def initNN(self, file, hlayers, hnodes, classification):
input = file.shape[1] - 1
if classification == 'regression':
output = 1
if classification == 'classification':
self.classes = list(file['class'].unique())
output = file['class'].nunique()
neuralNet = NN.getNN(self, input, hlayers, hnodes, output)
return (neuralNet)
def initNN(self, file, hlayers, hnodes, classification):
'''Kieran Ringel
Gets information needs to get shape of NN and then calls a function to get the shape'''
input = file.shape[1] - 1
if classification == 'regression': #for regression there is one output node
output = 1
if classification == 'classification': #for classification there is an output node for each class
self.classes = list(file['class'].unique()) #creates a class variable to be reference later
output = file['class'].nunique()
neuralNet = NN.getNN(self, input, hlayers, hnodes, output) #initialized the shape of the NN
return(neuralNet)
