.5326080641011224 0.0 1.0 0.5 0.0 0.0 0.9999995436475715 0.0 0.0 0.0 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +800.0 16.0 8000.0 120.0 7980.0 1.2077605151471122 2.5326080641011224 0.0 1.0 0.5 0.0 0.0 0.9999999999999993 0.0 0.0 0.0 0.0 1.0 0.0 0.2857142857142857 0.7142857142857143 +800.0 16.0 8000.0 340.0 -100.0 0.820295307339647 2.4639444841218645 0.0 1.0 0.5 0.0 0.0 1.0530617357553813E-20 0.0 0.0 0.0 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +800.0 16.0 8000.0 340.0 1920.0 0.9192006146193857 2.505645073461167 0.0 1.0 0.5 0.0 0.0 6.240255430523462E-12 0.8857142857142857 1.0 0.11428571428571432 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +800.0 16.0 8000.0 340.0 3940.0 1.1537797403644874 2.4639444841218645 0.0 1.0 0.5 0.0 0.0 1.0 0.53 0.0 0.0 0.0 1.0 0.0 0.8095238095238095 0.19047619047619047 +800.0 16.0 8000.0 340.0 5960.0 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1.1504460544265884 2.4590142044032066 0.0 1.0 0.5 0.0 0.0 0.9999999999999993 0.0 0.0 0.0 0.0 1.0 0.0 0.9333333333333333 0.0 +800.0 16.0 8000.0 780.0 -100.0 0.8888909594184756 2.5500000000000065 0.0 1.0 0.5 0.0 0.0 1.0530617357553813E-20 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +800.0 16.0 8000.0 780.0 1920.0 0.9793380472272434 2.588115161996651 0.0 1.0 0.5 0.0 0.0 6.240255430523462E-12 0.8857142857142857 1.0 0.11428571428571432 0.0 1.0 0.0 0.0 0.0 +800.0 16.0 8000.0 780.0 3940.0 1.2737927103000592 2.5500000000000065 0.0 1.0 0.5 0.0 0.0 1.0 0.53 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +800.0 16.0 8000.0 780.0 5960.0 1.2737927103000592 2.5500000000000065 0.0 1.0 0.5 0.0 0.0 0.9999995436475715 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +800.0 16.0 8000.0 780.0 7980.0 1.2737927103000592 2.5500000000000065 0.0 1.0 0.5 0.0 0.0 0.9999999999999993 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 diff --git a/Examples/jFuzzyLogic/qualify_optimized.fcl b/Examples/jFuzzyLogic/qualify_optimized.fcl new file mode 100644 index 0000000..91d3b71 --- /dev/null +++ b/Examples/jFuzzyLogic/qualify_optimized.fcl @@ -0,0 +1,98 @@ +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 diff --git a/Examples/jFuzzyLogic/qualify_optimized.fld b/Examples/jFuzzyLogic/qualify_optimized.fld new file mode 100644 index 0000000..37df397 --- /dev/null +++ b/Examples/jFuzzyLogic/qualify_optimized.fld @@ -0,0 +1,3126 @@ +scoring occupation_type city scoring_partner sel credLimMul qualify 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 +0.0 0.0 0.0 -100.0 -100.0 -1.206666659202866 1.0000000000000029 0.0 0.0 0.0 0.0 1.0 4.6334716373236764E-21 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 -100.0 1920.0 1.1992903655117537 2.5654942150946973 0.0 0.0 0.0 0.0 1.0 2.7457123894303223E-12 0.8857142857142857 0.6699999999999998 0.0822857142857143 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 -100.0 3940.0 0.863385903964759 2.313754145354425 0.0 0.0 0.0 0.0 1.0 0.43999999999999984 0.53 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +0.0 0.0 0.0 -100.0 5960.0 -0.17671504534678786 2.2210623803058884 0.0 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2.3288814838498215 0.0 0.39527102803738295 0.11715481171548105 0.0 0.0 0.43999999999999984 0.53 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +800.0 16.0 8000.0 780.0 5960.0 0.8958285270699143 2.2429882898420095 0.0 0.39527102803738295 0.11715481171548105 0.0 0.0 0.43999979920493126 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 +800.0 16.0 8000.0 780.0 7980.0 0.8958286437237324 2.2429885099069744 0.0 0.39527102803738295 0.11715481171548105 0.0 0.0 0.43999999999999956 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 diff --git a/Examples/jFuzzyLogic/qurat.fcl b/Examples/jFuzzyLogic/qurat.fcl new file mode 100644 index 0000000..1f9ff7d --- /dev/null +++ b/Examples/jFuzzyLogic/qurat.fcl @@ -0,0 +1,62 @@ +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 diff --git a/Examples/jFuzzyLogic/qurat.fld b/Examples/jFuzzyLogic/qurat.fld new file mode 100644 index 0000000..073d50d --- /dev/null +++ b/Examples/jFuzzyLogic/qurat.