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simple_neurone #28

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s464843 wants to merge 3 commits from simple_neuore into master
pull from: simple_neuore
merge into: s462072:master
Conversation 0 Commits 3 Files Changed 7 +135 -43
7 changed files with 135 additions and 43 deletions
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28
Untitled.ipynb
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@ -31,11 +31,33 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"id": "04bbec40",
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pybrain3'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpybrain3\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpylab\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m \n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pybrain3'"
]
}
],
"source": [
"import pybrain3\n",
"import pickle\n",
"import matplotlib.pylab as plt \n",
"from numpy import ravel\n",
"from pybrain3.tools.shortcuts import buildNetwork\n",
"from pybrain3.datasets import SupervisedDataSet\n",
"from pybrain3.supervised.trainers import BackpropTrainer \n",
"from pybrain3.tools.xml.networkwriter import NetworkWriter \n",
"from pybrain3.tools.xml.networkreader import NetworkReader"
]
},
{
"cell_type": "code",

74
decision_tree/tree_as_txt.txt
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@ -4,40 +4,18 @@
| | | |--- class: 0
| | |--- feature_0 > 1.50
| | | |--- feature_3 <= 3.50
| | | | |--- feature_4 <= 2.50
| | | | |--- feature_2 <= 2.50
| | | | | |--- class: 1
| | | | |--- feature_4 > 2.50
| | | | | |--- feature_2 <= 2.50
| | | | |--- feature_2 > 2.50
| | | | | |--- feature_4 <= 2.50
| | | | | | |--- class: 1
| | | | | |--- feature_2 > 2.50
| | | | | |--- feature_4 > 2.50
| | | | | | |--- class: 0
| | | |--- feature_3 > 3.50
| | | | |--- feature_3 <= 4.50
| | | | | |--- feature_1 <= 2.50
| | | | | | |--- feature_0 <= 2.50
| | | | | | | |--- feature_1 <= 1.50
| | | | | | | | |--- feature_4 <= 2.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- feature_4 > 2.50
| | | | | | | | | |--- feature_2 <= 2.00
| | | | | | | | | | |--- class: 1
| | | | | | | | | |--- feature_2 > 2.00
| | | | | | | | | | |--- class: 0
| | | | | | | |--- feature_1 > 1.50
| | | | | | | | |--- class: 0
| | | | | | |--- feature_0 > 2.50
| | | | | | | |--- feature_2 <= 2.50
| | | | | | | | |--- class: 1
| | | | | | | |--- feature_2 > 2.50
| | | | | | | | |--- feature_4 <= 2.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- feature_4 > 2.50
| | | | | | | | | |--- class: 0
| | | | | |--- feature_1 > 2.50
| | | | | | |--- feature_0 <= 3.50
| | | | | | | |--- class: 0
| | | | | | |--- feature_0 > 3.50
| | | | | | | |--- feature_1 <= 3.50
| | | | | | | | |--- feature_2 <= 2.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- feature_2 > 2.50
@ -45,8 +23,30 @@
| | | | | | | | | | |--- class: 1
| | | | | | | | | |--- feature_4 > 2.00
| | | | | | | | | | |--- class: 0
| | | | | | | |--- feature_1 > 3.50
| | | | | | | |--- feature_1 > 1.50
| | | | | | | | |--- class: 0
| | | | | | |--- feature_0 > 2.50
| | | | | | | |--- feature_4 <= 2.50
| | | | | | | | |--- class: 1
| | | | | | | |--- feature_4 > 2.50
| | | | | | | | |--- feature_2 <= 2.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- feature_2 > 2.50
| | | | | | | | | |--- class: 0
| | | | | |--- feature_1 > 2.50
| | | | | | |--- feature_1 <= 3.50
| | | | | | | |--- feature_0 <= 3.50
| | | | | | | | |--- class: 0
| | | | | | | |--- feature_0 > 3.50
| | | | | | | | |--- feature_4 <= 2.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- feature_4 > 2.50
| | | | | | | | | |--- feature_2 <= 2.00
| | | | | | | | | | |--- class: 1
| | | | | | | | | |--- feature_2 > 2.00
| | | | | | | | | | |--- class: 0
| | | | | | |--- feature_1 > 3.50
| | | | | | | |--- class: 0
| | | | |--- feature_3 > 4.50
| | | | | |--- class: 0
| |--- feature_4 > 3.50
@ -59,13 +59,13 @@
| | | | | | |--- class: 1
| | | | |--- feature_3 > 3.50
| | | | | |--- feature_1 <= 2.50
| | | | | | |--- feature_3 <= 4.50
| | | | | | | |--- feature_0 <= 2.50
| | | | | | | | |--- class: 0
| | | | | | | |--- feature_0 > 2.50
| | | | | | | | |--- class: 1
| | | | | | |--- feature_3 > 4.50
| | | | | | |--- feature_0 <= 2.50
| | | | | | | |--- class: 0
| | | | | | |--- feature_0 > 2.50
| | | | | | | |--- feature_3 <= 4.50
| | | | | | | | |--- class: 1
| | | | | | | |--- feature_3 > 4.50
| | | | | | | | |--- class: 0
| | | | | |--- feature_1 > 2.50
| | | | | | |--- class: 0
| | | |--- feature_4 > 4.50
@ -73,8 +73,8 @@
| | |--- feature_2 > 1.50
| | | |--- class: 0
|--- feature_2 > 3.50
| |--- feature_4 <= 1.50
| | |--- feature_1 <= 1.50
| |--- feature_1 <= 1.50
| | |--- feature_4 <= 1.50
| | | |--- feature_2 <= 4.50
| | | | |--- feature_3 <= 4.50
| | | | | |--- feature_0 <= 1.50
@ -85,7 +85,7 @@
| | | | | |--- class: 0
| | | |--- feature_2 > 4.50
| | | | |--- class: 0
| | |--- feature_1 > 1.50
| | |--- feature_4 > 1.50
| | | |--- class: 0
| |--- feature_4 > 1.50
| |--- feature_1 > 1.50
| | |--- class: 0

