From 3109c12ec8936957d1aba94bafb0f24d0fcac486 Mon Sep 17 00:00:00 2001 From: Thomas Neidhart Date: Thu, 2 Feb 2012 21:02:54 +0000 Subject: [PATCH] Changed deprecated MathRuntimeException in package stat.regression JIRA: MATH-459 git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1239842 13f79535-47bb-0310-9956-ffa450edef68 --- .../AbstractMultipleLinearRegression.java | 84 ++++++++++--------- .../stat/regression/SimpleRegression.java | 33 ++++---- 2 files changed, 60 insertions(+), 57 deletions(-) diff --git a/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java b/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java index 7e4195d3d..6e4ede60a 100644 --- a/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java +++ b/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java @@ -16,8 +16,13 @@ */ package org.apache.commons.math.stat.regression; -import org.apache.commons.math.MathRuntimeException; +import org.apache.commons.math.exception.DimensionMismatchException; +import org.apache.commons.math.exception.MathIllegalArgumentException; +import org.apache.commons.math.exception.NoDataException; +import org.apache.commons.math.exception.NullArgumentException; +import org.apache.commons.math.exception.NumberIsTooSmallException; import org.apache.commons.math.exception.util.LocalizedFormats; +import org.apache.commons.math.linear.NonSquareMatrixException; import org.apache.commons.math.linear.RealMatrix; import org.apache.commons.math.linear.Array2DRowRealMatrix; import org.apache.commons.math.linear.RealVector; @@ -87,20 +92,21 @@ public abstract class AbstractMultipleLinearRegression implements * @param data input data array * @param nobs number of observations (rows) * @param nvars number of independent variables (columns, not counting y) - * @throws IllegalArgumentException if the preconditions are not met + * @throws NullArgumentException if the data array is null + * @throws DimensionMismatchException if the length of the data array is not equal + * to nobs * (nvars + 1) + * @throws NumberIsTooSmallException if nobs is smaller than + * nvars */ public void newSampleData(double[] data, int nobs, int nvars) { if (data == null) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NULL_NOT_ALLOWED); + throw new NullArgumentException(); } if (data.length != nobs * (nvars + 1)) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.INVALID_REGRESSION_ARRAY, data.length, nobs, nvars); + throw new DimensionMismatchException(data.length, nobs * (nvars + 1)); } if (nobs <= nvars) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS); + throw new NumberIsTooSmallException(nobs, nvars, false); } double[] y = new double[nobs]; final int cols = noIntercept ? nvars: nvars + 1; @@ -123,16 +129,15 @@ public abstract class AbstractMultipleLinearRegression implements * Loads new y sample data, overriding any previous data. * * @param y the array representing the y sample - * @throws IllegalArgumentException if y is null or empty + * @throws NullArgumentException if y is null + * @throws NoDataException if y is empty */ protected void newYSampleData(double[] y) { if (y == null) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NULL_NOT_ALLOWED); + throw new NullArgumentException(); } if (y.length == 0) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NO_DATA); + throw new NoDataException(); } this.Y = new ArrayRealVector(y); } @@ -158,16 +163,16 @@ public abstract class AbstractMultipleLinearRegression implements * specifying a model including an intercept term. *

* @param x the rectangular array representing the x sample - * @throws IllegalArgumentException if x is null, empty or not rectangular + * @throws NullArgumentException if x is null + * @throws NoDataException if x is empty + * @throws DimensionMismatchException if x is not rectangular */ protected void newXSampleData(double[][] x) { if (x == null) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NULL_NOT_ALLOWED); + throw new NullArgumentException(); } if (x.length == 0) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NO_DATA); + throw new NoDataException(); } if (noIntercept) { this.X = new Array2DRowRealMatrix(x, true); @@ -176,9 +181,7 @@ public abstract class AbstractMultipleLinearRegression implements final double[][] xAug = new double[x.length][nVars + 1]; for (int i = 0; i < x.length; i++) { if (x[i].length != nVars) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.DIFFERENT_ROWS_LENGTHS, - x[i].length, nVars); + throw new DimensionMismatchException(x[i].length, nVars); } xAug[i][0] = 1.0d; System.arraycopy(x[i], 0, xAug[i], 1, nVars); @@ -198,24 +201,27 @@ public abstract class AbstractMultipleLinearRegression implements * * @param x the [n,k] array representing the x data * @param y the [n,1] array representing the y data - * @throws IllegalArgumentException if any of the checks fail - * + * @throws NullArgumentException if {@code x} or {@code y} is null + * @throws DimensionMismatchException if {@code x} and {@code y} do not + * have the same length + * @throws NoDataException if {@code x} or {@code y} are zero-length + * @throws MathIllegalArgumentException if the number of rows of {@code x} + * is not larger than the number of columns + 1 */ protected void validateSampleData(double[][] x, double[] y) { - if ((x == null) || (y == null) || (x.length != y.length)) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, - (x == null) ? 0 : x.length, - (y == null) ? 0 : y.length); + if ((x == null) || (y == null)) { + throw new NullArgumentException(); + } + if (x.length != y.length) { + throw new DimensionMismatchException(y.length, x.length); } if (x.length == 0) { // Must be no y data either - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NO_DATA); + throw new NoDataException(); } if (x[0].length + 1 > x.length) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS, - x.length, x[0].length); + throw new MathIllegalArgumentException( + LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS, + x.length, x[0].length); } } @@ -225,18 +231,16 @@ public abstract class AbstractMultipleLinearRegression implements * * @param x the [n,k] array representing the x sample * @param covariance the [n,n] array representing the covariance matrix - * @throws IllegalArgumentException if the number of rows in x is not equal - * to the number of rows in covariance or covariance is not square. + * @throws DimensionMismatchException if the number of rows in x is not equal + * to the number of rows in covariance + * @throws NonSquareMatrixException if the covariance matrix is not square */ protected void validateCovarianceData(double[][] x, double[][] covariance) { if (x.length != covariance.length) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, x.length, covariance.length); + throw new DimensionMismatchException(x.length, covariance.length); } if (covariance.length > 0 && covariance.length != covariance[0].length) { - throw MathRuntimeException.createIllegalArgumentException( - LocalizedFormats.NON_SQUARE_MATRIX, - covariance.length, covariance[0].length); + throw new NonSquareMatrixException(covariance.length, covariance[0].length); } } diff --git a/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java b/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java index 220a492f1..963350cb5 100644 --- a/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java +++ b/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java @@ -18,7 +18,6 @@ package org.apache.commons.math.stat.regression; import java.io.Serializable; -import org.apache.commons.math.MathException; import org.apache.commons.math.exception.OutOfRangeException; import org.apache.commons.math.distribution.TDistribution; import org.apache.commons.math.exception.MathIllegalArgumentException; @@ -137,7 +136,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg } else { if( hasIntercept ){ final double fact1 = 1.0 + n; - final double fact2 = (n) / (1.0 + n); + final double fact2 = n / (1.0 + n); final double dx = x - xbar; final double dy = y - ybar; sumXX += dx * dx * fact2; @@ -176,7 +175,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg if (n > 0) { if (hasIntercept) { final double fact1 = n - 1.0; - final double fact2 = (n) / (n - 1.0); + final double fact2 = n / (n - 1.0); final double dx = x - xbar; final double dy = y - ybar; sumXX -= dx * dx * fact2; @@ -609,9 +608,9 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * Bivariate Normal Distribution.

