# Batch Processing¶

## Algorithm Input¶

The correlation and variance-covariance matrices algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm.

Algorithm Input for Correlation and Variance-Covariance Matrices Algorithm (Batch Processing)

Input ID

Input

data

Pointer to the $$n \times p$$ numeric table for which the variance-covariance or correlation matrix $$C$$ is computed. While the input for defaultDense, singlePassDense, or sumDense method can be an object of any class derived from NumericTable, the input for fastCSR, singlePassCSR, or sumCSR method can only be an object of the CSRNumericTable class.

## Algorithm Parameters¶

The correlation and variance-covariance matrices algorithm has the following parameters:

Algorithm Parameters for Correlation and Variance-Covariance Matrices Algorithm (Batch Processing)

Parameter

Default Value

Description

algorithmFPType

float

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

method

defaultDense

Available methods for computation of correlation and variance-covariance matrices:

For CPU:

• defaultDense - default performance-oriented method

• singlePassDense - implementation of the single-pass algorithm proposed by D.H.D. West

• sumDense - implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; returns an error if pre-computed sums are not defined

• fastCSR - performance-oriented method for CSR numeric tables

• singlePassCSR - implementation of the single-pass algorithm proposed by D.H.D. West; optimized for CSR numeric tables

• sumCSR - implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; optimized for CSR numeric tables; returns an error if pre-computed sums are not defined

For GPU:

• defaultDense - default performance-oriented method

outputMatrixType

covarianceMatrix

The type of the output matrix. Can be:

• covarianceMatrix - variance-covariance matrix

• correlationMatrix - correlation matrix

## Algorithm Output¶

The correlation and variance-covariance matrices algorithm calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm.

Algorithm Output for Correlation and Variance-Covariance Matrices Algorithm (Batch Processing)

Result ID

Result

covariance

Use when outputMatrixType=covarianceMatrix. Pointer to the numeric table with the $$p \times p$$ variance-covariance matrix.

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 PackedTriangularMatrix and CSRNumericTable.

correlation

Use when outputMatrixType=correlationMatrix. Pointer to the numeric table with the $$p \times p$$ correlation matrix.

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 PackedTriangularMatrix and CSRNumericTable.

mean

Pointer to the $$1 \times p$$ numeric table with means.

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 PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

Product and Performance Information

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex​.

Notice revision #20201201