org.meteoinfo.math.stats.StatsUtil.java Source code

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Here is the source code for org.meteoinfo.math.stats.StatsUtil.java

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package org.meteoinfo.math.stats;

import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.stat.correlation.Covariance;
import org.apache.commons.math3.stat.correlation.KendallsCorrelation;
import org.apache.commons.math3.stat.correlation.PearsonsCorrelation;
import org.apache.commons.math3.stat.correlation.SpearmansCorrelation;
import org.meteoinfo.data.ArrayUtil;
import ucar.ma2.Array;
import ucar.ma2.DataType;

/**
 *
 * @author Yaqiang Wang
 */
public class StatsUtil {

    /**
     * Computes covariances for pairs of arrays or columns of a matrix.
     *
     * @param x X data
     * @param y Y data
     * @param bias If true, returned value will be bias-corrected
     * @return The covariance matrix
     */
    public static Array cov(Array x, Array y, boolean bias) {
        int m = x.getShape()[0];
        int n = 1;
        if (x.getRank() == 2)
            n = x.getShape()[1];
        double[][] aa = new double[m][n * 2];
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n * 2; j++) {
                if (j < n) {
                    aa[i][j] = x.getDouble(i * n + j);
                } else {
                    aa[i][j] = y.getDouble(i * n + j - n);
                }
            }
        }
        RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
        Covariance cov = new Covariance(matrix, bias);
        RealMatrix mcov = cov.getCovarianceMatrix();
        m = mcov.getColumnDimension();
        n = mcov.getRowDimension();
        Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n; j++) {
                r.setDouble(i * n + j, mcov.getEntry(i, j));
            }
        }

        return r;
    }

    /**
     * Computes covariances for columns of a matrix.
     * @param a Matrix data
     * @param bias If true, returned value will be bias-corrected
     * @return Covariant matrix or value
     */
    public static Object cov(Array a, boolean bias) {
        if (a.getRank() == 1) {
            double[] ad = (double[]) ArrayUtil.copyToNDJavaArray(a);
            Covariance cov = new Covariance();
            return cov.covariance(ad, ad);
        } else {
            double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
            RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
            Covariance cov = new Covariance(matrix, bias);
            RealMatrix mcov = cov.getCovarianceMatrix();
            int m = mcov.getColumnDimension();
            int n = mcov.getRowDimension();
            Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
            for (int i = 0; i < m; i++) {
                for (int j = 0; j < n; j++) {
                    r.setDouble(i * n + j, mcov.getEntry(i, j));
                }
            }

            return r;
        }
    }

    /**
     * Calculates Kendall's tau, a correlation measure for ordinal data.
     *
     * @param x X data
     * @param y Y data
     * @return Kendall's tau correlation.
     */
    public static double kendalltau(Array x, Array y) {
        double[] xd = (double[]) ArrayUtil.copyToNDJavaArray(x);
        double[] yd = (double[]) ArrayUtil.copyToNDJavaArray(y);
        KendallsCorrelation kc = new KendallsCorrelation();
        double r = kc.correlation(xd, yd);
        return r;
    }

    /**
     * Calculates a Pearson correlation coefficient.
     *
     * @param x X data
     * @param y Y data
     * @return Pearson correlation and p-value.
     */
    public static double[] pearsonr(Array x, Array y) {
        int m = x.getShape()[0];
        int n = 1;
        double[][] aa = new double[m][n * 2];
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n * 2; j++) {
                if (j < n) {
                    aa[i][j] = x.getDouble(i * n + j);
                } else {
                    aa[i][j] = y.getDouble(i * n + j - n);
                }
            }
        }
        RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
        PearsonsCorrelation pc = new PearsonsCorrelation(matrix);
        double r = pc.getCorrelationMatrix().getEntry(0, 1);
        double pvalue = pc.getCorrelationPValues().getEntry(0, 1);
        return new double[] { r, pvalue };
    }

    /**
     * Computes Spearman's rank correlation for pairs of arrays or columns of a matrix.
     *
     * @param x X data
     * @param y Y data
     * @return Spearman's rank correlation
     */
    public static Array spearmanr(Array x, Array y) {
        int m = x.getShape()[0];
        int n = 1;
        if (x.getRank() == 2)
            n = x.getShape()[1];
        double[][] aa = new double[m][n * 2];
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n * 2; j++) {
                if (j < n) {
                    aa[i][j] = x.getDouble(i * n + j);
                } else {
                    aa[i][j] = y.getDouble(i * n + j - n);
                }
            }
        }
        RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
        SpearmansCorrelation cov = new SpearmansCorrelation(matrix);
        RealMatrix mcov = cov.getCorrelationMatrix();
        m = mcov.getColumnDimension();
        n = mcov.getRowDimension();
        Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < n; j++) {
                r.setDouble(i * n + j, mcov.getEntry(i, j));
            }
        }

        return r;
    }

    /**
     * Computes Spearman's rank correlation for columns of a matrix.
     * @param a Matrix data
     * @return Spearman's rank correlation
     */
    public static Object spearmanr(Array a) {
        if (a.getRank() == 1) {
            double[] ad = (double[]) ArrayUtil.copyToNDJavaArray(a);
            Covariance cov = new Covariance();
            return cov.covariance(ad, ad);
        } else {
            double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
            RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
            SpearmansCorrelation cov = new SpearmansCorrelation(matrix);
            RealMatrix mcov = cov.getCorrelationMatrix();
            int m = mcov.getColumnDimension();
            int n = mcov.getRowDimension();
            Array r = Array.factory(DataType.DOUBLE, new int[] { m, n });
            for (int i = 0; i < m; i++) {
                for (int j = 0; j < n; j++) {
                    r.setDouble(i * n + j, mcov.getEntry(i, j));
                }
            }

            return r;
        }
    }
}