#include <stdlib.h>
#include "neuronal_network.h"
#include <stdio.h>
#include <math.h>

double sigmoid(double input);
Matrix* predict(Neural_Network* network, Matrix* image_data);
double square(double input);
Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old);

Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
    Neural_Network *network = malloc(sizeof(Neural_Network));
    // initialize networks variables
    network->hidden_size = hidden_size;
    network->input_size = input_size;
    network->output_size = output_size;
    network->learning_rate = learning_rate;

    network->weights_1 = matrix_create(hidden_size, input_size);
    network->weights_2 = matrix_create(hidden_size, hidden_size);
    network->weights_3 = matrix_create(hidden_size, hidden_size);
    network->weights_output = matrix_create(output_size, hidden_size);
    network->bias_1 = matrix_create(hidden_size, 1);
    network->bias_2 = matrix_create(hidden_size, 1);
    network->bias_3 = matrix_create(hidden_size, 1);
    network->bias_output = matrix_create(output_size, 1);

    return network;
}

void randomize_network(Neural_Network* network, int scope){
    matrix_randomize(network->weights_1, scope);
    matrix_randomize(network->weights_2, scope);
    matrix_randomize(network->weights_3, scope);
    matrix_randomize(network->weights_output, scope);
//    matrix_randomize(network->bias_1, scope);
//    matrix_randomize(network->bias_2, scope);
//    matrix_randomize(network->bias_3, scope);
//    matrix_randomize(network->bias_output, scope);

    matrix_fill(network->bias_1, 1);
    matrix_fill(network->bias_2, 1);
    matrix_fill(network->bias_3, 1);
    matrix_fill(network->bias_output, 1);
}

void free_network(Neural_Network* network){
    matrix_free(network->weights_1);
    matrix_free(network->weights_2);
    matrix_free(network->weights_3);
    matrix_free(network->weights_output);
    matrix_free(network->bias_1);
    matrix_free(network->bias_2);
    matrix_free(network->bias_3);
    matrix_free(network->bias_output);
    free(network);
}

void save_network(Neural_Network* network) {

    char* file_name = "../networks/newest_network.txt";

    // create file
    FILE* save_file = fopen(file_name, "w");

    // check if file is successfully opened
    if(save_file == NULL) {
        printf("ERROR: Something went wrong in file creation! (save_network)");
        exit(1);
    }

    // save network size to first line of the file
    fprintf(save_file, "%d\n", network->input_size);
    fprintf(save_file, "%d\n", network->hidden_size);
    fprintf(save_file, "%d\n", network->output_size);

    // close the file
    fclose(save_file);

    // save first layer
    matrix_save(network->bias_1, file_name);
    matrix_save(network->weights_1, file_name);

    // save second layer
    matrix_save(network->bias_2, file_name);
    matrix_save(network->weights_2, file_name);

    // save third layer
    matrix_save(network->bias_3, file_name);
    matrix_save(network->weights_3, file_name);

    // save output weights
    matrix_save(network->bias_output, file_name);
    matrix_save(network->weights_output, file_name);

    printf("Network Saved!");
}

Neural_Network* load_network(char* file) {

    // create file pointer and open file
    FILE* save_file = fopen(file, "r");

    // check if file could be opened
    if(save_file == NULL) {
        printf("ERROR: File could not be opened/found! (load_network)");
        exit(1);
    }

    // read & store the information on the size of the network from the save file
    char buffer[MAX_BYTES];
    fgets(buffer, MAX_BYTES, save_file);
    int input_size = (int) strtol(buffer, NULL, 10);
    fgets(buffer, MAX_BYTES, save_file);
    int hidden_size = (int) strtol(buffer, NULL, 10);
    fgets(buffer, MAX_BYTES, save_file);
    int output_size = (int) strtol(buffer, NULL, 10);

    // create a new network to fill with the saved data
    Neural_Network* saved_network = new_network(input_size, hidden_size, output_size, 0);

    // load matrices from file into struct
    saved_network->bias_1 = load_next_matrix(save_file);
    saved_network->weights_1 = load_next_matrix(save_file);
    saved_network->bias_2 = load_next_matrix(save_file);
    saved_network->weights_2 = load_next_matrix(save_file);
    saved_network->bias_3 = load_next_matrix(save_file);
    saved_network->weights_3 = load_next_matrix(save_file);
    saved_network->bias_output = load_next_matrix(save_file);
    saved_network->weights_output = load_next_matrix(save_file);

