mirror of https://github.com/Relintai/pmlpp.git
55 lines
1.2 KiB
C++
55 lines
1.2 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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#include <iostream>
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#include <random>
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namespace MLPP {
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PCA::PCA(std::vector<std::vector<double>> inputSet, int k) :
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inputSet(inputSet), k(k) {
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}
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std::vector<std::vector<double>> PCA::principalComponents() {
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LinAlg alg;
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Data data;
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auto [U, S, Vt] = alg.SVD(alg.cov(inputSet));
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X_normalized = data.meanCentering(inputSet);
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U_reduce.resize(U.size());
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for (int i = 0; i < k; i++) {
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for (int j = 0; j < U.size(); j++) {
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U_reduce[j].push_back(U[j][i]);
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}
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}
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Z = alg.matmult(alg.transpose(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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double PCA::score() {
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LinAlg alg;
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std::vector<std::vector<double>> X_approx = alg.matmult(U_reduce, Z);
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double num, den = 0;
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for (int i = 0; i < X_normalized.size(); i++) {
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num += alg.norm_sq(alg.subtraction(X_normalized[i], X_approx[i]));
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}
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num /= X_normalized.size();
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for (int i = 0; i < X_normalized.size(); i++) {
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den += alg.norm_sq(X_normalized[i]);
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}
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den /= X_normalized.size();
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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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} //namespace MLPP
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