added plot generators and current c++ code

renamed some matlab files

franke/plots/net/net_0.gp

@@ -0,0 +1,613 @@
+set terminal emf size 600,250
+ set output 'net_0.emf'
+ unset xtics
+ unset key
+ set format y ' '
+plot '-' with lines  lw 2 lc rgb '#ff0000' title 'JUMPING_JACK', '-' with lines  lw 2 lc rgb '#00ff00' title 'SITUPS', '-' with lines  lw 2 lc rgb '#0000ff' title 'PUSHUPS', '-' with lines  lw 2 lc rgb '#ffff00' title 'KNEEBEND', '-' with lines  lw 2 lc rgb '#000000' title 'FORWARDBEND', 
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+

franke/plots/net/net_1.gp

@@ -0,0 +1,613 @@
+set terminal emf size 600,250
+ set output 'net_1.emf'
+ unset xtics
+ unset key
+ set format y ' '
+plot '-' with lines  lw 2 lc rgb '#ff0000' title 'JUMPING_JACK', '-' with lines  lw 2 lc rgb '#00ff00' title 'SITUPS', '-' with lines  lw 2 lc rgb '#0000ff' title 'PUSHUPS', '-' with lines  lw 2 lc rgb '#ffff00' title 'KNEEBEND', '-' with lines  lw 2 lc rgb '#000000' title 'FORWARDBEND', 
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+
+

franke/plots/net/net_2.gp

@@ -0,0 +1,613 @@
+set terminal emf size 600,250
+ set output 'net_2.emf'
+ unset xtics
+ unset key
+ set format y ' '
+plot '-' with lines  lw 2 lc rgb '#ff0000' title 'JUMPING_JACK', '-' with lines  lw 2 lc rgb '#00ff00' title 'SITUPS', '-' with lines  lw 2 lc rgb '#0000ff' title 'PUSHUPS', '-' with lines  lw 2 lc rgb '#ffff00' title 'KNEEBEND', '-' with lines  lw 2 lc rgb '#000000' title 'FORWARDBEND', 
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+

franke/plots/net/net_3.gp

@@ -0,0 +1,613 @@
+set terminal emf size 600,250
+ set output 'net_3.emf'
+ unset xtics
+ unset key
+ set format y ' '
+plot '-' with lines  lw 2 lc rgb '#ff0000' title 'JUMPING_JACK', '-' with lines  lw 2 lc rgb '#00ff00' title 'SITUPS', '-' with lines  lw 2 lc rgb '#0000ff' title 'PUSHUPS', '-' with lines  lw 2 lc rgb '#ffff00' title 'KNEEBEND', '-' with lines  lw 2 lc rgb '#000000' title 'FORWARDBEND', 
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+

franke/plots/net/net_4.gp

@@ -0,0 +1,613 @@
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+ set output 'net_4.emf'
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+ unset key
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franke/plots/net/raw_2.gp

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franke/plots/net/raw_3.gp

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+e
+
+

franke/plots/net/raw_4.gp

@@ -0,0 +1,373 @@
+set terminal emf size 600,250
+ set output 'raw_4.emf'
+ unset xtics
+ unset key
+ unset arrow 1
+ unset arrow 2
+ set format y ' '
+plot '-' with lines  lw 1 lc rgb '#ff0000' title 'x', '-' with lines  lw 1 lc rgb '#00ff00' title 'y', '-' with lines  lw 1 lc rgb '#0000ff' title 'z', 
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+e
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+11900 -0.520382
+e
+
+

workspace/main.cpp

@@ -1,198 +1,32 @@
 
 //#include "usingneuralnet.h"
-#include "usingpca.h"
+
 #include <omp.h>
-#include "pca/TrainPCA.h"
+
 #include <KLib/misc/gnuplot/Gnuplot.h>
 #include <KLib/misc/gnuplot/GnuplotSplot.h>
 #include <KLib/misc/gnuplot/GnuplotSplotElementLines.h>
 
-#include "pca/KNN.h"
+
-#include "pca/aKNN.h"
 
 #include <vector>
 
-std::vector<std::string> COLORS = {"#000000", "#0000ff", "#00ff00", "#ff0000", "#00ffff"};
-
-std::string getClass(const std::vector<ClassifiedFeature>& nns) {
-	std::unordered_map<std::string, int> map;
-	for(const ClassifiedFeature& nn : nns) { map[nn.className] += 1; }
-	for (auto& it : map) {
-		if (it.second > nns.size() * 0.75) {return it.first;}
-	}
-	return "";
-}
-
-struct ClassStats {
-	int counts[6] = {};
-};
-
-struct Stats{
-	int match;
-	int error;
-	int unknown;
-	Stats() : match(0), error(0), unknown(0) {;}
-	float getSum() {return match+error+unknown;}
-};
 
