Move files in src/ include/
This commit is contained in:
Patrick Niklaus
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/*
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Copyright (c) 2015, Project OSRM contributors
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All rights reserved.
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list
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of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright notice, this
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list of conditions and the following disclaimer in the documentation and/or
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other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
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ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef BAYES_CLASSIFIER_HPP
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#define BAYES_CLASSIFIER_HPP
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#include <cmath>
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#include <vector>
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#include <utility>
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struct NormalDistribution
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{
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NormalDistribution(const double mean, const double standard_deviation)
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: mean(mean), standard_deviation(standard_deviation)
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{
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}
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// FIXME implement log-probability version since its faster
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double density_function(const double val) const
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{
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const double x = val - mean;
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return 1.0 / (std::sqrt(2. * M_PI) * standard_deviation) *
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std::exp(-x * x / (standard_deviation * standard_deviation));
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}
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double mean;
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double standard_deviation;
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};
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struct LaplaceDistribution
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{
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LaplaceDistribution(const double location, const double scale)
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: location(location), scale(scale)
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{
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}
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// FIXME implement log-probability version since its faster
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double density_function(const double val) const
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{
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const double x = std::abs(val - location);
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return 1.0 / (2. * scale) * std::exp(-x / scale);
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}
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double location;
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double scale;
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};
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template <typename PositiveDistributionT, typename NegativeDistributionT, typename ValueT>
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class BayesClassifier
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{
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public:
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enum class ClassLabel : unsigned
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{
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NEGATIVE,
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POSITIVE
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};
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using ClassificationT = std::pair<ClassLabel, double>;
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BayesClassifier(PositiveDistributionT positive_distribution,
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NegativeDistributionT negative_distribution,
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const double positive_apriori_probability)
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: positive_distribution(std::move(positive_distribution)),
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negative_distribution(std::move(negative_distribution)),
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positive_apriori_probability(positive_apriori_probability),
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negative_apriori_probability(1. - positive_apriori_probability)
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{
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}
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// Returns label and the probability of the label.
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ClassificationT classify(const ValueT &v) const
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{
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const double positive_postpriori =
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positive_apriori_probability * positive_distribution.density_function(v);
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const double negative_postpriori =
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negative_apriori_probability * negative_distribution.density_function(v);
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const double norm = positive_postpriori + negative_postpriori;
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if (positive_postpriori > negative_postpriori)
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{
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return std::make_pair(ClassLabel::POSITIVE, positive_postpriori / norm);
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}
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return std::make_pair(ClassLabel::NEGATIVE, negative_postpriori / norm);
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}
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private:
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PositiveDistributionT positive_distribution;
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NegativeDistributionT negative_distribution;
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double positive_apriori_probability;
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double negative_apriori_probability;
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};
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#endif // BAYES_CLASSIFIER_HPP
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@@ -0,0 +1,170 @@
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/*
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Copyright (c) 2015, Project OSRM contributors
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All rights reserved.
