mirror of https://github.com/Relintai/pmlpp.git
139 lines
3.6 KiB
C++
139 lines
3.6 KiB
C++
//
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// OutlierFinder.cpp
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//
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// Created by Marc Melikyan on 11/13/20.
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//
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#include "outlier_finder.h"
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#include "../stat/stat.h"
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real_t MLPPOutlierFinder::get_threshold() {
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return _threshold;
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}
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void MLPPOutlierFinder::set_threshold(real_t val) {
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_threshold = val;
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}
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Vector<Vector<real_t>> MLPPOutlierFinder::model_set_test(const Ref<MLPPMatrix> &input_set) {
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ERR_FAIL_COND_V(!input_set.is_valid(), Vector<Vector<real_t>>());
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MLPPStat stat;
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Size2i input_set_size = input_set->size();
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Vector<Vector<real_t>> outliers;
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outliers.resize(input_set_size.y);
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Ref<MLPPVector> input_set_i_row_tmp;
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input_set_i_row_tmp.instance();
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input_set_i_row_tmp->resize(input_set_size.x);
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for (int i = 0; i < input_set_size.y; ++i) {
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input_set->row_get_into_mlpp_vector(i, input_set_i_row_tmp);
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real_t meanv = stat.meanv(input_set_i_row_tmp);
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real_t s_dev_v = stat.standard_deviationv(input_set_i_row_tmp);
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for (int j = 0; j < input_set_size.x; ++j) {
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real_t input_set_i_j = input_set->element_get(i, j);
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real_t z = (input_set_i_j - meanv) / s_dev_v;
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if (ABS(z) > _threshold) {
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outliers.write[i].push_back(input_set_i_j);
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}
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}
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}
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return outliers;
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}
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Array MLPPOutlierFinder::model_set_test_bind(const Ref<MLPPMatrix> &input_set) {
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Vector<Vector<real_t>> res = model_set_test(input_set);
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Array arr;
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for (int i = 0; i < res.size(); ++i) {
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//will get converted to PoolRealArray
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arr.push_back(Variant(res[i]));
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}
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return arr;
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}
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PoolVector2iArray MLPPOutlierFinder::model_set_test_indices(const Ref<MLPPMatrix> &input_set) {
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ERR_FAIL_COND_V(!input_set.is_valid(), PoolVector2iArray());
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MLPPStat stat;
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Size2i input_set_size = input_set->size();
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PoolVector2iArray outliers;
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Ref<MLPPVector> input_set_i_row_tmp;
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input_set_i_row_tmp.instance();
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input_set_i_row_tmp->resize(input_set_size.x);
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for (int i = 0; i < input_set_size.y; ++i) {
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input_set->row_get_into_mlpp_vector(i, input_set_i_row_tmp);
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real_t meanv = stat.meanv(input_set_i_row_tmp);
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real_t s_dev_v = stat.standard_deviationv(input_set_i_row_tmp);
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for (int j = 0; j < input_set_size.x; ++j) {
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real_t z = (input_set->element_get(i, j) - meanv) / s_dev_v;
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if (ABS(z) > _threshold) {
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outliers.push_back(Vector2i(j, i));
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}
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}
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}
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return outliers;
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}
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PoolRealArray MLPPOutlierFinder::model_test(const Ref<MLPPVector> &input_set) {
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ERR_FAIL_COND_V(!input_set.is_valid(), PoolRealArray());
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MLPPStat stat;
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PoolRealArray outliers;
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real_t mean = stat.meanv(input_set);
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real_t s_dev = stat.standard_deviationv(input_set);
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int input_set_size = input_set->size();
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const real_t *input_set_ptr = input_set->ptr();
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for (int i = 0; i < input_set_size; ++i) {
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real_t input_set_i = input_set_ptr[i];
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real_t z = (input_set_i - mean) / s_dev;
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if (ABS(z) > _threshold) {
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outliers.push_back(input_set_i);
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}
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}
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return outliers;
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}
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MLPPOutlierFinder::MLPPOutlierFinder(real_t threshold) {
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_threshold = threshold;
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}
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MLPPOutlierFinder::MLPPOutlierFinder() {
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_threshold = 0;
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}
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MLPPOutlierFinder::~MLPPOutlierFinder() {
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}
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void MLPPOutlierFinder::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_threshold"), &MLPPOutlierFinder::get_threshold);
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ClassDB::bind_method(D_METHOD("set_threshold", "val"), &MLPPOutlierFinder::set_threshold);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "threshold"), "set_threshold", "get_threshold");
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ClassDB::bind_method(D_METHOD("model_set_test", "input_set"), &MLPPOutlierFinder::model_set_test_bind);
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ClassDB::bind_method(D_METHOD("model_set_test_indices", "input_set"), &MLPPOutlierFinder::model_set_test_indices);
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ClassDB::bind_method(D_METHOD("model_test", "input_set"), &MLPPOutlierFinder::model_test);
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}
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