/**
* 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.clustering.fuzzykmeans;
import java.util.Collection;
import java.util.List;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
public class FuzzyKMeansClusterer {
private static final double MINIMAL_VALUE = 0.0000000001;
private double m = 2.0; // default value
public Vector computePi(Collection<SoftCluster> clusters, List<Double> clusterDistanceList) {
Vector pi = new DenseVector(clusters.size());
for (int i = 0; i < clusters.size(); i++) {
double probWeight = computeProbWeight(clusterDistanceList.get(i), clusterDistanceList);
pi.set(i, probWeight);
}
return pi;
}
/** Computes the probability of a point belonging to a cluster */
public double computeProbWeight(double clusterDistance, Iterable<Double> clusterDistanceList) {
if (clusterDistance == 0) {
clusterDistance = MINIMAL_VALUE;
}
double denom = 0.0;
for (double eachCDist : clusterDistanceList) {
if (eachCDist == 0.0) {
eachCDist = MINIMAL_VALUE;
}
denom += Math.pow(clusterDistance / eachCDist, 2.0 / (m - 1));
}
return 1.0 / denom;
}
public void setM(double m) {
this.m = m;
}
}