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Remove trailing whitespace

This commit is contained in:
aherbert 2022-10-13 11:38:37 +01:00
parent a8d392580d
commit 5a8234a2fe
44 changed files with 383 additions and 383 deletions
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2
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/analysis/interpolation/LoessInterpolatorTest.java
View File

@ -322,7 +322,7 @@ public class LoessInterpolatorTest {
Assert.assertEquals(yref[i], res[i], delta); Assert.assertEquals(yref[i], res[i], delta);
} }
} }
private void generateSineData(double[] xval, double[] yval, double xnoise, double ynoise) { private void generateSineData(double[] xval, double[] yval, double xnoise, double ynoise) {
double dx = 2 * JdkMath.PI / xval.length; double dx = 2 * JdkMath.PI / xval.length;
double x = 0; double x = 0;

2
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/analysis/solvers/MullerSolverTest.java
View File

@ -109,7 +109,7 @@ public final class MullerSolverTest {
UnivariateFunction f = new Expm1(); UnivariateFunction f = new Expm1();
UnivariateSolver solver = new MullerSolver(); UnivariateSolver solver = new MullerSolver();
double min; double min;
double max; double max;
double expected; double expected;
double result; double result;
double tolerance; double tolerance;

2
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/optim/SimplePointCheckerTest.java
View File

@ -23,7 +23,7 @@ import org.junit.Assert;
public class SimplePointCheckerTest { public class SimplePointCheckerTest {
@Test(expected=NotStrictlyPositiveException.class) @Test(expected=NotStrictlyPositiveException.class)
public void testIterationCheckPrecondition() { public void testIterationCheckPrecondition() {
new SimplePointChecker<PointValuePair>(1e-1, 1e-2, 0); new SimplePointChecker<>(1e-1, 1e-2, 0);
} }
@Test @Test

4
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/MultivariateFunctionPenaltyAdapterTest.java
View File

@ -83,7 +83,7 @@ public class MultivariateFunctionPenaltyAdapterTest {
biQuadratic.getUpper(), biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 }); 1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-11, 1.0e-20)); SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<>(1.0e-11, 1.0e-20));
final PointValuePair optimum final PointValuePair optimum
= optimizer.optimize(new MaxEval(600), = optimizer.optimize(new MaxEval(600),
@ -133,7 +133,7 @@ public class MultivariateFunctionPenaltyAdapterTest {
biQuadratic.getUpper(), biQuadratic.getUpper(),
1000.0, new double[] { 100.0, 100.0 }); 1000.0, new double[] { 100.0, 100.0 });
SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<>(1.0e-10, 1.0e-20)); SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-10, 1.0e-20));
final PointValuePair optimum final PointValuePair optimum
= optimizer.optimize(new MaxEval(400), = optimizer.optimize(new MaxEval(400),

6
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/FrequencyTest.java
View File

@ -86,7 +86,7 @@ public final class FrequencyTest {
Assert.assertEquals("-2 cum pct", 0, fInteger.getCumPct(-2), TOLERANCE); Assert.assertEquals("-2 cum pct", 0, fInteger.getCumPct(-2), TOLERANCE);
Assert.assertEquals("10 cum pct", 1, fInteger.getCumPct(10), TOLERANCE); Assert.assertEquals("10 cum pct", 1, fInteger.getCumPct(10), TOLERANCE);
fString = new Frequency<String>(String.CASE_INSENSITIVE_ORDER); fString = new Frequency<>(String.CASE_INSENSITIVE_ORDER);
fString.addValue("one"); fString.addValue("one");
fString.addValue("One"); fString.addValue("One");
fString.addValue("oNe"); fString.addValue("oNe");
@ -260,10 +260,10 @@ public final class FrequencyTest {
Assert.assertEquals(1, f.getCount(ONE_LONG)); Assert.assertEquals(1, f.getCount(ONE_LONG));
Assert.assertEquals(0, f.getCount(TWO_LONG)); Assert.assertEquals(0, f.getCount(TWO_LONG));
Frequency<Long> g = new Frequency<>(); Frequency<Long> g = new Frequency<Long>();
g.addValue(TWO_LONG); g.addValue(TWO_LONG);
Frequency<Long> h = new Frequency<>(); Frequency<Long> h = new Frequency<Long>();
h.addValue(THREE_LONG); h.addValue(THREE_LONG);
List<Frequency<Long>> coll = new ArrayList<>(); List<Frequency<Long>> coll = new ArrayList<>();

2
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/descriptive/rank/PSquarePercentileTest.java
View File

@ -387,7 +387,7 @@ public class PSquarePercentileTest extends
@Test(expected = MathIllegalArgumentException.class) @Test(expected = MathIllegalArgumentException.class)
public void testInitial() { public void testInitial() {
PSquarePercentile.newMarkers(new ArrayList<Double>(), 0.5); PSquarePercentile.newMarkers(new ArrayList<>(), 0.5);
Assert.fail(); Assert.fail();
} }

2
commons-math-neuralnet/src/main/java/org/apache/commons/math4/neuralnet/Network.java
View File

@ -183,7 +183,7 @@ public class Network
} }
neuronMap.put(id, new Neuron(id, features.clone())); neuronMap.put(id, new Neuron(id, features.clone()));
linkMap.put(id, new HashSet<Long>()); linkMap.put(id, new HashSet<>());
if (id > nextId.get()) { if (id > nextId.get()) {
nextId.set(id); nextId.set(id);

