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Java example source code file (SpearmansCorrelation.java)

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

Learn more about this Java project at its project page.

Java - Java tags/keywords

arraylist, blockrealmatrix, hashset, list, naturalranking, pearsonscorrelation, rankingalgorithm, realmatrix, set, spearmanscorrelation, util

The SpearmansCorrelation.java Java example 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.math3.stat.correlation;

import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.linear.BlockRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.stat.ranking.NaNStrategy;
import org.apache.commons.math3.stat.ranking.NaturalRanking;
import org.apache.commons.math3.stat.ranking.RankingAlgorithm;

/**
 * Spearman's rank correlation. This implementation performs a rank
 * transformation on the input data and then computes {@link PearsonsCorrelation}
 * on the ranked data.
 * <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.
 *
 * @since 2.0
 */
public class SpearmansCorrelation {

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

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

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

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

    /**
     * Create a SpearmansCorrelation with the given ranking algorithm.
     * <p>
     * From version 4.0 onwards this constructor will throw an exception
     * if the provided {@link NaturalRanking} uses a {@link NaNStrategy#REMOVED} strategy.
     *
     * @param rankingAlgorithm ranking algorithm
     * @since 3.1
     */
    public SpearmansCorrelation(final RankingAlgorithm rankingAlgorithm) {
        data = null;
        this.rankingAlgorithm = rankingAlgorithm;
        rankCorrelation = null;
    }

    /**
     * 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 with the given input data matrix
     * and ranking algorithm.
     * <p>
     * From version 4.0 onwards this constructor will throw an exception
     * if the provided {@link NaturalRanking} uses a {@link NaNStrategy#REMOVED} strategy.
     *
     * @param dataMatrix matrix of data with columns representing
     * variables to correlate
     * @param rankingAlgorithm ranking algorithm
     */
    public SpearmansCorrelation(final RealMatrix dataMatrix, final RankingAlgorithm rankingAlgorithm) {
        this.rankingAlgorithm = rankingAlgorithm;
        this.data = rankTransform(dataMatrix);
        rankCorrelation = new PearsonsCorrelation(data);
    }

    /**
     * Calculate the Spearman Rank Correlation Matrix.
     *
     * @return Spearman Rank Correlation Matrix
     * @throws NullPointerException if this instance was created with no data
     */
    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.
     *
     * <p>Returns null if this instance was created with no data.

* * @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(final RealMatrix matrix) { final RealMatrix matrixCopy = rankTransform(matrix); 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(final double[][] matrix) { return computeCorrelationMatrix(new BlockRealMatrix(matrix)); } /** * Computes the Spearman's rank correlation coefficient between the two arrays. * * @param xArray first data array * @param yArray second data array * @return Returns Spearman's rank correlation coefficient for the two arrays * @throws DimensionMismatchException if the arrays lengths do not match * @throws MathIllegalArgumentException if the array length is less than 2 */ public double correlation(final double[] xArray, final double[] yArray) { if (xArray.length != yArray.length) { throw new DimensionMismatchException(xArray.length, yArray.length); } else if (xArray.length < 2) { throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION, xArray.length, 2); } else { double[] x = xArray; double[] y = yArray; if (rankingAlgorithm instanceof NaturalRanking && NaNStrategy.REMOVED == ((NaturalRanking) rankingAlgorithm).getNanStrategy()) { final Set<Integer> nanPositions = new HashSet(); nanPositions.addAll(getNaNPositions(xArray)); nanPositions.addAll(getNaNPositions(yArray)); x = removeValues(xArray, nanPositions); y = removeValues(yArray, nanPositions); } return new PearsonsCorrelation().correlation(rankingAlgorithm.rank(x), rankingAlgorithm.rank(y)); } } /** * Applies rank transform to each of the columns of <code>matrix * using the current <code>rankingAlgorithm. * * @param matrix matrix to transform * @return a rank-transformed matrix */ private RealMatrix rankTransform(final RealMatrix matrix) { RealMatrix transformed = null; if (rankingAlgorithm instanceof NaturalRanking && ((NaturalRanking) rankingAlgorithm).getNanStrategy() == NaNStrategy.REMOVED) { final Set<Integer> nanPositions = new HashSet(); for (int i = 0; i < matrix.getColumnDimension(); i++) { nanPositions.addAll(getNaNPositions(matrix.getColumn(i))); } // if we have found NaN values, we have to update the matrix size if (!nanPositions.isEmpty()) { transformed = new BlockRealMatrix(matrix.getRowDimension() - nanPositions.size(), matrix.getColumnDimension()); for (int i = 0; i < transformed.getColumnDimension(); i++) { transformed.setColumn(i, removeValues(matrix.getColumn(i), nanPositions)); } } } if (transformed == null) { transformed = matrix.copy(); } for (int i = 0; i < transformed.getColumnDimension(); i++) { transformed.setColumn(i, rankingAlgorithm.rank(transformed.getColumn(i))); } return transformed; } /** * Returns a list containing the indices of NaN values in the input array. * * @param input the input array * @return a list of NaN positions in the input array */ private List<Integer> getNaNPositions(final double[] input) { final List<Integer> positions = new ArrayList(); for (int i = 0; i < input.length; i++) { if (Double.isNaN(input[i])) { positions.add(i); } } return positions; } /** * Removes all values from the input array at the specified indices. * * @param input the input array * @param indices a set containing the indices to be removed * @return the input array without the values at the specified indices */ private double[] removeValues(final double[] input, final Set<Integer> indices) { if (indices.isEmpty()) { return input; } final double[] result = new double[input.length - indices.size()]; for (int i = 0, j = 0; i < input.length; i++) { if (!indices.contains(i)) { result[j++] = input[i]; } } return result; } }

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