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91 lines
4.2 KiB
C++
91 lines
4.2 KiB
C++
#ifndef MLPP_STAT_H
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#define MLPP_STAT_H
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/*************************************************************************/
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/* stat.h */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#ifdef USING_SFW
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#include "sfw.h"
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#else
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#endif
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include <vector>
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class MLPPStat : public Reference {
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GDCLASS(MLPPStat, Reference);
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public:
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// These functions are for univariate lin reg module- not for users.
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real_t b0_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
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real_t b1_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
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// Statistical Functions
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real_t median(const Ref<MLPPVector> &x);
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Ref<MLPPVector> mode(const Ref<MLPPVector> &x);
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real_t range(const Ref<MLPPVector> &x);
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real_t midrange(const Ref<MLPPVector> &x);
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real_t abs_avg_deviation(const Ref<MLPPVector> &x);
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real_t correlation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
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real_t r2(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
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real_t chebyshev_ineq(const real_t k);
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real_t meanv(const Ref<MLPPVector> &x);
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real_t standard_deviationv(const Ref<MLPPVector> &x);
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real_t variancev(const Ref<MLPPVector> &x);
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real_t covariancev(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
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// Extras
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real_t weighted_mean(const Ref<MLPPVector> &x, const Ref<MLPPVector> &weights);
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real_t geometric_mean(const Ref<MLPPVector> &x);
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real_t harmonic_mean(const Ref<MLPPVector> &x);
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real_t rms(const Ref<MLPPVector> &x);
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real_t power_mean(const Ref<MLPPVector> &x, const real_t p);
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real_t lehmer_mean(const Ref<MLPPVector> &x, const real_t p);
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real_t weighted_lehmer_mean(const Ref<MLPPVector> &x, const Ref<MLPPVector> &weights, const real_t p);
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real_t contra_harmonic_mean(const Ref<MLPPVector> &x);
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real_t heronian_mean(const real_t A, const real_t B);
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real_t heinz_mean(const real_t A, const real_t B, const real_t x);
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real_t neuman_sandor_mean(const real_t a, const real_t b);
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real_t stolarsky_mean(const real_t x, const real_t y, const real_t p);
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real_t identric_mean(const real_t x, const real_t y);
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real_t log_mean(const real_t x, const real_t y);
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protected:
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static void _bind_methods();
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};
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#endif /* Stat_hpp */
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