92
src/changes/changes.xml
View File

@ -36,7 +36,7 @@ The <action> type attribute can be add,update,fix,remove.
--> -->
<!-- NOTE: <!-- NOTE:
The description attribute entries below are specially formatted The description attribute entries below are specially formatted
so as to improve the layout of the generated text release notes. so as to improve the layout of the generated text release notes.
The parsing process removes all line feeds, replacing them with a single space. The parsing process removes all line feeds, replacing them with a single space.
@ -477,7 +477,7 @@ Caveat:
<action dev="tn" type="add" issue="MATH-1039" due-to="Aleksei Dievskii"> <action dev="tn" type="add" issue="MATH-1039" due-to="Aleksei Dievskii">
Added logDensity(double) to RealDistribution and logProbability(int) Added logDensity(double) to RealDistribution and logProbability(int)
to IntegerDistribution interface. The implementations have already been to IntegerDistribution interface. The implementations have already been
updated in release 3.3. updated in release 3.3.
</action> </action>
<action dev="tn" type="update" issue="MATH-1155"> <action dev="tn" type="update" issue="MATH-1155">
WELL type pseudo-random number generators have been refactored: WELL type pseudo-random number generators have been refactored:
@ -577,7 +577,7 @@ Users are encouraged to upgrade to this version as this release not
Fixed potential race condition in PolynomialUtils#buildPolynomial in Fixed potential race condition in PolynomialUtils#buildPolynomial in
case polynomials are generated from multiple threads. Furthermore, the case polynomials are generated from multiple threads. Furthermore, the
synchronization is now performed on the coefficient list instead of the class. synchronization is now performed on the coefficient list instead of the class.
</action> </action>
<action dev="psteitz" type="update" issue="MATH-1246"> <action dev="psteitz" type="update" issue="MATH-1246">
Added bootstrap method to KolmogorovSmirnov test. Added bootstrap method to KolmogorovSmirnov test.
</action> </action>
@ -599,7 +599,7 @@ Users are encouraged to upgrade to this version as this release not
</action> </action>
<action dev="tn" type="fix" issue="MATH-1283" due-to="Jean Noel Delavalade"> <action dev="tn" type="fix" issue="MATH-1283" due-to="Jean Noel Delavalade">
Fixed "Gamma#gamma(double)" for negative values smaller than -20. Fixed "Gamma#gamma(double)" for negative values smaller than -20.
</action> </action>
<action dev="tn" type="fix" issue="MATH-1237" due-to="Ken Williams"> <action dev="tn" type="fix" issue="MATH-1237" due-to="Ken Williams">
Fixed javadoc of methods {floorDiv,floorMod} in class "FastMath". Fixed javadoc of methods {floorDiv,floorMod} in class "FastMath".
</action> </action>
@ -697,7 +697,7 @@ Users are encouraged to upgrade to this version as this release not
</action> </action>
<action dev="psteitz" type="fix" issue="MATH-1245"> <action dev="psteitz" type="fix" issue="MATH-1245">
Fixed error in computing discrete distribution of D statistics for small-sample Fixed error in computing discrete distribution of D statistics for small-sample
2-sample Kolmogorov-Smirnov tests. Error was causing incorrect p-values returned 2-sample Kolmogorov-Smirnov tests. Error was causing incorrect p-values returned
by exactP and monteCarloP methods (used by default for small, mid-size samples). by exactP and monteCarloP methods (used by default for small, mid-size samples).
</action> </action>
<action dev="erans" type="update" issue="MATH-1243"> <action dev="erans" type="update" issue="MATH-1243">
@ -719,16 +719,16 @@ Users are encouraged to upgrade to this version as this release not
<action dev="tn" type="fix" issue="MATH-1242" due-to="Otmar Ertl"> <action dev="tn" type="fix" issue="MATH-1242" due-to="Otmar Ertl">
Improved performance to calculate the two-sample Kolmogorov-Smirnov test Improved performance to calculate the two-sample Kolmogorov-Smirnov test
via monte carlo simulation ("KolmogorovSmirnovTets#monteCarloP(...)"). via monte carlo simulation ("KolmogorovSmirnovTets#monteCarloP(...)").
</action> </action>
<action dev="tn" type="fix" issue="MATH-1241" due-to="Aleksei Dievskii"> <action dev="tn" type="fix" issue="MATH-1241" due-to="Aleksei Dievskii">
A "StackOverflowException" was thrown when passing Double.NaN or infinity A "StackOverflowException" was thrown when passing Double.NaN or infinity
to "Gamma#digamma(double)" or "Gamma#trigamma(double)". Now the input value to "Gamma#digamma(double)" or "Gamma#trigamma(double)". Now the input value
is propagated to the output if it is not a real number. is propagated to the output if it is not a real number.
</action> </action>
<action dev="tn" type="fix" issue="MATH-1232" due-to="Otmar Ertl"> <action dev="tn" type="fix" issue="MATH-1232" due-to="Otmar Ertl">
Improved performance of calculating the two-sample Kolmogorov-Smirnov Improved performance of calculating the two-sample Kolmogorov-Smirnov
test statistic. test statistic.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1232"> <action dev="luc" type="fix" issue="MATH-1232">
Fixed error message for unknown parameter name in ODE. Fixed error message for unknown parameter name in ODE.
</action> </action>
@ -738,7 +738,7 @@ Users are encouraged to upgrade to this version as this release not
<action dev="tn" type="fix"> <action dev="tn" type="fix">
Fix potential branching errors in "FastMath#pow(double, double)" when Fix potential branching errors in "FastMath#pow(double, double)" when
passing special values, i.e. infinity, due to erroneous JIT optimization. passing special values, i.e. infinity, due to erroneous JIT optimization.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1118" > <action dev="luc" type="fix" issue="MATH-1118" >
Fixed equals/hashcode contract failure for Dfp. Fixed equals/hashcode contract failure for Dfp.
</action> </action>
@ -751,27 +751,27 @@ Users are encouraged to upgrade to this version as this release not
<action dev="tn" type="fix" issue="MATH-1197"> <action dev="tn" type="fix" issue="MATH-1197">
Computation of 2-sample Kolmogorov-Smirnov statistic in case of ties Computation of 2-sample Kolmogorov-Smirnov statistic in case of ties
was not correct. was not correct.
</action> </action>
</release> </release>
<release version="3.5" date="2015-04-17" description=" <release version="3.5" date="2015-04-17" description="
This is a minor release: It combines bug fixes and new features. This is a minor release: It combines bug fixes and new features.
Changes to existing features were made in a backwards-compatible Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.4.1 JAR file. way such as to allow drop-in replacement of the v3.4.1 JAR file.
Most notable among the new features are: Most notable among the new features are:
getQuadraticMean method added to DescriptiveStatistics SummaryStatistics getQuadraticMean method added to DescriptiveStatistics SummaryStatistics
which calculates the root mean square, and a way to build polyhedrons sets which calculates the root mean square, and a way to build polyhedrons sets
from list of vertices and the facets they belong too. There have been from list of vertices and the facets they belong too. There have been
numerous bug fixes. See below for a full list. numerous bug fixes. See below for a full list.
The minimum version of the Java platform required to compile and use The minimum version of the Java platform required to compile and use
Apache Commons Math is Java 5. Apache Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0). methods that will be deleted from the next major release (4.0).
Caveat: Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha 1. The implementation of the BOBYQA optimization algorithm is in alpha
state (cf. MATH-621): Many code paths are untested, and we are looking state (cf. MATH-621): Many code paths are untested, and we are looking
@ -783,26 +783,26 @@ Users are encouraged to upgrade to this version as this release not
<action dev="luc" type="fix" issue="MATH-1195"> <action dev="luc" type="fix" issue="MATH-1195">
