/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.common.distance;
import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;
import com.google.common.io.Closeables;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.common.ClassUtils;
import org.apache.mahout.common.parameters.ClassParameter;
import org.apache.mahout.common.parameters.Parameter;
import org.apache.mahout.common.parameters.PathParameter;
import org.apache.mahout.math.Algebra;
import org.apache.mahout.math.CardinalityException;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.MatrixWritable;
import org.apache.mahout.math.SingularValueDecomposition;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import java.io.DataInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.util.Collection;
import java.util.List;
//See http://en.wikipedia.org/wiki/Mahalanobis_distance for details
public class MahalanobisDistanceMeasure implements DistanceMeasure {
private Matrix inverseCovarianceMatrix;
private Vector meanVector;
private ClassParameter vectorClass;
private ClassParameter matrixClass;
private List<Parameter<?>> parameters;
private Parameter<Path> inverseCovarianceFile;
private Parameter<Path> meanVectorFile;
/*public MahalanobisDistanceMeasure(Vector meanVector,Matrix inputMatrix, boolean inversionNeeded)
{
this.meanVector=meanVector;
if(inversionNeeded)
setCovarianceMatrix(inputMatrix);
else
setInverseCovarianceMatrix(inputMatrix);
}*/
@Override
public void configure(Configuration jobConf) {
if (parameters == null) {
ParameteredGeneralizations.configureParameters(this, jobConf);
}
try {
if (inverseCovarianceFile.get() != null) {
FileSystem fs = FileSystem.get(inverseCovarianceFile.get().toUri(), jobConf);
MatrixWritable inverseCovarianceMatrix =
ClassUtils.instantiateAs((Class<? extends MatrixWritable>) matrixClass.get(), MatrixWritable.class);
if (!fs.exists(inverseCovarianceFile.get())) {
throw new FileNotFoundException(inverseCovarianceFile.get().toString());
}
DataInputStream in = fs.open(inverseCovarianceFile.get());
try {
inverseCovarianceMatrix.readFields(in);
} finally {
Closeables.closeQuietly(in);
}
this.inverseCovarianceMatrix = inverseCovarianceMatrix.get();
Preconditions.checkArgument(this.inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
}
if (meanVectorFile.get() != null) {
FileSystem fs = FileSystem.get(meanVectorFile.get().toUri(), jobConf);
VectorWritable meanVector =
ClassUtils.instantiateAs((Class<? extends VectorWritable>) vectorClass.get(), VectorWritable.class);
if (!fs.exists(meanVectorFile.get())) {
throw new FileNotFoundException(meanVectorFile.get().toString());
}
DataInputStream in = fs.open(meanVectorFile.get());
try {
meanVector.readFields(in);
} finally {
Closeables.closeQuietly(in);
}
this.meanVector = meanVector.get();
Preconditions.checkArgument(this.meanVector != null, "meanVector not initialized");
}
} catch (IOException e) {
throw new IllegalStateException(e);
}
}
@Override
public Collection<Parameter<?>> getParameters() {
return parameters;
}
@Override
public void createParameters(String prefix, Configuration jobConf) {
parameters = Lists.newArrayList();
inverseCovarianceFile = new PathParameter(prefix, "inverseCovarianceFile", jobConf, null,
"Path on DFS to a file containing the inverse covariance matrix.");
parameters.add(inverseCovarianceFile);
matrixClass = new ClassParameter(prefix, "maxtrixClass", jobConf, DenseMatrix.class,
"Class<Matix> file specified in parameter inverseCovarianceFile has been serialized with.");
parameters.add(matrixClass);
meanVectorFile = new PathParameter(prefix, "meanVectorFile", jobConf, null,
"Path on DFS to a file containing the mean Vector.");
parameters.add(meanVectorFile);
vectorClass = new ClassParameter(prefix, "vectorClass", jobConf, DenseVector.class,
"Class file specified in parameter meanVectorFile has been serialized with.");
parameters.add(vectorClass);
}
/**
* @param v The vector to compute the distance to
* @return Mahalanobis distance of a multivariate vector
*/
public double distance(Vector v) {
return Math.sqrt(v.minus(meanVector).dot(Algebra.mult(inverseCovarianceMatrix, v.minus(meanVector))));
}
@Override
public double distance(Vector v1, Vector v2) {
if (v1.size() != v2.size()) {
throw new CardinalityException(v1.size(), v2.size());
}
return Math.sqrt(v1.minus(v2).dot(Algebra.mult(inverseCovarianceMatrix, v1.minus(v2))));
}
@Override
public double distance(double centroidLengthSquare, Vector centroid, Vector v) {
return distance(centroid, v); // TODO
}
public void setInverseCovarianceMatrix(Matrix inverseCovarianceMatrix) {
Preconditions.checkArgument(inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
this.inverseCovarianceMatrix = inverseCovarianceMatrix;
}
/**
* Computes the inverse covariance from the input covariance matrix given in input.
*
* @param m A covariance matrix.
* @throws IllegalArgumentException if <tt>eigen values equal to 0 found</tt>.
*/
public void setCovarianceMatrix(Matrix m) {
if (m.numRows() != m.numCols()) {
throw new CardinalityException(m.numRows(), m.numCols());
}
// See http://www.mlahanas.de/Math/svd.htm for details,
// which specifically details the case of covariance matrix inversion
// Complexity: O(min(nm2,mn2))
SingularValueDecomposition svd = new SingularValueDecomposition(m);
Matrix sInv = svd.getS();
// Inverse Diagonal Elems
for (int i = 0; i < sInv.numRows(); i++) {
double diagElem = sInv.get(i, i);
if (diagElem > 0.0) {
sInv.set(i, i, 1 / diagElem);
} else {
throw new IllegalStateException("Eigen Value equals to 0 found.");
}
}
inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
Preconditions.checkArgument(inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
}
public Matrix getInverseCovarianceMatrix() {
return inverseCovarianceMatrix;
}
public void setMeanVector(Vector meanVector) {
Preconditions.checkArgument(meanVector != null, "meanVector not initialized");
this.meanVector = meanVector;
}
public Vector getMeanVector() {
return meanVector;
}
}