diff --git a/franke/octave/helper2.m b/franke/octave/pca.m
similarity index 100%
rename from franke/octave/helper2.m
rename to franke/octave/pca.m
diff --git a/franke/plots/net/input.gp b/franke/plots/net/input.gp
new file mode 100644
index 0000000..2d5e46b
--- /dev/null
+++ b/franke/plots/net/input.gp
@@ -0,0 +1,1210 @@
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diff --git a/franke/plots/net/net_0.gp b/franke/plots/net/net_0.gp
new file mode 100644
index 0000000..cc01d54
--- /dev/null
+++ b/franke/plots/net/net_0.gp
@@ -0,0 +1,613 @@
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diff --git a/franke/plots/net/net_1.gp b/franke/plots/net/net_1.gp
new file mode 100644
index 0000000..868f86e
--- /dev/null
+++ b/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
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diff --git a/franke/plots/net/net_2.gp b/franke/plots/net/net_2.gp
new file mode 100644
index 0000000..70bfc63
--- /dev/null
+++ b/franke/plots/net/net_2.gp
@@ -0,0 +1,613 @@
+set terminal emf size 600,250
+ set output 'net_2.emf'
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+ unset key
+ set format y ' '
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diff --git a/franke/plots/net/net_3.gp b/franke/plots/net/net_3.gp
new file mode 100644
index 0000000..3d65c0b
--- /dev/null
+++ b/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 ' '
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new file mode 100644
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new file mode 100644
index 0000000..5ddae63
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diff --git a/franke/plots/pca/input.gp b/franke/plots/pca/input.gp
new file mode 100644
index 0000000..0a859cb
--- /dev/null
+++ b/franke/plots/pca/input.gp
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diff --git a/franke/plots/pca/input_for_pca.gp b/franke/plots/pca/input_for_pca.gp
new file mode 100644
index 0000000..48e8bef
--- /dev/null
+++ b/franke/plots/pca/input_for_pca.gp
@@ -0,0 +1,1211 @@
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diff --git a/franke/plots/pca/pca.gp b/franke/plots/pca/pca.gp
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diff --git a/franke/plots/pca/pca1.gp b/franke/plots/pca/pca1.gp
new file mode 100644
index 0000000..597d9a9
--- /dev/null
+++ b/franke/plots/pca/pca1.gp
@@ -0,0 +1,16617 @@
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diff --git a/franke/plots/pca/pca2.gp b/franke/plots/pca/pca2.gp
new file mode 100644
index 0000000..2e43ed7
--- /dev/null
+++ b/franke/plots/pca/pca2.gp
@@ -0,0 +1,16620 @@
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+1.93811 -0.629005 -1.19691
+1.38332 -0.730821 -0.423907
+0.90196 -0.658 -0.0254326
+0.836456 -0.183703 1.04225
+0.806448 0.393281 1.18411
+1.69947 1.04659 0.890782
+2.16375 1.35348 -0.690064
+3.56082 1.58231 -1.7426
+4.67747 1.76474 -2.04856
+6.19884 1.60288 -3.47398
+7.44572 1.24401 -3.91056
+8.87679 0.747052 -4.18937
+10.3878 0.158596 -4.47326
+11.8098 -0.371372 -4.50845
+13.0373 -0.847575 -4.36255
+14.0396 -1.4076 -5.10077
+14.9277 -2.02984 -5.50817
+15.325 -2.58957 -5.60185
+15.7591 -3.10137 -5.42774
+15.6567 -3.41852 -4.64887
+15.2964 -3.56597 -3.90983
+14.6606 -3.53981 -2.71736
+13.6859 -3.60889 -2.65842
+12.3138 -3.78416 -2.69739
+11.1109 -3.8771 -2.14648
+9.13806 -3.86291 -1.72235
+7.88364 -3.66929 -0.990168
+5.72863 -3.25083 -0.425525
