Developer Guide for Intel® Data Analytics Acceleration Library 2018
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.
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.
For a description of the output, refer to Usage Model: Training and Prediction.
C++:
Java*:
Python*: