FHWS/Fusion2016
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started working on the tex-part

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started working on eval-graphics
ned helper methods
tested some new aspects
some fixes and changes
added some graphics
new test-floorplan
many cleanups
This commit is contained in:
FrankE
2016-02-03 21:17:15 +01:00
parent 8a57b4cdbd
commit c5a5acbbf6
40 changed files with 69163 additions and 275 deletions
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60
code/lukas/StepEvaluation.h
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@@ -8,8 +8,8 @@
#include "StepObservation.h"
#include <math.h>
static constexpr double mu_walk = 40;
static constexpr double sigma_walk = 15;
static constexpr double mu_walk = 90;
static constexpr double sigma_walk = 30;
static constexpr double mu_stop = 0;
static constexpr double sigma_stop = 5;
@@ -21,29 +21,49 @@ public:
return 1;
// double distance = state.distanceWalkedCM;
const float mdlWalked_m = state.walkState.distanceWalked_m;
// double a = 1.0;
// double mu_distance = 0; //cm
// double sigma_distance = 10.0; //cm
((MyState&)state).walkState.distanceWalked_m = 0;
// if(obs->step) {
// a = 1.0;
// mu_distance = mu_walk;//80.0; //cm
// sigma_distance = sigma_walk;//40.0; //cm
// }
const float stepSize_m = 0.71;
const float sensSigma_m = 0.05 + (0.05 * obs->steps);
const float sensWalked_m = obs->steps * stepSize_m;
// else {
// a = 0.0;
// mu_distance = mu_stop; //cm
// sigma_distance = sigma_stop; //cm
// }
if (obs->steps > 1) {
int i = 0;
int j = i+1; ++j;
}
// //Mixed Gaussian model: 1st Gaussian = step, 2nd Gaussian = no step
// const double p = a * K::NormalDistribution::getProbability(mu_distance, sigma_distance, distance) +
// (1.0-a) * K::NormalDistribution::getProbability(mu_distance, sigma_distance, distance);
const double prob = K::NormalDistribution::getProbability(sensWalked_m, sensSigma_m, mdlWalked_m);
if (prob != prob) {
throw 1;
}
return prob;
// float a = 1.0;
// float mu_distance = 0;
// float sigma_distance = 0;
// if(obs->step) {
// a = 1.0;
// mu_distance = mu_walk;
// sigma_distance = sigma_walk;
// }
// else {
// a = 0.0;
// mu_distance = mu_stop;
// sigma_distance = sigma_stop;
// }
// //Mixed Gaussian model: 1st Gaussian = step, 2nd Gaussian = no step
// const double p = a * K::NormalDistribution::getProbability(mu_distance, sigma_distance, distance) +
// (1.0-a) * K::NormalDistribution::getProbability(mu_distance, sigma_distance, distance);
// return p;
// return p;
}
};