import numpy as np
import matplotlib.pyplot as plt
import numpy.random
import scipy.special as sc


class NeuralNetwork:
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        self.inodes = input_nodes
        self.hnodes = hidden_nodes
        self.onodes = output_nodes

        self.lr = learning_rate

        self.wih = numpy.random.normal(0.0, pow(self.inodes, -0.5), (self.hnodes, self.inodes))
        self.who = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.onodes, self.hnodes))

        self.activation_func = lambda x: sc.expit(x)
        pass

    def train(self, inputs_list, targets_list):
        inputs = np.array(inputs_list, ndmin=2).T
        targets = np.array(targets_list, ndmin=2).T

        hidden_inputs = np.dot(self.wih, inputs)
        hidden_outputs = self.activation_func(hidden_inputs)

        final_inputs = np.dot(self.who, hidden_outputs)
        final_outputs = self.activation_func(final_inputs)

        output_errors = targets - final_outputs
        hidden_errors = np.dot(self.who.T, output_errors)

        self.who += self.lr * np.dot((output_errors * final_outputs * (1.0 - final_outputs)), np.transpose(hidden_outputs))
        self.wih += self.lr * np.dot((hidden_errors * hidden_outputs *(1.0 - hidden_outputs)), np.transpose(inputs))
        pass

    def query(self, inputs_list):
        inputs = np.array(inputs_list, ndmin=2).T
        hidden_inputs = np.dot(self.wih, inputs)
        hidden_outputs = self.activation_func(hidden_inputs)

        final_inputs = np.dot(self.who, hidden_outputs)
        final_outputs = self.activation_func(final_inputs)

        return final_outputs


if __name__ == "__main__":
    n = NeuralNetwork(784, 100, 10, 0.3)
    storecard = []
    # print(n.query([1.0, 0.5, -1.5]))

    with open("/home/gemini/PycharmProjects/neural/mnist_train.csv", 'r') as f:
        content = f.readlines()
        # print(content[0])
    #     print(len(content))

    for record in content:
        all_val = record.split(',')
        inputs = (np.asfarray(all_val[1:])/255.0 * 0.99) + 0.01
        targets = np.zeros(n.onodes) + 0.01
        # print(all_val)

        targets[int(all_val[0])] = 0.99
        # print(targets)
        n.train(inputs, targets)

    with open("/home/gemini/PycharmProjects/neural/mnist_test.csv", 'r') as f:
        testing = f.readlines()

    for record in testing:
        all_values = record.split(',')
        correct_answer = int(all_values[0])
        print(correct_answer, "Истинное значение")

        inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
        outputs = n.query(inputs)

        net_answer = np.argmax(outputs)
        print(net_answer,"Ответ сети")

        if net_answer == correct_answer:
            storecard.append(1)
        else:
            storecard.append(0)
            pass
    # print(storecard)
    storecard_array = np.asarray(storecard)
    print("Эффективность = ", storecard_array.sum() / storecard_array.size)
    # image_array = np.asfarray(test_val[1:]).reshape((28, 28))
    # plt.imshow(image_array, cmap='Greys', interpolation='None')
    # plt.show()

    # print(n.query((np.asfarray(test_val[1:])/ 255.0 * 0.99) + 0.01))