def printMatrix():
name = input(
'Input matrix\'s name to be printed. (Read from ".mtrx" file)\n>>>')
raw = IO.RAWReader()
raw.open(p.DATA_PATH + name + '.mtrx')
matrix = IO.getAMatrix(raw)
IO.printprettyMatrix(matrix)
def feedNeuralNetworkbymtrx(MyNeuralNetwork):
name = input('Input .mtrx\'s file name.\n')
rawreader = IO.RAWReader()
rawreader.open(p.DATA_PATH + name + '.mtrx')
M = IO.getAMatrix(rawreader)
np.set_printoptions(threshold=np.nan)
np.set_printoptions(precision=3)
np.set_printoptions(suppress=False)
IO.printprettyMatrix(MyNeuralNetwork.feed(M))
pass
def feedNeuralNetwork(MyNeuralNetwork):
# string = input('input "row(number of data amount)", "column(number of input layer\'s neuron size)"\n')
# string = string + ' ' + input('And then input "elements" row after row.\n')
string= '1 '+str(MyNeuralNetwork.LayerNeuronsCount[0])+' '
# Single set of data 1 * input size
string = string+input('Input single data set.(split by space and press enter)\n')
rawreader = IO.RAWReader()
rawreader.openString(string)
M = IO.getAMatrix(rawreader)
IO.printprettyMatrix(MyNeuralNetwork.feed(M))
def loadfromFile(self, Filename):
try:
MyRAWReader = IO.RAWReader()
MyRAWReader.open(p.SAVED_PATH + Filename + '.node')
self.Name = Filename
self.LayersCount = int(MyRAWReader.pop())
self.LayerNeuronsCount = []
self.Weight = []
self.Bias = []
# Get the LayersCount first and Initlalize LayerNeuronsCount, Weight and Bias
for layer in range(0, self.LayersCount):
self.LayerNeuronsCount.append(int(MyRAWReader.pop()))
# Get each layer's neurons count one by one
for layer in range(0, self.LayersCount - 1):
self.Weight.append(IO.getAMatrix(MyRAWReader))
# Get each layer's weight one by one
for layer in range(0, self.LayersCount - 1):
self.Bias.append(IO.getAMatrix(MyRAWReader))
# Get each layer's bias one by one
except:
print('warning: Loading ' + Filename + '.node error!')