    // return saved network
    fclose(save_file);
    return saved_network;
}

void print_network(Neural_Network* network) {
    matrix_print(network->bias_1);
    matrix_print(network->bias_2);
    matrix_print(network->bias_3);
    matrix_print(network->bias_output);

    matrix_print(network->weights_1);
    matrix_print(network->weights_2);
    matrix_print(network->weights_3);
    matrix_print(network->weights_output);
}

double measure_network_accuracy(Neural_Network* network, Image** images, int amount) {
    int num_correct = 0;
    for (int i = 0; i < amount; i++) {
        Matrix* prediction = predict_image(network, images[i]);
        if (matrix_argmax(prediction) == images[i]->label) {
            num_correct++;
        }
        matrix_free(prediction);
    }
    return ((double) num_correct) / amount;
}

Matrix* predict_image(Neural_Network* network, Image* image){
    Matrix* image_data = matrix_flatten(image->pixel_values, 0);
    Matrix* res = predict(network, image_data);
    matrix_free(image_data);
    return res;
}

Matrix* predict(Neural_Network* network, Matrix* image_data) {
    Matrix* h1_dot = dot(network->weights_1, image_data);
    Matrix* h1_add = add(h1_dot, network->bias_1);
    Matrix* h1_outputs = apply(sigmoid, h1_add);

    Matrix* h2_dot = dot(network->weights_2, h1_outputs);
    Matrix* h2_add = add(h2_dot, network->bias_2);
    Matrix* h2_outputs = apply(sigmoid, h2_add);

    Matrix* h3_dot = dot(network->weights_3, h2_outputs);
    Matrix* h3_add = add(h3_dot, network->bias_3);
    Matrix* h3_outputs = apply(sigmoid, h3_add);

    Matrix* final_dot = dot(network->weights_output, h3_outputs);
    Matrix* final_add = add(final_dot, network->bias_output);
    Matrix* final_outputs = apply(sigmoid, final_add);

    matrix_free(h1_dot);
    matrix_free(h1_add);
    matrix_free(h1_outputs);

    matrix_free(h2_dot);
    matrix_free(h2_add);
    matrix_free(h2_outputs);

    matrix_free(h3_dot);
    matrix_free(h3_add);
    matrix_free(h3_outputs);

    matrix_free(final_dot);
    matrix_free(final_add);

    return final_outputs;
}

void train_network(Neural_Network* network, Image *image, int label) {

    // Flatten the image into matrix
    Matrix* input = matrix_flatten(image->pixel_values, 0);

    // Perform forward propagation
    Matrix* h1_dot = dot(network->weights_1, input);
    Matrix* h1_add = add(h1_dot, network->bias_1);
    Matrix* h1_outputs = apply(sigmoid, h1_add);

    Matrix* h2_dot = dot(network->weights_2, h1_outputs);
    Matrix* h2_add = add(h2_dot, network->bias_2);
    Matrix* h2_outputs = apply(sigmoid, h2_add);

    Matrix* h3_dot = dot(network->weights_3, h2_outputs);
    Matrix* h3_add = add(h3_dot, network->bias_3);
    Matrix* h3_outputs = apply(sigmoid, h3_add);

    Matrix* final_dot = dot(network->weights_output, h3_outputs);
    Matrix* final_add = add(final_dot, network->bias_output);
    Matrix* final_outputs = apply(sigmoid, final_add);

    // begin backpropagation

    // The output of this is equal to an array of the size (10, 1) where each element is the derivative of the sigmoid function
    // with the input of the neuron prior to the application of the activation function
    Matrix* matrix_filled_with_ones = matrix_create(final_outputs->rows, 1);
    matrix_fill(matrix_filled_with_ones, 1);
    Matrix* temp1 = subtract(matrix_filled_with_ones, final_outputs);
    Matrix* derivative_input = multiply(final_outputs, temp1); // * soll-ist


    // create label matrix, which indicates the correct output of the neural network
    Matrix* correct_output = matrix_create(final_outputs->rows, final_outputs->columns);
    matrix_fill(correct_output, 0);
    correct_output->numbers[label][0] = 1;

    // calculate the difference between what the value should be and what it actually is (MAYBE USE MES)
    Matrix* error_difference = subtract(final_outputs, correct_output); // * output ist minus output soll

