added current c++ code

workspace/pca/TrainPCA.h

@@ -0,0 +1,108 @@
+#ifndef TRAINPCA_H
+#define TRAINPCA_H
+
+#include "Data.h"
+#include "Settings.h"
+#include <KLib/math/linearTransform/PCA.h>
+
+class TrainPCA {
+
+private:
+
+
+
+public:
+
+	struct Matrices {
+		K::DynMatrix<float> A1;
+		K::DynMatrix<float> A2;
+		K::DynMatrix<float> A3;
+	};
+
+	static std::vector<ClassifiedPattern> getTestData() {
+
+		const int windowSize_ms = 1000;
+		const int regionStart_ms = 1500 + 25;		// worst case: half-window-size offset
+		const float regionPercent = 0.85;
+		const int stepSize_ms = 50;
+
+		// get 10 data-files per class
+		std::vector<ClassifiedDataFile> files = Data::getDataFiles(30);
+
+		// get patterns for each class
+		std::vector<ClassifiedPattern> patterns;
+		for (ClassifiedDataFile cdf : files) {
+
+			std::cout << cdf.fileName << std::endl;
+			std::vector<std::vector<float>> samples = Data::getSamples(cdf.fileName, windowSize_ms, regionStart_ms, regionPercent, stepSize_ms);
+
+			for (const std::vector<float> vec : samples) {
+				patterns.push_back(ClassifiedPattern(cdf.className, vec));
+			}
+
+		}
+
+		return patterns;
+
+	}
+
+	/** train PCA features */
+	static std::vector<ClassifiedPattern> getTrainData() {
+
+		const int windowSize_ms = 1000;
+		const int regionStart_ms = 1500;
+		const float regionPercent = 0.4;
+		const int stepSize_ms = 50;
+
+		// get 5 data-files per class
+		std::vector<ClassifiedDataFile> files = Data::getDataFiles(30);
+
+		// get patterns for each class
+		std::vector<ClassifiedPattern> patterns;
+		for (ClassifiedDataFile cdf : files) {
+
+			std::cout << cdf.fileName << std::endl;
+			std::vector<std::vector<float>> samples = Data::getSamples(cdf.fileName, windowSize_ms, regionStart_ms, regionPercent, stepSize_ms);
+			std::cout << "\tgot" << samples.size() << " samples, each " << samples[0].size() << " values" << std::endl;
+
+			for (const std::vector<float> vec : samples) {
+				patterns.push_back(ClassifiedPattern(cdf.className, vec));
+			}
+
+		}
+
+		return patterns;
+
+	}
+
+	/** get the A1,A2,A3 matrices for the given training data */
+	static Matrices getMatrices(const std::vector<ClassifiedPattern>& data, const int numFeatures) {
+
+		K::LinearTransform<float>::PCA pca;
+		K::LinearTransform<float>::MaxInterClassDistance<std::string> inter;
+		K::LinearTransform<float>::MinIntraClassDistance<std::string> intra;
+
+		for (const ClassifiedPattern& pat : data) {
+			pca.add(pat.pattern);
+			inter.add(pat.className, pat.pattern);
+			intra.add(pat.className, pat.pattern);
+		}
+
+		Matrices m;
+		m.A1 = pca.getA(numFeatures);
+		m.A2 = inter.getA(numFeatures);
+		m.A3 = intra.getA(numFeatures);
+
+		std::cout << "A1: " << std::endl << m.A1 << std::endl;
+		std::cout << "A2: " << std::endl << m.A2 << std::endl;
+		std::cout << "A3: " << std::endl << m.A3 << std::endl;
+
+		return m;
+
+	}
+
+
+};
+
+#endif // TRAINPCA_H
+