#pragma once

#include "./core/omp_config.h"
#include "./utils/matrix.h"
#include "./utils/vector.h"
#include "./utils/random.h"

//#include <math.h>


namespace neural_networks{

		template <typename T>
		void create_spital_data(const uint64_t samples, const uint64_t classes, utils::Matrix<T>& X, utils::Vector<T>& y) {

			const uint64_t rows = samples*classes;
			T r, t;
			uint64_t row_idx;


			if ((rows != X.rows()) || (X.cols() != 2)){
				X.resize(samples*classes, 2);
			}
			if (rows != y.size()){
				y.resize(rows);
			}

			for (uint64_t i = 0; i < classes; ++i){
				for (uint64_t j = 0; j < samples; ++j){
					r = static_cast<T>(j)/static_cast<T>(samples);
					t =  static_cast<T>(i)*4.0 + (4.0+r);
					row_idx = (i*samples) + j;

					X(row_idx, 0) = r*std::cos(t*2.5) + utils::random(T{-0.15}, T{0.15});
					X(row_idx, 1) = r*std::sin(t*2.5) + utils::random(T{-0.15}, T{0.15});
					y[row_idx] = i;
				}
			}

			

			/*

			utils::Matrix<T> X(static_cast<uint64_t>(samples*classes), 3, T{0});
		    
		    const uint64_t rows = A.rows();
		    const uint64_t cols = A.cols();

		    if (rows != x.size()) {
		        throw std::runtime_error("inplace_matadd_colvec: dimension mismatch");
		    }

		    for (uint64_t i = 0; i < cols; ++i) {
		        for (uint64_t j = 0; j < rows; ++j) {
		            A(j, i) +=  x[j];
		        }
		    }*/
		}


} // end namesoace NN