Moved FastMathTestPerformance out of the main test tree, as is is Moved FastMathTestPerformance out of the main test tree, as is is
a benchmark rather than a test. a benchmark rather than a test.
</action> </action>
<action dev="luc" type="add"> <action dev="luc" type="add">
Added a way to build polyhedrons sets from a list of vertices and Added a way to build polyhedrons sets from a list of vertices and
facets specified using vertices indices. facets specified using vertices indices.
</action> </action>
<action dev="psteitz" type="update" issue="MATH-1213"> <action dev="psteitz" type="update" issue="MATH-1213">
Added Laguerre complex solve methods taking maxEval parameters. Added Laguerre complex solve methods taking maxEval parameters.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1191"> <action dev="luc" type="fix" issue="MATH-1191">
Fixed ignored method parameters in QRDecomposition protected methods. Fixed ignored method parameters in QRDecomposition protected methods.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1211" due-to="Mike Zimmerman"> <action dev="luc" type="fix" issue="MATH-1211" due-to="Mike Zimmerman">
Fixed wrong selection of line/polyhedron intersection point. Fixed wrong selection of line/polyhedron intersection point.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1162"> <action dev="luc" type="fix" issue="MATH-1162">
Improved fix for corner cases in BSP-tree merging, when cut sub-hyperplanes vanish. Improved fix for corner cases in BSP-tree merging, when cut sub-hyperplanes vanish.
</action> </action>
<action dev="tn" type="fix" issue="MATH-1209" due-to="Jonathan Ogilvie"> <action dev="tn" type="fix" issue="MATH-1209" due-to="Jonathan Ogilvie">
Fixed link to algorithm description in "PoissonDistribution#sample()". Fixed link to algorithm description in "PoissonDistribution#sample()".
</action> </action>
<action dev="psteitz" type="fix" issue="MATH-1208"> <action dev="psteitz" type="fix" issue="MATH-1208">
EmpiricalDistribution cumulativeProbability can return NaN when evaluated within a constant bin. EmpiricalDistribution cumulativeProbability can return NaN when evaluated within a constant bin.
</action> </action>
@ -835,7 +835,7 @@ This is a maintenance release: It fixes issue MATH-1188.
This is a minor release: It combines bug fixes and new features. This is a minor release: It combines bug fixes and new features.
Changes to existing features were made in a backwards-compatible Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.3 JAR file. way such as to allow drop-in replacement of the v3.3 JAR file.
Most notable among the new features are: Most notable among the new features are:
new distributions (Gumbel, Laplace, Logistic, Nakagami), and new distributions (Gumbel, Laplace, Logistic, Nakagami), and
improvements on percentiles algorithms (better handling for NaNs improvements on percentiles algorithms (better handling for NaNs
@ -844,14 +844,14 @@ Most notable among the new features are:
implementations added. There have been numerous bug fixes and implementations added. There have been numerous bug fixes and
several improvements on performances or robustness. See below several improvements on performances or robustness. See below
for a full list. for a full list.
The minimum version of the Java platform required to compile and use The minimum version of the Java platform required to compile and use
Apache Commons Math is Java 5. Apache Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0). methods that will be deleted from the next major release (4.0).
Caveat: Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha 1. The implementation of the BOBYQA optimization algorithm is in alpha
state (cf. MATH-621): Many code paths are untested, and we are looking state (cf. MATH-621): Many code paths are untested, and we are looking
@ -870,7 +870,7 @@ Users are encouraged to upgrade to this version as this release not
</action> </action>
<action dev="psteitz" type="fix" issue="MATH-1181"> <action dev="psteitz" type="fix" issue="MATH-1181">
Fixed integer overflow in KolmogorovSmirnovTest causing 2-sample test Fixed integer overflow in KolmogorovSmirnovTest causing 2-sample test
to use exact method when the product of the sample sizes exceeds to use exact method when the product of the sample sizes exceeds
Integer.MAX_VALUE, resulting in effectively hung execution. Integer.MAX_VALUE, resulting in effectively hung execution.
</action> </action>
<action dev="erans" type="add" issue="MATH-1180"> <action dev="erans" type="add" issue="MATH-1180">
@ -896,14 +896,14 @@ Users are encouraged to upgrade to this version as this release not
</action> </action>
<action dev="luc" type="update" > <action dev="luc" type="update" >
Spurious vertices in the middle of otherwise straight edges are now Spurious vertices in the middle of otherwise straight edges are now
filtered out when rebuilding polygons boundaries from BSP trees. filtered out when rebuilding polygons boundaries from BSP trees.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1174" > <action dev="luc" type="fix" issue="MATH-1174" >
Fixed a problem with too thin polygons considered to have infinite size. Fixed a problem with too thin polygons considered to have infinite size.
</action> </action>
<action dev="luc" type="add" > <action dev="luc" type="add" >
Boundary attributes in regions now provides the BSP tree nodes that Boundary attributes in regions now provides the BSP tree nodes that
were used to split the sub-hyperplane forming the boundary part of the facet. were used to split the sub-hyperplane forming the boundary part of the facet.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1162" > <action dev="luc" type="fix" issue="MATH-1162" >
Fixed a problem with vanishing cut sub-hyperplanes during BSP tree merging. Fixed a problem with vanishing cut sub-hyperplanes during BSP tree merging.
@ -999,7 +999,7 @@ Users are encouraged to upgrade to this version as this release not
Added lazy evaluation to "LeastSquaresFactory" (in "o.a.c.m.fitting.leastsquares") Added lazy evaluation to "LeastSquaresFactory" (in "o.a.c.m.fitting.leastsquares")
to avoid evaluating the model when the optimization algorithm does not actually to avoid evaluating the model when the optimization algorithm does not actually
require it. require it.
</action> </action>
<action dev="luc" type="fix" issue="MATH-1127"> <action dev="luc" type="fix" issue="MATH-1127">
Fixed overflow in Precision.equals with ulps (both double and float versions). Fixed overflow in Precision.equals with ulps (both double and float versions).
</action> </action>
@ -1019,20 +1019,20 @@ Users are encouraged to upgrade to this version as this release not
This is a minor release: It combines bug fixes and new features. This is a minor release: It combines bug fixes and new features.
Changes to existing features were made in a backwards-compatible Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.x JAR file. way such as to allow drop-in replacement of the v3.x JAR file.
Most notable among the new features are: Most notable among the new features are:
Framework for creating artificial neural nets, self organizing feature maps, Framework for creating artificial neural nets, self organizing feature maps,
computational geometry algorithms (convex hull, enclosing ball), performance computational geometry algorithms (convex hull, enclosing ball), performance
improvements of the linear simplex solver, refactoring of curve fitters, improvements of the linear simplex solver, refactoring of curve fitters,
low-discrepancy random generators (sobol, halton), least-squares fitting. low-discrepancy random generators (sobol, halton), least-squares fitting.
The minimum version of the Java platform required to compile and use The minimum version of the Java platform required to compile and use
Commons Math is Java 5. Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0). methods that will be deleted from the next major release (4.0).
Caveat: Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha 1. The implementation of the BOBYQA optimization algorithm is in alpha
