Moved unit tests close to the sources

src/libs/som/test/SomTest.cpp

@@ -0,0 +1,134 @@
+/*
+ * Copyright (C) 2019 Emeric Poupon
+ *
+ * This file is part of LMS.
+ *
+ * LMS is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * LMS is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with LMS.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#include <unordered_set>
+#include <gtest/gtest.h>
+#include "som/DataNormalizer.hpp"
+#include "som/Network.hpp"
+
+using namespace SOM;
+
+static constexpr InputVector::value_type EPSILON = 0.01;
+
+TEST(som, Matrix)
+{
+	{
+		Matrix<int> testMatrix {2, 2, 123};
+		{
+			const Position pos {0, 0};
+			EXPECT_EQ(testMatrix[pos],  123);
+		}
+		{
+			const Position pos {0, 1};
+			EXPECT_EQ(testMatrix[pos], 123);
+		}
+		{
+			const Position pos {1, 0};
+			EXPECT_EQ(testMatrix[pos], 123);
+		}
+		{
+			const Position pos {1, 1};
+			EXPECT_EQ(testMatrix[pos], 123);
+		}
+	}
+}
+
+TEST(som, InputVector)
+{
+
+	{
+		InputVector test1 {2};
+		test1[0] = 0;
+		test1[1] = 1;
+
+		InputVector test2 {2};
+		test2[0] = 1;
+		test2[1] = 0;
+
+		InputVector test3 {test1};
+		test3 += test2;
+		EXPECT_LT(std::abs(test3[0] - 1), EPSILON);
+		EXPECT_LT(std::abs(test3[1] - 1), EPSILON);
+	}
+}
+
+TEST(som, Network)
+{
+	Network network {2, 2, 1};
+
+	const InputVector weights {1, 1};
+	std::vector<InputVector> trainData
+	{
+		{ 1, 50 },
+		{ 1, 100 },
+		{ 1, 150 },
+		{ 1, 200 },
+	};
+
+	DataNormalizer normalizer {1};
+	normalizer.computeNormalizationFactors(trainData);
+	for (auto& data: trainData)
+		normalizer.normalizeData(data);
+
+	network.dump(std::cout);
+	network.train(trainData, 20);
+	network.dump(std::cout);
+
+	auto distFunc {network.getDistanceFunc()};
+
+	EXPECT_LT((std::abs(distFunc({1, 0}, {1, 1}, weights) - 1)), EPSILON);
+	EXPECT_LT((std::abs(distFunc({1, 0}, {1, 2}, weights) - 4)), EPSILON);
+	EXPECT_LT(std::abs(distFunc({1, 0}, {1, 0.33}, weights) - distFunc({1, 0.66}, {1, 1.}, weights)), EPSILON);
+
+	{
+		std::unordered_set<Position> positions;
+		for (const InputVector& data : trainData)
+			positions.insert(network.getClosestRefVectorPosition(data));
+
+		EXPECT_EQ(positions.size(), 4);
+	}
+
+	{
+		Position pos {network.getClosestRefVectorPosition(InputVector{1, 0.66})};
+		for (std::size_t i {}; i < 40; ++i)
+		{
+			InputVector input {1, 130 + static_cast<InputVector::value_type>(i) };
+			normalizer.normalizeData(input);
+
+			EXPECT_EQ(network.getClosestRefVectorPosition(input), pos);
+		}
+	}
+
+	{
+		Position pos {network.getClosestRefVectorPosition(InputVector{1, 1})};
+		for (std::size_t i {}; i < 40; ++i)
+		{
+			InputVector input {1, 180 + static_cast<InputVector::value_type>(i) };
+			normalizer.normalizeData(input);
+
+			EXPECT_EQ(network.getClosestRefVectorPosition(input), pos);
+		}
+	}
+}
+
+int main(int argc, char **argv)
+{
+	::testing::InitGoogleTest(&argc, argv);
+	return RUN_ALL_TESTS();
+}