Author: gregs
Date: Fri Sep 23 03:36:11 2011
New Revision: 1174509
URL: http://svn.apache.org/viewvc?rev=1174509&view=rev
Log:
JIRA: MATH-607 Adding support for UpdatingMultipleLinearRegression to
SimpleRegression
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/exception/util/LocalizedFormats.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/RegressionResults.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/UpdatingMultipleLinearRegression.java
commons/proper/math/trunk/src/site/xdoc/changes.xml
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/SimpleRegressionTest.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/exception/util/LocalizedFormats.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/exception/util/LocalizedFormats.java?rev=1174509&r1=1174508&r2=1174509&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/exception/util/LocalizedFormats.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/exception/util/LocalizedFormats.java
Fri Sep 23 03:36:11 2011
@@ -42,6 +42,7 @@ public enum LocalizedFormats implements
// CHECKSTYLE: stop JavadocVariable
ARGUMENT_OUTSIDE_DOMAIN("Argument {0} outside domain [{1} ; {2}]"),
+ ARRAY_SIZE_EXCEEDS_MAX_VARIABLES("array size cannot be greater than {0}"),
ARRAY_SIZES_SHOULD_HAVE_DIFFERENCE_1("array sizes should have difference 1
({0} != {1} + 1)"),
ARRAY_SUMS_TO_ZERO("array sums to zero"),
ASSYMETRIC_EIGEN_NOT_SUPPORTED("eigen decomposition of assymetric matrices
not supported yet"),
@@ -135,6 +136,7 @@ public enum LocalizedFormats implements
INVALID_INTERVAL_INITIAL_VALUE_PARAMETERS("invalid interval, initial value
parameters: lower={0}, initial={1}, upper={2}"),
INVALID_ITERATIONS_LIMITS("invalid iteration limits: min={0}, max={1}"),
INVALID_MAX_ITERATIONS("bad value for maximum iterations number: {0}"),
+ NOT_ENOUGH_DATA_REGRESSION("the number of observations is not sufficient
to conduct regression"),
INVALID_REGRESSION_ARRAY("input data array length = {0} does not match the
number of observations = {1} and the number of regressors = {2}"),
INVALID_REGRESSION_OBSERVATION("length of regressor array = {0} does not
match the number of variables = {1} in the model"),
INVALID_ROUNDING_METHOD("invalid rounding method {0}, valid methods: {1}
({2}), {3} ({4}), {5} ({6}), {7} ({8}), {9} ({10}), {11} ({12}), {13} ({14}),
{15} ({16})"),
@@ -239,6 +241,7 @@ public enum LocalizedFormats implements
NO_RESULT_AVAILABLE("no result available"),
NO_SUCH_MATRIX_ENTRY("no entry at indices ({0}, {1}) in a {2}x{3} matrix"),
NULL_NOT_ALLOWED("null is not allowed"), /* keep */
+ ARRAY_ZERO_LENGTH_OR_NULL_NOTALLOWED("A null or zero length array not
allowed"),
COVARIANCE_MATRIX("covariance matrix"), /* keep */
DENOMINATOR("denominator"), /* keep */
DENOMINATOR_FORMAT("denominator format"), /* keep */
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/RegressionResults.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/RegressionResults.java?rev=1174509&r1=1174508&r2=1174509&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/RegressionResults.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/RegressionResults.java
Fri Sep 23 03:36:11 2011
@@ -114,7 +114,7 @@ public class RegressionResults implement
this.globalFitInfo = new double[5];
Arrays.fill(this.globalFitInfo, Double.NaN);
- if (rank > 2) {
+ if (rank > 0) {
this.globalFitInfo[SST_IDX] = containsConstant ?