state (cf. MATH-621): Many code paths are untested, and we are looking state (cf. MATH-621): Many code paths are untested, and we are looking
@ -1043,7 +1043,7 @@ Users are encouraged to upgrade to this version as this release not
3. A few methods/constructors in the package o.a.c.m.geometry.partitioning 3. A few methods/constructors in the package o.a.c.m.geometry.partitioning
have changed their signature in a non backwards-compatible way. The respective have changed their signature in a non backwards-compatible way. The respective
classes are intended to be package private only and are not supposed to be classes are intended to be package private only and are not supposed to be
used for other purposes. used for other purposes.
"> ">
<action dev="tn" type="add" issue="MATH-1110" due-to="Edward Segall"> <action dev="tn" type="add" issue="MATH-1110" due-to="Edward Segall">
Added new constructor to "OLSMultipleLinearRegression" to be able Added new constructor to "OLSMultipleLinearRegression" to be able
@ -1397,7 +1397,7 @@ Users are encouraged to upgrade to this version as this release not
"o.a.c.m.analysis.integration.gauss"). "o.a.c.m.analysis.integration.gauss").
</action> </action>
<action dev="erans" type="update" issue="MATH-995"> <action dev="erans" type="update" issue="MATH-995">
Documented limitation of "IterativeLegendreGaussIntegrator" (added Documented limitation of "IterativeLegendreGaussIntegrator" (added
warning about potential wrong usage). warning about potential wrong usage).
</action> </action>
<action dev="erans" type="fix" issue="MATH-993"> <action dev="erans" type="fix" issue="MATH-993">
@ -1448,7 +1448,7 @@ Users are encouraged to upgrade to this version as this release not
This is a minor release: It combines bug fixes and new features. This is a minor release: It combines bug fixes and new features.
Changes to existing features were made in a backwards-compatible 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. way such as to allow drop-in replacement of the v3.1[.1] JAR file.
Most notable among the new features are: Framework for automatic Most notable among the new features are: Framework for automatic
differentiation, Lévy distribution, prime numbers, enumerated differentiation, Lévy distribution, prime numbers, enumerated
distributions, real field allowing to use several algorithms distributions, real field allowing to use several algorithms
@ -1456,14 +1456,14 @@ Most notable among the new features are: Framework for automatic
classes (high accuracy or automatic differentiation), spherical classes (high accuracy or automatic differentiation), spherical
coordinates with gradients and Hessians, reorganized clustering coordinates with gradients and Hessians, reorganized clustering
package with different distance implementations. package with different distance implementations.
The minimum version of the Java platform required to compile and use The minimum version of the Java platform required to compile and use
Commons Math is Java 5. Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0). methods that will be deleted from the next major release (4.0).
Caveat: Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha 1. The implementation of the BOBYQA optimization algorithm is in alpha
state (cf. MATH-621): Many code paths are untested, and we are looking state (cf. MATH-621): Many code paths are untested, and we are looking
@ -1601,17 +1601,17 @@ Users are encouraged to upgrade to this version as this release not
</action> </action>
<action dev="luc" type="add" > <action dev="luc" type="add" >
Added RealFieldElement interface to represent anything that is Added RealFieldElement interface to represent anything that is
real number like, implemented by both Decimal64, Dfp and DerivativeStructure. real number like, implemented by both Decimal64, Dfp and DerivativeStructure.
</action> </action>
<action dev="luc" type="add" > <action dev="luc" type="add" >
Added partial derivatives computation for 3D vectors and rotations. Added partial derivatives computation for 3D vectors and rotations.
</action> </action>
<action dev="luc" type="fix" issue="MATH-935" > <action dev="luc" type="fix" issue="MATH-935" >
Fixed DerivativeStructure.atan2 for special cases when both arguments are +/-0. Fixed DerivativeStructure.atan2 for special cases when both arguments are +/-0.
</action> </action>
<action dev="luc" type="add" > <action dev="luc" type="add" >
Added accurate linear combination of DerivativeStructure instances, Added accurate linear combination of DerivativeStructure instances,
avoiding cancellation. avoiding cancellation.
</action> </action>
<action dev="erans" type="update" issue="MATH-933"> <action dev="erans" type="update" issue="MATH-933">
Throw "MathUnsupportedOperationException" from optimizers that do Throw "MathUnsupportedOperationException" from optimizers that do
@ -1626,7 +1626,7 @@ Users are encouraged to upgrade to this version as this release not
Greater efficiency in "UnitSphereRandomVectorGenerator". Greater efficiency in "UnitSphereRandomVectorGenerator".
</action> </action>
<action dev="tn" type="fix" issue="MATH-930"> <action dev="tn" type="fix" issue="MATH-930">
Improved class javadoc wrt convergence criteria and added Improved class javadoc wrt convergence criteria and added
additional constructors to override the default epsilon and cut-off additional constructors to override the default epsilon and cut-off
values in class "SimplexSolver". values in class "SimplexSolver".
</action> </action>
@ -1653,7 +1653,7 @@ This is a point release: It fixes a bug that renders version 3.1 unusable
This is a minor release: It combines bug fixes and new features. This is a minor release: It combines bug fixes and new features.
Changes to existing features were made in a backwards-compatible Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.0 JAR file. way such as to allow drop-in replacement of the v3.0 JAR file.
Most notable among the new features are: Framework for automatic Most notable among the new features are: Framework for automatic
differentiation, multivariate mixture model distribution, quaternions, differentiation, multivariate mixture model distribution, quaternions,
Gauss integration framework, Hermite polynomial interpolation, Gauss integration framework, Hermite polynomial interpolation,
@ -1661,13 +1661,13 @@ This is a minor release: It combines bug fixes and new features.
Most notable among the changes are: Greatly improved precision in Most notable among the changes are: Greatly improved precision in
the implementation of the Gamma and Beta special functions, optimizers the implementation of the Gamma and Beta special functions, optimizers
API, deprecation of the sparse vector implementation. API, deprecation of the sparse vector implementation.
The minimal version of the Java platform required to compile and use The minimal version of the Java platform required to compile and use
Commons Math is Java 5. Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0). methods that will be deleted from the next major release (4.0).
Caveat: Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha 1. The implementation of the BOBYQA optimization algorithm is in alpha
state (cf. MATH-621): Many code paths are untested, and we are looking state (cf. MATH-621): Many code paths are untested, and we are looking
@ -2026,7 +2026,7 @@ Caveat:
for volunteers to improve the code readability, robustness and performance for volunteers to improve the code readability, robustness and performance
and to extend the unit tests suite. and to extend the unit tests suite.
2. A few methods in the FastMath class are in fact slower that their 2. A few methods in the FastMath class are in fact slower that their
counterpart in either Math or StrictMath (cf. MATH-740). counterpart in either Math or StrictMath (cf. MATH-740).
"> ">
<action dev="mikl" type="add" issue="MATH-431"> <action dev="mikl" type="add" issue="MATH-431">
Added statistical hypothesis tests "MannWhitneyUTest" and Added statistical hypothesis tests "MannWhitneyUTest" and

