Distributed Processing

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

This computation mode assumes that the data set is split in nblocks blocks across computation nodes.

Training

Algorithm Parameters

At the training stage, linear regression in the distributed processing mode has the following parameters:

Parameter	Default Value	Description
computeStep	Not applicable	The parameter required to initialize the algorithm. Can be: step1Local - the first step, performed on local nodes step2Master - the second step, performed on a master node
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.

Use the two-step computation schema for linear regression training in the distributed processing mode, as illustrated below:

Step 1 - on Local Nodes

Linear Regression Training, Distributed Processing, Workflow Step 1

In this step, linear regression training 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 `i`-th data block on the local node. 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 `i`-th data block. This table can be an object of any class derived from NumericTable.

In this step, linear regression training 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.

Result ID	Result
partialModel	Pointer to the partial linear regression model that corresponds to the `i`-th data block. The result can only be an object of the Model class.

Step 2 - on Master Node

Linear Regression Training, Distributed Processing, Workflow Step 2

In this step, linear regression training 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
partialModels	A collection of partial models computed on local nodes in Step 1. The collection contains objects of the Model class.

Result ID	Result
model	Pointer to the linear regression model being trained. The result can only be an object of the Model class.

Examples

C++:

Java*: