caca

test/test_plot.cpp

@@ -0,0 +1,109 @@
+#include <algorithm>
+#include <chrono>
+#include <cmath>
+#include <execution>
+#include <future>
+#include <matplot/matplot.h>
+
+#include "Gauss.h"
+#include "Matrix.h"
+#include "Solver.h"
+
+static constexpr int EXECUTION_COUNT = 100;
+static constexpr int MATRIX_MAX_SIZE = 300;
+
+static unsigned int GetRandomInt() {
+	return rand() % MATRIX_MAX_SIZE + 1;
+}
+
+static Matrix GetRandomMatrix(std::size_t a_Raw, std::size_t a_Column) {
+	Matrix matrix {a_Raw, a_Column};
+
+	for (std::size_t i = 0; i < matrix.GetRawCount(); i++) {
+		for (std::size_t j = 0; j < matrix.GetColumnCount(); j++) {
+			matrix.at(i, j) = GetRandomInt();
+		}
+	}
+
+	return matrix;
+}
+
+std::vector<double> GaussJordan(const std::vector<double>& x) {
+	std::vector<double> y;
+
+	std::for_each(x.begin(), x.end(), [&y](double size) {
+		auto start = std::chrono::system_clock::now();
+
+		for (int j = 0; j < EXECUTION_COUNT; j++) {
+			Matrix mat = GetRandomMatrix(size, size);
+			Gauss::GaussJordan(mat, false, false);
+		}
+
+		auto end = std::chrono::system_clock::now();
+		std::chrono::duration<double> elapsed_seconds = end - start;
+		std::cout << "S " << size << "\n";
+		y.push_back(elapsed_seconds.count() / static_cast<double>(EXECUTION_COUNT));
+	});
+
+	return y;
+}
+
+
+std::vector<double> GaussJordanReduite(const std::vector<double>& x) {
+	std::vector<double> y;
+
+	std::for_each(x.begin(), x.end(), [&y](double size) {
+		auto start = std::chrono::system_clock::now();
+
+		for (int j = 0; j < EXECUTION_COUNT; j++) {
+			Matrix mat = GetRandomMatrix(size, size);
+			Gauss::GaussJordan(mat, true, false);
+		}
+
+		auto end = std::chrono::system_clock::now();
+		std::chrono::duration<double> elapsed_seconds = end - start;
+		std::cout << "R " << size << "\n";
+		y.push_back(elapsed_seconds.count() / static_cast<double>(EXECUTION_COUNT));
+	});
+
+	return y;
+}
+
+int main() {
+	srand(time(0));
+
+	int start = 1;
+
+	std::vector<double> x = matplot::linspace(start, MATRIX_MAX_SIZE, MATRIX_MAX_SIZE - start + 1);
+	//std::vector<double> x = {5000};
+	std::vector<double> y, y1, y2, y3;
+
+	// y2.resize(x.size());
+
+	{
+		auto result1 = std::async(std::launch::async, &GaussJordan, x);
+		auto result2 = std::async(std::launch::async, &GaussJordanReduite, x);
+		y = result1.get();
+		y1 = result2.get();
+	}
+
+
+	std::cout << "Fini !\n";
+
+
+	// std::transform(x.begin(), x.end(), y2.begin(), [](double x) { return 1.0 / (100.0 * 100.0) * 0.6 * x * x; });
+
+	matplot::title("Echelonnage de matrices");
+	matplot::xlabel("Taille des matrices");
+	matplot::ylabel("Temps d'exécution (s)");
+
+	matplot::hold(matplot::on);
+	matplot::plot(x, y);
+	matplot::plot(x, y1);
+
+	auto l = matplot::legend({"Echelonnage non réduit", "Echelonnage réduit", "Echelonnage non réduit normalisé", "Echelonnage réduit normalisé"});
+	l->location(matplot::legend::general_alignment::topleft);
+
+	matplot::show();
+	return 0;
+}