 
 
-std::vector<ClassifiedPattern> removePatterns(const std::vector<ClassifiedPattern>& patAll, const std::string& fileName) {
-	std::vector<ClassifiedPattern> res;
-	for (const ClassifiedPattern& pat : patAll) {
-		if (pat.belongsToFile(fileName)) {
-			continue;
-		} else {
-			res.push_back(pat);
-		}
-	}
-	return res;
-}
 
-template <int numFeatures> struct PCA {
+#include "usingpca.h"
-
+//#include "plotFile.h"
-	aKNN<ClassifiedFeature, numFeatures> knn;
+//#include "usingneuralnet.h"
-	TrainPCA::Matrices m;
-
-};
-
-class Plot {
-
-	K::Gnuplot gp;
-	K::GnuplotSplot splot;
-	K::GnuplotSplotElementLines lines[5];
-
-
-public:
-
-	Plot() {
-		for (int i = 0; i < 5; ++i) {lines[i].setColorHex(COLORS[i]);}
-		for (int i = 0; i < 5; ++i) {splot.add(&lines[i]);}
-	}
-
-	void add(int idx, std::vector<float>& vec) {
-		K::GnuplotPoint3 p3(vec[0], vec[1], vec[2]);
-		lines[idx].add(p3);
-	}
-
-	void clear() {
-		for (int i = 0; i < 5; ++i) {lines[i].clear();}
-	}
-
-	void show() {
-		gp.setDebugOutput(false);
-		gp.draw(splot);
-		gp.flush();
-	}
-
-};
 
 int main(void) {
 
 	omp_set_dynamic(false);
 	omp_set_num_threads(3);
 
-	const int numFeatures = 10;
+	runPCA();
-
+	//runPlot();
-	TrainPCA::Settings setTrain;
+	//UsingNeuralNet::run();
-	TrainPCA::Settings setClass; setClass.regionStart_ms += 25;
-
-	Data::getAllDataFiles();
-	Plot p;
-
-	// convert all provided datasets into patterns
-	std::vector<ClassifiedPattern> srcTrain = TrainPCA::getAllData(setTrain);
-	std::vector<ClassifiedPattern> srcClass = TrainPCA::getAllData(setClass);
-	std::cout << "windows: " << srcTrain.size() << std::endl;
-
-	// error calculation
-	std::unordered_map<std::string, Stats> stats;
-	std::unordered_map<std::string, ClassStats> classStats;
-	//int xx = 0;
-	std::unordered_map<std::string, PCA<numFeatures>*> pcas;
-
-	// try to classify each pattern
-	for (const ClassifiedPattern& patClassify : srcClass) {
-
-		// construct knn search for this leave-one-out ONLY ONCE
-		if (pcas.find(patClassify.fileName) == pcas.end()) {
-
-			std::cout << "constructing PCA for all files but " << patClassify.fileName << std::endl;
-
-			// remove all training patterns belonging to the same source file as the to be classifed pattern
-			std::vector<ClassifiedPattern> srcTrainLOO = removePatterns(srcTrain, patClassify.fileName);
-
-			// sanity check (have we removed all patterns?)
-			int diff =  srcTrain.size() - srcTrainLOO.size();
-			if (diff < 200) {throw 1;}
-
-			p.clear();
-			PCA<numFeatures>* pca = new PCA<numFeatures>();
-			pcas[patClassify.fileName] = pca;
-
-			// train PCA using all pattern without those belonging to the same source file as the to-be-classified one
-			pca->m = TrainPCA::getMatrices(srcTrainLOO, numFeatures);
-
-			// calculate features and add them to the KNN
-			for (const ClassifiedPattern& pat : srcTrainLOO) {
-				K::DynColVector<float> vec = pca->m.A1 * K::PCAHelper<float>::toVector(pat.pattern);
-				std::vector<float> arr;
-				for (int i = 0; i < numFeatures; ++i) {arr.push_back(vec(i));}
-				pca->knn.add(ClassifiedFeature(pat.className, arr));
-
-				const int idx = Settings::classToInt(pat.className);
-				p.add(idx, arr);
-
-			}
-			pca->knn.build();
-			//p.show();
-			//sleep(100);
-
-		}
-
-		{
-
-			PCA<numFeatures>* pca = pcas[patClassify.fileName];
-
-			// calculate features for the to-be-classified pattern
-			//const int idx = Settings::classToInt(pat.className);
-			K::DynColVector<float> vec = pca->m.A1 * K::PCAHelper<float>::toVector(patClassify.pattern);
-
-			// get KNN's answer
-			std::vector<float> arr;
-			for (int i = 0; i < numFeatures; ++i) {arr.push_back(vec(i));}
-			std::vector<ClassifiedFeature> neighbors = pca->knn.get(arr.data(), 5);
-			std::string gotClass = getClass(neighbors);
-
-			if		(patClassify.className == gotClass) {stats["all"].match++;		stats[patClassify.fileName].match++;	stats[patClassify.className].match++;}
-			else if	(gotClass == "")					{stats["all"].unknown++;	stats[patClassify.fileName].unknown++;	stats[patClassify.className].unknown++;}
-			else										{stats["all"].error++;		stats[patClassify.fileName].error++;	stats[patClassify.className].error++;}
-
-			int gotIdx = (gotClass == "") ? (5) : Settings::classToInt(gotClass);
-			++classStats[patClassify.className].counts[gotIdx];
-
-		}
-
-
-	}
-
-	for (auto& it : stats) {
-		Stats& stats = it.second;
-		std::cout << "'" <<it.first << "',";
-		std::cout << stats.match/stats.getSum() << ",";
-		std::cout << stats.error/stats.getSum() << ",";
-		std::cout << stats.unknown/stats.getSum();
-		std::cout << std::endl;
-	}
-
-	for (auto& it : classStats) {
-		ClassStats& stats = it.second;
-		std::cout << "'" << it.first << "',";
-		for (int i = 0; i < 6; ++i) {
-			std::cout << stats.counts[i] << ",";
-		}
-		std::cout << std::endl;
-	}
 
