neuralNetwork

import numpy
# scipy.special for the sigmoid function expit()
import scipy.special
# neural network class definition
class neuralNetwork:

    # initialize the neural network
    def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
        # set number of nodes in each input, hidden, output layer
        self.inodes = inputnodes
        self.hnodes = hiddennodes
        self.onodes = outputnodes

        # link weight matrices, wih and who
        # weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
        # w11 w21
        # w12 w22
        # etc.
        self.wih = (2 * numpy.random.rand(self.hnodes, self.inodes) - 1)
        self.who = (2 * numpy.random.rand(self.onodes, self.hnodes) - 1)

        # learning rate
        self.lr = learningrate
        pass

        # activation function is the sigmoid function
        self.activation_function = lambda x: scipy.special.expit(x)
        pass

    #train the neural network
    def train():
        pass

    #query the neural network
    def query(self, inputs_list):
        # convert inputs list to 2d array
        inputs = numpy.array(inputs_list, ndmin=2).T

        # calculate signals into hidden layer
        hidden_inputs = numpy.dot(self.wih, inputs)
        # calculate the signals emerging from hidden layer
        hidden_outputs = self.activation_function(hidden_inputs)

        # calculate signals into final output layer
        final_inputs = numpy.dot(self.who, hidden_outputs)
        #calculate the signals emerging from final output layer
        final_outputs = self.activation_function(final_inputs)

        return final_outputs


# number of input, hidden and output nodes
input_nodes = 3
hidden_nodes = 3
output_nodes = 3

# learning rate is 0.3
learning_rate = 0.3

# create instance of neural network
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)