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Commons Math example source code file (SpearmansCorrelation.java)

This example Commons Math source code file (SpearmansCorrelation.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

blockrealmatrix, illegalargumentexception, naturalranking, naturalranking, pearsonscorrelation, pearsonscorrelation, rankingalgorithm, realmatrix, realmatrix, spearmanscorrelation, spearmanscorrelation

The Commons Math SpearmansCorrelation.java source code

/*
 * 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.commons.math.stat.correlation;

import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.linear.BlockRealMatrix;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.stat.ranking.NaturalRanking;
import org.apache.commons.math.stat.ranking.RankingAlgorithm;

/**
 * <p>Spearman's rank correlation. This implementation performs a rank
 * transformation on the input data and then computes {@link PearsonsCorrelation}
 * on the ranked data.</p>
 *
 * <p>By default, ranks are computed using {@link NaturalRanking} with default
 * strategies for handling NaNs and ties in the data (NaNs maximal, ties averaged).
 * The ranking algorithm can be set using a constructor argument.</p>
 *
 * @since 2.0
 * @version $Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $
 */

public class SpearmansCorrelation {

    /** Input data */
    private final RealMatrix data;

    /** Ranking algorithm  */
    private final RankingAlgorithm rankingAlgorithm;

    /** Rank correlation */
    private final PearsonsCorrelation rankCorrelation;

    /**
     * Create a SpearmansCorrelation with the given input data matrix
     * and ranking algorithm.
     *
     * @param dataMatrix matrix of data with columns representing
     * variables to correlate
     * @param rankingAlgorithm ranking algorithm
     */
    public SpearmansCorrelation(final RealMatrix dataMatrix, final RankingAlgorithm rankingAlgorithm) {
        this.data = dataMatrix.copy();
        this.rankingAlgorithm = rankingAlgorithm;
        rankTransform(data);
        rankCorrelation = new PearsonsCorrelation(data);
    }

    /**
     * Create a SpearmansCorrelation from the given data matrix.
     *
     * @param dataMatrix matrix of data with columns representing
     * variables to correlate
     */
    public SpearmansCorrelation(final RealMatrix dataMatrix) {
        this(dataMatrix, new NaturalRanking());
    }

    /**
     * Create a SpearmansCorrelation without data.
     */
    public SpearmansCorrelation() {
        data = null;
        this.rankingAlgorithm = new NaturalRanking();
        rankCorrelation = null;
    }

    /**
     * Calculate the Spearman Rank Correlation Matrix.
     *
     * @return Spearman Rank Correlation Matrix
     */
    public RealMatrix getCorrelationMatrix() {
        return rankCorrelation.getCorrelationMatrix();
    }

    /**
     * Returns a {@link PearsonsCorrelation} instance constructed from the
     * ranked input data. That is,
     * <code>new SpearmansCorrelation(matrix).getRankCorrelation()
     * is equivalent to
     * <code>new PearsonsCorrelation(rankTransform(matrix)) where
     * <code>rankTransform(matrix) is the result of applying the
     * configured <code>RankingAlgorithm to each of the columns of
     * <code>matrix.
     *
     * @return PearsonsCorrelation among ranked column data
     */
    public PearsonsCorrelation getRankCorrelation() {
        return rankCorrelation;
    }

    /**
     * Computes the Spearman's rank correlation matrix for the columns of the
     * input matrix.
     *
     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     */
    public RealMatrix computeCorrelationMatrix(RealMatrix matrix) {
        RealMatrix matrixCopy = matrix.copy();
        rankTransform(matrixCopy);
        return new PearsonsCorrelation().computeCorrelationMatrix(matrixCopy);
    }

    /**
     * Computes the Spearman's rank correlation matrix for the columns of the
     * input rectangular array.  The columns of the array represent values
     * of variables to be correlated.
     *
     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     */
    public RealMatrix computeCorrelationMatrix(double[][] matrix) {
       return computeCorrelationMatrix(new BlockRealMatrix(matrix));
    }

    /**
     * Computes the Spearman's rank correlation coefficient between the two arrays.
     *
     * </p>Throws IllegalArgumentException if the arrays do not have the same length
     * or their common length is less than 2</p>
     *
     * @param xArray first data array
     * @param yArray second data array
     * @return Returns Spearman's rank correlation coefficient for the two arrays
     * @throws  IllegalArgumentException if the arrays lengths do not match or
     * there is insufficient data
     */
    public double correlation(final double[] xArray, final double[] yArray)
    throws IllegalArgumentException {
        if (xArray.length == yArray.length && xArray.length > 1) {
            return new PearsonsCorrelation().correlation(rankingAlgorithm.rank(xArray),
                    rankingAlgorithm.rank(yArray));
        }
        else {
            throw MathRuntimeException.createIllegalArgumentException(
                    "invalid array dimensions. xArray has size {0}; yArray has {1} elements",
                    xArray.length, yArray.length);
        }
    }

    /**
     * Applies rank transform to each of the columns of <code>matrix
     * using the current <code>rankingAlgorithm
     *
     * @param matrix matrix to transform
     */
    private void rankTransform(RealMatrix matrix) {
        for (int i = 0; i < matrix.getColumnDimension(); i++) {
            matrix.setColumn(i, rankingAlgorithm.rank(matrix.getColumn(i)));
        }
    }
}

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