Merge branch 'master' of https://git.frank-ebner.de/FHWS/Indoor

data/HistoryTS.h

@@ -0,0 +1,68 @@
+#ifndef HISTORYTS_H
+#define HISTORYTS_H
+
+#include <vector>
+#include "Timestamp.h"
+#include <algorithm>
+
+/**
+ * keep the history of values for a given amount of time
+ */
+template <typename T> class HistoryTS {
+
+private:
+
+	/** timestamp -> value combination */
+	struct Entry {
+		Timestamp ts;
+		T value;
+		Entry(const Timestamp ts, const T& value) : ts(ts), value(value) {;}
+	};
+
+	/** the time-window to keep */
+	Timestamp window;
+
+	/** the value history for the window-size */
+	std::vector<Entry> history;
+
+public:
+
+
+	/** ctor with the time-window to keep */
+	HistoryTS(const Timestamp window) : window(window) {
+
+	}
+
+	/** add a new entry */
+	void add(const Timestamp ts, const T& data) {
+
+		// append to history
+		history.push_back(Entry(ts, data));
+
+		// remove too-old history entries
+		const Timestamp oldest = ts - window;
+		while(history.front().ts < oldest) {
+
+			// remove from history
+			history.erase(history.begin());
+
+		}
+
+	}
+
+	/** get the most recent entry */
+	T getMostRecent() const {
+		return history.back().value;
+	}
+
+	/** get the oldest entry available */
+	T getOldest() const {
+		return history.front().value;
+	}
+
+
+
+};
+
+
+#endif // HISTORYTS_H

floorplan/v2/FloorplanCeilings.h

@@ -73,7 +73,7 @@ namespace Floorplan {
 
 				const float myDistToCeil = (ap.z < person.z) ? (person.z - z) : (z - person.z);
 				if ( std::abs(myDistToCeil) < near ) {
-					cnt += sigmoid(myDistToCeil*6);
+					cnt += sigmoid(myDistToCeil * 6);
 				} else if (ap.z < z && person.z >= z+near) {	// AP below celing, me above ceiling
 					cnt += 1;
 				} else if (ap.z > z && person.z <= z-near) {	// AP above ceiling, me below ceiling
@@ -86,6 +86,22 @@ namespace Floorplan {
 
 		}
 
+		float numCeilingsBetweenLinearInt(const Point3 ap, const Point3 person) const {
+
+			// sanity checks
+			Assert::isNot0(ceilingsAtHeight_m.size(), "no ceilings available for testing! incorrect map?");
+
+			int cnt = 0;
+			float sum = 0;
+			for (float z = -1.0; z <= +1.0; z+= 0.25) {
+				sum += numCeilingsBetween(ap, person + Point3(0,0,z));
+				++cnt;
+			}
+
+			return sum/cnt;
+
+		}
+
 		/** get the number of ceilings between z1 and z2 */
 		int numCeilingsBetween(const Point3 pos1, const Point3 pos2) const {
 
@@ -98,8 +114,9 @@ namespace Floorplan {
 
 	#ifdef WITH_ASSERTIONS
 
-			static size_t numNear = 0;
+            static uint64_t numNear = 0;
-			static size_t numFar = 0;
+            static uint64_t numFar = 0;
+
 			for (const float z : ceilingsAtHeight_m) {
 				const float diff = std::min( std::abs(z-zMin), std::abs(z-zMax) );
 				if (diff < 0.1) {++numNear;} else {++numFar;}

main.cpp

@@ -30,7 +30,7 @@ int main(int argc, char** argv) {
 
 
 	//::testing::GTEST_FLAG(filter) = "*Offline.readWrite*";
-	::testing::GTEST_FLAG(filter) = "*Angle*";
+    ::testing::GTEST_FLAG(filter) = "*Jensen*";
 
 
     //::testing::GTEST_FLAG(filter) = "*Barometer*";

math/MovingAVG.h

@@ -11,7 +11,7 @@ private:
 	std::vector<T> values;
 
 	/** track the current sum of the vector's values */
-	T curSum = 0;
+	T curSum;
 
 	/** the number of elements to average */
 	int size;
@@ -19,7 +19,7 @@ private:
 public:
 
 	/** ctor */
-	MovingAVG(const int size) : size(size) {;}
+	MovingAVG(const int size) : curSum(), size(size) {;}
 
 	/** add a new value */
 	void add(const T val) {

math/Random.h

@@ -18,10 +18,10 @@ class RandomGenerator : public std::minstd_rand {
 public:
 
