WIP som network

tools/classifier/LmsClassifier.cpp

@@ -0,0 +1,80 @@
+#include <stdlib.h>
+
+#include <stdexcept>
+#include <iostream>
+#include <string>
+
+#include "classifier/SOM.hpp"
+#include "classifier/DataNormalizer.hpp"
+
+int main(int argc, char *argv[])
+{
+	if (argc != 2)
+	{
+		std::cerr << "Usage: <file>" << std::endl;
+		return EXIT_FAILURE;
+	}
+
+	auto iterationCount = std::stoul(argv[1]);
+
+	using namespace SOM;
+
+	SOM::Network network(5, 5, 2);
+
+	network.dump(std::cout);
+
+	std::vector< std::vector<double> > inputValues =
+	{
+		{ 160, 1 },
+		{ 80, -1 },
+		{ 80, -0.75 },
+		{ 240, 0.5 },
+		{ 120, -0.5 },
+		{ 140, -0.5 },
+	};
+
+	std::cout << "Before normalization:" << std::endl;
+	for (const auto& inputValue : inputValues)
+	{
+		std::cout << inputValue << std::endl;
+	}
+	std::cout << std::endl;
+
+	std::cout << "After normalization:" << std::endl;
+
+	SOM::DataNormalizer normalizer(2);
+
+	normalizer.computeNormalizationFactors(inputValues);
+
+	for (auto& inputValue : inputValues)
+		normalizer.normalizeData(inputValue);
+
+	for (const auto& inputValue : inputValues)
+	{
+		std::cout << inputValue << std::endl;
+	}
+
+	std::cout << std::endl;
+
+	for (const auto& inputValue : inputValues)
+	{
+		auto res = network.classify(inputValue);
+		std::cout << "Found at " << res.x << ", " << res.y << std::endl;
+	}
+
+	std::cout << "Training network for " << iterationCount << " iterations" << std::endl;
+
+	network.train(inputValues, iterationCount);
+
+	network.dump(std::cout);
+	std::cout << "OK" << std::endl;
+
+	for (const auto& inputValue : inputValues)
+	{
+		auto res = network.classify(inputValue);
+		std::cout << "Found at " << res.x << ", " << res.y << std::endl;
+	}
+
+	return EXIT_SUCCESS;
+}
+