 
 	/*

workspace/plotFile.h

@@ -0,0 +1,50 @@
+#ifndef PLOTFILE_H
+#define PLOTFILE_H
+
+#include <KLib/misc/gnuplot/Gnuplot.h>
+#include <KLib/misc/gnuplot/GnuplotPlot.h>
+#include <KLib/misc/gnuplot/GnuplotPlotElementLines.h>
+
+#include "pca/Data.h"
+#include "pca/Settings.h"
+
+void runPlot() {
+
+	// read all sensor-values within the given data-file
+	Recording rec = SensorReader::read("/mnt/firma/kunden/HandyGames/datenOK/jumpingjack/jumpingjack_gl_5_subject_3_left.txt");
+
+	// get the value-interpolator
+	K::Interpolator<uint64_t, SensorAccelerometer> intAccel;
+	for (const auto& val : rec.accel.values) {intAccel.add(val.ts, val.val);}
+	intAccel.makeRelative();
+
+	K::Gnuplot gp;
+	K::GnuplotPlot plot;
+	K::GnuplotPlotElementLines lines[3];
+	plot.add(&lines[0]); lines[0].setColorHex("#0000ff");
+	plot.add(&lines[1]); lines[1].setColorHex("#00FF00");
+	plot.add(&lines[2]); lines[2].setColorHex("#ff0000");
+
+	for (int ms = 0; ms < 20000; ms += 50) {
+		SensorAccelerometer sa = intAccel.get(ms);
+		lines[0].add(K::GnuplotPoint2(ms, sa.x));
+		lines[1].add(K::GnuplotPoint2(ms, sa.y));
+		lines[2].add(K::GnuplotPoint2(ms, sa.z));
+	}
+
+	gp.draw(plot);
+
+	std::ofstream out("/tmp/1.dat");
+	out << gp.getBuffer();
+	out.close();
+
+
+	gp.flush();
+
+	sleep(1000);
+
+
+}
+
+#endif // PLOTFILE_H
+

workspace/usingneuralnet.h

@@ -440,8 +440,8 @@ public:
 		opt.setElitism(0.07f);
 		opt.setPopulationSize(100);
 		opt.setMaxIterations(200);
-		opt.setMutation(0.20f);
+		opt.setMutation(0.40f);
-		opt.setValRange(0.25);
+		opt.setValRange(0.20);
 		opt.calculateOptimum(func, vec);
 
 
@@ -514,7 +514,7 @@ public:
 				netLines[i].clear();
 			}
 
-			for (int ms = 0; ms < 20000; ms += 50) {		//	K::Gnuplot gp;
+			for (int ms = 0; ms < 12000; ms += 100) {		//	K::Gnuplot gp;
 				//	K::GnuplotPlot plot;
 				//	K::GnuplotPlotElementLines line[3];
 				//	line[0].setColorHex("#ff0000"); line[0].setTitle("x");
@@ -546,7 +546,7 @@ public:
 			}
 
