Author: luc
Date: Wed Mar 6 10:29:51 2013
New Revision: 1453271
URL: http://svn.apache.org/r1453271
Log:
Allow covariance to be computed for one-dimensional variables.
JIRA: MATH-939
Modified:
commons/proper/math/trunk/src/changes/changes.xml
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/correlation/Covariance.java
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/correlation/CovarianceTest.java
Modified: commons/proper/math/trunk/src/changes/changes.xml
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/changes/changes.xml?rev=1453271&r1=1453270&r2=1453271&view=diff
==============================================================================
--- commons/proper/math/trunk/src/changes/changes.xml (original)
+++ commons/proper/math/trunk/src/changes/changes.xml Wed Mar 6 10:29:51 2013
@@ -55,6 +55,9 @@ This is a minor release: It combines bug
Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.1[.1] JAR file.
">
+ <action dev="luc" type="fix" issue="MATH-939" due-to="Piotr Wydrych" >
+ Allow covariance to be computed for one-dimensional variables.
+ </action>
<action dev="luc" type="fix" issue="MATH-938" >
Fixed accuracy of 3D Line.revert().
</action>
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/correlation/Covariance.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/correlation/Covariance.java?rev=1453271&r1=1453270&r2=1453271&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/correlation/Covariance.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/correlation/Covariance.java
Wed Mar 6 10:29:51 2013
@@ -17,6 +17,7 @@
package org.apache.commons.math3.stat.correlation;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
+import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.BlockRealMatrix;
@@ -70,16 +71,18 @@ public class Covariance {
* <p>The <code>biasCorrected</code> parameter determines whether or not
* covariance estimates are bias-corrected.</p>
*
- * <p>The input array must be rectangular with at least two columns
+ * <p>The input array must be rectangular with at least one column
* and two rows.</p>
*
* @param data rectangular array with columns representing covariates
* @param biasCorrected true means covariances are bias-corrected
* @throws MathIllegalArgumentException if the input data array is not
- * rectangular with at least two rows and two columns.
+ * rectangular with at least two rows and one column.
+ * @throws NotStrictlyPositiveException if the input data array is not
+ * rectangular with at least one row and one column.
*/
public Covariance(double[][] data, boolean biasCorrected)
- throws MathIllegalArgumentException {
+ throws MathIllegalArgumentException, NotStrictlyPositiveException {
this(new BlockRealMatrix(data), biasCorrected);
}
@@ -87,14 +90,17 @@ public class Covariance {
* Create a Covariance matrix from a rectangular array
* whose columns represent covariates.
*
- * <p>The input array must be rectangular with at least two columns
+ * <p>The input array must be rectangular with at least one column
* and two rows</p>
*
* @param data rectangular array with columns representing covariates
* @throws MathIllegalArgumentException if the input data array is not
- * rectangular with at least two rows and two columns.
+ * rectangular with at least two rows and one column.
+ * @throws NotStrictlyPositiveException if the input data array is not
+ * rectangular with at least one row and one column.
*/
- public Covariance(double[][] data) throws MathIllegalArgumentException {
+ public Covariance(double[][] data)
+ throws MathIllegalArgumentException, NotStrictlyPositiveException {
this(data, true);
}
@@ -105,12 +111,12 @@ public class Covariance {
* <p>The <code>biasCorrected</code> parameter determines whether or not
* covariance estimates are bias-corrected.</p>
*
- * <p>The matrix must have at least two columns and two rows</p>
+ * <p>The matrix must have at least one column and two rows</p>
*
* @param matrix matrix with columns representing covariates
* @param biasCorrected true means covariances are bias-corrected
* @throws MathIllegalArgumentException if the input matrix does not have
- * at least two rows and two columns
+ * at least two rows and one column
*/
public Covariance(RealMatrix matrix, boolean biasCorrected)
throws MathIllegalArgumentException {
@@ -123,11 +129,11 @@ public class Covariance {
* Create a covariance matrix from a matrix whose columns
* represent covariates.
*
- * <p>The matrix must have at least two columns and two rows</p>
+ * <p>The matrix must have at least one column and two rows</p>
*
* @param matrix matrix with columns representing covariates
* @throws MathIllegalArgumentException if the input matrix does not have
- * at least two rows and two columns
+ * at least two rows and one column
*/
public Covariance(RealMatrix matrix) throws MathIllegalArgumentException {
this(matrix, true);
@@ -154,7 +160,7 @@ public class Covariance {
/**
* Compute a covariance matrix from a matrix whose columns represent
* covariates.
