FHWS/OTHER2017
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This commit is contained in:
FrankE
2017-04-05 18:45:00 +02:00
parent 6171582b40
commit b2adb16b49
18 changed files with 1602 additions and 360 deletions
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224
EvalWiFi.h
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@@ -25,7 +25,10 @@
#include <KLib/math/statistics/Statistics.h>
#include "Structs.h"
#include "Plotty.h"
#include "plots/Plotty.h"
#include "plots/PlotErrTime.h"
#include "plots/PlotErrFunc.h"
#include "CSV.h"
#include <unordered_set>
@@ -89,6 +92,16 @@ private:
WiFiModel* wiModel = nullptr;
std::vector<int> gtIndices;
// error in meter
PlotErrFunc* pef;
PlotErrTime* pet;
// error in probability
PlotErrFunc* pef2;
PlotErrTime* pet2;
Plotty* plot;
public:
/** ctor with map and fingerprints */
@@ -108,30 +121,70 @@ public:
// the optimizer
// opt = new WiFiOptimizer::LogDistCeiling(map, *vap, *calib, WiFiOptimizer::LogDistCeiling::Mode::MEDIUM);
// how to handle VAPs
vap = new VAPGrouper(VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO, VAPGrouper::Aggregation::MEDIAN);
pef = new PlotErrFunc("\\small{error (m)}", "\\small{measurements (\\%)}");
pef->showMarkers(false);
pef2 = new PlotErrFunc("\\small{-log(p(..))}", "\\small{measurements (\\%)}");
pef2->showMarkers(false);
pet = new PlotErrTime("2", "3", "");
pet->getPlot().setRangeY(K::GnuplotAxisRange(0, 40));
pet2 = new PlotErrTime("2", "3", "");
plot = new Plotty(map);
plot->buildFloorplan();
plot->setGroundTruth(gtIndices);
}
void fixedParams(const float txp, const float exp, const float waf) {
// how to handle VAPs
vap = new VAPGrouper(VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO, VAPGrouper::Aggregation::AVERAGE);
void load(const std::string& xmlFile, const std::string& name) {
// setup the model
WiFiModelLogDistCeiling* wiModel = new WiFiModelLogDistCeiling(map);
wiModel->loadAPs(map, *vap, txp, exp, waf, false);
wiModel->loadXML(xmlFile);
this->wiModel = wiModel;
// fire
run();
build(name);
}
// void fixedParams(const float txp, const float exp, const float waf) {
// // how to handle VAPs
// vap = new VAPGrouper(VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO, VAPGrouper::Aggregation::AVERAGE);
// // setup the model
// WiFiModelLogDistCeiling* wiModel = new WiFiModelLogDistCeiling(map);
// wiModel->loadAPs(map, *vap, txp, exp, waf, false);
// this->wiModel = wiModel;
// // fire
// run();
// }
private:
void run() {
void build(const std::string& name) {
static int idx = -1; ++idx;
const Offline::FileReader::GroundTruth gtp = reader.getGroundTruth(map, gtIndices);
Plotty* plot = new Plotty(map);
Line<Point3> path;
plot->setGroundTruth(gtIndices);
K::GnuplotSplotElementLines* gpPath = new K::GnuplotSplotElementLines();
plot->splot.add(gpPath);
K::Statistics<float>* stats = new K::Statistics<float>();
K::Statistics<float>* statsProbOnGT = new K::Statistics<float>();
pef->add(name, stats);
pef2->add(name, statsProbOnGT);
// process each wifi entry within the offline file
for (const auto wifi : reader.wifi) {
@@ -146,42 +199,71 @@ private:
const WiFiMeasurements mes = vap->group(_mes);
// error calculation
auto func = [&] (float* params) -> double {
auto func = [&] (const float* params) -> double {
// crop z to 1 meter
params[2] = std::round(params[2]);
//params[2] = std::round(params[2]);
// suggested position
const Point3 pos_m(params[0], params[1], params[2]);
const float sigma = 6.0;
const float sigma = 8.0;
double prob = 1.0;
// calculate error for above position using the currently available measurements
for (const WiFiMeasurement& m : mes.entries) {
if (1 == 1) {
// skip non-FHWS APs
if (!LeHelper::isFHWS_AP(m.getAP().getMAC())) {continue;}
// calculate error for above position using the currently available measurements
for (const WiFiMeasurement& m : mes.entries) {
// get model's rssi for the given location
const float rssi_model = wiModel->getRSSI(m.getAP().getMAC(), pos_m);
// skip non-FHWS APs
if (!LeHelper::isFHWS_AP(m.getAP().getMAC())) {continue;}
// get model's rssi for the given location
const float rssi_model = wiModel->getRSSI(m.getAP().getMAC(), pos_m);
// skip APs unknown to the model
