/**
* 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.iterator;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.List;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.classify.ClusterClassifier;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.SequentialAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.function.TimesFunction;
public abstract class AbstractClusteringPolicy implements ClusteringPolicy {
@Override
public abstract void write(DataOutput out) throws IOException;
@Override
public abstract void readFields(DataInput in) throws IOException;
@Override
public Vector select(Vector probabilities) {
int maxValueIndex = probabilities.maxValueIndex();
Vector weights = new SequentialAccessSparseVector(probabilities.size());
weights.set(maxValueIndex, 1.0);
return weights;
}
@Override
public void update(ClusterClassifier posterior) {
// nothing to do in general here
}
@Override
public Vector classify(Vector data, ClusterClassifier prior) {
List<Cluster> models = prior.getModels();
int i = 0;
Vector pdfs = new DenseVector(models.size());
for (Cluster model : models) {
pdfs.set(i++, model.pdf(new VectorWritable(data)));
}
return pdfs.assign(new TimesFunction(), 1.0 / pdfs.zSum());
}
@Override
public void close(ClusterClassifier posterior) {
for (Cluster cluster : posterior.getModels()) {
cluster.computeParameters();
}
}
}