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/robot.fcl b/Examples/jFuzzyLogic/robot.fcl new file mode 100644 index 0000000..6b6c1fe --- /dev/null +++ b/Examples/jFuzzyLogic/robot.fcl @@ -0,0 +1,135 @@ +/* + 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 diff --git a/Examples/jFuzzyLogic/robot.fld b/Examples/jFuzzyLogic/robot.fld new file mode 100644 index 0000000..7254ea0 --- /dev/null +++ b/Examples/jFuzzyLogic/robot.fld @@ -0,0 +1,626 @@ +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 +0.0 0.0 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 +0.0 0.0 0.0 -27.0 -0.8968226725105329 -0.8968226725105329 0.3688 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +0.0 0.0 0.0 -9.0 -0.18451141786173775 -0.5804602711643171 0.1844 0.0 0.39984 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +0.0 0.0 0.0 9.0 -1.1512282770330305E-17 -0.5000000000000017 0.0 0.0 0.39984 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +0.0 0.0 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 +0.0 0.0 0.4 -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 +0.0 0.0 0.4 -27.0 -0.23797748610920041 -0.8144006488819687 0.3688 0.0 0.0 0.0 0.14552 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +0.0 0.0 0.4 -9.0 -0.01312616868213907 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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 diff --git a/Examples/jFuzzyLogic/robot_optimized.fcl b/Examples/jFuzzyLogic/robot_optimized.fcl new file mode 100644 index 0000000..914a7c3 --- /dev/null +++ b/Examples/jFuzzyLogic/robot_optimized.fcl @@ -0,0 +1,121 @@ +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 diff --git a/Examples/jFuzzyLogic/robot_optimized.fld b/Examples/jFuzzyLogic/robot_optimized.fld new file mode 100644 index 0000000..c8329bb --- /dev/null +++ b/Examples/jFuzzyLogic/robot_optimized.fld @@ -0,0 +1,626 @@ +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 +0.0 0.0 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 +0.0 0.0 0.0 -27.0 -0.8968226725105329 -0.8968226725105329 0.3688 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +0.0 0.0 0.0 -9.0 -0.5804602711643171 -0.18451141786173775 0.1844 0.0 0.39984 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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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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 diff --git a/Examples/jFuzzyLogic/s2.fcl b/Examples/jFuzzyLogic/s2.fcl new file mode 100644 index 0000000..78d0c13 --- /dev/null +++ b/Examples/jFuzzyLogic/s2.fcl @@ -0,0 +1,65 @@ +// 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 + diff --git a/Examples/jFuzzyLogic/s2.fld b/Examples/jFuzzyLogic/s2.fld new file mode 100644 index 0000000..50bcbb8 --- /dev/null +++ b/Examples/jFuzzyLogic/s2.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_1.fcl b/Examples/jFuzzyLogic/testDeMorgan_1.fcl new file mode 100644 index 0000000..9ae084b --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_1.fcl @@ -0,0 +1,42 @@ + +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_1.fld b/Examples/jFuzzyLogic/testDeMorgan_1.fld new file mode 100644 index 0000000..7c28809 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_1.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_2.fcl b/Examples/jFuzzyLogic/testDeMorgan_2.fcl new file mode 100644 index 0000000..f71f41d --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_2.fcl @@ -0,0 +1,42 @@ + +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_2.fld b/Examples/jFuzzyLogic/testDeMorgan_2.fld new file mode 100644 index 0000000..7c28809 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_2.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_3.fcl b/Examples/jFuzzyLogic/testDeMorgan_3.fcl new file mode 100644 index 0000000..6d0e099 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_3.fcl @@ -0,0 +1,42 @@ + +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_3.fld b/Examples/jFuzzyLogic/testDeMorgan_3.fld new file mode 100644 index 0000000..7c28809 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_3.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_4.fcl b/Examples/jFuzzyLogic/testDeMorgan_4.fcl new file mode 100644 index 0000000..358fb72 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_4.fcl @@ -0,0 +1,42 @@ + +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_4.fld b/Examples/jFuzzyLogic/testDeMorgan_4.fld new file mode 100644 index 0000000..7c28809 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_4.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_5.fcl b/Examples/jFuzzyLogic/testDeMorgan_5.fcl new file mode 100644 index 0000000..936d11e --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_5.fcl @@ -0,0 +1,42 @@ + +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_5.fld b/Examples/jFuzzyLogic/testDeMorgan_5.fld new file mode 100644 index 0000000..7c28809 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_5.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_6.fcl b/Examples/jFuzzyLogic/testDeMorgan_6.fcl new file mode 100644 index 0000000..3ba3954 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_6.fcl @@ -0,0 +1,42 @@ + +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 diff --git a/Examples/jFuzzyLogic/testDeMorgan_6.fld b/Examples/jFuzzyLogic/testDeMorgan_6.fld new file mode 100644 index 0000000..7c28809 --- /dev/null +++ b/Examples/jFuzzyLogic/testDeMorgan_6.