i_n = 11 #no. of input nodes
h_n = 21 #no. of hidden nodes 11 ~ sqrt(i_n * o_n)
o_n = 1 #np. of output nodes
lr = 0.15 #learning rate
NN1 = NeuralNetwork(i_n, h_n, o_n, lr) #setting up neural networks
NN2 = NeuralNetwork(i_n, h_n, o_n, lr)
NN3 = NeuralNetwork(i_n, h_n, o_n, lr)
NN4 = NeuralNetwork(i_n, h_n, o_n, lr)
NN5 = NeuralNetwork(i_n, h_n, o_n, lr)
NN6 = NeuralNetwork(i_n, h_n, o_n, lr)
NN7 = NeuralNetwork(i_n, h_n, o_n, lr)
NN8 = NeuralNetwork(i_n, h_n, o_n, lr)
NN1.load('a7', 'b7')
NN2.load('c7', 'd7')
NN3.load('e7', 'f7')
NN4.load('g7', 'h7')
NN5.load('i7', 'j7')
NN6.load('k7', 'l7')
NN7.load('m7', 'n7')
NN8.load('o7', 'p7')
test_data_file = open(
'X:/Documents/Carbon Emissions Data/Data/TestingDataBasicFinal.csv',
'r') #loading test data
test_data = test_data_file.readlines()
test_data_file.close()
i_n = 14 #no. of input nodes
h_n = 21 #no. of hidden nodes 11 ~ sqrt(i_n * o_n)
o_n = 1 #np. of output nodes
lr = 0.15 #learning rate
epochs = 1 #no. of epochs
NN1 = NeuralNetwork(i_n, h_n, o_n, lr) #setting up neural networks
NN2 = NeuralNetwork(i_n, h_n, o_n, lr)
NN3 = NeuralNetwork(i_n, h_n, o_n, lr)
NN4 = NeuralNetwork(i_n, h_n, o_n, lr)
NN5 = NeuralNetwork(i_n, h_n, o_n, lr)
NN6 = NeuralNetwork(i_n, h_n, o_n, lr)
NN7 = NeuralNetwork(i_n, h_n, o_n, lr)
NN8 = NeuralNetwork(i_n, h_n, o_n, lr)
NN1.load('a6', 'b6')
NN2.load('c6', 'd6')
NN3.load('e6', 'f6')
NN4.load('g6', 'h6')
NN5.load('i6', 'j6')
NN6.load('k6', 'l6')
NN7.load('m6', 'n6')
NN8.load('o6', 'p6')
def normalise_inputs(inputs):
property_type = (inputs[0] - 2.5)/1.5 #normalising each piece of data into range [1,-1] approx
built_form = (inputs[1] - 3.5)/2.5
floor_area = (inputs[2] - 149.41)/142.69
no_rooms = (inputs[3] - 15.5)/14.5
no_fire = (inputs[4] - 4.95)/5
hotwater =(inputs[5] - 5.95)/5
