template class xf::fintech::PCA¶

#include "pca.hpp"

Overview¶

Class that will compute the Principal Component Analysis of some input data. Data is assumed to be a set of at most MAX_VARS variables in the rows and at most MAX_OBS observations in the columns.

Parameters:

DT	The data type to be used
N	The number of components to select from the PCA .
NCU	Number of computational units to improve parallelism
MAX_VARS	Maximum number of synthetisable rows.
MAX_OBS	Maximum number of synthetisable cols.
IMPL_METHOD	Choice of implementation method. By default it will use the covariance matrix.

template <
    typename DT,
    unsigned int N,
    unsigned int NCU,
    unsigned int MAX_VARS,
    unsigned int MAX_OBS,
    pcaImplementationMethod IMPL_METHOD = pcaImplementationMethod::Covariance
    >
class PCA

Methods¶

PCA¶

PCA (
    const unsigned int noVars,
    const unsigned int noObs,
    DT data [MAX_VARS][MAX_OBS]
    )

Calculates the core Principal Component Analysis functionality from the provided matrix with the given method.

Parameters:

noVars	Number of variables (rows) in the matrix.
noObs	Number of observations (cols) in the matrix.
data	The matrix with the data to be analysed.

getExplainedVariance¶

void getExplainedVariance (DT values [N])

Gets the N principal components’ variance of the input data.

Parameters:

values Vector of N elements wide where the PCA values will be stored.

getComponents¶

void getComponents (DT vectors [MAX_VARS][N])

Gets the N principal components of the input data.

Parameters:

vectors Matrix of MAX_VARS x N elements wide where the principal components will be stored.

getLoadingsMatrix¶

void getLoadingsMatrix (DT loadings [MAX_VARS][N])

Calculate the loadings matrix of the fitted data.

.

\[loadings = components^T * \sqrt{explainedVariance}\]

Parameters:

loadings Matrix of MAX_VARS x N elements where the PCA loadings will be stored.