92 lines
2.7 KiB
C++
92 lines
2.7 KiB
C++
#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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