 	/** ctor with default seed */
-	RandomGenerator() : std::minstd_rand(RANDOM_SEED) {;}
+    RandomGenerator() : std::minstd_rand(RANDOM_SEED) {;}
 
 	/** ctor with custom seed */
-	RandomGenerator(result_type seed) : std::minstd_rand(seed) {;}
+    RandomGenerator(result_type seed) : std::minstd_rand(seed) {;}
 
 };
 

math/distribution/NormalN.h

@@ -91,7 +91,7 @@ namespace Distribution {
                 Assert::notEqual(numElements, 0, "data is empty, thats not enough for getting the distribution!");
 
                 const Eigen::MatrixXd centered = data.rowwise() - mean.transpose();
-                const Eigen::MatrixXd cov = (centered.adjoint() * centered) / double(data.rows() - 1);
+                Eigen::MatrixXd cov = (centered.adjoint() * centered) / double(data.rows() - 1);
 
                 return NormalDistributionN(mean, cov);
         }

math/divergence/JensenShannon.h

@@ -18,6 +18,13 @@ public:
      * @param Q is a vector containg the densities of the same samples set then P
     */
     static inline Scalar getGeneralFromSamples(Eigen::VectorXd P, Eigen::VectorXd Q, LOGMODE mode){
+        // normalize
+        P /= P.sum();
+        Q /= Q.sum();
+
+        Assert::isNear((double)P.sum(), 1.0, 0.01,"Normalization failed.. this shouldn't happen");
+        Assert::isNear((double)Q.sum(), 1.0, 0.01, "Normalization failed.. this shouldn't happen");
+
         Eigen::VectorXd M = 0.5 * (P + Q);
 
         return (0.5 * KullbackLeibler<Scalar>::getGeneralFromSamples(P, M, mode)) + (0.5 * KullbackLeibler<Scalar>::getGeneralFromSamples(Q, M, mode));

sensors/activity/ActivityDetector.h

@@ -11,13 +11,20 @@
 #include "../../math/MovingAverageTS.h"
 #include "../../math/MovingStdDevTS.h"
 
+#include "../../data/HistoryTS.h"
+
 #include "../activity/Activity.h"
 
+//#define ACT_DET_DEBUG_PLOT
+
+#ifdef ACT_DET_DEBUG_PLOT
 #include <KLib/misc/gnuplot/Gnuplot.h>
 #include <KLib/misc/gnuplot/GnuplotSplot.h>
 #include <KLib/misc/gnuplot/GnuplotSplotElementLines.h>
 #include <KLib/misc/gnuplot/GnuplotPlot.h>
 #include <KLib/misc/gnuplot/GnuplotPlotElementLines.h>
+#endif
+
 
 /**
  * simple step detection based on accelerometer magnitude.
@@ -37,50 +44,49 @@ private:
 	MovingAverageTS<float> baroAvgSlow;
 	MovingAverageTS<float> baroAvgFast;
 
+	MovingAverageTS<float> baroAvg;
+	HistoryTS<float> baroHistory;
+
+	Activity current;
 
 public:
 
+#ifdef ACT_DET_DEBUG_PLOT
 	K::Gnuplot gp;
 	K::GnuplotPlot gplot;
 	K::GnuplotPlotElementLines l1;
 	K::GnuplotPlotElementLines l2;
+#endif
 
 	/** ctor */
 	ActivityDetector() : avgLong(Timestamp::fromMS(1500), 0), avgShort(Timestamp::fromMS(500), 0),
-		stdDev(Timestamp::fromMS(200), 0), stdDev2(Timestamp::fromMS(2000), 0) {
+		stdDev(Timestamp::fromMS(150), 0), stdDev2(Timestamp::fromMS(2000), 0),
+		baroAvg(Timestamp::fromMS(500), 0), baroHistory(Timestamp::fromMS(4000)) {
 		;
-		gplot.add(&l1);
+
-		gplot.add(&l2); l2.getStroke().getColor().setHexStr("#ff0000");
+		#ifdef ACT_DET_DEBUG_PLOT
+			gplot.add(&l1);
+			gplot.add(&l2); l2.getStroke().getColor().setHexStr("#ff0000");
+		#endif
+
 	}
 
+	//int xx = 0;
+
 	/** add barometer data */
 	void add(const Timestamp ts, const BarometerData& baro) {
 		if (baro.isValid()) {
-			baroAvgSlow.add(ts, baro.hPa);
+			baroAvg.add(ts, baro.hPa);
-			baroAvgFast.add(ts, baro.hPa);
+			const float avg = baroAvg.get();
+			baroHistory.add(ts, avg);
+			//l1.add(K::GnuplotPoint2(xx, avg));
+			update();
 		}
 	}
 
