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Removed new things from MLPPStatOld.

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Relintai 2023-02-13 19:30:30 +01:00
parent a30a92171d
commit aab33650b7
2 changed files with 1 additions and 79 deletions
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60
mlpp/stat/stat_old.cpp
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@ -22,13 +22,6 @@ real_t MLPPStatOld::b1Estimation(const std::vector<real_t> &x, const std::vector
return covariance(x, y) / variance(x);
}
real_t MLPPStatOld::b0_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
return meanv(y) - b1_estimation(x, y) * meanv(x);
}
real_t MLPPStatOld::b1_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
return covariancev(x, y) / variancev(x);
}
real_t MLPPStatOld::mean(const std::vector<real_t> &x) {
real_t sum = 0;
for (uint32_t i = 0; i < x.size(); i++) {
@ -121,56 +114,6 @@ real_t MLPPStatOld::chebyshevIneq(const real_t k) {
return 1 - 1 / (k * k);
}
real_t MLPPStatOld::meanv(const Ref<MLPPVector> &x) {
int x_size = x->size();
const real_t *x_ptr = x->ptr();
real_t sum = 0;
for (int i = 0; i < x_size; ++i) {
sum += x_ptr[i];
}
return sum / x_size;
}
real_t MLPPStatOld::standard_deviationv(const Ref<MLPPVector> &x) {
return Math::sqrt(variancev(x));
}
real_t MLPPStatOld::variancev(const Ref<MLPPVector> &x) {
real_t x_mean = meanv(x);
int x_size = x->size();
const real_t *x_ptr = x->ptr();
real_t sum = 0;
for (int i = 0; i < x_size; ++i) {
real_t xi = x_ptr[i];
sum += (xi - x_mean) * (xi - x_mean);
}
return sum / (x_size - 1);
}
real_t MLPPStatOld::covariancev(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y) {
ERR_FAIL_COND_V(x->size() != y->size(), 0);
real_t x_mean = meanv(x);
real_t y_mean = meanv(y);
int x_size = x->size();
const real_t *x_ptr = x->ptr();
const real_t *y_ptr = y->ptr();
real_t sum = 0;
for (int i = 0; i < x_size; ++i) {
sum += (x_ptr[i] - x_mean) * (y_ptr[i] - y_mean);
}
return sum / (x_size - 1);
}
real_t MLPPStatOld::weightedMean(const std::vector<real_t> &x, const std::vector<real_t> &weights) {
real_t sum = 0;
real_t weights_sum = 0;
@ -270,6 +213,3 @@ real_t MLPPStatOld::logMean(const real_t x, const real_t y) {
}
return (y - x) / (log(y) - std::log(x));
}
void MLPPStatOld::_bind_methods() {
}

20
mlpp/stat/stat_old.h
View File

@ -10,24 +10,14 @@
#include "core/math/math_defs.h"
#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include <vector>
class MLPPStatOld : public Reference {
GDCLASS(MLPPStatOld, Reference);
class MLPPStatOld {
public:
// These functions are for univariate lin reg module- not for users.
real_t b0Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y);
real_t b1Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y);
real_t b0_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
real_t b1_estimation(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
// Statistical Functions
real_t mean(const std::vector<real_t> &x);
real_t median(std::vector<real_t> x);
@ -42,11 +32,6 @@ public:
real_t R2(const std::vector<real_t> &x, const std::vector<real_t> &y);
real_t chebyshevIneq(const real_t k);
real_t meanv(const Ref<MLPPVector> &x);
real_t standard_deviationv(const Ref<MLPPVector> &x);
real_t variancev(const Ref<MLPPVector> &x);
real_t covariancev(const Ref<MLPPVector> &x, const Ref<MLPPVector> &y);
// Extras
real_t weightedMean(const std::vector<real_t> &x, const std::vector<real_t> &weights);
real_t geometricMean(const std::vector<real_t> &x);
@ -62,9 +47,6 @@ public:
real_t stolarskyMean(const real_t x, const real_t y, const real_t p);
real_t identricMean(const real_t x, const real_t y);
real_t logMean(const real_t x, const real_t y);
protected:
static void _bind_methods();
};
#endif /* Stat_hpp */