Online Processing¶

Online processing computation mode assumes that data arrives in blocks \(i = 1, 2, 3, \ldots \text{nblocks}\).

Computation of correlation and variance-covariance matrices in the online processing mode follows the general computation schema for online processing described in Algorithms.

Algorithm Input¶

The correlation and variance-covariance matrices algorithm in the online processing mode 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 Correlation and Variance-Covariance Matrices Algorithm (Online Processing)¶
Input ID	Input
`data`	Pointer to the numeric table of size \(n_i \times p\) that represents the current data block. 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 in the online processing mode:

Algorithm Parameters for for Correlation and Variance-Covariance Matrices Algorithm (Online Processing)¶
Parameter	Default Valude	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: 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
`outputMatrixType`	`covarianceMatrix`	The type of the output matrix. Can be: `covarianceMatrix` - variance-covariance matrix `correlationMatrix` - correlation matrix
`initializationProcedure`	Not applicable	The procedure for setting initial parameters of the algorithm in the online processing mode. By default, the algorithm sets the `nObservations`, `sum`, and `crossProduct` parameters to zero.

Partial Results¶

The correlation and variance-covariance matrices algorithm in the online processing mode calculates partial 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.

Partial Results for Correlation and Variance-Covariance Matrices Algorithm (Online Processing)¶
Result ID	Result
`nObservations`	Pointer to the \(1 \times 1\) numeric table that contains the number of observations processed so far. 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 `CSRNumericTable`.
`crossProduct`	Pointer to \(p \times p\) numeric table with the cross-product matrix computed so far. Note By default, this table 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`.
`sum`	Pointer to \(1 \times p\) numeric table with partial sums computed so far. Note By default, this table 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`.

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. For more details, see Algorithms.

Algorithm Output for Correlation and Variance-Covariance Matrices Algorithm (Online 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.

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