import numpy as np

class NeuralNetwork(object):
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        # Set number of nodes in input, hidden and output layers.
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes

        # Initialize weights
        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
                                       (self.input_nodes, self.hidden_nodes))

        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
                                       (self.hidden_nodes, self.output_nodes))
        self.lr = learning_rate

        self.activation_function = lambda x: 1/(1 + np.exp(-x))

    def train(self, features, targets):
        """Train the network on batch of features and targets.

            Arguments:
            - features: 2D array, each row is one data record,
                        each column is a feature
            - targets: 1D array of target values
        """

        n_records = features.shape[0]
        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)

        for X, y in zip(features, targets):
            final_outputs, hidden_outputs = self.forward_pass_train(X)
            delta_weights_i_h, delta_weights_h_o = self.backpropagation(final_outputs, hidden_outputs, X, y,
                                                                        delta_weights_i_h, delta_weights_h_o)
        self.update_weights(delta_weights_i_h, delta_weights_h_o, n_records)

    def forward_pass_train(self, X):
        """Forward Pass Method.

            Arguments:
            - X: features batch
        """
        hidden_inputs = np.dot(X, self.weights_input_to_hidden)
        hidden_outputs = self.activation_function(hidden_inputs)

        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
        final_outputs = final_inputs

        return final_outputs, hidden_outputs

    def backpropagation(self, final_outputs, hidden_outputs, X, y,
                        delta_weights_i_h, delta_weights_h_o):
        """Backpropagation.

            Arguments:
            - final_outputs: output from forward pass
            - y: target (i.e. label) batch
            - delta_weights_i_h: change in weights from input to hidden layers
            - delta_weights_h_o: change in weights from hidden to output layers
        """

        # Calculate error: difference between desired target & output
        error = y - final_outputs

        # Calculate the hidden layer's contribution to the error
        hidden_error = np.dot(self.weights_hidden_to_output, error)

        # Backpropagated error terms
        output_error_term = error * 1.0
        hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)

        # Weight step (input to hidden)
        delta_weights_i_h += hidden_error_term * X[:, None]
        # Weight step (hidden to output)
        delta_weights_h_o += output_error_term * hidden_outputs[:, None]
        return delta_weights_i_h, delta_weights_h_o

    def update_weights(self, delta_weights_i_h, delta_weights_h_o, n_records):
        """Update weights on gradient descent step.

            Arguments:
            - delta_weights_i_h: change in weights from input to hidden layers
            - delta_weights_h_o: change in weights from hidden to output layers
            - n_records: number of records
        """

        # Update hidden-to-output weights with gradient descent step
        self.weights_hidden_to_output += self.lr * delta_weights_h_o/n_records

        # Update input-to-hidden weights with gradient descent step
        self.weights_input_to_hidden += self.lr * delta_weights_i_h/n_records

    def run(self, features):
        """Run a forward pass through the network with input features.

            Arguments:
            - features: 1D array of feature values
        """

        hidden_inputs = np.dot(features, self.weights_input_to_hidden)
        hidden_outputs = self.activation_function(hidden_inputs)

        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
        final_outputs = final_inputs

        return final_outputs

#########################################################
# Set your hyperparameters here
#########################################################
iterations = 5000
learning_rate = 0.5
hidden_nodes = 10
output_nodes = 1