 			// process every (positive) occurence within the practice
-			for (int ts = 1000; ts < 10000; ts += 50) {
+			for (int ts = 0; ts < 12000; ts += 100) {
 
 				std::vector<float> values = getNetworkInput(interpol, ts);
 				std::vector<float> res = net.get(values);
@@ -566,11 +566,32 @@ public:
 				gp2.draw(plot2);
 				gp2.flush();
 
-				usleep(1000*50);
+
+
+				//usleep(1000*50);
 
 
 			}
 
+			gp1.flush();;
+			gp2.flush();;
+
+			std::string fileRaw = "raw_" + std::to_string((int)p.type);
+			std::string fileNet = "net_" + std::to_string((int)p.type);
+			gp1 << "set terminal emf size 600,250\n set output '"<<fileRaw<<".emf'\n unset xtics\n unset key\n unset arrow 1\n unset arrow 2\n set format y ' '\n";
+			gp2 << "set terminal emf size 600,250\n set output '"<<fileNet<<".emf'\n unset xtics\n unset key\n set format y ' '\n";
+
+			gp1.draw(plot1);
+			gp2.draw(plot2);
+
+			std::ofstream out1("/tmp/"+fileRaw+".gp");	out1 << gp1.getBuffer(); out1.close();
+			std::ofstream out2("/tmp/"+fileNet+".gp");	out2 << gp2.getBuffer(); out2.close();
+			gp1.flush();
+			gp2.flush();
+
+
+
+
 		}
 
 		sleep(1000);

