Online Processing

You can use linear regression in the online processing mode only at the training stage.

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

Training

Linear regression training in the online processing mode follows the general workflow described in Usage Model: Training and Prediction.

Linear regression training 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, `i`-th, data block. This table can be an object of any class derived from NumericTable.
dependentVariables	Pointer to the `n`_i x `k` numeric table with responses associated with the current, `i`-th, data block. This table can be an object of any class derived from NumericTable.

Linear regression training in the online processing mode has the following algorithm-specific parameters:

Parameter	Default Value	Description
algorithmFPType	double	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	Available methods for linear regression training: defaultDense - the normal equations method qrDense - the method based on QR decomposition
interceptFlag	true	A flag that indicates a need to compute β_0j.

Parameter

Default Value

Description

algorithmFPType

double

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

method

defaultDense

Available methods for linear regression training:

defaultDense - the normal equations method
qrDense - the method based on QR decomposition

interceptFlag

true

A flag that indicates a need to compute β_0j.

For a description of the output, refer to Usage Model: Training and Prediction.

Examples

C++:

Java*: