quic_svd_method.hpp
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1
14 #ifndef MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_QUIC_SVD_METHOD_HPP
15 #define MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_QUIC_SVD_METHOD_HPP
16
17 #include <mlpack/prereqs.hpp>
19
20 namespace mlpack {
21 namespace pca {
22
27 {
28  public:
35  QUICSVDPolicy(const double epsilon = 0.03, const double delta = 0.1) :
36  epsilon(epsilon),
37  delta(delta)
38  {
39  /* Nothing to do here */
40  }
41
53  void Apply(const arma::mat& data,
54  const arma::mat& centeredData,
55  arma::mat& transformedData,
56  arma::vec& eigVal,
57  arma::mat& eigvec,
58  const size_t /* rank */)
59  {
60  // This matrix will store the right singular values; we do not need them.
61  arma::mat v, sigma;
62
63  // Do singular value decomposition using the QUIC-SVD algorithm.
64  svd::QUIC_SVD quicsvd(centeredData, eigvec, v, sigma, epsilon, delta);
65
66  // Now we must square the singular values to get the eigenvalues.
67  // In addition we must divide by the number of points, because the
68  // covariance matrix is X * X' / (N - 1).
69  eigVal = arma::pow(arma::diagvec(sigma), 2) / (data.n_cols - 1);
70
71  // Project the samples to the principals.
72  transformedData = arma::trans(eigvec) * centeredData;
73  }
74
76  double Epsilon() const { return epsilon; }
78  double& Epsilon() { return epsilon; }
79
81  double Delta() const { return delta; }
83  double& Delta() { return delta; }
84
85  private:
87  double epsilon;
88
90  double delta;
91 };
92
93 } // namespace pca
94 } // namespace mlpack
95
96 #endif
double & Epsilon()
Modify the error tolerance fraction for calculated subspace.
void Apply(const arma::mat &data, const arma::mat &centeredData, arma::mat &transformedData, arma::vec &eigVal, arma::mat &eigvec, const size_t)
Apply Principal Component Analysis to the provided data set using the QUIC-SVD method.
.hpp