2
src/main/resources/checkstyle/checkstyle-legacy.xml
View File

@ -160,7 +160,7 @@
<module name="EmptyStatement" /> <module name="EmptyStatement" />
<module name="EqualsHashCode" /> <module name="EqualsHashCode" />
<!-- Method parameters and local variables should not hide fields, except in constructors and setters --> <!-- Method parameters and local variables should not hide fields, except in constructors and setters -->
<!-- <!--
<module name="HiddenField"> <module name="HiddenField">
<property name="ignoreConstructorParameter" value="true" /> <property name="ignoreConstructorParameter" value="true" />
<property name="ignoreSetter" value="true" /> <property name="ignoreSetter" value="true" />

4
src/site/site.xml
View File

@ -50,9 +50,9 @@
<item name="Javadoc (2.2 release)" <item name="Javadoc (2.2 release)"
href="http://commons.apache.org/math/javadocs/api-2.2/index.html"/> href="http://commons.apache.org/math/javadocs/api-2.2/index.html"/>
<item name="Issue Tracking" href="/issue-tracking.html"/> <item name="Issue Tracking" href="/issue-tracking.html"/>
<item name="Source Repository (current)" <item name="Source Repository (current)"
href="http://gitbox.apache.org/repos/asf/commons-math.git"/> href="http://gitbox.apache.org/repos/asf/commons-math.git"/>
<item name="Wiki" <item name="Wiki"
href="http://wiki.apache.org/commons/Math"/> href="http://wiki.apache.org/commons/Math"/>
<item name="Developers Guide" href="/developers.html"/> <item name="Developers Guide" href="/developers.html"/>
<item name="Proposal" href="/proposal.html"/> <item name="Proposal" href="/proposal.html"/>

