org.apache.spark.mllib.regression

Class IsotonicRegressionModel

Object

org.apache.spark.mllib.regression.IsotonicRegressionModel

All Implemented Interfaces:

java.io.Serializable

public class IsotonicRegressionModel
extends Object
implements java.io.Serializable

:: Experimental ::

Regression model for isotonic regression.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
IsotonicRegressionModel(double[] boundaries, double[] predictions, boolean isotonic)

Method Summary

Methods

Modifier and Type	Method and Description
double[]	boundaries()
boolean	isotonic()
double	predict(double testData) Predict a single label.
JavaDoubleRDD	predict(JavaDoubleRDD testData) Predict labels for provided features.
RDD<Object>	predict(RDD<Object> testData) Predict labels for provided features.
double[]	predictions()

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

IsotonicRegressionModel

public IsotonicRegressionModel(double[] boundaries,
                       double[] predictions,
                       boolean isotonic)

Method Detail

boundaries

public double[] boundaries()

predictions

public double[] predictions()

isotonic

public boolean isotonic()

predict

public RDD<Object> predict(RDD<Object> testData)

Predict labels for provided features. Using a piecewise linear function.

Parameters:

testData - Features to be labeled.

Returns:

Predicted labels.

predict

public JavaDoubleRDD predict(JavaDoubleRDD testData)

Predict labels for provided features. Using a piecewise linear function.

Parameters:

testData - Features to be labeled.

Returns:

Predicted labels.

predict

public double predict(double testData)

Predict a single label. Using a piecewise linear function.

Parameters:

testData - Feature to be labeled.

Returns:

Predicted label. 1) If testData exactly matches a boundary then associated prediction is returned. In case there are multiple predictions with the same boundary then one of them is returned. Which one is undefined (same as java.util.Arrays.binarySearch). 2) If testData is lower or higher than all boundaries then first or last prediction is returned respectively. In case there are multiple predictions with the same boundary then the lowest or highest is returned respectively. 3) If testData falls between two values in boundary array then prediction is treated as piecewise linear function and interpolated value is returned. In case there are multiple values with the same boundary then the same rules as in 2) are used.