PearsonsCorrelation

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 *      https://www.apache.org/licenses/LICENSE-2.0
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/*
 * This is not the original file distributed by the Apache Software Foundation
 * It has been modified by the Hipparchus project
 */
package org.hipparchus.stat.correlation;

import org.hipparchus.distribution.continuous.TDistribution;
import org.hipparchus.exception.LocalizedCoreFormats;
import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.linear.BlockRealMatrix;
import org.hipparchus.linear.RealMatrix;
import org.hipparchus.stat.LocalizedStatFormats;
import org.hipparchus.stat.regression.SimpleRegression;
import org.hipparchus.util.FastMath;
import org.hipparchus.util.MathArrays;
import org.hipparchus.util.MathUtils;

/**
 * Computes Pearson's product-moment correlation coefficients for pairs of arrays
 * or columns of a matrix.
 * <p>
 * The constructors that take <code>RealMatrix</code> or
 * <code>double[][]</code> arguments generate correlation matrices.  The
 * columns of the input matrices are assumed to represent variable values.
 * Correlations are given by the formula:
 * <p>
 * <code>cor(X, Y) = &Sigma;[(x<sub>i</sub> - E(X))(y<sub>i</sub> - E(Y))] / [(n - 1)s(X)s(Y)]</code>
 * <p>
 * where <code>E(X)</code> is the mean of <code>X</code>, <code>E(Y)</code>
 * is the mean of the <code>Y</code> values and s(X), s(Y) are standard deviations.
 * <p>
 * To compute the correlation coefficient for a single pair of arrays, use {@link #PearsonsCorrelation()}
 * to construct an instance with no data and then {@link #correlation(double[], double[])}.
 * Correlation matrices can also be computed directly from an instance with no data using
 * {@link #computeCorrelationMatrix(double[][])}. In order to use {@link #getCorrelationMatrix()},
 * {@link #getCorrelationPValues()},  or {@link #getCorrelationStandardErrors()}; however, one of the
 * constructors supplying data or a covariance matrix must be used to create the instance.
 */
public class PearsonsCorrelation {

    /** correlation matrix */
    private final RealMatrix correlationMatrix;

    /** number of observations */
    private final int nObs;

    /**
     * Create a PearsonsCorrelation instance without data.
     */
    public PearsonsCorrelation() {
        super();
        correlationMatrix = null;
        nObs = 0;
    }

    /**
     * Create a PearsonsCorrelation from a rectangular array
     * whose columns represent values of variables to be correlated.
     *
     * Throws MathIllegalArgumentException if the input array does not have at least
     * two columns and two rows.  Pairwise correlations are set to NaN if one
     * of the correlates has zero variance.
     *
     * @param data rectangular array with columns representing variables
     * @throws MathIllegalArgumentException if the input data array is not
     * rectangular with at least two rows and two columns.
     * @see #correlation(double[], double[])
     */
    public PearsonsCorrelation(double[][] data) {
        this(new BlockRealMatrix(data));
    }

    /**
     * Create a PearsonsCorrelation from a RealMatrix whose columns
     * represent variables to be correlated.
     *
     * Throws MathIllegalArgumentException if the matrix does not have at least
     * two columns and two rows.  Pairwise correlations are set to NaN if one
     * of the correlates has zero variance.
     *
     * @param matrix matrix with columns representing variables to correlate
     * @throws MathIllegalArgumentException if the matrix does not contain sufficient data
     * @see #correlation(double[], double[])
     */
    public PearsonsCorrelation(RealMatrix matrix) {
        nObs = matrix.getRowDimension();
        correlationMatrix = computeCorrelationMatrix(matrix);
    }

    /**
     * Create a PearsonsCorrelation from a {@link Covariance}.  The correlation
     * matrix is computed by scaling the Covariance's covariance matrix.
     * The Covariance instance must have been created from a data matrix with
     * columns representing variable values.
     *
     * @param covariance Covariance instance
     */
    public PearsonsCorrelation(Covariance covariance) {
        RealMatrix covarianceMatrix = covariance.getCovarianceMatrix();
        MathUtils.checkNotNull(covarianceMatrix, LocalizedStatFormats.COVARIANCE_MATRIX);
        nObs = covariance.getN();
        correlationMatrix = covarianceToCorrelation(covarianceMatrix);
    }

