329 lines
12 KiB
C++
Executable File
329 lines
12 KiB
C++
Executable File
//#include "main.h"
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#include "navMesh/mesh.h"
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#include "navMesh/filter.h"
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#include "Settings.h"
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#include "navMesh/meshPlotter.h"
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#include "Plotty.h"
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#include <memory>
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#include <thread>
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#include <experimental/filesystem>
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#include <Indoor/floorplan/v2/FloorplanReader.h>
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#include <Indoor/sensors/radio/model/WiFiModelLogDistCeiling.h>
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#include <Indoor/sensors/offline/FileReader.h>
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#include <Indoor/geo/Heading.h>
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#include <Indoor/geo/Point2.h>
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#include <Indoor/sensors/imu/TurnDetection.h>
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#include <Indoor/sensors/imu/StepDetection.h>
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#include <Indoor/sensors/imu/MotionDetection.h>
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#include <Indoor/sensors/pressure/RelativePressure.h>
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#include <Indoor/data/Timestamp.h>
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#include <Indoor/sensors/radio/setup/WiFiOptimizerLogDistCeiling.h>
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#include <Indoor/math/stats/Statistics.h>
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#include <Indoor/smc/filtering/resampling/ParticleFilterResamplingSimpleImpoverishment.h>
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#include <sys/stat.h>
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Stats::Statistics<float> run(Settings::DataSetup setup, int numFile, std::string folder) {
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// reading file
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Floorplan::IndoorMap* map = Floorplan::Reader::readFromFile(setup.map);
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Offline::FileReader fr(setup.training[numFile]);
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WiFiFingerprints fingerprints(setup.fingerprints);
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std::ifstream inp(setup.wifiModel, std::ifstream::binary);
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// ground truth
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std::vector<int> gtPath;
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for(int i = 0; i < setup.numGTPoints; ++i){gtPath.push_back(i);}
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Interpolator<uint64_t, Point3> gtInterpolator = fr.getGroundTruthPath(map, gtPath);
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Stats::Statistics<float> errorStats;
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// error file
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const long int t = static_cast<long int>(time(NULL));
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auto evalDir = std::experimental::filesystem::path(Settings::errorDir);
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evalDir.append(folder);
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if (!std::experimental::filesystem::exists(evalDir)) {
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std::experimental::filesystem::create_directory(evalDir);
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}
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std::ofstream errorFile;
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errorFile.open (evalDir.string() + "/" + std::to_string(numFile) + "_" + std::to_string(t) + ".csv");
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// wifi
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WiFiModelLogDistCeiling WiFiModel(map);
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// with optimization
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if(Settings::WiFiModel::optimize){
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if (!inp.good() || (inp.peek()&&0) || inp.eof()) {
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Assert::isFalse(fingerprints.getFingerprints().empty(), "no fingerprints available!");
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WiFiOptimizer::LogDistCeiling opt(map, Settings::WiFiModel::vg_calib);
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for (const WiFiFingerprint& fp : fingerprints.getFingerprints()) {
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opt.addFingerprint(fp);
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}
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const WiFiOptimizer::LogDistCeiling::APParamsList res = opt.optimizeAll(opt.NONE);
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for (const WiFiOptimizer::LogDistCeiling::APParamsMAC& ap : res.get()) {
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const WiFiModelLogDistCeiling::APEntry entry(ap.params.getPos(), ap.params.txp, ap.params.exp, ap.params.waf);
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WiFiModel.addAP(ap.mac, entry);
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}
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WiFiModel.saveXML(setup.wifiModel);
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} else {
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WiFiModel.loadXML(setup.wifiModel);
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}
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} else {
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// without optimization
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WiFiModel.loadAPs(map, Settings::WiFiModel::TXP, Settings::WiFiModel::EXP, Settings::WiFiModel::WAF);
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Assert::isFalse(WiFiModel.getAllAPs().empty(), "no AccessPoints stored within the map.xml");
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}
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// mesh
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NM::NavMeshSettings set;
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MyNavMesh mesh;
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MyNavMeshFactory fac(&mesh, set);
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fac.build(map);
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const Point3 srcPath0(26, 43, 7.5);
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const Point3 srcPath1(62, 38, 1.7);
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const Point3 srcPath2(62, 38, 1.8);
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const Point3 srcPath3(62, 38, 1.8);
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// add shortest-path to destination
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//const Point3 dst(51, 45, 1.7);
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//const Point3 dst(25, 45, 0);
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//NM::NavMeshDijkstra::stamp<MyNavMeshTriangle>(mesh, dst);
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// debug show
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//MeshPlotter dbg;
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//dbg.addFloors(map);
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//dbg.addOutline(map);
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//dbg.addMesh(mesh);
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//dbg.addDijkstra(mesh);
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//dbg.draw();
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Plotty plot(map);
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plot.buildFloorplan();
