org.apache.mahout.common.distance
Class MahalanobisDistanceMeasure
java.lang.Object
org.apache.mahout.common.distance.MahalanobisDistanceMeasure
- All Implemented Interfaces:
- DistanceMeasure, Parametered
public class MahalanobisDistanceMeasure
- extends Object
- implements DistanceMeasure
Fields inherited from interface org.apache.mahout.common.parameters.Parametered |
log |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
MahalanobisDistanceMeasure
public MahalanobisDistanceMeasure()
configure
public void configure(org.apache.hadoop.conf.Configuration jobConf)
- Specified by:
configure
in interface Parametered
getParameters
public Collection<Parameter<?>> getParameters()
- Specified by:
getParameters
in interface Parametered
createParameters
public void createParameters(String prefix,
org.apache.hadoop.conf.Configuration jobConf)
- Description copied from interface:
Parametered
- EXPERT: consumers should never have to call this method. It would be friendly visible to
Parametered.ParameteredGeneralizations
if java supported it. Calling this method should create a new list of
parameters and is called
- Specified by:
createParameters
in interface Parametered
- Parameters:
prefix
- ends with a dot if not empty.jobConf
- configuration used for retrieving values- See Also:
invoking method
,
invoking method
distance
public double distance(Vector v)
- Parameters:
v
- The vector to compute the distance to
- Returns:
- Mahalanobis distance of a multivariate vector
distance
public double distance(Vector v1,
Vector v2)
- Description copied from interface:
DistanceMeasure
- Returns the distance metric applied to the arguments
- Specified by:
distance
in interface DistanceMeasure
- Parameters:
v1
- a Vector defining a multidimensional point in some feature spacev2
- a Vector defining a multidimensional point in some feature space
- Returns:
- a scalar doubles of the distance
distance
public double distance(double centroidLengthSquare,
Vector centroid,
Vector v)
- Description copied from interface:
DistanceMeasure
- Optimized version of distance metric for sparse vectors. This distance computation requires operations
proportional to the number of non-zero elements in the vector instead of the cardinality of the vector.
- Specified by:
distance
in interface DistanceMeasure
- Parameters:
centroidLengthSquare
- Square of the length of centroidcentroid
- Centroid vector
setInverseCovarianceMatrix
public void setInverseCovarianceMatrix(Matrix inverseCovarianceMatrix)
setCovarianceMatrix
public void setCovarianceMatrix(Matrix m)
- Computes the inverse covariance from the input covariance matrix given in input.
- Parameters:
m
- A covariance matrix.
- Throws:
IllegalArgumentException
- if eigen values equal to 0 found.
getInverseCovarianceMatrix
public Matrix getInverseCovarianceMatrix()
setMeanVector
public void setMeanVector(Vector meanVector)
getMeanVector
public Vector getMeanVector()
Copyright © 2008–2014 The Apache Software Foundation. All rights reserved.