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
        self.lr = learning_rate

        # Initialize weights
        self.input_hidden_weights = np.random.randn(hidden_nodes, input_nodes) # 10x7
        self.hidden_output_weights = np.random.randn(output_nodes, hidden_nodes) # 1x10


        # Sigmoid activation funciton
        self.sigmoid = lambda x: 1/(1+np.exp(-x))
        self.diff_sigm = lambda x: x*(1-x)

    def train(self, input_list, label_list):

        # Create an array of inputs and labels
        inputs = np.array(input_list, ndmin=2).T # 7x1
        labels = np.array(label_list, ndmin=2) # 1x1

        # Forward propagation
        hidden_layer = self.sigmoid(np.dot(self.input_hidden_weights, inputs))
        output_layer = self.sigmoid(np.dot(self.hidden_output_weights, hidden_layer))

        final_output = output_layer


        # Error function
        output_errors = labels-final_output

        # Backpropagation
        output_delta = output_errors * self.diff_sigm(output_layer)
        hidden_delta = np.dot(self.hidden_output_weights.T, output_delta) * self.diff_sigm(hidden_layer)

        # Update the weights
        self.hidden_output_weights += np.dot(output_delta, hidden_layer.T) * self.lr
        self.input_hidden_weights += np.dot(hidden_delta, inputs.T) * self.lr


        """
        # Backpropagation
        hidden_errors = np.dot(self.hidden_output_weights.T, output_errors)
        hidden_grad = hidden_layer * (1.0 - hidden_layer)

        # Update the weights
        self.hidden_output_weights += self.lr * np.dot(output_errors.T, output_layer.T) # update hidden-to-output weights with gradient descent step
        self.input_hidden_weights += self.lr * np.dot(hidden_errors * hidden_grad, inputs.T)  # update input-to-hidden weights with gradient descent step
        """