+4.15413 -3.09227 -1.10422
+2.78184 -2.73687 0.0465589
+1.9261 -2.62185 -0.5957
+1.1506 -2.36708 0.31101
+0.350626 -2.32146 -0.260386
+-0.160137 -2.13817 0.443599
+-0.807303 -2.01435 0.391189
+-0.418467 -1.61234 1.12671
+-0.372665 -0.995209 1.81994
+0.312519 -0.718984 0.252624
+1.21116 -0.510777 -0.292913
+1.93342 -0.619872 -1.55251
+3.67949 -0.93194 -2.41754
+5.30631 -1.34042 -2.63038
+6.96389 -1.80084 -2.32778
+8.65254 -2.25633 -2.23984
+10.1857 -2.60921 -1.85973
+11.8836 -2.88072 -1.73903
+13.4412 -2.91415 -1.33126
+14.8084 -2.80434 -1.34993
+15.7953 -2.52238 -1.20424
+16.2533 -2.1246 -1.02364
+16.2647 -1.59662 -0.624442
+15.9589 -1.17819 -0.912909
+15.2943 -0.640625 -0.071682
+14.0104 -0.366781 -0.642118
+13.1237 -0.113187 -0.351836
+11.0705 -0.00397974 -0.679362
+9.56408 -0.156773 -1.21938
+7.46156 -0.0325832 -0.0517609
+5.32788 0.0541956 -0.436231
+3.9506 0.184976 -0.125336
+1.85613 0.249033 -0.259366
+0.606901 0.123336 -0.714506
+-0.52948 -0.188969 -0.677855
+-1.23227 -0.713782 -1.24908
+-1.58474 -1.0441 -0.312618
+-1.96525 -1.28745 -0.0563745
+-1.81779 -1.29904 0.551082
+-2.01247 -1.33857 0.556907
+-1.18574 -1.38178 0.230074
+-0.371628 -1.31924 0.264123
+1.43103 -1.57874 -1.42394
+3.20829 -1.60069 -1.0218
+4.70925 -2.02088 -2.10145
+6.38604 -2.43117 -1.79365
+7.88241 -2.74865 -1.14461
+9.79061 -3.04712 -1.2121
+11.5718 -3.12958 -0.76216
+13.4617 -3.00653 -0.594106
+14.8598 -2.7725 -0.762897
+15.9075 -2.33118 -0.578806
+16.3951 -1.96813 -1.14829
+16.5263 -1.56611 -1.22379
+16.229 -1.15313 -0.863327
+15.563 -0.711527 -0.465625
+14.4108 -0.532182 -0.971816
+13.127 -0.318494 -0.345246
+11.3187 -0.252665 -0.772045
+10.0572 -0.170969 -0.367169
+7.98328 -0.00320661 -0.297055
+6.33995 0.0529043 -0.756796
+3.96375 0.278547 0.0195746
+2.03532 0.20949 -0.989882
+0.91546 0.0527408 -0.475491
+-0.00204801 -0.288924 -0.775891
+-0.442689 -0.802372 -1.00194
+-1.24481 -0.946098 0.44691
+-1.66725 -1.11158 0.123244
+-1.84279 -1.0301 1.00305
+-2.02337 -0.832817 1.30901
+e
+
+
diff --git a/workspace/main.cpp b/workspace/main.cpp
index 3d026d4..032887c 100644
--- a/workspace/main.cpp
+++ b/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 {
-
-	aKNN<ClassifiedFeature, numFeatures> knn;
-	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();
-	}
-
-};
+#include "usingpca.h"
+//#include "plotFile.h"
+//#include "usingneuralnet.h"
 
 int main(void) {
 
 	omp_set_dynamic(false);
 	omp_set_num_threads(3);
 
-	const int numFeatures = 10;
-
-	TrainPCA::Settings setTrain;
-	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;
-	}
+	runPCA();
+	//runPlot();
+	//UsingNeuralNet::run();
 
 
 	/*
diff --git a/workspace/plotFile.h b/workspace/plotFile.h
new file mode 100644
index 0000000..ca9c37c
--- /dev/null
+++ b/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
+
diff --git a/workspace/usingneuralnet.h b/workspace/usingneuralnet.h
index 5d2449e..52c1389 100644
--- a/workspace/usingneuralnet.h
+++ b/workspace/usingneuralnet.h
@@ -440,8 +440,8 @@ public:
 		opt.setElitism(0.07f);
 		opt.setPopulationSize(100);
 		opt.setMaxIterations(200);
-		opt.setMutation(0.20f);
-		opt.setValRange(0.25);
+		opt.setMutation(0.40f);
+		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);
diff --git a/workspace/usingpca.h b/workspace/usingpca.h
index b02f37e..e654e3c 100644
--- a/workspace/usingpca.h
+++ b/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