32
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@ -15,7 +15,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<document> <document>
<properties> <properties>
<title>Developers Guide</title> <title>Developers Guide</title>
@ -43,7 +43,7 @@
<p> <p>
<ol> <ol>
<li>Start by reviewing the overall objectives stated in the <li>Start by reviewing the overall objectives stated in the
<a href="proposal.html">proposal</a> upon which the project is <a href="proposal.html">proposal</a> upon which the project is
founded. founded.
</li> </li>
<li>Download the Commons Math source code. Follow the instructions <li>Download the Commons Math source code. Follow the instructions
@ -57,8 +57,8 @@
read-write access. read-write access.
</li> </li>
<li>Like most commons components, Commons Math uses Apache Maven as our <li>Like most commons components, Commons Math uses Apache Maven as our
build tool. The sources can also be built using Ant (a working build tool. The sources can also be built using Ant (a working
Ant build.xml is included in the top level project directory). Ant build.xml is included in the top level project directory).
To build Commons Math using Maven, you can follow the instructions for To build Commons Math using Maven, you can follow the instructions for
<a href="http://maven.apache.org/run-maven/index.html">Building a <a href="http://maven.apache.org/run-maven/index.html">Building a
project with Maven</a>. Launch Maven from the top-level directory project with Maven</a>. Launch Maven from the top-level directory
@ -72,7 +72,7 @@
on the <a href="http://wiki.apache.org/commons/MathWishList"> on the <a href="http://wiki.apache.org/commons/MathWishList">
Math Wish List Wiki Page.</a> Math Wish List Wiki Page.</a>
</li> </li>
<li>Be sure to join the commons-dev and commons-user <li>Be sure to join the commons-dev and commons-user
<a href="mail-lists.html"> <a href="mail-lists.html">
email lists</a> and use them appropriately (make sure the string email lists</a> and use them appropriately (make sure the string
"[math]" starts the Subject line of all your postings). "[math]" starts the Subject line of all your postings).
@ -81,15 +81,15 @@
<li> <li>
<a href="https://issues.apache.org/jira/secure/Signup!default.jspa"> <a href="https://issues.apache.org/jira/secure/Signup!default.jspa">
Setup an account on JIRA</a> and use it to submit patches and Setup an account on JIRA</a> and use it to submit patches and
identify bugs. Read the identify bugs. Read the
<a href="https://issues.apache.org/bugwritinghelp.html"> <a href="https://issues.apache.org/bugwritinghelp.html">
directions</a> for submitting bugs and search the database to directions</a> for submitting bugs and search the database to
determine if an issue exists or has already been dealt with. determine if an issue exists or has already been dealt with.
<p> <p>
See the <a href="http://commons.apache.org/math/issue-tracking.html"> See the <a href="http://commons.apache.org/math/issue-tracking.html">
Commons Math Issue Tracking Page</a> for more information on how to Commons Math Issue Tracking Page</a> for more information on how to
search for or submit bugs or enhancement requests. search for or submit bugs or enhancement requests.
</p> </p>
<li> <li>
Generating patches: The requested format for generating patches is Generating patches: The requested format for generating patches is
the Unified Diff format, which can be easily generated using the git the Unified Diff format, which can be easily generated using the git
@ -105,7 +105,7 @@
<p> <p>
Follow the steps below when making suggestions for additions or Follow the steps below when making suggestions for additions or
enhancements to Commons Math. This will make it easier for the community enhancements to Commons Math. This will make it easier for the community
to comment on your ideas and for the committers to keep track of them. to comment on your ideas and for the committers to keep track of them.
Thanks in advance! Thanks in advance!
<ol> <ol>
<li>Start with a post to the commons-dev mailing list, with [math] at <li>Start with a post to the commons-dev mailing list, with [math] at
@ -122,7 +122,7 @@
useful</li> useful</li>
</ul></li> </ul></li>
<li>Assuming a generally favorable response to the idea on commons-dev, <li>Assuming a generally favorable response to the idea on commons-dev,
the next step is to add an entry to the the next step is to add an entry to the
<a href="http://wiki.apache.org/commons/MathWishList">Math Wish <a href="http://wiki.apache.org/commons/MathWishList">Math Wish
List</a> corresponding to the idea. Include a reference to the List</a> corresponding to the idea. Include a reference to the
discussion thread. </li> discussion thread. </li>
@ -139,7 +139,7 @@
ticket if a patch goes more than a few days with no comment or commit. ticket if a patch goes more than a few days with no comment or commit.
</li> </li>
</ol> </ol>
</p> </p>
</subsection> </subsection>
<subsection name='Coding Style'> <subsection name='Coding Style'>
<p> <p>
@ -191,7 +191,7 @@
formulas and escape special characters.</li> formulas and escape special characters.</li>
<li> <li>
Implementations <i>should</i> use standard algorithms and Implementations <i>should</i> use standard algorithms and
references or full descriptions of all algorithms <i>should</i> be references or full descriptions of all algorithms <i>should</i> be
provided.</li> provided.</li>
<li> <li>
Additions and enhancements <i>should</i> include updates to the User Additions and enhancements <i>should</i> include updates to the User
@ -232,11 +232,11 @@
impossible to specify because of numerical properties of the method. impossible to specify because of numerical properties of the method.
If a solver fails to find a root or a quadrature method fails to converge If a solver fails to find a root or a quadrature method fails to converge
for a given set of parameters, <i>unless those parameters violate the for a given set of parameters, <i>unless those parameters violate the
advertised preconditions</i> it is not appropriate to throw advertised preconditions</i> it is not appropriate to throw
MathIllegalArgumentException.</li> MathIllegalArgumentException.</li>
<li> <li>
State information included in exception messages <i>must</i> be available State information included in exception messages <i>must</i> be available
in exception properties - i.e., successful handling or reporting of in exception properties - i.e., successful handling or reporting of
Commons Math exceptions must not require parsing exception messages.</li> Commons Math exceptions must not require parsing exception messages.</li>
</ul> </ul>
</subsection> </subsection>
@ -259,7 +259,7 @@
All new source file submissions <i>must</i> include the Apache Software All new source file submissions <i>must</i> include the Apache Software
License in a comment that begins the file </li> License in a comment that begins the file </li>
<li> <li>
All contributions must comply with the terms of the Apache All contributions must comply with the terms of the Apache
<a href="http://www.apache.org/licenses/cla.pdf">Contributor License <a href="http://www.apache.org/licenses/cla.pdf">Contributor License
Agreement (CLA)</a></li> Agreement (CLA)</a></li>
<li> <li>
@ -271,7 +271,7 @@
<li> <li>
References to source materials covered by restrictive proprietary References to source materials covered by restrictive proprietary
licenses should be avoided. In particular, contributions should not licenses should be avoided. In particular, contributions should not
implement or include references to algorithms in implement or include references to algorithms in
<a href="http://www.nr.com/">Numerical Recipes (NR)</a>. <a href="http://www.nr.com/">Numerical Recipes (NR)</a>.
Any questions about copyright or patent issues should be raised on Any questions about copyright or patent issues should be raised on
the commons-dev mailing list before contributing or committing code. the commons-dev mailing list before contributing or committing code.

