baseCode.math
Class Distance

java.lang.Object
  baseCode.math.Distance

public class Distance

extends java.lang.Object

Alternative distance and similarity metrics for vectors.

Copyright (c) 2004

Institution:: Columbia University

Version:

$Id: Distance.java,v 1.7 2004/08/14 20:38:35 pavlidis Exp $

Author:

Paul Pavlidis

Constructor Summary

Distance()

Method Summary

static double	correlationOfStandardized(double[] xe, double[] ye) Highly optimized implementation of the Pearson correlation.
static double	correlationOfStandardized(cern.colt.list.DoubleArrayList x, cern.colt.list.DoubleArrayList y) Like correlationofNormedFast, but takes DoubleArrayLists as inputs, handles missing values correctly, and does more error checking.
double	euclDistance(cern.colt.list.DoubleArrayList x, cern.colt.list.DoubleArrayList y) Calculate the Euclidean distance between two vectors.
double	manhattanDistance(cern.colt.list.DoubleArrayList x, cern.colt.list.DoubleArrayList y) Calculate the Manhattan distance between two vectors.
static double	spearmanRankCorrelation(cern.colt.list.DoubleArrayList x, cern.colt.list.DoubleArrayList y) Spearman Rank Correlation.

Methods inherited from class java.lang.Object

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

Constructor Detail

Distance

public Distance()

Method Detail

manhattanDistance

public double manhattanDistance(cern.colt.list.DoubleArrayList x,
                                cern.colt.list.DoubleArrayList y)

Calculate the Manhattan distance between two vectors.

Parameters:

x - DoubleArrayList

y - DoubleArrayList

Returns:

Manhattan distance between x and y

euclDistance

public double euclDistance(cern.colt.list.DoubleArrayList x,
                           cern.colt.list.DoubleArrayList y)

Calculate the Euclidean distance between two vectors.

Parameters:

x - DoubleArrayList

y - DoubleArrayList

Returns:

Euclidean distance between x and y

spearmanRankCorrelation

public static double spearmanRankCorrelation(cern.colt.list.DoubleArrayList x,
                                             cern.colt.list.DoubleArrayList y)

Spearman Rank Correlation. This does the rank transformation of the data.

Parameters:

x - DoubleArrayList

y - DoubleArrayList

Returns:

Spearman's rank correlation between x and y.

correlationOfStandardized

public static double correlationOfStandardized(double[] xe,
                                               double[] ye)

Highly optimized implementation of the Pearson correlation. The inputs must be standardized - mean zero, variance one, without any missing values.

Parameters:

xe - A standardized vector

ye - A standardized vector

Returns:

Pearson correlation coefficient.

correlationOfStandardized

public static double correlationOfStandardized(cern.colt.list.DoubleArrayList x,
                                               cern.colt.list.DoubleArrayList y)

Like correlationofNormedFast, but takes DoubleArrayLists as inputs, handles missing values correctly, and does more error checking. Assumes the data has been converted to z scores already.

Parameters:

x - A standardized vector

y - A standardized vector

Returns:

The Pearson correlation between x and y.