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plot.setGroundTruth(gtPath);
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// particle-filter
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const int numParticles = 5000;
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//auto init = std::make_unique<MyPFInitFixed>(&mesh, srcPath1); // known position
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auto init = std::make_unique<MyPFInitUniform>(&mesh); // uniform distribution
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auto eval = std::make_unique<MyPFEval>(WiFiModel);
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auto trans = std::make_unique<MyPFTrans>(mesh, WiFiModel);
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//auto resample = std::make_unique<SMC::ParticleFilterResamplingSimple<MyState>>();
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//auto resample = std::make_unique<SMC::ParticleFilterResamplingSimpleImpoverishment<MyState, MyNavMeshTriangle>>();
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auto resample = std::make_unique<SMC::ParticleFilterResamplingKLD<MyState>>();
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auto estimate = std::make_unique<SMC::ParticleFilterEstimationBoxKDE<MyState>>(map, 0.2, Point2(1,1));
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//auto estimate = std::make_unique<SMC::ParticleFilterEstimationWeightedAverage<MyState>>();
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//auto estimate = std::make_unique<SMC::ParticleFilterEstimationMax<MyState>>();
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// setup
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MyFilter pf(numParticles, std::move(init));
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pf.setEvaluation(std::move(eval));
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pf.setTransition(std::move(trans));
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pf.setResampling(std::move(resample));
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pf.setEstimation(std::move(estimate));
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pf.setNEffThreshold(0.85);
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// sensors
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MyControl ctrl;
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MyObservation obs;
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StepDetection sd;
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PoseDetection pd;
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TurnDetection td(&pd);
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RelativePressure relBaro;
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ActivityDetector act;
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relBaro.setCalibrationTimeframe( Timestamp::fromMS(5000) );
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Timestamp lastTimestamp = Timestamp::fromMS(0);
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// parse each sensor-value within the offline data
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for (const Offline::Entry& e : fr.getEntries()) {
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const Timestamp ts = Timestamp::fromMS(e.ts);
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if (e.type == Offline::Sensor::WIFI) {
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obs.wifi = fr.getWiFiGroupedByTime()[e.idx].data;
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ctrl.wifi = fr.getWiFiGroupedByTime()[e.idx].data;
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} else if (e.type == Offline::Sensor::ACC) {
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if (sd.add(ts, fr.getAccelerometer()[e.idx].data)) {
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++ctrl.numStepsSinceLastEval;
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}
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const Offline::TS<AccelerometerData>& _acc = fr.getAccelerometer()[e.idx];
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pd.addAccelerometer(ts, _acc.data);
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//simpleActivity walking / standing
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act.add(ts, fr.getAccelerometer()[e.idx].data);
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} else if (e.type == Offline::Sensor::GYRO) {
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const Offline::TS<GyroscopeData>& _gyr = fr.getGyroscope()[e.idx];
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const float delta_gyro = td.addGyroscope(ts, _gyr.data);
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ctrl.headingChangeSinceLastEval += delta_gyro;
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} else if (e.type == Offline::Sensor::BARO) {
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relBaro.add(ts, fr.getBarometer()[e.idx].data);
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obs.relativePressure = relBaro.getPressureRealtiveToStart();
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obs.sigmaPressure = relBaro.getSigma();
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//simpleActivity stairs up / down
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act.add(ts, fr.getBarometer()[e.idx].data);
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obs.activity = act.get();
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}
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if (ctrl.numStepsSinceLastEval > 0) {
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obs.currentTime = ts;
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ctrl.currentTime = ts;
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// if(ctrl.numStepsSinceLastEval > 0){
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// pf.updateTransitionOnly(&ctrl);
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// }
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MyState est = pf.update(&ctrl, obs); //pf.updateEvaluationOnly(obs);
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ctrl.afterEval();
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Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms())) + Point3(0,0,0.1);
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lastTimestamp = ts;
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ctrl.lastEstimate = est.pos.pos;
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//plot
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//dbg.showParticles(pf.getParticles());
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//dbg.setCurPos(est.pos.pos);
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//dbg.setGT(gtPos);
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//dbg.addEstimationNode(est.pos.pos);
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//dbg.addGroundTruthNode(gtPos);
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//dbg.setTimeInMinute(static_cast<int>(ts.sec()) / 60, static_cast<int>(static_cast<int>(ts.sec())%60));
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//dbg.draw();
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plot.showParticles(pf.getParticles());
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plot.setCurEst(est.pos.pos);
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plot.setGroundTruth(gtPos);
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plot.addEstimationNode(est.pos.pos);
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plot.setActivity((int) act.get());
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//plot.plot();
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// error calc
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float err_m = gtPos.getDistance(est.pos.pos);
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errorStats.add(err_m);
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errorFile << ts.ms() << " " << err_m << "\n";
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//dbg.gp.setOutput("/tmp/123/" + std::to_string(i) + ".png");
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//dbg.gp.setTerminal("pngcairo", K::GnuplotSize(60, 30));
