ubic.basecode.math

Class Distance

java.lang.Object

ubic.basecode.math.Distance

public class Distance
extends Object

Alternative distance and similarity metrics for vectors.

Author:

Paul Pavlidis

Constructor Summary

Constructors

Constructor and Description
Distance()

Method Summary

Modifier and Type	Method and Description
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.
static double	euclDistance(cern.colt.list.DoubleArrayList x, cern.colt.list.DoubleArrayList y) Calculate the Euclidean distance between two vectors.
static 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) Convenience function to compute the rank correlation when we just want to know if the values are "in order".
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

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.

euclDistance

public static 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

manhattanDistance

public static 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

spearmanRankCorrelation

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

Convenience function to compute the rank correlation when we just want to know if the values are "in order". Values in perfect ascending order are a correlation of 1, descending is -1.

Parameters:

x -

Returns:

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. Only mutually non-NaN values are used.

Parameters:

x - DoubleArrayList

y - DoubleArrayList

Returns:

Spearman's rank correlation between x and y or NaN if it could not be computed.