(sumysq - sumy * sumy / ((double) nobs)) : sumysq;
}
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java?rev=1174509&r1=1174508&r2=1174509&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java
Fri Sep 23 03:36:11 2011
@@ -22,8 +22,11 @@ import org.apache.commons.math.MathExcep
import org.apache.commons.math.exception.OutOfRangeException;
import org.apache.commons.math.distribution.TDistribution;
import org.apache.commons.math.distribution.TDistributionImpl;
+import org.apache.commons.math.exception.MathIllegalArgumentException;
+import org.apache.commons.math.exception.NoDataException;
import org.apache.commons.math.exception.util.LocalizedFormats;
import org.apache.commons.math.util.FastMath;
+import org.apache.commons.math.util.MathUtils;
/**
* Estimates an ordinary least squares regression model
@@ -55,7 +58,7 @@ import org.apache.commons.math.util.Fast
*
* @version $Id$
*/
-public class SimpleRegression implements Serializable {
+public class SimpleRegression implements Serializable,
UpdatingMultipleLinearRegression {
/** Serializable version identifier */
private static final long serialVersionUID = -3004689053607543335L;
@@ -98,7 +101,7 @@ public class SimpleRegression implements
* Secondary constructor which allows the user the ability to
include/exclude const
* @param includeIntercept boolean flag, true includes an intercept
*/
- public SimpleRegression(boolean includeIntercept){
+ public SimpleRegression(boolean includeIntercept) {
super();
hasIntercept = includeIntercept;
}
@@ -116,7 +119,7 @@ public class SimpleRegression implements
* @param x independent variable value
* @param y dependent variable value
*/
- public void addData(final double x, final double y){
+ public void addData(final double x,final double y) {
if (n == 0) {
xbar = x;
ybar = y;
@@ -158,7 +161,7 @@ public class SimpleRegression implements
* @param x independent variable value
* @param y dependent variable value
*/
- public void removeData(double x, double y) {
+ public void removeData(final double x,final double y) {
if (n > 0) {
if (hasIntercept) {
final double fact1 = (double) n - 1.0;
@@ -200,13 +203,69 @@ public class SimpleRegression implements
* data.</p>
*
* @param data array of observations to be added
+ * @throws ModelSpecificationException if the length of {@code data[i]} is
not
+ * greater than or equal to 2
*/
- public void addData(double[][] data) {
+ public void addData(final double[][] data) {
for (int i = 0; i < data.length; i++) {
+ if( data[i].length < 2 ){
+ throw new
ModelSpecificationException(LocalizedFormats.INVALID_REGRESSION_OBSERVATION,
+ data[i].length, 2);
+ }
addData(data[i][0], data[i][1]);
}
+ return;
+ }
+
+ /**
+ * Adds one observation to the regression model.
+ *
+ * @param x the independent variables which form the design matrix
+ * @param y the dependent or response variable
+ * @throws ModelSpecificationException if the length of {@code x} does not
equal
+ * the number of independent variables in the model
+ */
+ public void addObservation(final double[] x,final double y) throws
ModelSpecificationException{
+ if( x == null || x.length == 0 ){
+ throw new
ModelSpecificationException(LocalizedFormats.INVALID_REGRESSION_OBSERVATION,x!=null?x.length:0,
1);
+ }
+ addData( x[0], y );
+ return;
}
+ /**
+ * Adds a series of observations to the regression model. The lengths of
+ * x and y must be the same and x must be rectangular.
+ *
+ * @param x a series of observations on the independent variables
+ * @param y a series of observations on the dependent variable
+ * The length of x and y must be the same
+ * @throws ModelSpecificationException if {@code x} is not rectangular,
does not match
+ * the length of {@code y} or does not contain sufficient data to estimate
the model
+ */
+ public void addObservations(final double[][] x,final double[] y) {
+ if ((x == null) || (y == null) || (x.length != y.length)) {
+ throw new ModelSpecificationException(
+ LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
+ (x == null) ? 0 : x.length,
+ (y == null) ? 0 : y.length);
+ }
+ boolean obsOk=true;
+ for( int i = 0 ; i < x.length; i++){
+ if( x[i] == null || x[i].length == 0 ){
+ obsOk = false;
+ }
+ }
+ if( !obsOk ){
+ throw new ModelSpecificationException(
+ LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
+ 0, 1);
+ }
+ for( int i = 0 ; i < x.length ; i++){
+ addData( x[i][0], y[i] );
+ }
+ return;
+ }
/**
* Removes observations represented by the elements in <code>data</code>.