    /**
     * Create a PearsonsCorrelation from a covariance matrix. The correlation
     * matrix is computed by scaling the covariance matrix.
     *
     * @param covarianceMatrix covariance matrix
     * @param numberOfObservations the number of observations in the dataset used to compute
     * the covariance matrix
     */
    public PearsonsCorrelation(RealMatrix covarianceMatrix, int numberOfObservations) {
        nObs = numberOfObservations;
        correlationMatrix = covarianceToCorrelation(covarianceMatrix);
    }

    /**
     * Returns the correlation matrix.
     *
     * <p>This method will return null if the argumentless constructor was used
     * to create this instance, even if {@link #computeCorrelationMatrix(double[][])}
     * has been called before it is activated.</p>
     *
     * @return correlation matrix
     */
    public RealMatrix getCorrelationMatrix() {
        return correlationMatrix;
    }

    /**
     * Returns a matrix of standard errors associated with the estimates
     * in the correlation matrix.<br>
     * <code>getCorrelationStandardErrors().getEntry(i,j)</code> is the standard
     * error associated with <code>getCorrelationMatrix.getEntry(i,j)</code>
     *
     * <p>The formula used to compute the standard error is <br>
     * <code>SE<sub>r</sub> = ((1 - r<sup>2</sup>) / (n - 2))<sup>1/2</sup></code>
     * where <code>r</code> is the estimated correlation coefficient and
     * <code>n</code> is the number of observations in the source dataset.</p>
     *
     * <p>To use this method, one of the constructors that supply an input
     * matrix must have been used to create this instance.</p>
     *
     * @return matrix of correlation standard errors
     * @throws NullPointerException if this instance was created with no data
     */
    public RealMatrix getCorrelationStandardErrors() {
        int nVars = correlationMatrix.getColumnDimension();
        double[][] out = new double[nVars][nVars];
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < nVars; j++) {
                double r = correlationMatrix.getEntry(i, j);
                out[i][j] = FastMath.sqrt((1 - r * r) /(nObs - 2));
            }
        }
        return new BlockRealMatrix(out);
    }

    /**
     * Returns a matrix of p-values associated with the (two-sided) null
     * hypothesis that the corresponding correlation coefficient is zero.
     *
     * <p><code>getCorrelationPValues().getEntry(i,j)</code> is the probability
     * that a random variable distributed as <code>t<sub>n-2</sub></code> takes
     * a value with absolute value greater than or equal to <br>
     * <code>|r|((n - 2) / (1 - r<sup>2</sup>))<sup>1/2</sup></code></p>
     *
     * <p>The values in the matrix are sometimes referred to as the
     * <i>significance</i> of the corresponding correlation coefficients.</p>
     *
     * <p>To use this method, one of the constructors that supply an input
     * matrix must have been used to create this instance.</p>
     *
     * @return matrix of p-values
     * @throws org.hipparchus.exception.MathIllegalStateException
     * if an error occurs estimating probabilities
     * @throws NullPointerException if this instance was created with no data
     */
    public RealMatrix getCorrelationPValues() {
        TDistribution tDistribution = new TDistribution(nObs - 2);
        int nVars = correlationMatrix.getColumnDimension();
        double[][] out = new double[nVars][nVars];
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < nVars; j++) {
                if (i == j) {
                    out[i][j] = 0d;
                } else {
                    double r = correlationMatrix.getEntry(i, j);
                    double t = FastMath.abs(r * FastMath.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * tDistribution.cumulativeProbability(-t);
                }
            }
        }
        return new BlockRealMatrix(out);
    }


