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Added MLPPStatOld.
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@ -80,6 +80,7 @@ sources = [
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"mlpp/gauss_markov_checker/gauss_markov_checker_old.cpp",
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"mlpp/utilities/utilities_old.cpp",
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"mlpp/transforms/transforms_old.cpp",
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"mlpp/stat/stat_old.cpp",
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"test/mlpp_tests.cpp",
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]
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275
mlpp/stat/stat_old.cpp
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275
mlpp/stat/stat_old.cpp
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//
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// Stat.cpp
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//
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// Created by Marc Melikyan on 9/29/20.
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//
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#include "stat_old.h"
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#include "../activation/activation.h"
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#include "../data/data.h"
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#include "../lin_alg/lin_alg.h"
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#include <algorithm>
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#include <cmath>
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#include <map>
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#include <iostream>
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real_t MLPPStatOld::b0Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y) {
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return mean(y) - b1Estimation(x, y) * mean(x);
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}
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real_t MLPPStatOld::b1Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y) {
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return covariance(x, y) / variance(x);
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}
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real_t MLPPStatOld::b0_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
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return meanv(y) - b1_estimation(x, y) * meanv(x);
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}
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real_t MLPPStatOld::b1_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
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return covariancev(x, y) / variancev(x);
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}
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real_t MLPPStatOld::mean(const std::vector<real_t> &x) {
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real_t sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += x[i];
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}
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return sum / x.size();
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}
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real_t MLPPStatOld::median(std::vector<real_t> x) {
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real_t center = real_t(x.size()) / real_t(2);
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sort(x.begin(), x.end());
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if (x.size() % 2 == 0) {
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return mean({ x[center - 1], x[center] });
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} else {
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return x[center - 1 + 0.5];
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}
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}
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std::vector<real_t> MLPPStatOld::mode(const std::vector<real_t> &x) {
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MLPPData data;
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std::vector<real_t> x_set = data.vecToSet(x);
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std::map<real_t, int> element_num;
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for (uint32_t i = 0; i < x_set.size(); i++) {
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element_num[x[i]] = 0;
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}
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for (uint32_t i = 0; i < x.size(); i++) {
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element_num[x[i]]++;
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}
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std::vector<real_t> modes;
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real_t max_num = element_num[x_set[0]];
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for (uint32_t i = 0; i < x_set.size(); i++) {
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if (element_num[x_set[i]] > max_num) {
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max_num = element_num[x_set[i]];
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modes.clear();
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modes.push_back(x_set[i]);
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} else if (element_num[x_set[i]] == max_num) {
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modes.push_back(x_set[i]);
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}
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}
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return modes;
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}
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real_t MLPPStatOld::range(const std::vector<real_t> &x) {
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MLPPLinAlg alg;
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return alg.max(x) - alg.min(x);
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}
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real_t MLPPStatOld::midrange(const std::vector<real_t> &x) {
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return range(x) / 2;
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}
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real_t MLPPStatOld::absAvgDeviation(const std::vector<real_t> &x) {
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real_t sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += std::abs(x[i] - mean(x));
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}
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return sum / x.size();
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}
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real_t MLPPStatOld::standardDeviation(const std::vector<real_t> &x) {
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return std::sqrt(variance(x));
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}
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real_t MLPPStatOld::variance(const std::vector<real_t> &x) {
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real_t sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += (x[i] - mean(x)) * (x[i] - mean(x));
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}
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return sum / (x.size() - 1);
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}
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real_t MLPPStatOld::covariance(const std::vector<real_t> &x, const std::vector<real_t> &y) {
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real_t sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += (x[i] - mean(x)) * (y[i] - mean(y));
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}
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return sum / (x.size() - 1);
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}
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real_t MLPPStatOld::correlation(const std::vector<real_t> &x, const std::vector<real_t> &y) {
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return covariance(x, y) / (standardDeviation(x) * standardDeviation(y));
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}
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real_t MLPPStatOld::R2(const std::vector<real_t> &x, const std::vector<real_t> &y) {
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return correlation(x, y) * correlation(x, y);
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}
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real_t MLPPStatOld::chebyshevIneq(const real_t k) {
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// X may or may not belong to a Gaussian Distribution
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return 1 - 1 / (k * k);
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}
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real_t MLPPStatOld::meanv(const Ref<MLPPVector> &x) {
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int x_size = x->size();
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const real_t *x_ptr = x->ptr();
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real_t sum = 0;
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for (int i = 0; i < x_size; ++i) {
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sum += x_ptr[i];
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}
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return sum / x_size;
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}
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real_t MLPPStatOld::standard_deviationv(const Ref<MLPPVector> &x) {
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return Math::sqrt(variancev(x));
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}
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real_t MLPPStatOld::variancev(const Ref<MLPPVector> &x) {
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real_t x_mean = meanv(x);
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int x_size = x->size();
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const real_t *x_ptr = x->ptr();
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real_t sum = 0;
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for (int i = 0; i < x_size; ++i) {
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real_t xi = x_ptr[i];
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sum += (xi - x_mean) * (xi - x_mean);
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}
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return sum / (x_size - 1);
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}
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real_t MLPPStatOld::covariancev(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
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ERR_FAIL_COND_V(x->size() != y->size(), 0);
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real_t x_mean = meanv(x);
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real_t y_mean = meanv(y);
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int x_size = x->size();
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const real_t *x_ptr = x->ptr();
