Batch Processing

Algorithm Input

The PCA algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID

Input

data

Use when the input data is a normalized or non-normalized data set. Pointer to the n x p numeric table that contains the input data set. This input can be an object of any class derived from NumericTable.

correlation

Use when the input data is a correlation matrix. Pointer to the p x p numeric table that contains the correlation matrix. This input can be an object of any class derived from NumericTable except PackedTriangularMatrix.

Algorithm Parameters

The PCA algorithm has the following parameters, depending on the computation method parameter method:

Parameter

method

Default Value

Description

algorithmFPType

defaultDense or svdDense

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

Not applicable

defaultDense

Available methods for PCA computation:

  • defaultDense - the correlation method
  • svdDense - the SVD method
covariance

defaultDense

SharedPtr<covariance::Batch<algorithmFPType, covariance::defaultDense> >

The correlation and variance-covariance matrices algorithm to be used for PCA computations with the correlation method. For details, see Correlation and Variance-covariance Matrices. Batch Processing.

normalization

svdDense

SharedPtr<normalization::zscore::Batch<algorithmFPType,normalization::zscore::defaultDense> >

The data normalization algorithm to be used for PCA computations with the SVD method. For details, see Normalization. Zscore. Batch Processing.

nComponents

defaultDense, svdDense

0

Number of principal components pr. If it is zero, the algorithm will compute the result for pr = p.

isDeterministic

defaultDense, svdDense

false

If true, the algorithm applies the "sign flip" technique to the results.

resultsToCompute

defaultDense, svdDense

none

The 64-bit integer flag that specifies which optional result to compute.

Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:

mean

mean

variance

variance

eigenvalue

eigenvalue

Algorithm Output

The PCA algorithm calculates the results described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

eigenvalues

Pointer to the 1 x pr numeric table that contains eigenvalues in the descending order. By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

eigenvectors

Pointer to the pr x p numeric table that contains eigenvectors in the row-major order. By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

means

Pointer to the 1 x pr numeric table that contains mean values for each feature.

Optional.

If correlation is provided then the vector is filed with zeroes.

variances

Pointer to the 1 x pr numeric table that contains mean values for each feature.

Optional.

If correlation is provided then the vector is filed with zeroes.

dataForTransform

Pointer to key value data collection containing the aggregated data for normalization and whitening with the following key value pairs:

mean

mean

variance

variance

eigenvalue

eigenvalue

If resultsToCompute does not contain mean, the dataForTransform means table is NULL. If resultsToCompute does not contain variances, the dataForTransform variances table is NULL. If resultsToCompute does not contain eigenvalues, the dataForTransform eigenvalues table is NULL.

Note

  • If the function result is not requested through the resultsToCompute parameter, the respective element of the result contains a NULL pointer.
  • By default, each numeric table specified by the collection elements is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable, except for PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.
  • For the svdDense method n should not be less than p. If n > p, svdDense returns an error.
For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)