add backpropTrainer model with controled data set

neurone/main.py

@@ -0,0 +1,71 @@
+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()
+        
+
+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 = getModel()
+print(model.activate([2, 3, 80, 1]))
+