water.util

Class ModelUtils

java.lang.Object

water.util.ModelUtils

public class ModelUtils
extends java.lang.Object

Constructor Summary

Constructors

Constructor and Description
ModelUtils()

Method Summary

Methods

Modifier and Type	Method and Description
static java.lang.String[]	createConfusionMatrixHeader(long[] xs, java.lang.String[] ds)
static int	getPrediction(float[] preds, double[] data) Utility function to get a best prediction from an array of class prediction distribution.
static int	getPrediction(float[] preds, int row)
static int[]	getPredictions(int numK, float[] preds, double[] data) Create labels from per-class probabilities with pseudo-random tie-breaking, if needed.

Methods inherited from class java.lang.Object

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

Constructor Detail

ModelUtils

public ModelUtils()

Method Detail

createConfusionMatrixHeader

public static java.lang.String[] createConfusionMatrixHeader(long[] xs,
                                             java.lang.String[] ds)

getPredictions

public static int[] getPredictions(int numK,
                   float[] preds,
                   double[] data)

Create labels from per-class probabilities with pseudo-random tie-breaking, if needed.

Parameters:

numK - Number of top probabilities to make labels for

preds - Predictions (first element is ignored here: placeholder for a label)

data - Data to break ties (typically, the test set data for this row)

Returns:

Array of predicted labels

getPrediction

public static int getPrediction(float[] preds,
                int row)

getPrediction

public static int getPrediction(float[] preds,
                double[] data)

Utility function to get a best prediction from an array of class prediction distribution. It returns index of max value if predicted values are unique. In the case of tie, the implementation solve it in pseudo-random way.

Parameters:

preds - an array of prediction distribution. Length of arrays is equal to a number of classes+1.

Returns:

the best prediction (index of class, zero-based)