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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
This commit is contained in:
Thomas Neidhart 2012-02-02 21:02:54 +00:00
parent ab3935c4c9
commit 3109c12ec8
2 changed files with 60 additions and 57 deletions
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84
src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java
View File

@ -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 <code>nobs * (nvars + 1)</code>
* @throws NumberIsTooSmallException if <code>nobs</code> is smaller than
* <code>nvars</code>
*/
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.
* </p>
* @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);
}
}

33
src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java
View File

@ -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</a>.</p>
*
* @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
* <code>Double.NaN</code>.
* </li>
* <li><code>(0 < alpha < 1)</code>; otherwise an
* <code>IllegalArgumentException</code> is thrown.
* <code>OutOfRangeException</code> is thrown.
* </li></ul></p>
*
* @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
* <code>Double.NaN</code>.</p>
*
* @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) ){