ml_csharp/exercise2.cs at master · mtxset/ml_csharp · GitHub

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using System;
using OxyPlot;
using OxyPlot.Axes;
using OxyPlot.Series;
using utils;

namespace ml {
	public static class exercise2 {
		public static void test_cost_logistic_regression_regularized() {
			var file_path = "./data/ex2data2.txt";
			const double lambda = 1;

			double[][] train_data;
			double[] result_data, initial_theta;

			var parse_result = file_utils.parse_file(file_path, out train_data, out result_data);

			if (parse_result.has_errors()) {
				Console.WriteLine(parse_result.all_errors_to_string());
				return;
			}

			initial_theta = new double[train_data[0].Length + 1];
			double cost; // 0.69315
			double[] gradient;  // 0.008474576 0.018788093 0.000077771
			var result = ml_funcs.cost_logistic_regression_regularized(train_data, result_data, initial_theta, lambda, out cost, out gradient);

			if (result.has_errors()) {
				Console.WriteLine(result.all_errors_to_string());
				return;
			}

			var x = ml_funcs.matrix_column_to_vector(train_data, 0);
			var y = ml_funcs.matrix_column_to_vector(train_data, 1);
			double[][] featured_train_data;
			result = ml_funcs.map_feature(x, y, out featured_train_data, 6);

			Console.Write("cost_logistic_regression_regularized cost");
			if (Math.Round(cost, 5) == 0.69315)
				Console.WriteLine(" .. OK");
			else
				Console.WriteLine(" .. FAILED");

			var test_results = new double[] { 0.008474576, 0.018788093, 0.000077771 };
			Console.Write("cost_logistic_regression_regularized gradient");
			var correct = 0;
			for (var i = 0; i < test_results.Length; i++)
				if (Math.Round(gradient[i], 9) == test_results[i]) correct++;

			if (correct == test_results.Length)
				Console.WriteLine(" .. OK");
			else
				Console.WriteLine(" .. FAILED");

			Console.Write("map_feature");
			if (Math.Round(featured_train_data[0][0], 7) == 0.051267 &&
				Math.Round(featured_train_data[0][26], 5) == 0.11721 &&
				Math.Round(featured_train_data[117][0], 5) == 0.63265 &&
				Math.Round(featured_train_data[117][26], 14) == 0.00000000082291)
				Console.WriteLine(" .. OK");
			else
				Console.WriteLine(" .. FAILED");

			var featured_theta = new double[featured_train_data[0].Length + 1];
			result = ml_funcs.cost_logistic_regression_regularized(featured_train_data, result_data, featured_theta, lambda, out cost, out gradient);
			Console.Write("cost_logistic_regression_regularized after map_feature cost");
			if (Math.Round(cost, 5) == 0.69315)
				Console.WriteLine(" .. OK");
			else
				Console.WriteLine(" .. FAILED");

			test_results = new double[] { 0.008474576, 0.018788093, 0.000077771 };
			Console.Write("cost_logistic_regression_regularized after map_feature gradient");
			correct = 0;
			for (var i = 0; i < test_results.Length; i++)
				if (Math.Round(gradient[i], 9) == test_results[i]) correct++;

			if (correct == test_results.Length)
				Console.WriteLine(" .. OK");
			else
				Console.WriteLine(" .. FAILED");
		}

		public static void test_log_regression_cost() {
			var file_path = "./data/ex2data1.txt";
			double[][] train_data;
			double[] result_data;

			var parse_result = file_utils.parse_file(file_path, out train_data, out result_data);

			if (parse_result.has_errors()) {
				Console.WriteLine(parse_result.all_errors_to_string());
				return;
			}

			double cost; // 0.69315
			double[] gradient; // -0.1, -12.00922, -11.26284
			ml_funcs.cost_logistic_regression(train_data, result_data, new double[] { 0, 0, 0 }, out cost, out gradient);

			Console.Write("cost_logistic_regression cost");
			if (Math.Round(cost, 5) == 0.69315)
				Console.WriteLine(" .. OK");
			else
				Console.WriteLine(" .. FAILED");

			double[] test_results = new double[] { -0.1,  -12.00922, -11.26284 };

			Console.Write("cost_logistic_regression gradient");
			var correct = 0;
			for (var i = 0; i < test_results.Length; i++)
				if (Math.Round(gradient[i], 5) == test_results[i]) correct++;

			if (correct == test_results.Length)
				Console.WriteLine(" .. OK");
			else
				Console.WriteLine(" .. FAILED");
		}
	}
}