@@ -265,8 +324,8 @@ public class SimpleRegression implements
* @param x input <code>x</code> value
* @return predicted <code>y</code> value
*/
- public double predict(double x) {
- double b1 = getSlope();
+ public double predict(final double x) {
+ final double b1 = getSlope();
if (hasIntercept) {
return getIntercept(b1) + b1 * x;
}
@@ -298,7 +357,7 @@ public class SimpleRegression implements
*
* @return true if constant exists, false otherwise
*/
- public boolean hasIntercept(){
+ public boolean hasIntercept() {
return hasIntercept;
}
@@ -572,7 +631,7 @@ public class SimpleRegression implements
* @return half-width of 95% confidence interval for the slope estimate
* @throws MathException if the confidence interval can not be computed.
*/
- public double getSlopeConfidenceInterval(double alpha)
+ public double getSlopeConfidenceInterval(final double alpha)
throws MathException {
if (alpha >= 1 || alpha <= 0) {
throw new OutOfRangeException(LocalizedFormats.SIGNIFICANCE_LEVEL,
@@ -620,7 +679,7 @@ public class SimpleRegression implements
* @param slope current slope
* @return the intercept of the regression line
*/
- private double getIntercept(double slope){
+ private double getIntercept(final double slope) {
if( hasIntercept){
return (sumY - slope * sumX) / n;
}
@@ -633,7 +692,134 @@ public class SimpleRegression implements
* @param slope regression slope estimate
* @return sum of squared deviations of predicted y values
*/
- private double getRegressionSumSquares(double slope) {
+ private double getRegressionSumSquares(final double slope) {
return slope * slope * sumXX;
}
+
+ /**
+ * Performs a regression on data present in buffers and outputs a
RegressionResults object
+ * @return RegressionResults acts as a container of regression output
+ * @throws ModelSpecificationException if the model is not correctly
specified
+ */
+ public RegressionResults regress() throws ModelSpecificationException{
+ if( hasIntercept ){
+ if( n < 3 ){
+ throw new NoDataException(
LocalizedFormats.NOT_ENOUGH_DATA_REGRESSION );
+ }
+ if( FastMath.abs( sumXX ) > MathUtils.SAFE_MIN ){
+ final double[] params = new double[]{ getIntercept(), getSlope()
};
+ final double mse = getMeanSquareError();
+ final double _syy = sumYY + sumY * sumY / ((double) n);
+ final double[] vcv = new double[]{
+ mse * (xbar *xbar /sumXX + 1.0 / ((double) 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/((double) n),
Double.NaN };
+ //final double mse = getMeanSquareError();
+ final double[] vcv = new double[]{
+ ybar / ((double) n - 1.0),
+ Double.NaN,
+ Double.NaN };
+ return new RegressionResults(
+ params, new double[][]{vcv}, true, n, 1,
+ sumY, sumYY, getSumSquaredErrors(),true,false);
+ }
+ }else{
+ if( n < 2 ){
+ throw new NoDataException(
LocalizedFormats.NOT_ENOUGH_DATA_REGRESSION );
+ }
+ if( !Double.isNaN(sumXX) ){
+ final double[] vcv = new double[]{ getMeanSquareError() / sumXX };
+ final double[] params = new double[]{ sumXY/sumXX };
+ return new RegressionResults(
+ params, new double[][]{vcv}, true, n, 1,
+ sumY, sumYY, getSumSquaredErrors(),false,false);
+ }else{
+ final double[] vcv = new double[]{Double.NaN };
+ final double[] params = new double[]{ Double.NaN };
+ return new RegressionResults(
+ params, new double[][]{vcv}, true, n, 1,
+ Double.NaN, Double.NaN, Double.NaN,false,false);
+ }
+ }
+ }
+
+ /**
+ * Performs a regression on data present in buffers including only
regressors
+ * indexed in variablesToInclude and outputs a RegressionResults object
+ * @param variablesToInclude an array of indices of regressors to include
+ * @return RegressionResults acts as a container of regression output
+ * @throws ModelSpecificationException if the model is not correctly
specified
+ * @throws MathIllegalArgumentException if the variablesToInclude array is
null or zero length
+ * @throws OutOfRangeException if a requested variable is not present in
