Online Processing

Online processing computation mode assumes that data arrives in blocks i = 1, 2, 3, … nblocks.

PCA computation in the online processing mode follows the general computation schema for online processing described in Algorithms.

Algorithm Input

The PCA 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.

Input ID	Input
data	Pointer to the `n`_i x `p` numeric table that represents the current data block. The input can be an object of any class derived from NumericTable.

Algorithm Parameters

The PCA algorithm in the online processing mode has the following parameters, depending on the computation method parameter method:

Parameter	method	Default Value	Description
algorithmFPType	defaultDense or svdDense	double	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
initializationProcedure	defaultDense	Not applicable	The procedure for setting initial parameters of the algorithm in the online processing mode. By default, the algorithm initializes nObservationsCorrelation, sumCorrelation, and crossProductCorrelation with zeros.
initializationProcedure	svdDense	Not applicable	The procedure for setting initial parameters of the algorithm in the online processing mode. By default, the algorithm initializes nObservationsSVD, sumSVD, and sumSquaresSVD with zeros.
covariance	defaultDense	`SharedPtr<covariance::Online<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. Online Processing.

Partial Results

The PCA algorithm in the online processing mode calculates partial results described below. They depend on the computation method. 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
Correlation method (defaultDense):
nObservationsCorrelation	Pointer to the 1 x 1 numeric table with the number of observations processed so far. By default, this result is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except CSRNumericTable.
crossProductCorrelation	Pointer to the `p` x `p` numeric table with the partial cross-product matrix computed so far. By default, this table is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.
sumCorrelation	Pointer to the 1 x `p` numeric table with partial sums computed so far. By default, this table is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.
SVD method (svdDense):
nObservationsSVD	Pointer to the 1 x 1 numeric table with the number of observations processed so far. By default, this result is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except CSRNumericTable.
sumSVD	Pointer to the 1 x `p` numeric table with partial sums computed so far. By default, this table is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.
sumSquaresSVD	Pointer to the 1 x `p` numeric table with partial sums of squares computed so far. By default, this table is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Algorithm Output

The PCA algorithm in the online processing mode 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 `p` 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 `p` 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.

Examples

C++:

Java*: