LinearRegressionWithSGDTrain - Aster Analytics

Teradata AsterĀ® Spark Connector User Guide

Product
Aster Analytics
Release Number
7.00.00.01
Published
May 2017
Language
English (United States)
Last Update
2018-04-13
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Product Category
Software

The LinearRegrWithSGDTrain class defines a wrapper function that uses the Aster Spark API and implements the training phase of the Spark MLlib LinearRegressionWithSGD algorithm. The function generates a model that is typically used by the LinearRegressionWithSGDRun function.

Run Method Signature

run(input: RDD[DataRow], sparkFunctParams: String): RDD[DataRow]

Parameters

String representing the parameters specific to the function you are implementing. The string has this syntax:
'--option_value_pair [,...]'
option_value_pair is one of the following:
  • initialWeights initial_weights

    Array of initial weights, one for each feature in the data either "random" or "k-means||" (default).

  • miniBatchFraction mini_batch_fraction

    Fraction of data to use in each iteration.

  • modelLocation model_location

    Required. Specifies the HDFS path to the location where the function is to save the model.

  • numIterations iterations

    Number of iterations of gradient descent to run.

  • stepSize step_size

    Step size for each iteration of gradient descent.

Returns

A single value, the mean square error.

Side Effects

Function saves model in model_location.

Version

Spark 1.3 and later.