# 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.

Algorithm Input for Principal Component Analysis (Batch Processing)

Input ID

Input

data

Use when the input data is a normalized or non-normalized data set. Pointer to the $$n \times p$$ numeric table that contains the input data set.

Note

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 $$p \times p$$ numeric table that contains the correlation matrix.

Note

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:

Algorithm Parameters for Principal Component Analysis (Batch Processing)

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:

For CPU:

• defaultDense - the correlation method

• svdDense - the SVD method

For GPU:

• defaultDense - the correlation 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.

normalization

svdDense

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

The data normalization algorithm to be used for PCA computations with the SVD method.

nComponents

defaultDense, svdDense

$$0$$

The number of principal components $$p_r$$. If it is zero, the algorithm will compute the result for $$p_r = p$$.

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

• variance

• 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.

Algorithm Output for Principal Component Analysis (Batch Processing)

Result ID

Result

eigenvalues

Pointer to the $$1 \times p_r$$ numeric table that contains eigenvalues in the descending order.

Note

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 $$p_r \times p$$ numeric table that contains eigenvectors in the row-major order.

Note

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 $$1 \times p_r$$ 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 $$1 \times p_r$$ 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.

• 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.