org.apache.solr.client.solrj.io.eval.FuzzyKmeansEvaluator.java Source code

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/*
 * 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.solr.client.solrj.io.eval;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.HashMap;

import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.ml.clustering.CentroidCluster;
import org.apache.commons.math3.ml.distance.EuclideanDistance;
import org.apache.commons.math3.ml.clustering.FuzzyKMeansClusterer;
import org.apache.solr.client.solrj.io.stream.expr.StreamExpression;
import org.apache.solr.client.solrj.io.stream.expr.StreamExpressionNamedParameter;
import org.apache.solr.client.solrj.io.stream.expr.StreamFactory;

public class FuzzyKmeansEvaluator extends RecursiveObjectEvaluator implements TwoValueWorker {
    protected static final long serialVersionUID = 1L;

    private int maxIterations = 1000;
    private double fuzziness = 1.2;

    public FuzzyKmeansEvaluator(StreamExpression expression, StreamFactory factory) throws IOException {
        super(expression, factory);

        List<StreamExpressionNamedParameter> namedParams = factory.getNamedOperands(expression);

        for (StreamExpressionNamedParameter namedParam : namedParams) {
            if (namedParam.getName().equals("fuzziness")) {
                this.fuzziness = Double.parseDouble(namedParam.getParameter().toString().trim());
            } else if (namedParam.getName().equals("maxIterations")) {
                this.maxIterations = Integer.parseInt(namedParam.getParameter().toString().trim());
            } else {
                throw new IOException("Unexpected named parameter:" + namedParam.getName());
            }
        }
    }

    @Override
    public Object doWork(Object value1, Object value2) throws IOException {

        Matrix matrix = null;
        int k = 0;

        if (value1 instanceof Matrix) {
            matrix = (Matrix) value1;
        } else {
            throw new IOException("The first parameter for fuzzyKmeans should be the observation matrix.");
        }

        if (value2 instanceof Number) {
            k = ((Number) value2).intValue();
        } else {
            throw new IOException("The second parameter for fuzzyKmeans should be k.");
        }

        FuzzyKMeansClusterer<KmeansEvaluator.ClusterPoint> kmeans = new FuzzyKMeansClusterer(k, fuzziness,
                maxIterations, new EuclideanDistance());
        List<KmeansEvaluator.ClusterPoint> points = new ArrayList();
        double[][] data = matrix.getData();

        List<String> ids = matrix.getRowLabels();

        for (int i = 0; i < data.length; i++) {
            double[] vec = data[i];
            points.add(new KmeansEvaluator.ClusterPoint(ids.get(i), vec));
        }

        Map fields = new HashMap();

        fields.put("k", k);
        fields.put("fuzziness", fuzziness);
        fields.put("distance", "euclidean");
        fields.put("maxIterations", maxIterations);

        List<CentroidCluster<KmeansEvaluator.ClusterPoint>> clusters = kmeans.cluster(points);
        RealMatrix realMatrix = kmeans.getMembershipMatrix();
        double[][] mmData = realMatrix.getData();
        Matrix mmMatrix = new Matrix(mmData);
        mmMatrix.setRowLabels(matrix.getRowLabels());
        return new KmeansEvaluator.ClusterTuple(fields, clusters, matrix.getColumnLabels(), mmMatrix);
    }
}