    /**
     * Computes the correlation matrix for the columns of the
     * input matrix, using {@link #correlation(double[], double[])}.
     *
     * Throws MathIllegalArgumentException if the matrix does not have at least
     * two columns and two rows.  Pairwise correlations are set to NaN if one
     * of the correlates has zero variance.
     *
     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     * @throws MathIllegalArgumentException if the matrix does not contain sufficient data
     * @see #correlation(double[], double[])
     */
    public RealMatrix computeCorrelationMatrix(RealMatrix matrix) {
        checkSufficientData(matrix);
        int nVars = matrix.getColumnDimension();
        RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < i; j++) {
              double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
              outMatrix.setEntry(i, j, corr);
              outMatrix.setEntry(j, i, corr);
            }
            outMatrix.setEntry(i, i, 1d);
        }
        return outMatrix;
    }

    /**
     * Computes the correlation matrix for the columns of the
     * input rectangular array.  The columns of the array represent values
     * of variables to be correlated.
     *
     * Throws MathIllegalArgumentException if the matrix does not have at least
     * two columns and two rows or if the array is not rectangular. Pairwise
     * correlations are set to NaN if one of the correlates has zero variance.
     *
     * @param data matrix with columns representing variables to correlate
     * @return correlation matrix
     * @throws MathIllegalArgumentException if the array does not contain sufficient data
     * @see #correlation(double[], double[])
     */
    public RealMatrix computeCorrelationMatrix(double[][] data) {
       return computeCorrelationMatrix(new BlockRealMatrix(data));
    }

    /**
     * Computes the Pearson's product-moment correlation coefficient between two arrays.
     *
     * <p>Throws MathIllegalArgumentException if the arrays do not have the same length
     * or their common length is less than 2.  Returns {@code NaN} if either of the arrays
     * has zero variance (i.e., if one of the arrays does not contain at least two distinct
     * values).</p>
     *
     * @param xArray first data array
     * @param yArray second data array
     * @return Returns Pearson's correlation coefficient for the two arrays
     * @throws MathIllegalArgumentException if the arrays lengths do not match
     * @throws MathIllegalArgumentException if there is insufficient data
     */
    public double correlation(final double[] xArray, final double[] yArray) {
        MathArrays.checkEqualLength(xArray, yArray);
        if (xArray.length < 2) {
            throw new MathIllegalArgumentException(LocalizedCoreFormats.INSUFFICIENT_DIMENSION,
                                                   xArray.length, 2);
        }

        SimpleRegression regression = new SimpleRegression();
        for(int i = 0; i < xArray.length; i++) {
            regression.addData(xArray[i], yArray[i]);
        }
        return regression.getR();
    }

    /**
     * Derives a correlation matrix from a covariance matrix.
     *
     * <p>Uses the formula <br>
     * <code>r(X,Y) = cov(X,Y)/s(X)s(Y)</code> where
     * <code>r(&middot;,&middot;)</code> is the correlation coefficient and
     * <code>s(&middot;)</code> means standard deviation.</p>
     *
     * @param covarianceMatrix the covariance matrix
     * @return correlation matrix
     */
    public RealMatrix covarianceToCorrelation(RealMatrix covarianceMatrix) {
        int nVars = covarianceMatrix.getColumnDimension();
        RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
        for (int i = 0; i < nVars; i++) {
            double sigma = FastMath.sqrt(covarianceMatrix.getEntry(i, i));
            outMatrix.setEntry(i, i, 1d);
            for (int j = 0; j < i; j++) {
                double entry = covarianceMatrix.getEntry(i, j) /
                       (sigma * FastMath.sqrt(covarianceMatrix.getEntry(j, j)));
                outMatrix.setEntry(i, j, entry);
                outMatrix.setEntry(j, i, entry);
            }
        }
        return outMatrix;
    }

    /**
     * Throws MathIllegalArgumentException if the matrix does not have at least
     * two columns and two rows.
     *
     * @param matrix matrix to check for sufficiency
     * @throws MathIllegalArgumentException if there is insufficient data
     */
    private void checkSufficientData(final RealMatrix matrix) {
        int nRows = matrix.getRowDimension();
        int nCols = matrix.getColumnDimension();
        if (nRows < 2 || nCols < 2) {
            throw new MathIllegalArgumentException(LocalizedCoreFormats.INSUFFICIENT_ROWS_AND_COLUMNS,
                                                   nRows, nCols);
        }
    }
}