model
+ */
+ public RegressionResults regress(int[] variablesToInclude) throws
ModelSpecificationException{
+ if( variablesToInclude == null || variablesToInclude.length == 0){
+ throw new
MathIllegalArgumentException(LocalizedFormats.ARRAY_ZERO_LENGTH_OR_NULL_NOTALLOWED);
+ }
+ if( variablesToInclude.length > 2 || (variablesToInclude.length > 1 &&
!hasIntercept) ){
+ throw new ModelSpecificationException(
+ LocalizedFormats.ARRAY_SIZE_EXCEEDS_MAX_VARIABLES,
+ (variablesToInclude.length > 1 && !hasIntercept) ? 1 : 2);
+ }
+
+ if( hasIntercept ){
+ if( variablesToInclude.length == 2 ){
+ if( variablesToInclude[0] == 1 ){
+ throw new
ModelSpecificationException(LocalizedFormats.NOT_INCREASING_SEQUENCE);
+ }else if( variablesToInclude[0] != 0 ){
+ throw new OutOfRangeException( variablesToInclude[0], 0,1
);
+ }
+ if( variablesToInclude[1] != 1){
+ throw new OutOfRangeException( variablesToInclude[0], 0,1
);
+ }
+ return regress();
+ }else{
+ if( variablesToInclude[0] != 1 && variablesToInclude[0] != 0 ){
+ throw new OutOfRangeException( variablesToInclude[0],0,1
);
+ }
+ final double _mean = sumY * sumY / ((double) n);
+ final double _syy = sumYY + _mean;
+ if( variablesToInclude[0] == 0 ){
+ //just the mean
+ final double[] vcv = new double[]{
sumYY/((double)((n-1)*n)) };
+ final double[] params = new double[]{ ybar };
+ return new RegressionResults(
+ params, new double[][]{vcv}, true, n, 1,
+ sumY, _syy+_mean, sumYY,true,false);
+
+ }else if( variablesToInclude[0] == 1){
+ //final double _syy = sumYY + sumY * sumY / ((double) n);
+ final double _sxx = sumXX + sumX * sumX / ((double) n);
+ final double _sxy = sumXY + sumX * sumY / ((double) n);
+ final double _sse = FastMath.max(0d, _syy - _sxy * _sxy /
_sxx);
+ final double _mse = _sse/((double)(n-1));
+ if( !Double.isNaN(_sxx) ){
+ final double[] vcv = new double[]{ _mse / _sxx };
+ final double[] params = new double[]{ _sxy/_sxx };
+ return new RegressionResults(
+ params, new double[][]{vcv}, true, n, 1,
+ sumY, _syy, _sse,false,false);
+ }else{
+ final double[] vcv = new double[]{Double.NaN };
+ final double[] params = new double[]{ Double.NaN };
+ return new RegressionResults(
+ params, new double[][]{vcv}, true, n, 1,
+ Double.NaN, Double.NaN,
Double.NaN,false,false);
+ }
+ }
+ }
+ }else{
+ if( variablesToInclude[0] != 0 ){
+ throw new OutOfRangeException(variablesToInclude[0],0,0);
+ }
+ return regress();
+ }
+
+ return null;
+ }
}
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/UpdatingMultipleLinearRegression.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/UpdatingMultipleLinearRegression.java?rev=1174509&r1=1174508&r2=1174509&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/UpdatingMultipleLinearRegression.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/UpdatingMultipleLinearRegression.java
Fri Sep 23 03:36:11 2011
@@ -61,7 +61,7 @@ public interface UpdatingMultipleLinearR
* @throws ModelSpecificationException if {@code x} is not rectangular,
does not match
* the length of {@code y} or does not contain sufficient data to estimate
the model
*/
- void addObservations(double[][] x, double[] y);
+ void addObservations(double[][] x, double[] y) throws
ModelSpecificationException;
/**
* Clears internal buffers and resets the regression model. This means all
Modified: commons/proper/math/trunk/src/site/xdoc/changes.xml
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/site/xdoc/changes.xml?rev=1174509&r1=1174508&r2=1174509&view=diff
==============================================================================
--- commons/proper/math/trunk/src/site/xdoc/changes.xml (original)
+++ commons/proper/math/trunk/src/site/xdoc/changes.xml Fri Sep 23 03:36:11 2011
@@ -52,6 +52,9 @@ The <action> type attribute can be add,u
If the output is not quite correct, check for invisible trailing spaces!
