Online Processing

You can use linear or ridge 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 or ridge regression training in the online processing mode follows the general workflow described in Usage Model: Training and Prediction.

Linear or ridge 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 ni 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 ni 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.

The following table lists parameters of linear and ridge regressions at the training stage in the online processing mode. Some of these parameters or their values are specific to a linear or ridge regression algorithm.

Parameter

Algorithm

Default Value

Description

algorithmFPType

any

float

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

method

linear regression

defaultDense

Available methods for linear regression training:

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

ridge regression

Default computation method used by the ridge regression. The only method supported at the training stage is the normal equations method.

ridgeParameters

ridge regression

Numeric table of size 1 x 1 that contains the default ridge parameter equal to 1.

The numeric table of size 1 x k (k is the number of dependent variables) or 1 x 1. The contents of the table depend on its size:

  • size = 1 x k: values of the ridge parameters λj for j = 1, …, k.
  • size = 1 x 1: the value of the ridge parameter for each dependent variable λ1 = ... = λk.

This parameter can be an object of any class derived from NumericTable, except for PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

interceptFlag

any

true

A flag that indicates a need to compute β0j.

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

For more complete information about compiler optimizations, see our Optimization Notice.
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