4
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<document> <document>
<properties> <properties>
@ -65,7 +65,7 @@
<section name="Download Math"> <section name="Download Math">
<subsection name="Releases"> <subsection name="Releases">
<p> <p>
Download the Download the
<a href="http://commons.apache.org/math/download_math.cgi"> <a href="http://commons.apache.org/math/download_math.cgi">
Latest Release</a> of Commons Math. Latest Release</a> of Commons Math.
</p> </p>

12
src/site/xdoc/mail-lists.xml
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@ -48,7 +48,7 @@ limitations under the License.
<section name="Overview"> <section name="Overview">
<p> <p>
<a href="index.html">Commons Math</a> shares mailing lists with all the other <a href="index.html">Commons Math</a> shares mailing lists with all the other
<a href="http://commons.apache.org/components.html">Commons Components</a>. <a href="http://commons.apache.org/components.html">Commons Components</a>.
To make it easier for people to only read messages related to components they are interested in, To make it easier for people to only read messages related to components they are interested in,
the convention in Commons is to prefix the subject line of messages with the component's name, the convention in Commons is to prefix the subject line of messages with the component's name,
@ -68,8 +68,8 @@ limitations under the License.
</p> </p>
<p> <p>
<strong>Note:</strong> please don't send patches or attachments to any of the mailing lists. <strong>Note:</strong> please don't send patches or attachments to any of the mailing lists.
Patches are best handled via the <a href="issue-tracking.html">Issue Tracking</a> system. Patches are best handled via the <a href="issue-tracking.html">Issue Tracking</a> system.
Otherwise, please upload the file to a public server and include the URL in the mail. Otherwise, please upload the file to a public server and include the URL in the mail.
</p> </p>
</section> </section>
@ -185,10 +185,10 @@ limitations under the License.
General announcements of Apache project releases. General announcements of Apache project releases.
<br /><br /> <br /><br />
</td> </td>
<td><a class="externalLink" href="mailto:announce-subscribe@apache.org">Subscribe</a></td> <td><a class="externalLink" href="mailto:announce-subscribe@apache.org">Subscribe</a></td>
<td><a class="externalLink" href="mailto:announce-unsubscribe@apache.org">Unsubscribe</a></td> <td><a class="externalLink" href="mailto:announce-unsubscribe@apache.org">Unsubscribe</a></td>
<td><i>read only</i></td> <td><i>read only</i></td>
<td><a class="externalLink" href="https://mail-archives.apache.org/mod_mbox/www-announce/">mail-archives.apache.org</a></td> <td><a class="externalLink" href="https://mail-archives.apache.org/mod_mbox/www-announce/">mail-archives.apache.org</a></td>
<td><a class="externalLink" href="http://markmail.org/list/org.apache.announce/">markmail.org</a><br /> <td><a class="externalLink" href="http://markmail.org/list/org.apache.announce/">markmail.org</a><br />
<a class="externalLink" href="http://old.nabble.com/Apache-News-and-Announce-f109.html">old.nabble.com</a><br /> <a class="externalLink" href="http://old.nabble.com/Apache-News-and-Announce-f109.html">old.nabble.com</a><br />
<a class="externalLink" href="http://www.mail-archive.com/announce@apache.org/">www.mail-archive.com</a><br /> <a class="externalLink" href="http://www.mail-archive.com/announce@apache.org/">www.mail-archive.com</a><br />

