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Changed to use InsufficientDataException when the model does not contain sufficient data for the number of regerssors; fixed error in precondition statement. 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1540502 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz 2013-11-10 18:03:41 +00:00
parent fdd505d985
commit 78504bde01
1 changed files with 4 additions and 4 deletions
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8
src/main/java/org/apache/commons/math3/stat/regression/AbstractMultipleLinearRegression.java
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@ -17,10 +17,10 @@
package org.apache.commons.math3.stat.regression;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.InsufficientDataException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NoDataException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.linear.NonSquareMatrixException;
import org.apache.commons.math3.linear.RealMatrix;
@ -109,8 +109,8 @@ public abstract class AbstractMultipleLinearRegression implements
* @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>
* @throws InsufficientDataException if <code>nobs</code> is less than
* <code>nvars + 1</code>
*/
public void newSampleData(double[] data, int nobs, int nvars) {
if (data == null) {
@ -120,7 +120,7 @@ public abstract class AbstractMultipleLinearRegression implements
throw new DimensionMismatchException(data.length, nobs * (nvars + 1));
}
if (nobs <= nvars) {
throw new NumberIsTooSmallException(nobs, nvars, false);
throw new InsufficientDataException(LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, nobs, nvars + 1);
}
double[] y = new double[nobs];
final int cols = noIntercept ? nvars: nvars + 1;