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decision_tree/tree_model
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4
main.py
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@ -21,7 +21,7 @@ import itertools
def getTree():
tree = decisionTree.tree()
decisionTree.tree_as_txt(tree)
# decisionTree.tree_to_png(tree)
decisionTree.tree_to_png(tree)
decisionTree.tree_to_structure(tree)
drzewo = decisionTree.tree_from_structure('./decision_tree/tree_model')
# print("Dla losowych danych predykcja czy wziąć kosz to: ")
@ -117,8 +117,6 @@ class Game():
# print(i)
# print('----')
print('positive actions')
print(len(self.positive_actions))
for i in self.positive_decision:
# print(i.get_coords())
trash_x, trash_y = i.get_coords()

72
neurone/main.py Normal file
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@ -0,0 +1,72 @@
import pybrain3
import pickle
import matplotlib.pylab as plt
from numpy import ravel
from pybrain3.tools.shortcuts import buildNetwork
from pybrain3.datasets import SupervisedDataSet
from pybrain3.supervised.trainers import BackpropTrainer
from pybrain3.tools.xml.networkwriter import NetworkWriter
from pybrain3.tools.xml.networkreader import NetworkReader
# https://www.machinelearningmastery.ru/how-to-configure-the-number-of-layers-and-nodes-in-a-neural-network/
class SupervisedDataSetModel():
def __init__(self, metrics:int = 4,
predictions:int = 1,
input_layer:int = 4,
hidden_layer:int = 3,
output_layer:int = 1):
# 4 метрики, 1 предикшн
self.metrics = metrics
self.predictions = predictions
self.input_layer = input_layer
self.hidden_layer = hidden_layer
self.output_layer = output_layer
self.ds = SupervisedDataSet(metrics, predictions)
def activateModel(self):
self.net = buildNetwork(self.input_layer, self.hidden_layer, self.output_layer, bias=True)
self.trainer = BackpropTrainer(self.net, dataset=self.ds, momentum=0.1, learningrate=0.01, verbose=True, weightdecay=0.01)
self.trnerr, self.valerr = self.trainer.trainUntilConvergence()
plt.plot(self.trnerr, 'b', self.valerr, 'r')
plt.show()
def addDataToModel(self, input:list, target:list):
self.ds.addSample(inp=input, target=target)
def predict(self, data:list):
y = self.net.activate(data)
print(y)
return y
def saveModel(self):
fileObject = open('model.txt', 'wb')
pickle.dump(self.net, fileObject)
fileObject.close()
@staticmethod
def getModel():
fileObject = open('model.txt', 'rb')
net2 = pickle.load(fileObject)
fileObject.close()
return net2
model = SupervisedDataSetModel()
model.addDataToModel([2, 3, 80, 1], [5])
model.addDataToModel([5, 5, 50, 2], [4])
model.addDataToModel([10, 7, 40, 3], [3])
model.addDataToModel([15, 9, 20, 4], [2])
model.addDataToModel([20, 11, 10, 5], [1])
model.activateModel()
model.saveModel()
# USE MODEL - >
# model = BackPropagationModel.getModel()
# print(model.activate([2, 3, 80, 1]))

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neurone/model.txt Normal file
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0
neurone/neurone_client.py Normal file
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