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}
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}
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// get someting on console
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std::cout << "Statistical Analysis Filtering: " << std::endl;
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std::cout << "Median: " << errorStats.getMedian() << " Average: " << errorStats.getAvg() << " Std: " << errorStats.getStdDev() << std::endl;
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// save the statistical data in file
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errorFile << "========================================================== \n";
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errorFile << "Average of all statistical data: \n";
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errorFile << "Median: " << errorStats.getMedian() << "\n";
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errorFile << "Average: " << errorStats.getAvg() << "\n";
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errorFile << "Standard Deviation: " << errorStats.getStdDev() << "\n";
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errorFile << "75 Quantil: " << errorStats.getQuantile(0.75) << "\n";
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errorFile.close();
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/* plot in gp file */
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std::ofstream plotFile;
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plotFile.open(evalDir.string() + "/" + std::to_string(numFile) + "_" + std::to_string(t) + ".gp");
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plot.saveToFile(plotFile);
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plotFile.close();
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//save also a png image, just for a better overview
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plot.printOverview(evalDir.string() + "/" + std::to_string(numFile) + "_" + std::to_string(t));
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plot.plot();
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return errorStats;
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}
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int main(int argc, char** argv) {
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Stats::Statistics<float> statsAVG;
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Stats::Statistics<float> statsMedian;
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Stats::Statistics<float> statsSTD;
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Stats::Statistics<float> statsQuantil;
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Stats::Statistics<float> tmp;
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std::string evaluationName = "museum/tmp";
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for(int i = 0; i < 1; ++i){
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//TODO: in transition die distance über KLD noch einkommentieren als Test
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// for(int j = 0; j < Settings::data.Path0.training.size(); ++j){
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// tmp = run(Settings::data.Path0, j, evaluationName);
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// statsMedian.add(tmp.getMedian());
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// statsAVG.add(tmp.getAvg());
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// statsSTD.add(tmp.getStdDev());
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// statsQuantil.add(tmp.getQuantile(0.75));
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// }
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// for(int j = 0; j < Settings::data.Path1.training.size(); ++j){
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// tmp = run(Settings::data.Path1, j, evaluationName);
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// statsMedian.add(tmp.getMedian());
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// statsAVG.add(tmp.getAvg());
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// statsSTD.add(tmp.getStdDev());
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// statsQuantil.add(tmp.getQuantile(0.75));
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// }
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// for(int j = 0; j < Settings::data.Path2.training.size(); ++j){
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// tmp = run(Settings::data.Path2, j, evaluationName);
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// statsMedian.add(tmp.getMedian());
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// statsAVG.add(tmp.getAvg());
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// statsSTD.add(tmp.getStdDev());
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// statsQuantil.add(tmp.getQuantile(0.75));
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// }
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for(int j = 0; j < Settings::data.Path3.training.size(); ++j){
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tmp = run(Settings::data.Path3, j, evaluationName);
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statsMedian.add(tmp.getMedian());
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statsAVG.add(tmp.getAvg());
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statsSTD.add(tmp.getStdDev());
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statsQuantil.add(tmp.getQuantile(0.75));
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}
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std::cout << "Iteration " << i << " completed" << std::endl;;
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}
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std::cout << "==========================================================" << std::endl;
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std::cout << "Average of all statistical data: " << std::endl;
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std::cout << "Median: " << statsMedian.getAvg() << std::endl;
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std::cout << "Average: " << statsAVG.getAvg() << std::endl;
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std::cout << "Standard Deviation: " << statsSTD.getAvg() << std::endl;
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std::cout << "75 Quantil: " << statsQuantil.getAvg() << std::endl;
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std::cout << "==========================================================" << std::endl;
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//EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS
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std::ofstream finalStatisticFile;
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finalStatisticFile.open (Settings::errorDir + evaluationName + ".csv", std::ios_base::app);
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finalStatisticFile << "========================================================== \n";
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finalStatisticFile << "Average of all statistical data: \n";
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finalStatisticFile << "Median: " << statsMedian.getAvg() << "\n";
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finalStatisticFile << "Average: " << statsAVG.getAvg() << "\n";
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finalStatisticFile << "Standard Deviation: " << statsSTD.getAvg() << "\n";
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finalStatisticFile << "75 Quantil: " << statsQuantil.getAvg() << "\n";
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finalStatisticFile << "========================================================== \n";
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finalStatisticFile.close();
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//std::this_thread::sleep_for(std::chrono::seconds(60));
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}
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