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/tipper.fcl b/Examples/jFuzzyLogic/tipper.fcl new file mode 100644 index 0000000..59c9137 --- /dev/null +++ b/Examples/jFuzzyLogic/tipper.fcl @@ -0,0 +1,56 @@ +/* + 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 diff --git a/Examples/jFuzzyLogic/tipper.fld b/Examples/jFuzzyLogic/tipper.fld new file mode 100644 index 0000000..50bcbb8 --- /dev/null +++ b/Examples/jFuzzyLogic/tipper.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/tipper_with.fcl b/Examples/jFuzzyLogic/tipper_with.fcl new file mode 100644 index 0000000..9255e0b --- /dev/null +++ b/Examples/jFuzzyLogic/tipper_with.fcl @@ -0,0 +1,56 @@ +/* + 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 diff --git a/Examples/jFuzzyLogic/tipper_with.fld b/Examples/jFuzzyLogic/tipper_with.fld new file mode 100644 index 0000000..6280790 --- /dev/null +++ b/Examples/jFuzzyLogic/tipper_with.fld @@ -0,0 +1,26 @@ +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 diff --git a/Examples/jFuzzyLogic/trust/LarsenFewRules.fcl b/Examples/jFuzzyLogic/trust/LarsenFewRules.fcl new file mode 100644 index 0000000..ad2c8a3 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/LarsenFewRules.fcl @@ -0,0 +1,61 @@ + +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 diff --git a/Examples/jFuzzyLogic/trust/LarsenFewRules.fld b/Examples/jFuzzyLogic/trust/LarsenFewRules.fld new file mode 100644 index 0000000..4778855 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/LarsenFewRules.fld @@ -0,0 +1,126 @@ +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 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+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 diff --git a/Examples/jFuzzyLogic/trust/LarsenManyRules.fcl b/Examples/jFuzzyLogic/trust/LarsenManyRules.fcl new file mode 100644 index 0000000..47ad9c3 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/LarsenManyRules.fcl @@ -0,0 +1,86 @@ +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 diff --git a/Examples/jFuzzyLogic/trust/LarsenManyRules.fld b/Examples/jFuzzyLogic/trust/LarsenManyRules.fld new file mode 100644 index 0000000..37cbdde --- /dev/null +++ b/Examples/jFuzzyLogic/trust/LarsenManyRules.fld @@ -0,0 +1,126 @@ +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 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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 diff --git a/Examples/jFuzzyLogic/trust/MamdaniFewRules.fcl b/Examples/jFuzzyLogic/trust/MamdaniFewRules.fcl new file mode 100644 index 0000000..48952cf --- /dev/null +++ b/Examples/jFuzzyLogic/trust/MamdaniFewRules.fcl @@ -0,0 +1,58 @@ +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 diff --git a/Examples/jFuzzyLogic/trust/MamdaniFewRules.fld b/Examples/jFuzzyLogic/trust/MamdaniFewRules.fld new file mode 100644 index 0000000..e3ac975 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/MamdaniFewRules.fld @@ -0,0 +1,126 @@ +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 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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 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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 diff --git a/Examples/jFuzzyLogic/trust/MamdaniManyRules.fcl b/Examples/jFuzzyLogic/trust/MamdaniManyRules.fcl new file mode 100644 index 0000000..1aec12c --- /dev/null +++ b/Examples/jFuzzyLogic/trust/MamdaniManyRules.fcl @@ -0,0 +1,85 @@ +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 diff --git a/Examples/jFuzzyLogic/trust/MamdaniManyRules.fld b/Examples/jFuzzyLogic/trust/MamdaniManyRules.fld new file mode 100644 index 0000000..699ece5 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/MamdaniManyRules.fld @@ -0,0 +1,126 @@ +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 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a/Examples/jFuzzyLogic/trust/SingletonFewRules.fcl b/Examples/jFuzzyLogic/trust/SingletonFewRules.fcl new file mode 100644 index 0000000..1f2a809 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/SingletonFewRules.fcl @@ -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 diff --git a/Examples/jFuzzyLogic/trust/SingletonFewRules.fld b/Examples/jFuzzyLogic/trust/SingletonFewRules.fld new file mode 100644 index 0000000..bb49f50 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/SingletonFewRules.fld @@ -0,0 +1,126 @@ +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 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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 