#include "./core/omp_config.h"

#include "utils/utils.h"
#include "numerics/numerics.h"
#include "decomp/decomp.h"

#include "modules/neural_networks/neural_networks.h"



//#include <iostream>
//#include <stdexcept>
//#include <chrono>





int main(int argc, char const *argv[])
{   

    utils::Mf X(10,2, 0);
    utils::Matrix<int64_t> y(10,1, 0);
    utils::Vector<int64_t> class_targets;
    float loss;
    float accuracy;

    //neural_networks::create_spital_data<float, uint64_t>(10000, 3, X, y);
    neural_networks::create_vertical_data<float, int64_t>(100, 3, X, y);

    neural_networks::Dense_Layer<float> dense1(2, 3);
    neural_networks::Activation_ReLU<float> activation1;
    neural_networks::Dense_Layer<float> dense2(3, 3);
    neural_networks::Activation_Softmax<float> activation2;
    
    neural_networks::Loss_CategoricalCrossentrophy<float, int64_t> loss_funtion;

    float lowest_loss = 9999999;
    utils::Mf best_dense_1_weights = dense1.weights;
    utils::Vf best_dense_1_biases = dense1.biases;
    utils::Mf best_dense_2_weights = dense2.weights;
    utils::Vf best_dense_2_biases = dense2.biases;

    utils::Vf vectRND;

    utils::Vector<int64_t> predections;





        for (uint64_t i = 0; i < 10; ++i){


            // Generate a new set of weights for iteration
            numerics::inplace_matrandom_mul(dense1.weights,0.98f, 1.02f);
            numerics::inplace_vecrandom_mul(dense1.biases,0.98f, 1.02f);

            numerics::inplace_matrandom_mul(dense2.weights,0.98f, 1.02f);
            numerics::inplace_vecrandom_mul(dense2.biases,0.98f, 1.02f);

            // Perform a forward pass of the training data through this layer
            dense1.forward(X);
            activation1.forward(dense1.outputs);
            dense2.forward(activation1.outputs);
            activation2.forward(dense2.outputs);

            // Perform a farward pass through activation function
            // it takes the output of the second dense layer here and returns loss
            loss = loss_funtion.calculate(activation2.outputs, y);

            predections = numerics::matargmax_row<int64_t, float>(activation2.outputs);

            if (y.cols() < 1){
                class_targets = numerics::matargmax_row<int64_t, int64_t>(y);
            }else{
                class_targets = y.get_col(0);
            }

            accuracy = numerics::vecmean_equal<float>(predections, class_targets);

            if (loss < lowest_loss){
                //std::cout << "New set of weights found, iteration:" << i << ", loss:" << loss << ", acc:" << accuracy << std::endl;
                best_dense_1_weights = dense1.weights;
                best_dense_1_biases = dense1.biases;
                best_dense_2_weights = dense2.weights;
                best_dense_2_biases = dense2.biases;
                lowest_loss = loss;
            } else{
                //std::cout << "HERE" << std::endl;
                dense1.weights = best_dense_1_weights;
                dense1.biases = best_dense_1_biases;
                dense2.weights = best_dense_2_weights;
                dense2.biases = best_dense_2_biases;
            }


        }


    //std::cout << loss << std::endl;
    //std::cout << accuracy << std::endl;

    utils::Matrix<float> softmax_outputs{{0.7, 0.1, 0.2},
                                {0.1, 0.5, 0.4},
                                {0.02, 0.9, 0.08}};
    utils::Matrix<int64_t> clas_targets{{0},{1},{1}};

    neural_networks::Activation_Softmax_Loss_CategoricalCrossentropy<float, int64_t> softmax_loss;
    softmax_loss.backward(softmax_outputs, clas_targets);
    utils::Matrix<float> dvalues1 = softmax_loss.dinputs;

    neural_networks::Activation_Softmax<float> activation;
    activation.outputs = softmax_outputs;
    
    //neural_networks::Loss_CategoricalCrossentrophy<float, int64_t> loss;


    dvalues1.print();





    /*
    utils::Vd a = utils::linspace<double>(1, 10, 10, true);
    a.print();
    mesh::Mesh1D<double> mesh(a);
    mesh.generate_vertices(0.5, 10.5);
    double Gamma = 1.0;

    
    utils::Md A;
    utils::Vd b, s(10,1);


    core::Configs<double>& cfg = core::Configs<double>::defaults();
    cfg.grid = core::GridKind::Uniform;
    cfg.left = {core::FDKind::Forward, core::BCKind::Neumann, 0.0};
    cfg.right = {core::FDKind::Backward, core::BCKind::Neumann, 0.0};
    cfg.solver    = core::SolverKind::LU;


    fluids::Diffusion1D<double> diffusion(cfg, mesh, Gamma);
    diffusion.assemble(A, b, s);
*/
    
    return 0;
}