def initilizeData(self):
chosenFeatures = self.chosenFeatures.get()
feature1 = int(chosenFeatures[1])
feature2 = int(chosenFeatures[6])
learnRate = float(self.learnRate.get())
epochsNo = int(self.epochsNo.get())
bias = self.bias.get()
rd = ReadData()
rd.readData()
featureX = self.returnFeature(feature1, rd)
featureY = self.returnFeature(feature2, rd)
return (featureX, featureY)
def manageTrainingFeatures(self):
#initilize X1 & X2 & X3 & X4 & Y
rd = ReadData()
rd.readData()
# Reading first chunk of data
self.training_features['X1'] = rd.IrisX1[0:30]
self.training_features['X2'] = rd.IrisX2[0:30]
self.training_features['X3'] = rd.IrisX3[0:30]
self.training_features['X4'] = rd.IrisX4[0:30]
self.training_features['Y'] = [1 for i in range(0, 30)]
# Reading second chunk of data
self.training_features['X1'].extend(rd.IrisX1[50:80])
self.training_features['X2'].extend(rd.IrisX2[50:80])
self.training_features['X3'].extend(rd.IrisX3[50:80])
self.training_features['X4'].extend(rd.IrisX4[50:80])
self.training_features['Y'].extend([2 for i in range(50, 80)])
# Reading third chunk of data
self.training_features['X1'].extend(rd.IrisX1[100:130])
self.training_features['X2'].extend(rd.IrisX2[100:130])
self.training_features['X3'].extend(rd.IrisX3[100:130])
self.training_features['X4'].extend(rd.IrisX4[100:130])
self.training_features['Y'].extend([3 for i in range(100, 130)])
self.testing_features['X1'] = rd.IrisX1[30:50]
self.testing_features['X2'] = rd.IrisX2[30:50]
self.testing_features['X3'] = rd.IrisX3[30:50]
self.testing_features['X4'] = rd.IrisX4[30:50]
self.testing_features['Y'] = [1 for i in range(30, 50)]
# Reading second chunk of data
self.testing_features['X1'].extend(rd.IrisX1[80:100])
self.testing_features['X2'].extend(rd.IrisX2[80:100])
self.testing_features['X3'].extend(rd.IrisX3[80:100])
self.testing_features['X4'].extend(rd.IrisX4[80:100])
self.testing_features['Y'].extend([2 for i in range(80, 100)])
# Reading third chunk of data
self.testing_features['X1'].extend(rd.IrisX1[130:150])
self.testing_features['X2'].extend(rd.IrisX2[130:150])
self.testing_features['X3'].extend(rd.IrisX3[130:150])
self.testing_features['X4'].extend(rd.IrisX4[130:150])
self.testing_features['Y'].extend([3 for i in range(130, 150)])
def slope(b):
return 2 * (b - 4)
w1 = numpy.random.randn()
w2 = numpy.random.randn()
b = numpy.random.randn()
o = NN(3,1,w1,w2,b)
print(o)
print(b)
for i in range(10):
b = b - .1 * slope(b)
print(b)
dataset = ReadData()
dataset.readData()
X1_training = dataset.IrisX1[0:30]
X1_training.extend(dataset.IrisX1[50:80])
X1_training.extend(dataset.IrisX1[100:130])
vector = [X1_training[0],X1_training[1]]
print(vector)
x = [1 for i in range (0,5)]
x.extend([0 for i in range(1,5)])
x.extend([2 for i in range(1,5)])
print(x)
#pi = PlotIris()
#pi.plot(rd.IrisX1, rd.IrisX2, 'X1', 'X2')
'''
IrisX1 = []
IrisX2 = []
IrisX3 = []