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list
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of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright notice, this
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list of conditions and the following disclaimer in the documentation and/or
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other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
|
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ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
|
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef HIDDEN_MARKOV_MODEL
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#define HIDDEN_MARKOV_MODEL
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#include "../util/integer_range.hpp"
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#include <boost/assert.hpp>
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#include <cmath>
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#include <limits>
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#include <vector>
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namespace osrm
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{
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namespace matching
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{
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static const double log_2_pi = std::log(2. * M_PI);
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static const double IMPOSSIBLE_LOG_PROB = -std::numeric_limits<double>::infinity();
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static const double MINIMAL_LOG_PROB = std::numeric_limits<double>::lowest();
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static const std::size_t INVALID_STATE = std::numeric_limits<std::size_t>::max();
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} // namespace matching
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} // namespace osrm
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// closures to precompute log -> only simple floating point operations
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struct EmissionLogProbability
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{
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double sigma_z;
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double log_sigma_z;
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EmissionLogProbability(const double sigma_z) : sigma_z(sigma_z), log_sigma_z(std::log(sigma_z))
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{
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}
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double operator()(const double distance) const
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{
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return -0.5 * (osrm::matching::log_2_pi + (distance / sigma_z) * (distance / sigma_z)) -
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log_sigma_z;
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}
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};
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struct TransitionLogProbability
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{
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double beta;
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double log_beta;
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TransitionLogProbability(const double beta) : beta(beta), log_beta(std::log(beta)) {}
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double operator()(const double d_t) const { return -log_beta - d_t / beta; }
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};
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template <class CandidateLists> struct HiddenMarkovModel
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{
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std::vector<std::vector<double>> viterbi;
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std::vector<std::vector<std::pair<unsigned, unsigned>>> parents;
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std::vector<std::vector<float>> path_lengths;
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std::vector<std::vector<bool>> pruned;
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std::vector<std::vector<bool>> suspicious;
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std::vector<bool> breakage;
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const CandidateLists &candidates_list;
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const EmissionLogProbability &emission_log_probability;
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HiddenMarkovModel(const CandidateLists &candidates_list,
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const EmissionLogProbability &emission_log_probability)
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: breakage(candidates_list.size()), candidates_list(candidates_list),
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emission_log_probability(emission_log_probability)
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{
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viterbi.resize(candidates_list.size());
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parents.resize(candidates_list.size());
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path_lengths.resize(candidates_list.size());
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suspicious.resize(candidates_list.size());
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pruned.resize(candidates_list.size());
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breakage.resize(candidates_list.size());
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for (const auto i : osrm::irange<std::size_t>(0u, candidates_list.size()))
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{
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const auto& num_candidates = candidates_list[i].size();
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// add empty vectors
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if (num_candidates > 0)
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{
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viterbi[i].resize(num_candidates);
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parents[i].resize(num_candidates);
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path_lengths[i].resize(num_candidates);
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suspicious[i].resize(num_candidates);
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pruned[i].resize(num_candidates);
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}
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}
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clear(0);
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}
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void clear(std::size_t initial_timestamp)
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{
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BOOST_ASSERT(viterbi.size() == parents.size() && parents.size() == path_lengths.size() &&
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path_lengths.size() == pruned.size() && pruned.size() == breakage.size());
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for (const auto t : osrm::irange(initial_timestamp, viterbi.size()))
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{
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std::fill(viterbi[t].begin(), viterbi[t].end(), osrm::matching::IMPOSSIBLE_LOG_PROB);
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std::fill(parents[t].begin(), parents[t].end(), std::make_pair(0u, 0u));
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std::fill(path_lengths[t].begin(), path_lengths[t].end(), 0);
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std::fill(suspicious[t].begin(), suspicious[t].end(), true);
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std::fill(pruned[t].begin(), pruned[t].end(), true);
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}
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std::fill(breakage.begin() + initial_timestamp, breakage.end(), true);
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}
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std::size_t initialize(std::size_t initial_timestamp)
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{
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auto num_points = candidates_list.size();
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do
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{
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BOOST_ASSERT(initial_timestamp < num_points);
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for (const auto s : osrm::irange<std::size_t>(0u, viterbi[initial_timestamp].size()))
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{
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viterbi[initial_timestamp][s] =
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emission_log_probability(candidates_list[initial_timestamp][s].distance);
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parents[initial_timestamp][s] = std::make_pair(initial_timestamp, s);
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pruned[initial_timestamp][s] =
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viterbi[initial_timestamp][s] < osrm::matching::MINIMAL_LOG_PROB;
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suspicious[initial_timestamp][s] = false;
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breakage[initial_timestamp] =
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breakage[initial_timestamp] && pruned[initial_timestamp][s];
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}
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++initial_timestamp;
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} while (initial_timestamp < num_points && breakage[initial_timestamp - 1]);
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if (initial_timestamp >= num_points)
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{
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return osrm::matching::INVALID_STATE;
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}
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BOOST_ASSERT(initial_timestamp > 0);
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--initial_timestamp;
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BOOST_ASSERT(breakage[initial_timestamp] == false);
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return initial_timestamp;
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}
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};
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#endif // HIDDEN_MARKOV_MODEL
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