-->
<release version="3.0" date="TBD" description="TBD">
+ <action dev="gregs" type="update" issue="MATH-607">
+ SimpleRegression implements UpdatingMultipleLinearRegression interface.
+ </action>
<action dev="gregs" type="update" issue="MATH-675">
Added isMonotone methods in MathUtils. Optimized checkOrder method.
</action>
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/SimpleRegressionTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/SimpleRegressionTest.java?rev=1174509&r1=1174508&r2=1174509&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/SimpleRegressionTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/SimpleRegressionTest.java
Fri Sep 23 03:36:11 2011
@@ -80,9 +80,9 @@ public final class SimpleRegressionTest
{5, -1 }, {6, 12 }
};
-
+
/*
- * Data from NIST NOINT1
+ * Data from NIST NOINT1
*/
private double[][] noint1 = {
{130.0,60.0},
@@ -95,26 +95,74 @@ public final class SimpleRegressionTest
{137.0,67.0},
{138.0,68.0},
{139.0,69.0},
- {140.0,70.0}
+ {140.0,70.0}
};
-
+
/*
- * Data from NIST NOINT2
- *
+ * Data from NIST NOINT2
+ *
*/
private double[][] noint2 = {
{3.0,4},
{4,5},
{4,6}
};
-
+
+ @Test
+ public void testRegressIfaceMethod(){
+ final SimpleRegression regression = new SimpleRegression(true);
+ final UpdatingMultipleLinearRegression iface = regression;
+ final SimpleRegression regressionNoint = new SimpleRegression( false );
+ final SimpleRegression regressionIntOnly= new SimpleRegression( false
);
+ for (int i = 0; i < data.length; i++) {
+ iface.addObservation( new double[]{data[i][1]}, data[i][0]);
+ regressionNoint.addData(data[i][1], data[i][0]);
+ regressionIntOnly.addData(1.0, data[i][0]);
+ }
+
+ //should not be null
+ final RegressionResults fullReg = iface.regress( );
+ Assert.assertTrue(fullReg != null);
+ Assert.assertEquals("intercept", regression.getIntercept(),
fullReg.getParameterEstimate(0), 1.0e-16);
+ Assert.assertEquals("intercept std
err",regression.getInterceptStdErr(), fullReg.getStdErrorOfEstimate(0),1.0E-16);
+ Assert.assertEquals("slope", regression.getSlope(),
fullReg.getParameterEstimate(1), 1.0e-16);
+ Assert.assertEquals("slope std err",regression.getSlopeStdErr(),
fullReg.getStdErrorOfEstimate(1),1.0E-16);
+ Assert.assertEquals("number of observations",regression.getN(),
fullReg.getN());
+ Assert.assertEquals("r-square",regression.getRSquare(),
fullReg.getRSquared(), 1.0E-16);
+ Assert.assertEquals("SSR", regression.getRegressionSumSquares(),
fullReg.getRegressionSumSquares() ,1.0E-16);
+ Assert.assertEquals("MSE", regression.getMeanSquareError(),
fullReg.getMeanSquareError() ,1.0E-16);
+ Assert.assertEquals("SSE", regression.getSumSquaredErrors(),
fullReg.getErrorSumSquares() ,1.0E-16);
+
+