-	Activity get() {
+	/** get the currently detected activity */
-
+	Activity get() const {
-		// delta in acceleration
+		return current;
-		const float delta_acc = std::abs(avgLong.get() - avgShort.get());
-
-		if (delta_acc < 0.3) {
-			return Activity::STANDING;
-		}
-
-		// delta in pressure
-		const float delta_hPa = baroAvgFast.get() - baroAvgSlow.get();
-
-		if (std::abs(delta_hPa) < 0.1) {
-			return Activity::WALKING;
-		} else if (delta_hPa > 0) {
-			return Activity::WALKING_DOWN;
-		} else {
-			return Activity::WALKING_UP;
-		}
-
 	}
 
 	/** does the given data indicate a step? */
@@ -96,13 +102,6 @@ public:
 
 //		static int x = 0; ++x;
 
-////		if (x % 10 == 0) {
-////			l1.add({x, avgLong.get()});
-////			l2.add({x, avgShort.get()});
-////			gp.draw(gplot);
-////			gp.flush();
-////		}
-
 //		if (delta < 0.3) {
 //			return Activity::STANDING;
 //		}
@@ -118,8 +117,41 @@ public:
 	}
 
 
+private:
 
+	/** estimate the current activity based on the sensor data */
+	void update() {
 
+		// delta in acceleration
+		const float delta_acc = std::abs(avgLong.get() - avgShort.get());
+
+		if (delta_acc < 0.015) {
+			current = Activity::STANDING;
+			return;
+		}
+
+		// delta in pressure
+		const float delta_hPa = baroHistory.getMostRecent() - baroHistory.getOldest();
+
+		#ifdef ACT_DET_DEBUG_PLOT
+			l2.add(K::GnuplotPoint2(xx, delta_hPa));
+			gp.draw(gplot);
+			gp.flush();
+			++xx;
+		#endif
+
+		if (std::abs(delta_hPa) < 0.042) {
+			current = Activity::WALKING;
+			return;
+		} else if (delta_hPa > 0) {
+			current = Activity::WALKING_DOWN;
+			return;
+		} else {
+			current = Activity::WALKING_UP;
+			return;
+		}
+
+	}
 
 };
 

sensors/radio/VAPGrouper.h

@@ -50,18 +50,27 @@ private:
 	/** the signal-strength aggregation algorithm to use */
 	const Aggregation agg;
 
+	/** respect only outputs with at-least X occurences of one physical hardware [can be used to prevent issues] */
+	int minOccurences;
+
 public:
 
 	/** ctor */
-	VAPGrouper(const Mode mode, const Aggregation agg) : mode(mode), agg(agg) {
+	VAPGrouper(const Mode mode, const Aggregation agg, const int minOccurences = 2) :
-
+		mode(mode), agg(agg), minOccurences(minOccurences) {
+		;
 	}
 
+	/** set the number of needed occurences per VAP-group to be accepted */
+	void setMinOccurences(const int min) {
+		this->minOccurences = min;
+	}
 
 	/** get a vap-grouped version of the given input */
 	WiFiMeasurements group(const WiFiMeasurements& original) const {
 
 		// first, group all VAPs into a vector [one vector per VAP-group]
+		// by using the modified MAC address that all VAPs have in common
 		std::unordered_map<MACAddress, std::vector<WiFiMeasurement>> grouped;
 
 		for (const WiFiMeasurement& m : original.entries) {
@@ -72,8 +81,9 @@ public:
 
 		}
 
-		// output
+		// to-be-constructed output
 		WiFiMeasurements result;
+		int skipped = 0;
 
 		// perform aggregation on each VAP-group
 		for (auto it : grouped) {
@@ -81,6 +91,9 @@ public:
 			const MACAddress& base = it.first;
 			const std::vector<WiFiMeasurement>& vaps = it.second;
 
+			// remove entries that do not satify the min-occurences metric
+			if ((int)vaps.size() < minOccurences) {++skipped; continue;}
+
 			// group all VAPs into one measurement
 			const WiFiMeasurement groupedMeasurement = groupVAPs(base, vaps);
 
@@ -89,8 +102,12 @@ public:
 
 		}
 
-		Log::add(name, "grouped " + std::to_string(original.entries.size()) + " measurements into " + std::to_string(result.entries.size()), true);
+		// debug
-
+		Log::add(name,
+				 "grouped " + std::to_string(original.entries.size()) + " measurements " +
+				 "into " + std::to_string(result.entries.size()) + " [omitted: " + std::to_string(skipped) + "]",
+				true
+		);
 