    // multiply the derivative of the activation function with the input to the neuron
    Matrix* sigma1 = multiply(derivative_input, error_difference);

    // Calculate the delta for the weights
    Matrix* temp5 = transpose(h3_outputs);
    Matrix* temp6 = dot(sigma1, temp5);
    Matrix* weights_delta = scale(temp6, network->learning_rate);
    Matrix* bias_delta = scale(sigma1, network->learning_rate);

    Matrix* temp7 = add(network->weights_output, weights_delta);
    for (int i = 0; i < network->weights_output->rows; ++i) {
        for (int j = 0; j < network->weights_output->columns; ++j) {
            network->weights_output->numbers[i][j] = temp7->numbers[i][j];
        }
    }

//    Matrix* temp8 = add(network->bias_output, bias_delta);
//    for (int i = 0; i < network->bias_output->rows; ++i) {
//        for (int j = 0; j < network->bias_output->columns; ++j) {
//            network->bias_output->numbers[i][j] = temp8->numbers[i][j];
//        }
//    }

    // other levels
    Matrix* sigma2 = backPropagation(network->learning_rate, network->weights_3, network->bias_3, h3_outputs, h2_outputs, sigma1);
    Matrix* sigma3 = backPropagation(network->learning_rate, network->weights_2, network->bias_2, h2_outputs, h1_outputs, sigma2);
    Matrix* sigma4 = backPropagation(network->learning_rate, network->weights_1, network->bias_1, h1_outputs, input, sigma3);

    matrix_free(input);

    matrix_free(h1_dot);
    matrix_free(h1_add);
    matrix_free(h1_outputs);

    matrix_free(h2_dot);
    matrix_free(h2_add);
    matrix_free(h2_outputs);

    matrix_free(h3_dot);
    matrix_free(h3_add);
    matrix_free(h3_outputs);

    matrix_free(final_dot);
    matrix_free(final_add);
    matrix_free(final_outputs);

    matrix_free(weights_delta);
    matrix_free(bias_delta);

    matrix_free(sigma1);
    matrix_free(sigma2);
    matrix_free(sigma3);
    matrix_free(sigma4);


    matrix_free(temp1);
    matrix_free(derivative_input);
    matrix_free(correct_output);
    matrix_free(error_difference);
    matrix_free(temp5);
    matrix_free(temp6);
    matrix_free(temp7);
//    matrix_free(temp8);
    matrix_free(matrix_filled_with_ones);
}

Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) {
    Matrix* temp7 = matrix_create(current_layer_activation->rows, 1);
    matrix_fill(temp7, 1);

    Matrix* temp1 = subtract(temp7, current_layer_activation);
    Matrix* temp2 = multiply(temp1, current_layer_activation); // *sum(delta*weights)

    for(int i = 0; i < current_layer_activation->rows; i++) {
        double sum = 0;
        for (int j = 0; j < sigma_old->rows; j++) {
            sum += current_layer_activation->numbers[i][j] * sigma_old->numbers[j][0];
        }
        temp1->numbers[i][0] = sum;
    }
    Matrix* sigma_new = multiply(temp2, temp1);

    // new sigma done

    Matrix* temp3 = transpose(previous_layer_activation);
    Matrix* temp4 = dot(sigma_new, temp3);
    Matrix* weights_delta = scale(temp4, learning_rate);
    Matrix* bias_delta = scale(sigma_new, learning_rate);

    Matrix* temp5 = add(weights, weights_delta);
    for (int i = 0; i < weights->rows; ++i) {
        for (int j = 0; j < weights->columns; ++j) {
            weights->numbers[i][j] = temp5->numbers[i][j];
        }
    }

//    Matrix* temp6 = add(biases, bias_delta);
//    for (int i = 0; i < biases->rows; ++i) {
//        for (int j = 0; j < biases->columns; ++j) {
//            biases->numbers[i][j] = temp6->numbers[i][j];
//        }
//    }

    matrix_free(temp1);
    matrix_free(temp2);
    matrix_free(temp3);
    matrix_free(temp4);
    matrix_free(temp5);
//    matrix_free(temp6);
    matrix_free(temp7);
    matrix_free(weights_delta);
    matrix_free(bias_delta);

    return sigma_new;
}

double sigmoid(double input) {
    return 1.0 / (1 + exp(-1 * input));
}

double square(double input) {
    return input * input;
}