FrankE
a2c9e575a2
- worked on about everything - grid walker using plugable modules - wifi models - new distributions - worked on geometric data-structures - added typesafe timestamps - worked on grid-building - added sensor-classes - added sensor analysis (step-detection, turn-detection) - offline data reader - many test-cases
61 lines
1.2 KiB
C++
61 lines
1.2 KiB
C++
#ifndef NORMAL_H
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#define NORMAL_H
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#include <cmath>
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#include <random>
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#include "../Random.h"
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#include "../../Assertions.h"
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namespace Distribution {
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/** normal distribution */
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template <typename T> class Normal {
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private:
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const T mu;
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const T sigma;
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const T _a;
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RandomGenerator gen;
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std::normal_distribution<T> dist;
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public:
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/** ctor */
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Normal(const T mu, const T sigma) :
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mu(mu), sigma(sigma), _a(1.0 / (sigma * std::sqrt(2.0 * M_PI))), gen(RANDOM_SEED), dist(mu,sigma) {
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}
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/** get probability for the given value */
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T getProbability(const T val) const {
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const T b = -0.5 * ((val-mu)/sigma) * ((val-mu)/sigma);
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return _a * std::exp(b);
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}
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/** get a normally distributed random number */
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T draw() {
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return dist(gen);
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}
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/** set the seed to use */
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void setSeed(const long seed) {
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gen.seed(seed);
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}
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/** get the probability for the given value */
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static T getProbability(const T mu, const T sigma, const T val) {
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Assert::isTrue(sigma > 0, "sigma must be >= 0");
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const T a = 1.0 / (sigma * std::sqrt(2.0 * M_PI));
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const T b = -0.5 * ((val-mu)/sigma) * ((val-mu)/sigma);
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return a * std::exp(b);
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}
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};
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}
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#endif // NORMAL_H
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