 		// done
 		return result;

sensors/radio/WiFiMeasurements.h

@@ -32,6 +32,46 @@ struct WiFiMeasurements {
 		return nullptr;
 	}
 
+	/** remove the entry for the given MAC (if any) */
+	void remove(const MACAddress& mac) {
+		for (size_t i = 0; i < entries.size(); ++i) {
+			if (entries[i].getAP().getMAC() == mac) {
+				entries.erase(entries.begin() + i);
+				break;
+			}
+		}
+	}
+
+
+	/** create a combination */
+	static WiFiMeasurements mix(const WiFiMeasurements& a, const WiFiMeasurements& b, float sec = 3) {
+
+		Timestamp max;
+		WiFiMeasurements res;
+
+		for (const WiFiMeasurement& m : a.entries) {
+			res.entries.push_back(m);
+			if (m.getTimestamp() > max) {max = m.getTimestamp();}
+		}
+
+		for (const WiFiMeasurement& m : b.entries) {
+			res.entries.push_back(m);
+			if (m.getTimestamp() > max) {max = m.getTimestamp();}
+		}
+
+		std::vector<WiFiMeasurement> tmp;
+		std::swap(res.entries, tmp);
+
+		for (const WiFiMeasurement& m : tmp) {
+			if ((max - m.getTimestamp()).sec() < sec) {
+				res.entries.push_back(m);
+			}
+		}
+
+		return res;
+
+	}
+
 };
 
 #endif // WIFIMEASUREMENTS_H

sensors/radio/WiFiProbabilityFree.h

@@ -45,7 +45,6 @@ public:
 
 		double prob = 1.0;
 		//double prob = 0;
-		double error = 0;
 
 		int numMatchingAPs = 0;
 
@@ -77,14 +76,13 @@ public:
 			const float sigma = this->sigma + this->sigmaPerSecond * age.sec();
 
 			// probability for this AP
-			//double local = Distribution::Normal<double>::getProbability(modelRSSI, sigma, scanRSSI);
+			double local = Distribution::Normal<double>::getProbability(modelRSSI, sigma, scanRSSI);
-			double local = Distribution::Exponential<double>::getProbability(0.1, std::abs(modelRSSI-scanRSSI));
+			//double local = Distribution::Exponential<double>::getProbability(0.1, std::abs(modelRSSI-scanRSSI));
 
 			// also add the error value? [location is OK but model is wrong]
 			if (useError) {
 				local = 0.95 * local + 0.05 * (1.0-local);
 				//local = 0.95 * local + 0.05;
-#warning "TODO"
 			}
 
 			// update probability

sensors/radio/WiFiQualityAnalyzer.h

@@ -68,8 +68,8 @@ private:
 		const float stdDev = std::sqrt(avg2 - avg*avg);
 
 		// avg rssi score
-		const float minRSSI = -90;
+		const float minRSSI = -85;
-		const float maxRSSI = -65;
+		const float maxRSSI = -70;
 		float score1 = (avg-minRSSI) / (maxRSSI-minRSSI);	// min = 0; max = 1
 		if (score1 > 1) {score1 = 1;}
 		if (score1 < 0) {score1 = 0;}

sensors/radio/model/WiFiModelLogDistCeiling.h

@@ -149,6 +149,7 @@ public:
 		// WAF loss (params.waf is a negative value!) -> WAF loss is also a negative value
 		//const float wafLoss = params.waf * ceilings.numCeilingsBetween(position_m, params.position_m);
 		const float wafLoss = params.waf * ceilings.numCeilingsBetweenFloat(position_m, params.position_m);
+		//const float wafLoss = params.waf * ceilings.numCeilingsBetweenLinearInt(position_m, params.position_m);
 
 		// combine
 		const float res = rssiLOS + wafLoss;

sensors/radio/model/WiFiModelPerBBox.h

@@ -52,15 +52,18 @@ public:
 
 	/** get a list of all APs known to the model */
 	std::vector<AccessPoint> getAllAPs() const override {
+
+		// combine all submodels
 		std::vector<AccessPoint> res;
 		for (const ModelForBBoxes& sub : models) {
 			for (const AccessPoint& ap : sub.mdl->getAllAPs()) {
-				if (std::find(res.begin(), res.end(), ap) == res.end()) {
+				if (std::find(res.begin(), res.end(), ap) == res.end()) {	// TODO use map instead?
 					res.push_back(ap);
 				}
 			}
 		}
 		return res;
+
 	}
 
 	void add(WiFiModel* mdl, const BBoxes3 bboxes) {

sensors/radio/model/WiFiModelPerFloor.h

@@ -49,7 +49,18 @@ public:
 