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const real_t *y_ptr = y->ptr();
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real_t sum = 0;
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for (int i = 0; i < x_size; ++i) {
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sum += (x_ptr[i] - x_mean) * (y_ptr[i] - y_mean);
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}
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return sum / (x_size - 1);
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}
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real_t MLPPStatOld::weightedMean(const std::vector<real_t> &x, const std::vector<real_t> &weights) {
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real_t sum = 0;
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real_t weights_sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += x[i] * weights[i];
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weights_sum += weights[i];
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}
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return sum / weights_sum;
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}
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real_t MLPPStatOld::geometricMean(const std::vector<real_t> &x) {
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real_t product = 1;
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for (uint32_t i = 0; i < x.size(); i++) {
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product *= x[i];
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}
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return std::pow(product, 1.0 / x.size());
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}
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real_t MLPPStatOld::harmonicMean(const std::vector<real_t> &x) {
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real_t sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += 1 / x[i];
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}
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return x.size() / sum;
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}
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real_t MLPPStatOld::RMS(const std::vector<real_t> &x) {
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real_t sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += x[i] * x[i];
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}
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return sqrt(sum / x.size());
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}
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real_t MLPPStatOld::powerMean(const std::vector<real_t> &x, const real_t p) {
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real_t sum = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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sum += std::pow(x[i], p);
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}
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return std::pow(sum / x.size(), 1 / p);
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}
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real_t MLPPStatOld::lehmerMean(const std::vector<real_t> &x, const real_t p) {
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real_t num = 0;
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real_t den = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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num += std::pow(x[i], p);
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den += std::pow(x[i], p - 1);
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}
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return num / den;
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}
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real_t MLPPStatOld::weightedLehmerMean(const std::vector<real_t> &x, const std::vector<real_t> &weights, const real_t p) {
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real_t num = 0;
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real_t den = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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num += weights[i] * std::pow(x[i], p);
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den += weights[i] * std::pow(x[i], p - 1);
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}
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return num / den;
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}
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real_t MLPPStatOld::heronianMean(const real_t A, const real_t B) {
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return (A + sqrt(A * B) + B) / 3;
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}
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real_t MLPPStatOld::contraHarmonicMean(const std::vector<real_t> &x) {
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return lehmerMean(x, 2);
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}
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real_t MLPPStatOld::heinzMean(const real_t A, const real_t B, const real_t x) {
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return (std::pow(A, x) * std::pow(B, 1 - x) + std::pow(A, 1 - x) * std::pow(B, x)) / 2;
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}
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real_t MLPPStatOld::neumanSandorMean(const real_t a, const real_t b) {
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MLPPActivation avn;
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return (a - b) / 2 * avn.arsinh((a - b) / (a + b));
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}
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real_t MLPPStatOld::stolarskyMean(const real_t x, const real_t y, const real_t p) {
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if (x == y) {
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return x;
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}
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return std::pow((std::pow(x, p) - std::pow(y, p)) / (p * (x - y)), 1 / (p - 1));
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}
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real_t MLPPStatOld::identricMean(const real_t x, const real_t y) {
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if (x == y) {
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return x;
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}
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return (1 / M_E) * std::pow(std::pow(x, x) / std::pow(y, y), 1 / (x - y));
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}
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real_t MLPPStatOld::logMean(const real_t x, const real_t y) {
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if (x == y) {
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return x;
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}
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return (y - x) / (log(y) - std::log(x));
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}
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void MLPPStatOld::_bind_methods() {
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}
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70
mlpp/stat/stat_old.h
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70
mlpp/stat/stat_old.h
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#ifndef MLPP_STAT_OLD_H
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#define MLPP_STAT_OLD_H
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//
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// Stat.hpp
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//
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// Created by Marc Melikyan on 9/29/20.
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//
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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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 MLPPStatOld : public Reference {
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GDCLASS(MLPPStatOld, 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 b0Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y);
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real_t b1Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y);
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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 mean(const std::vector<real_t> &x);
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real_t median(std::vector<real_t> x);
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std::vector<real_t> mode(const std::vector<real_t> &x);
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real_t range(const std::vector<real_t> &x);
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real_t midrange(const std::vector<real_t> &x);
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real_t absAvgDeviation(const std::vector<real_t> &x);
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real_t standardDeviation(const std::vector<real_t> &x);
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real_t variance(const std::vector<real_t> &x);
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real_t covariance(const std::vector<real_t> &x, const std::vector<real_t> &y);
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real_t correlation(const std::vector<real_t> &x, const std::vector<real_t> &y);
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real_t R2(const std::vector<real_t> &x, const std::vector<real_t> &y);
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real_t chebyshevIneq(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 weightedMean(const std::vector<real_t> &x, const std::vector<real_t> &weights);
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real_t geometricMean(const std::vector<real_t> &x);
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real_t harmonicMean(const std::vector<real_t> &x);
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real_t RMS(const std::vector<real_t> &x);
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real_t powerMean(const std::vector<real_t> &x, const real_t p);
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real_t lehmerMean(const std::vector<real_t> &x, const real_t p);
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real_t weightedLehmerMean(const std::vector<real_t> &x, const std::vector<real_t> &weights, const real_t p);
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real_t contraHarmonicMean(const std::vector<real_t> &x);
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real_t heronianMean(const real_t A, const real_t B);
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real_t heinzMean(const real_t A, const real_t B, const real_t x);
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real_t neumanSandorMean(const real_t a, const real_t b);
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real_t stolarskyMean(const real_t x, const real_t y, const real_t p);
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real_t identricMean(const real_t x, const real_t y);
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real_t logMean(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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