* * @return half-width of 95% confidence interval for the slope estimate - * @throws MathException if the confidence interval can not be computed. + * @throws OutOfRangeException if the confidence interval can not be computed. */ - public double getSlopeConfidenceInterval() throws MathException { + public double getSlopeConfidenceInterval() { return getSlopeConfidenceInterval(0.05d); } @@ -639,15 +638,14 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * Double.NaN. * *
  • (0 < alpha < 1); otherwise an - * IllegalArgumentException is thrown. + * OutOfRangeException is thrown. *

    • * * @param alpha the desired significance level * @return half-width of 95% confidence interval for the slope estimate - * @throws MathException if the confidence interval can not be computed. + * @throws OutOfRangeException if the confidence interval can not be computed. */ - public double getSlopeConfidenceInterval(final double alpha) - throws MathException { + public double getSlopeConfidenceInterval(final double alpha) { if (alpha >= 1 || alpha <= 0) { throw new OutOfRangeException(LocalizedFormats.SIGNIFICANCE_LEVEL, alpha, 0, 1); @@ -676,9 +674,10 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg * Double.NaN.

    * * @return significance level for slope/correlation - * @throws MathException if the significance level can not be computed. + * @throws org.apache.commons.math.exception.MaxCountExceededException + * if the significance level can not be computed. */ - public double getSignificance() throws MathException { + public double getSignificance() { TDistribution distribution = new TDistribution(n - 2); return 2d * (1.0 - distribution.cumulativeProbability( FastMath.abs(getSlope()) / getSlopeStdErr())); @@ -724,16 +723,16 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg if( FastMath.abs( sumXX ) > Precision.SAFE_MIN ){ final double[] params = new double[]{ getIntercept(), getSlope() }; final double mse = getMeanSquareError(); - final double _syy = sumYY + sumY * sumY / (n); + final double _syy = sumYY + sumY * sumY / n; final double[] vcv = new double[]{ - mse * (xbar *xbar /sumXX + 1.0 / (n)), + mse * (xbar *xbar /sumXX + 1.0 / n), -xbar*mse/sumXX, mse/sumXX }; return new RegressionResults( params, new double[][]{vcv}, true, n, 2, sumY, _syy, getSumSquaredErrors(),true,false); }else{ - final double[] params = new double[]{ sumY/(n), Double.NaN }; + final double[] params = new double[]{ sumY / n, Double.NaN }; //final double mse = getMeanSquareError(); final double[] vcv = new double[]{ ybar / (n - 1.0), @@ -797,7 +796,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg if( variablesToInclude[0] != 1 && variablesToInclude[0] != 0 ){ throw new OutOfRangeException( variablesToInclude[0],0,1 ); } - final double _mean = sumY * sumY / (n); + final double _mean = sumY * sumY / n; final double _syy = sumYY + _mean; if( variablesToInclude[0] == 0 ){ //just the mean @@ -809,8 +808,8 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg }else if( variablesToInclude[0] == 1){ //final double _syy = sumYY + sumY * sumY / ((double) n); - final double _sxx = sumXX + sumX * sumX / (n); - final double _sxy = sumXY + sumX * sumY / (n); + final double _sxx = sumXX + sumX * sumX / n; + final double _sxy = sumXY + sumX * sumY / n; final double _sse = FastMath.max(0d, _syy - _sxy * _sxy / _sxx); final double _mse = _sse/((n-1)); if( !Double.isNaN(_sxx) ){