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126 lines
3.1 KiB
C++
126 lines
3.1 KiB
C++
//
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// PCA.cpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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#include "pca.h"
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#include "../data/data.h"
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#include "../lin_alg/lin_alg.h"
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Ref<MLPPMatrix> MLPPPCA::get_input_set() {
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return _input_set;
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}
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void MLPPPCA::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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}
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int MLPPPCA::get_k() {
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return _k;
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}
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void MLPPPCA::set_k(const int val) {
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_k = val;
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}
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Ref<MLPPMatrix> MLPPPCA::principal_components() {
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ERR_FAIL_COND_V(!_input_set.is_valid() || _k == 0, Ref<MLPPMatrix>());
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MLPPLinAlg alg;
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MLPPData data;
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MLPPLinAlg::SVDResult svr_res = alg.svd(alg.covnm(_input_set));
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_x_normalized = data.mean_centering(_input_set);
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Size2i svr_res_u_size = svr_res.U->size();
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_u_reduce->resize(Size2i(_k, svr_res_u_size.y));
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for (int i = 0; i < _k; ++i) {
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for (int j = 0; j < svr_res_u_size.y; ++j) {
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_u_reduce->set_element(j, i, svr_res.U->get_element(j, i));
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}
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}
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_z = alg.matmultnm(alg.transposenm(_u_reduce), _x_normalized);
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return _z;
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}
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// Simply tells us the percentage of variance maintained.
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real_t MLPPPCA::score() {
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ERR_FAIL_COND_V(!_input_set.is_valid() || _k == 0, 0);
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MLPPLinAlg alg;
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Ref<MLPPMatrix> x_approx = alg.matmultnm(_u_reduce, _z);
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real_t num = 0;
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real_t den = 0;
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Size2i x_normalized_size = _x_normalized->size();
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int x_normalized_size_y = x_normalized_size.y;
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Ref<MLPPVector> x_approx_row_tmp;
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x_approx_row_tmp.instance();
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x_approx_row_tmp->resize(x_approx->size().x);
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Ref<MLPPVector> x_normalized_row_tmp;
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x_normalized_row_tmp.instance();
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x_normalized_row_tmp->resize(x_normalized_size.x);
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for (int i = 0; i < x_normalized_size_y; ++i) {
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_x_normalized->get_row_into_mlpp_vector(i, x_normalized_row_tmp);
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x_approx->get_row_into_mlpp_vector(i, x_approx_row_tmp);
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num += alg.norm_sqv(alg.subtractionnv(x_normalized_row_tmp, x_approx_row_tmp));
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}
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num /= x_normalized_size_y;
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for (int i = 0; i < x_normalized_size_y; ++i) {
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_x_normalized->get_row_into_mlpp_vector(i, x_normalized_row_tmp);
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den += alg.norm_sqv(x_normalized_row_tmp);
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}
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den /= x_normalized_size_y;
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if (den == 0) {
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den += 1e-10; // For numerical sanity as to not recieve a domain error
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}
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return 1 - num / den;
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}
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MLPPPCA::MLPPPCA(const Ref<MLPPMatrix> &p_input_set, int p_k) {
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_k = p_k;
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_input_set = p_input_set;
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_x_normalized.instance();
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_u_reduce.instance();
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_z.instance();
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}
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MLPPPCA::MLPPPCA() {
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_k = 0;
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_x_normalized.instance();
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_u_reduce.instance();
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_z.instance();
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}
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MLPPPCA::~MLPPPCA() {
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}
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void MLPPPCA::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPPCA::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPPCA::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "get_input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_k"), &MLPPPCA::get_k);
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ClassDB::bind_method(D_METHOD("set_k", "val"), &MLPPPCA::set_k);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "k"), "set_k", "get_k");
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ClassDB::bind_method(D_METHOD("principal_components"), &MLPPPCA::principal_components);
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ClassDB::bind_method(D_METHOD("score"), &MLPPPCA::score);
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}
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