- * @param matrix input matrix (must have at least two columns and two rows)
+ * @param matrix input matrix (must have at least one column and two rows)
* @param biasCorrected determines whether or not covariance estimates are
bias-corrected
* @return covariance matrix
* @throws MathIllegalArgumentException if the matrix does not contain
sufficient data
@@ -178,7 +184,7 @@ public class Covariance {
/**
* Create a covariance matrix from a matrix whose columns represent
* covariates. Covariances are computed using the bias-corrected formula.
- * @param matrix input matrix (must have at least two columns and two rows)
+ * @param matrix input matrix (must have at least one column and two rows)
* @return covariance matrix
* @throws MathIllegalArgumentException if matrix does not contain
sufficient data
* @see #Covariance
@@ -191,26 +197,31 @@ public class Covariance {
/**
* Compute a covariance matrix from a rectangular array whose columns
represent
* covariates.
- * @param data input array (must have at least two columns and two rows)
+ * @param data input array (must have at least one column and two rows)
* @param biasCorrected determines whether or not covariance estimates are
bias-corrected
* @return covariance matrix
* @throws MathIllegalArgumentException if the data array does not contain
sufficient
* data
+ * @throws NotStrictlyPositiveException if the input data array is not
+ * rectangular with at least one row and one column.
*/
protected RealMatrix computeCovarianceMatrix(double[][] data, boolean
biasCorrected)
- throws MathIllegalArgumentException {
+ throws MathIllegalArgumentException, NotStrictlyPositiveException {
return computeCovarianceMatrix(new BlockRealMatrix(data),
biasCorrected);
}
/**
* Create a covariance matrix from a rectangular array whose columns
represent
* covariates. Covariances are computed using the bias-corrected formula.
- * @param data input array (must have at least two columns and two rows)
+ * @param data input array (must have at least one column and two rows)
* @return covariance matrix
* @throws MathIllegalArgumentException if the data array does not contain
sufficient data
+ * @throws NotStrictlyPositiveException if the input data array is not
+ * rectangular with at least one row and one column.
* @see #Covariance
*/
- protected RealMatrix computeCovarianceMatrix(double[][] data) throws
MathIllegalArgumentException {
+ protected RealMatrix computeCovarianceMatrix(double[][] data)
+ throws MathIllegalArgumentException, NotStrictlyPositiveException {
return computeCovarianceMatrix(data, true);
}
@@ -268,7 +279,7 @@ public class Covariance {
/**
* Throws MathIllegalArgumentException if the matrix does not have at least
- * two columns and two rows.
+ * one column and two rows.
* @param matrix matrix to check
* @throws MathIllegalArgumentException if the matrix does not contain
sufficient data
* to compute covariance
@@ -276,7 +287,7 @@ public class Covariance {
private void checkSufficientData(final RealMatrix matrix) throws
MathIllegalArgumentException {
int nRows = matrix.getRowDimension();
int nCols = matrix.getColumnDimension();
- if (nRows < 2 || nCols < 2) {
+ if (nRows < 2 || nCols < 1) {
throw new MathIllegalArgumentException(
LocalizedFormats.INSUFFICIENT_ROWS_AND_COLUMNS,
nRows, nCols);
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/correlation/CovarianceTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/correlation/CovarianceTest.java?rev=1453271&r1=1453270&r2=1453271&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/correlation/CovarianceTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/correlation/CovarianceTest.java
Wed Mar 6 10:29:51 2013
@@ -17,6 +17,7 @@
package org.apache.commons.math3.stat.correlation;
import org.apache.commons.math3.TestUtils;
+import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
@@ -161,6 +162,16 @@ public class CovarianceTest {
Assert.assertEquals(0d, new Covariance().covariance(noVariance,
noVariance, true), Double.MIN_VALUE);
}
+ /**
+ * One column
+ */
+ @Test
+ public void testOneColumn() {
+ RealMatrix cov = new Covariance(new double[][] {{1}, {2}},
false).getCovarianceMatrix();
+ Assert.assertEquals(1, cov.getRowDimension());
+ Assert.assertEquals(1, cov.getColumnDimension());
+ Assert.assertEquals(0.25, cov.getEntry(0, 0), 1.0e-15);
+ }
/**
* Insufficient data
@@ -175,11 +186,10 @@ public class CovarianceTest {
} catch (IllegalArgumentException ex) {
// Expected
}
- RealMatrix matrix = new Array2DRowRealMatrix(new double[][] {{0},{1}});
try {
- new Covariance(matrix);
- Assert.fail("Expecting IllegalArgumentException");
- } catch (IllegalArgumentException ex) {
+ new Covariance(new double[][] {{},{}});
+ Assert.fail("Expecting NotStrictlyPositiveException");
+ } catch (NotStrictlyPositiveException ex) {
// Expected
}
}