+ final RegressionResults noInt = iface.regress( new int[]{1} );
+ Assert.assertTrue(noInt != null);
+ Assert.assertEquals("slope", regressionNoint.getSlope(),
noInt.getParameterEstimate(0), 1.0e-12);
+ Assert.assertEquals("slope std err",regressionNoint.getSlopeStdErr(),
noInt.getStdErrorOfEstimate(0),1.0E-16);
+ Assert.assertEquals("number of observations",regressionNoint.getN(),
noInt.getN());
+ Assert.assertEquals("r-square",regressionNoint.getRSquare(),
noInt.getRSquared(), 1.0E-16);
+ Assert.assertEquals("SSR", regressionNoint.getRegressionSumSquares(),
noInt.getRegressionSumSquares() ,1.0E-8);
+ Assert.assertEquals("MSE", regressionNoint.getMeanSquareError(),
noInt.getMeanSquareError() ,1.0E-16);
+ Assert.assertEquals("SSE", regressionNoint.getSumSquaredErrors(),
noInt.getErrorSumSquares() ,1.0E-16);
+
+ final RegressionResults onlyInt = iface.regress( new int[]{0} );
+ Assert.assertTrue( onlyInt != null );
+ Assert.assertEquals("slope", regressionIntOnly.getSlope(),
onlyInt.getParameterEstimate(0), 1.0e-12);
+ Assert.assertEquals("slope std
err",regressionIntOnly.getSlopeStdErr(),
onlyInt.getStdErrorOfEstimate(0),1.0E-12);
+ Assert.assertEquals("number of observations",regressionIntOnly.getN(),
onlyInt.getN());
+ Assert.assertEquals("r-square",regressionIntOnly.getRSquare(),
onlyInt.getRSquared(), 1.0E-14);
+ Assert.assertEquals("SSE", regressionIntOnly.getSumSquaredErrors(),
onlyInt.getErrorSumSquares() ,1.0E-8);
+ Assert.assertEquals("SSR",
regressionIntOnly.getRegressionSumSquares(), onlyInt.getRegressionSumSquares()
,1.0E-8);
+ Assert.assertEquals("MSE", regressionIntOnly.getMeanSquareError(),
onlyInt.getMeanSquareError() ,1.0E-8);
+
+ }
+
@Test
public void testNoInterceot_noint2(){
SimpleRegression regression = new SimpleRegression(false);
regression.addData(noint2[0][1], noint2[0][0]);
regression.addData(noint2[1][1], noint2[1][0]);
regression.addData(noint2[2][1], noint2[2][0]);
- Assert.assertEquals("slope", 0.727272727272727,
+ Assert.assertEquals("slope", 0.727272727272727,
regression.getSlope(), 10E-12);
Assert.assertEquals("slope std err", 0.420827318078432E-01,
regression.getSlopeStdErr(),10E-12);
@@ -128,8 +176,8 @@ public final class SimpleRegressionTest
Assert.assertEquals("SSE", 0.272727272727273,
regression.getSumSquaredErrors(),10E-9);
}
-
- @Test
+
+ @Test
public void testNoIntercept_noint1(){
SimpleRegression regression = new SimpleRegression(false);
for (int i = 0; i < noint1.length; i++) {
@@ -147,9 +195,9 @@ public final class SimpleRegressionTest
regression.getMeanSquareError(), 10E-10);
Assert.assertEquals("SSE", 127.272727272727,
regression.getSumSquaredErrors(),10E-9);
-
- }
-
+
+ }
+
@Test
public void testNorris() {
SimpleRegression regression = new SimpleRegression();