if (rssi_model != rssi_model) {
std::cout << "unknown ap: " << m.getAP().getMAC().asString() << std::endl;
continue;
}
// get scan's rssi
const float rssi_scan = m.getRSSI();
// likelyhood
const double p = Distribution::Normal<double>::getProbability(rssi_model, sigma, rssi_scan);
//const double p = Distribution::Region<double>::getProbability(rssi_model, sigma, rssi_scan);
// adjust
prob *= p;
// skip APs unknown to the model
if (rssi_model != rssi_model) {
std::cout << "unknown ap: " << m.getAP().getMAC().asString() << std::endl;
continue;
}
// get scan's rssi
const float rssi_scan = m.getRSSI();
} else {
// likelyhood
const double p = Distribution::Normal<double>::getProbability(rssi_model, sigma, rssi_scan);
// //const float limit = -85;
// adjust
prob *= p;
// for (const AccessPoint& ap : wiModel->getAllAPs()) {
// // get model's rssi for the given location
// float rssi_model = wiModel->getRSSI(ap.getMAC(), pos_m);
// if (rssi_model < limit) {rssi_model = limit;}
// // get scan's rssi
// const WiFiMeasurement* mesModel = mes.getForMac(ap.getMAC());
// float rssi_scan = (mesModel) ? (mesModel->getRSSI()) : (limit);
// if (rssi_scan < limit) {rssi_scan = limit;}
// // likelyhood
// const double p = Distribution::Normal<double>::getProbability(rssi_model, sigma, rssi_scan);
// // adjust
// prob *= p;
// }
}
@@ -190,36 +272,80 @@ private:
};
std::minstd_rand gen;
std::uniform_real_distribution<float> distX(mapBBox.getMin().x, mapBBox.getMax().x);
std::uniform_real_distribution<float> distY(mapBBox.getMin().y, mapBBox.getMax().y);
std::uniform_real_distribution<float> distZ(mapBBox.getMin().z, mapBBox.getMax().z);
// initializer for the optimizer: random position within the map's bbox
auto init = [&] (const int childIdx, float* params) {
params[0] = distX(gen);
params[1] = distY(gen);
params[2] = distZ(gen);
};
// parameters
float params[3];
K::NumOptAlgoGenetic<float> opt(3);
// std::minstd_rand gen;
// std::uniform_real_distribution<float> distX(mapBBox.getMin().x, mapBBox.getMax().x);
// std::uniform_real_distribution<float> distY(mapBBox.getMin().y, mapBBox.getMax().y);
// std::uniform_real_distribution<float> distZ(mapBBox.getMin().z, mapBBox.getMax().z);
// // initializer for the optimizer: random position within the map's bbox
// auto init = [&] (const int childIdx, float* params) {
// (void) childIdx;
// params[0] = distX(gen);
// params[1] = distY(gen);
// params[2] = distZ(gen);
// };
// K::NumOptAlgoGenetic<float> opt(3);
// opt.setPopulationSize(400);
// opt.setMaxIterations(20);
// opt.calculateOptimum(func, params, init);
using LeOpt = K::NumOptAlgoRangeRandom<float>;
const std::vector<LeOpt::MinMax> valRegion = {
LeOpt::MinMax(mapBBox.getMin().x, mapBBox.getMax().x), // x
LeOpt::MinMax(mapBBox.getMin().y, mapBBox.getMax().y), // y
LeOpt::MinMax(mapBBox.getMin().z, mapBBox.getMax().z), // z
};
K::NumOptAlgoRangeRandom<float> opt(valRegion);
opt.setPopulationSize(200);
opt.setMaxIterations(20);
opt.calculateOptimum(func, params, init);
opt.setNumIerations(50);
//opt.calculateOptimum(func, params);
std::cout << params[0] << "," << params[1] << "," << params[2] << std::endl;
path.add(Point3(params[0], params[1], params[2]));
const Point3 curEst(params[0], params[1], params[2]);
path.add(curEst);
// draw a smoothed version of th epath
plot->pathEst.clear();
const Timestamp ts = mes.entries.front().getTimestamp();
// draw a smoothed version of the path
gpPath->clear();
for (const Point3 p : path.getAverage(2)) {
const K::GnuplotPoint3 gp3(p.x, p.y, p.z);
plot->pathEst.add(gp3);
gpPath->add(gp3);
}
// groud-truth
const Point3 gt = gtp.get(ts);
plot->gp << "set arrow 1 at " << gt.x << "," << gt.y << "," << gt.z << " to " << gt.x << "," << gt.y << "," << (gt.z+1) << "\n";
// error
//const float err_m = gt.xy().getDistance(curEst.xy());
const float err_m = gt.getDistance(curEst);
stats->add(err_m);
float gtFloat[3] = {gt.x, gt.y, gt.z};
const double probOnGT = -func(gtFloat);
const double logProbOnGT = -log(probOnGT);
statsProbOnGT->add(logProbOnGT);
// plot err
pet->addErr(ts, err_m, idx);
pet2->addErr(ts, logProbOnGT/mes.entries.size(), idx);
// fire
plot->plot();
// pet->plot();
// pef->plot();
pet2->plot();
pef2->plot();
}