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
        
        # learning rate
        self.lr = learningrate
        
        # 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
        # basic version 
        # self.wih = (numpy.random.rand(self.hnodes, self.inodes) - 0.5)
        # self.who = (numpy.random.rand(self.onodes, self.hnodes) - 0.5)
        # adjusted version
        self.wih = numpy.random.normal(0.0, 
                                       pow(self.hnodes, -0.5),
                                       (self.hnodes, self.inodes))
        self.who = numpy.random.normal(0.0, 
                                       pow(self.onodes, -0.5), 
                                       (self.onodes, self.hnodes))

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

        pass
    
    # train the neural network
    def train(self, inputs_list, targets_list):
        # convert inputs list to 2d array
        inputs = numpy.array(inputs_list, ndmin=2).T
        targets = numpy.array(targets_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)

        # error is the (target - actual)
        output_errors = targets - final_outputs
        
        # hidden layer error is the output_errors, split by weights, recombined 
        # at hidden nodes
        hidden_errors = numpy.dot(self.who.T, output_errors)
        
        # update the weights for the links between the hidden and output layers
        # * is to multiply each item at the same position (j,k) in two matrices
        # numpy.dot() is to do dot product with two matrices
        self.who += self.lr * numpy.dot(
                (output_errors * final_outputs * (1.0 - final_outputs)),
                numpy.transpose(hidden_outputs))
        
        # update the weights for the links between the input and hidden layers
        self.wih += self.lr * numpy.dot(
                (hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
                numpy.transpose(inputs))
        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 

    def getWIH(self):
        return self.wih
    def getWHO(self):
        return self.who
    def setWIH(self, wih):
        self.wih = wih
    def setWHO(self, who):
        self.who = who

