#include "core.hpp"
#include <cmath>
#include <cstdlib>
#include <omp.h> 

#define MAX_CORES 16

NeuralNetwork::NeuralNetwork(LayerStructure_t layers[], int count) : numLayers(count) {
    for (int i = 0; i < count; i++) sizes.push_back(layers[i].size);
    for (int i = 0; i < count - 1; i++) {
        std::vector<std::vector<double>> layerW;
        double scale = sqrt(2.0 / sizes[i]);
        for (int j = 0; j < sizes[i+1]; j++) {
            std::vector<double> nodeW;
            for (int k = 0; k < sizes[i]; k++) 
                nodeW.push_back(((double)rand()/RAND_MAX * 2 - 1) * scale);
            layerW.push_back(nodeW);
        }
        weights.push_back(layerW);
        biases.push_back(std::vector<double>(sizes[i+1], 0.0));
    }
}

std::vector<double> NeuralNetwork::feedForward(const std::vector<double>& input) {
    outputs.clear();
    outputs.push_back(input);

    std::vector<double> curr = input;

    for (int i = 0; i < numLayers - 1; i++) {
        std::vector<double> next;
        for (int j = 0; j < sizes[i+1]; j++) {
            double sum = biases[i][j];
            for (int k = 0; k < (int)curr.size(); k++) sum += curr[k] * weights[i][j][k];
            next.push_back(1.0 / (1.0 + exp(-sum)));
        }
        curr = next;
        outputs.push_back(curr);
    }
    return curr;
}


double NeuralNetwork::train(const std::vector<double>& input, const std::vector<double>& target, double lr) {
    omp_set_num_threads(MAX_CORES);

    std::vector<double> pred = feedForward(input);
    std::vector<std::vector<double>> errors(numLayers);
    errors[numLayers - 1].resize(sizes[numLayers - 1]);
    
    double totalErr = 0;

    for (int i = 0; i < sizes[numLayers - 1]; i++) {
        double e = target[i] - pred[i];
        errors[numLayers - 1][i] = e * pred[i] * (1.0 - pred[i]);
        totalErr += e * e;
    }

    for (int i = numLayers - 2; i > 0; i--) {
        errors[i].resize(sizes[i]);
    
        #pragma omp parallel for
        for (int j = 0; j < sizes[i]; j++) {
            double e = 0;
            for (int k = 0; k < sizes[i + 1]; k++) {
                e += errors[i + 1][k] * weights[i][k][j];
            }
            errors[i][j] = e * outputs[i][j] * (1.0 - outputs[i][j]);
        }
    }
    for (int i = 0; i < numLayers - 1; i++) {
        #pragma omp parallel for
        for (int j = 0; j < sizes[i + 1]; j++) {
            double errorTerm = lr * errors[i + 1][j];
            
            // Вложенный цикл обновления весов
            for (int k = 0; k < sizes[i]; k++) {
                weights[i][j][k] += errorTerm * outputs[i][k];
            }
            biases[i][j] += errorTerm;
        }
    }

    return totalErr;
}