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