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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 diff --git a/Examples/jFuzzyLogic/trust/SingletonManyRules.fcl b/Examples/jFuzzyLogic/trust/SingletonManyRules.fcl new file mode 100644 index 0000000..7cf9a66 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/SingletonManyRules.fcl @@ -0,0 +1,86 @@ +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 diff --git a/Examples/jFuzzyLogic/trust/SingletonManyRules.fld b/Examples/jFuzzyLogic/trust/SingletonManyRules.fld new file mode 100644 index 0000000..578d6a4 --- /dev/null +++ b/Examples/jFuzzyLogic/trust/SingletonManyRules.fld @@ -0,0 +1,126 @@ +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 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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 diff --git a/Examples/jFuzzyLogic/z.fcl b/Examples/jFuzzyLogic/z.fcl new file mode 100644 index 0000000..082fdda --- /dev/null +++ b/Examples/jFuzzyLogic/z.fcl @@ -0,0 +1,35 @@ +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 diff --git a/Examples/jFuzzyLogic/z.fld b/Examples/jFuzzyLogic/z.fld new file mode 100644 index 0000000..e0b8823 --- /dev/null +++ b/Examples/jFuzzyLogic/z.fld @@ -0,0 +1,6 @@ +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 diff --git a/README.md b/README.md new file mode 100644 index 0000000..fa4ec41 --- /dev/null +++ b/README.md @@ -0,0 +1,242 @@ +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. \ No newline at end of file diff --git a/extramf.py b/extramf.py new file mode 100644 index 0000000..7bad1d9 --- /dev/null +++ b/extramf.py @@ -0,0 +1,205 @@ +# -*- 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())) diff --git a/fcl_parser.py b/fcl_parser.py new file mode 100644 index 0000000..1486100 --- /dev/null +++ b/fcl_parser.py @@ -0,0 +1,674 @@ +# -*- 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) diff --git a/fcl_scanner.py b/fcl_scanner.py new file mode 100644 index 0000000..2b29531 --- /dev/null +++ b/fcl_scanner.py @@ -0,0 +1,337 @@ +''' + 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) diff --git a/fcl_symbols.py b/fcl_symbols.py new file mode 100644 index 0000000..108a811 --- /dev/null +++ b/fcl_symbols.py @@ -0,0 +1,418 @@ +# -*- 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 diff --git a/hedges.py b/hedges.py new file mode 100644 index 0000000..c6daf02 --- /dev/null +++ b/hedges.py @@ -0,0 +1,134 @@ +# -*- 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; 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 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) diff --git a/main.py b/main.py new file mode 100644 index 0000000..b3ec488 --- /dev/null +++ b/main.py @@ -0,0 +1,44 @@ +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])) \ No newline at end of file diff --git a/matches_list.csv b/matches_list.csv new file mode 100644 index 0000000..23810e5 --- /dev/null +++ b/matches_list.csv @@ -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 diff --git a/simulate.py b/simulate.py new file mode 100644 index 0000000..d67c10f --- /dev/null +++ b/simulate.py @@ -0,0 +1,443 @@ +# -*- 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) diff --git a/teams_list.csv b/teams_list.csv new file mode 100644 index 0000000..992dada --- /dev/null +++ b/teams_list.csv @@ -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 diff --git a/tests/multiple.fcl b/tests/multiple.fcl new file mode 100644 index 0000000..145fa92 --- /dev/null +++ b/tests/multiple.fcl @@ -0,0 +1,68 @@ +// 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 diff --git a/tests/test_controlsystem.py b/tests/test_controlsystem.py new file mode 100644 index 0000000..40347d1 --- /dev/null +++ b/tests/test_controlsystem.py @@ -0,0 +1,424 @@ +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() diff --git a/tests/test_fcl_parser.py b/tests/test_fcl_parser.py new file mode 100644 index 0000000..bc55bea --- /dev/null +++ b/tests/test_fcl_parser.py @@ -0,0 +1,484 @@ +# -*- 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() diff --git a/tests/tipper.fcl b/tests/tipper.fcl new file mode 100644 index 0000000..3e6bdd4 --- /dev/null +++ b/tests/tipper.fcl @@ -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 diff --git a/tnorms.py b/tnorms.py new file mode 100644 index 0000000..c0241e6 --- /dev/null +++ b/tnorms.py @@ -0,0 +1,240 @@ +# -*- 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) diff --git a/worker.fcl b/worker.fcl new file mode 100644 index 0000000..c030d50 --- /dev/null +++ b/worker.fcl @@ -0,0 +1,58 @@ +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