def LoadData():
dr = DataReader(train_file, test_file)
dr.ReadData()
#dr.NormalizeX()
#dr.NormalizeY(YNormalizationMethod.Regression)
dr.Shuffle()
dr.GenerateValidationSet()
return dr
def LoadData():
dr = DataReader(train_file, test_file)
dr.ReadData()
dr.NormalizeX()
dr.NormalizeY(YNormalizationMethod.MultipleClassifier, base=1)
dr.Shuffle()
dr.GenerateValidationSet()
return dr
def LoadData():
dataReader = DataReader(x_data_name, y_data_name)
dataReader.ReadData()
dataReader.Normalize(False, False, False)
return dataReader
net.inference(input)
return X, Y
def ShowResult2D(net, dr):
ShowDataHelper(dr.XTrain[:,0], dr.XTrain[:,1], dr.YTrain[:,0],
"Classifier Result", "x1", "x2", False, False)
count = 50
X,Y = Prepare3DData(net, count)
Z = net.output.reshape(count,count)
plt.contourf(X, Y, Z, cmap=plt.cm.Spectral, zorder=1)
plt.show()
#end def
if __name__ == '__main__':
dataReader = DataReader(train_data_name, test_data_name)
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.Shuffle()
dataReader.GenerateValidationSet()
num_input = 2
num_hidden = 2
num_output = 1
max_epoch = 10000
batch_size = 5
learning_rate = 0.1
eps = 1e-3
params = HyperParameters(
def LoadData():
dataReader = DataReader(x_data_name, y_data_name)
dataReader.ReadData()
dataReader.Normalize(normalize_x=True, normalize_y=True, to_one_hot=True)
return dataReader