 	/** get a list of all APs known to the model */
 	std::vector<AccessPoint> getAllAPs() const override {
-		return models.front().mdl->getAllAPs();
+
+		// combine all submodels
+		std::vector<AccessPoint> res;
+		for (const ModelForFloor& sub : models) {
+			for (const AccessPoint& ap : sub.mdl->getAllAPs()) {
+				if (std::find(res.begin(), res.end(), ap) == res.end()) {	// TODO use map instead?
+					res.push_back(ap);
+				}
+			}
+		}
+		return res;
+
 	}
 
 	void add(WiFiModel* mdl, const Floorplan::Floor* floor) {

tests/math/divergence/TestJensenShannon.cpp

@@ -0,0 +1,94 @@
+#ifdef WITH_TESTS
+
+#include "../../Tests.h"
+#include "../../../math/divergence/JensenShannon.h"
+#include "../../../math/Distributions.h"
+
+#include <random>
+
+
+TEST(JensenShannon, generalFromSamples) {
+    //ge cov
+    Eigen::VectorXd mu1(2);
+    mu1[0] = 1.0;
+    mu1[1] = 1.0;
+
+    Eigen::VectorXd mu2(2);
+    mu2[0] = 1.0;
+    mu2[1] = 1.0;
+
+    Eigen::VectorXd mu3(2);
+    mu3[0] = 1.0;
+    mu3[1] = 1.0;
+
+    Eigen::VectorXd mu4(2);
+    mu4[0] = 9.0;
+    mu4[1] = 9.0;
+
+    Eigen::MatrixXd cov1(2,2);
+    cov1(0,0) = 1.0;
+    cov1(0,1) = 0.0;
+    cov1(1,0) = 0.0;
+    cov1(1,1) = 1.0;
+
+    Eigen::MatrixXd cov2(2,2);
+    cov2(0,0) = 1.0;
+    cov2(0,1) = 0.0;
+    cov2(1,0) = 0.0;
+    cov2(1,1) = 1.0;
+
+    Eigen::MatrixXd cov3(2,2);
+    cov3(0,0) = 1.0;
+    cov3(0,1) = 0.0;
+    cov3(1,0) = 0.0;
+    cov3(1,1) = 1.0;
+
+    Eigen::MatrixXd cov4(2,2);
+    cov4(0,0) = 3.0;
+    cov4(0,1) = 0.0;
+    cov4(1,0) = 0.0;
+    cov4(1,1) = 13.0;
+
+    Distribution::NormalDistributionN norm1(mu1, cov1);
+    Distribution::NormalDistributionN norm2(mu2, cov2);
+    Distribution::NormalDistributionN norm3(mu3, cov3);
+    Distribution::NormalDistributionN norm4(mu4, cov4);
+
+    int size = 10000;
+    Eigen::VectorXd samples1(size);
+    Eigen::VectorXd samples2(size);
+    Eigen::VectorXd samples3(size);
+    Eigen::VectorXd samples4(size);
+
+    //random numbers
+    std::mt19937_64 rng;
+   // initialize the random number generator with time-dependent seed
+   uint64_t timeSeed = std::chrono::high_resolution_clock::now().time_since_epoch().count();
+   std::seed_seq ss{uint32_t(timeSeed & 0xffffffff), uint32_t(timeSeed>>32)};
+   rng.seed(ss);
+   // initialize a uniform distribution between 0 and 1
+   std::uniform_real_distribution<double> unif(-9, 10);
+
+    //generate samples
+    for(int i = 0; i < size; ++i){
+
+        double r1 = unif(rng);
+        double r2 = unif(rng);
+        Eigen::VectorXd v(2);
+        v << r1, r2;
+
+        samples1[i] = norm1.getProbability(v);
+        samples2[i] = norm2.getProbability(v);
+        samples3[i] = norm3.getProbability(v);
+        samples4[i] = norm4.getProbability(v);
+    }
+
+    double kld12 = Divergence::JensenShannon<float>::getGeneralFromSamples(samples1, samples2, Divergence::LOGMODE::NATURALIS);
+    double kld34 = Divergence::JensenShannon<float>::getGeneralFromSamples(samples3, samples4, Divergence::LOGMODE::NATURALIS);
+    std::cout << kld34 << " > " << kld12 << std::endl;
+
+    ASSERT_GE(kld34, kld12);
+
+}
+
+#endif