From 99d592a71ef05abca277edc5a8d43b441e528531 Mon Sep 17 00:00:00 2001
From: emeric <itmfr@yahoo.fr>
Date: Sat, 9 Mar 2019 14:19:24 +0100
Subject: [PATCH] Add a SOM test

---
 test/som-test/SomTest.cpp | 99 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 test/som-test/SomTest.cpp

diff --git a/test/som-test/SomTest.cpp b/test/som-test/SomTest.cpp
new file mode 100644
index 00000000..2c96f54e
--- /dev/null
+++ b/test/som-test/SomTest.cpp
@@ -0,0 +1,99 @@
+#include <cassert>
+#include <assert.h>
+#include <iostream>
+
+#include "DataNormalizer.hpp"
+#include "Network.hpp"
+
+#define CHECK(pred)	assert((pred))
+
+using namespace SOM;
+
+int main(int argc, char* argv[])
+{
+	static const InputVector::value_type EPSILON = 0.01;
+	{
+		Matrix<int> testMatrix {2, 2, 123};
+		assert((testMatrix[{0,0}] == 123));
+		assert((testMatrix[{0,1}] == 123));
+		assert((testMatrix[{1,0}] == 123));
+		assert((testMatrix[{1,1}] == 123));
+	}
+
+	{
+		InputVector test1 {2};
+		test1[0] = 0;
+		test1[1] = 1;
+
+		InputVector test2 {2};
+		test2[0] = 1;
+		test2[1] = 0;
+
+		InputVector test3 {test1};
+		test3 += test2;
+		assert(std::abs(test3[0] - 1) < EPSILON);
+		assert(std::abs(test3[1] - 1) < EPSILON);
+	}
+
+	{
+		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);
+
+		std::cout << "MEAN dist = " << network.computeRefVectorsDistanceMean() << std::endl;
+		std::cout << "MEDIAN dist = " << network.computeRefVectorsDistanceMedian() << std::endl;
+
+		auto distFunc {network.getDistanceFunc()};
+
+		assert((std::abs(distFunc({1, 0}, {1, 1}, weights) - 1) < EPSILON));
+		assert((std::abs(distFunc({1, 0}, {1, 2}, weights) - 4) < EPSILON));
+		assert((std::abs(distFunc({1, 0}, {1, 0.33}, weights) - distFunc({1, 0.66}, {1, 1.}, weights)) < EPSILON));
+
+		{
+			std::set<Position> positions;
+			for (const InputVector& data : trainData)
+				positions.insert(network.getClosestRefVectorPosition(data));
+			assert(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);
+
+				assert( 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);
+
+				assert( network.getClosestRefVectorPosition(input) == pos);
+			}
+		}
+
+	}
+
+	return 0;
+}