workspace/usingpca.h

@@ -1,6 +1,204 @@
 #ifndef USINGPCA_H
 #define USINGPCA_H
 
+#include "pca/TrainPCA.h"
+#include "pca/KNN.h"
+#include "pca/aKNN.h"
+
+std::vector<std::string> COLORS = {"#000000", "#0000ff", "#00ff00", "#ff0000", "#00ffff"};
+
+
+
+struct Plot {
+
+	K::Gnuplot gp;
+	K::GnuplotSplot splot;
+	K::GnuplotSplotElementLines lines[5];
+
+
+public:
+
+	Plot() {
+		for (int i = 0; i < 5; ++i) {lines[i].setColorHex(COLORS[i]);}
+		for (int i = 0; i < 5; ++i) {splot.add(&lines[i]);}
+	}
+
+	void add(int idx, std::vector<float>& vec) {
+		K::GnuplotPoint3 p3(vec[0], vec[1], vec[2]);
+		lines[idx].add(p3);
+	}
+
+	void clear() {
+		for (int i = 0; i < 5; ++i) {lines[i].clear();}
+	}
+
+	void show() {
+		gp.setDebugOutput(false);
+		gp.draw(splot);
+		gp.flush();
+	}
+
+};
+
+std::string getClass(const std::vector<ClassifiedFeature>& nns) {
+	std::unordered_map<std::string, int> map;
+	for(const ClassifiedFeature& nn : nns) { map[nn.className] += 1; }
+	for (auto& it : map) {
+		if (it.second > nns.size() * 0.75) {return it.first;}
+	}
+	return "";
+}
+
+struct ClassStats {
+	int counts[6] = {};
+};
+
+struct Stats{
+	int match;
+	int error;
+	int unknown;
+	Stats() : match(0), error(0), unknown(0) {;}
+	float getSum() {return match+error+unknown;}
+};
+
+
+
+std::vector<ClassifiedPattern> removePatterns(const std::vector<ClassifiedPattern>& patAll, const std::string& fileName) {
+	std::vector<ClassifiedPattern> res;
+	for (const ClassifiedPattern& pat : patAll) {
+		if (pat.belongsToFile(fileName)) {
+			continue;
+		} else {
+			res.push_back(pat);
+		}
+	}
+	return res;
+}
+
+template <int numFeatures> struct PCA {
+
+	aKNN<ClassifiedFeature, numFeatures> knn;
+	TrainPCA::Matrices m;
+
+};
+
+
+void runPCA() {
+
+	const int numFeatures = 10;
+
+	TrainPCA::Settings setTrain;
+	TrainPCA::Settings setClass; setClass.regionStart_ms += 25;
+
+
+	Plot p;
+
+	// convert all provided datasets into patterns
+	std::vector<ClassifiedPattern> srcTrain = TrainPCA::getAllData(setTrain);
+	std::vector<ClassifiedPattern> srcClass = TrainPCA::getAllData(setClass);
+	std::cout << "windows: " << srcTrain.size() << std::endl;
+
+
+
+
+	// error calculation
+	std::unordered_map<std::string, Stats> stats;
+	std::unordered_map<std::string, ClassStats> classStats;
+	int xx = 0;
+	std::unordered_map<std::string, PCA<numFeatures>*> pcas;
+
+	// try to classify each pattern
+	for (const ClassifiedPattern& patClassify : srcClass) {
+
+		// construct knn search for this leave-one-out ONLY ONCE
+		if (pcas.find(patClassify.fileName) == pcas.end()) {
+
+			std::cout << "constructing PCA for all files but " << patClassify.fileName << std::endl;
+
+			// remove all training patterns belonging to the same source file as the to be classifed pattern
+			std::vector<ClassifiedPattern> srcTrainLOO = removePatterns(srcTrain, patClassify.fileName);
+
+			// sanity check (have we removed all patterns?)
+			int diff =  srcTrain.size() - srcTrainLOO.size();
+			if (diff < 200) {throw 1;}
+
+			p.clear();
+			PCA<numFeatures>* pca = new PCA<numFeatures>();
+			pcas[patClassify.fileName] = pca;
+
+			// train PCA using all pattern without those belonging to the same source file as the to-be-classified one
+			pca->m = TrainPCA::getMatrices(srcTrainLOO, numFeatures);
+
+			// calculate features and add them to the KNN
+			for (const ClassifiedPattern& pat : srcTrainLOO) {
+				K::DynColVector<float> vec = pca->m.A1 * K::PCAHelper<float>::toVector(pat.pattern);
+				std::vector<float> arr;
+				for (int i = 0; i < numFeatures; ++i) {arr.push_back(vec(i));}
+				pca->knn.add(ClassifiedFeature(pat.className, arr));
+
+				const int idx = Settings::classToInt(pat.className);
+				p.add(idx, arr);
+
+			}
+			pca->knn.build();
+
+			if (xx == 0) {
+				++xx;
+				std::ofstream out("/tmp/pca.gp"); p.gp.draw(p.splot); out << p.gp.getBuffer(); out.close();
+			}
+			//p.show();
+			//sleep(100);
+
+		}
+
+		{
+
+			PCA<numFeatures>* pca = pcas[patClassify.fileName];
+
+			// calculate features for the to-be-classified pattern
+			//const int idx = Settings::classToInt(pat.className);
+			K::DynColVector<float> vec = pca->m.A1 * K::PCAHelper<float>::toVector(patClassify.pattern);
+
+			// get KNN's answer
+			std::vector<float> arr;
+			for (int i = 0; i < numFeatures; ++i) {arr.push_back(vec(i));}
+			std::vector<ClassifiedFeature> neighbors = pca->knn.get(arr.data(), 5);
+			std::string gotClass = getClass(neighbors);
+
+			if		(patClassify.className == gotClass) {stats["all"].match++;		stats[patClassify.fileName].match++;	stats[patClassify.className].match++;}
+			else if	(gotClass == "")					{stats["all"].unknown++;	stats[patClassify.fileName].unknown++;	stats[patClassify.className].unknown++;}
+			else										{stats["all"].error++;		stats[patClassify.fileName].error++;	stats[patClassify.className].error++;}
+
+			int gotIdx = (gotClass == "") ? (5) : Settings::classToInt(gotClass);
+			++classStats[patClassify.className].counts[gotIdx];
+
+		}
+
+
+	}
+
+	for (auto& it : stats) {
+		Stats& stats = it.second;
+		std::cout << "'" <<it.first << "',";
+		std::cout << stats.match/stats.getSum() << ",";
+		std::cout << stats.error/stats.getSum() << ",";
+		std::cout << stats.unknown/stats.getSum();
+		std::cout << std::endl;
+	}
+
+	for (auto& it : classStats) {
+		ClassStats& stats = it.second;
+		std::cout << "'" << it.first << "',";
+		for (int i = 0; i < 6; ++i) {
+			std::cout << stats.counts[i] << ",";
+		}
+		std::cout << std::endl;
+	}
+
+}
+
+/*
+
 #include <vector>
 
 #include "sensors/SensorReader.h"
@@ -8,6 +206,7 @@
 
 #include <eigen3/Eigen/Dense>
 
+
 enum class PracticeType {
 	//REST,
 	JUMPING_JACK,
@@ -108,6 +307,7 @@ public:
 	}
 
 };
+*/
 
 #endif // USINGPCA_H