30
src/site/xdoc/proposal.xml
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<document> <document>
<properties> <properties>
@ -29,40 +29,40 @@
<section name='Proposal for math Package'> <section name='Proposal for math Package'>
<subsection name='(0) Rationale'> <subsection name='(0) Rationale'>
<p>The Java programming language and the math extensions in commons-lang provide implementations <p>The Java programming language and the math extensions in commons-lang provide implementations
for only the most basic mathematical algorithms. Routine development tasks such as computing for only the most basic mathematical algorithms. Routine development tasks such as computing
basic statistics or solving a system of linear equations require components not available in java basic statistics or solving a system of linear equations require components not available in java
or commons-lang.</p> or commons-lang.</p>
<p>Most basic mathematical or statistical algorithms are available in open source implementations, <p>Most basic mathematical or statistical algorithms are available in open source implementations,
but to assemble a simple set of capabilities one has to use multiple libraries, many of which have but to assemble a simple set of capabilities one has to use multiple libraries, many of which have
more restrictive licensing terms than the ASF. In addition, many of the best open source more restrictive licensing terms than the ASF. In addition, many of the best open source
implementations (e.g. the R statistical package) are either not available in Java or require large implementations (e.g. the R statistical package) are either not available in Java or require large
support libraries and/or external dependencies to work.</p> support libraries and/or external dependencies to work.</p>
<p>A commons-math community will provide a productive environment for aggregation, testing and <p>A commons-math community will provide a productive environment for aggregation, testing and
support of efficient Java implementations of commonly used mathematical and statistical algorithms.</p> support of efficient Java implementations of commonly used mathematical and statistical algorithms.</p>
</subsection> </subsection>
<subsection name='(1) Scope of the Package'> <subsection name='(1) Scope of the Package'>
<p>The Math project shall create and maintain a library of lightweight, self-contained mathematics <p>The Math project shall create and maintain a library of lightweight, self-contained mathematics
and statistics components addressing the most common practical problems not immediately available in and statistics components addressing the most common practical problems not immediately available in
the Java programming language or commons-lang. The guiding principles for commons-math will be: the Java programming language or commons-lang. The guiding principles for commons-math will be:
<ol> <ol>
<li>Real-world application use cases determine priority</li> <li>Real-world application use cases determine priority</li>
<li>Emphasis on small, easily integrated components rather than large libraries with complex <li>Emphasis on small, easily integrated components rather than large libraries with complex
dependencies</li> dependencies</li>
<li>All algorithms are fully documented and follow generally accepted best practices</li> <li>All algorithms are fully documented and follow generally accepted best practices</li>
<li>In situations where multiple standard algorithms exist, use the Strategy pattern to support <li>In situations where multiple standard algorithms exist, use the Strategy pattern to support
multiple implementations</li> multiple implementations</li>
<li>Limited dependencies. No external dependencies beyond Commons components and the JDK</li> <li>Limited dependencies. No external dependencies beyond Commons components and the JDK</li>
</ol> </ol>
</p> </p>
</subsection> </subsection>
<subsection name='(1.5) Interaction With Other Packages'> <subsection name='(1.5) Interaction With Other Packages'>
<p><em>math</em> relies only on standard JDK 1.2 (or later) APIs for <p><em>math</em> relies only on standard JDK 1.2 (or later) APIs for
@ -75,7 +75,7 @@ developers of the component.</p>
</subsection> </subsection>
<subsection name='(2) Initial Source of the Package'> <subsection name='(2) Initial Source of the Package'>
<p>The initial codebase will consist of implementations of basic statistical algorithms such <p>The initial codebase will consist of implementations of basic statistical algorithms such
as the following: as the following:
<ul> <ul>
<li>Simple univariate statistics (mean, standard deviation, n, confidence intervals)</li> <li>Simple univariate statistics (mean, standard deviation, n, confidence intervals)</li>

14
src/site/xdoc/userguide/analysis.xml
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="analysis.html"> <document url="analysis.html">
<properties> <properties>
@ -215,7 +215,7 @@
This may again result in roots way off the true value, without This may again result in roots way off the true value, without
indication. There is not much a generic algorithm can do if indication. There is not much a generic algorithm can do if
ill-conditioned problems are met. A way around this is to transform ill-conditioned problems are met. A way around this is to transform
the problem in order to get a better conditioned function. Proper the problem in order to get a better conditioned function. Proper
selection of a root-finding algorithm and its configuration parameters selection of a root-finding algorithm and its configuration parameters
requires knowledge of the analytical properties of the function under requires knowledge of the analytical properties of the function under
analysis and numerical analysis techniques. Users are encouraged analysis and numerical analysis techniques. Users are encouraged
@ -339,7 +339,7 @@ double c = UnivariateSolverUtils.forceSide(100, function,
+0.0001, the value may be decreased. For computing roots +0.0001, the value may be decreased. For computing roots
much larger in absolute value than 100, the default absolute much larger in absolute value than 100, the default absolute
accuracy may never be reached because the given relative accuracy may never be reached because the given relative
accuracy is reached first. accuracy is reached first.
</td> </td>
</tr> </tr>
<tr> <tr>
@ -375,7 +375,7 @@ double c = UnivariateSolverUtils.forceSide(100, function,
<p> <p>
A <a href="../apidocs/org/apache/commons/math4/analysis/interpolation/UnivariateInterpolator.html"> A <a href="../apidocs/org/apache/commons/math4/analysis/interpolation/UnivariateInterpolator.html">
UnivariateInterpolator</a> is used to find a univariate real-valued UnivariateInterpolator</a> is used to find a univariate real-valued
function <code>f</code> which for a given set of ordered pairs function <code>f</code> which for a given set of ordered pairs
(<code>x<sub>i</sub></code>,<code>y<sub>i</sub></code>) yields (<code>x<sub>i</sub></code>,<code>y<sub>i</sub></code>) yields
<code>f(x<sub>i</sub>)=y<sub>i</sub></code> to the best accuracy possible. The result <code>f(x<sub>i</sub>)=y<sub>i</sub></code> to the best accuracy possible. The result
is provided as an object implementing the <a is provided as an object implementing the <a
@ -387,7 +387,7 @@ double c = UnivariateSolverUtils.forceSide(100, function,
interpolation algorithm is more determined by the kind of the interpolated function interpolation algorithm is more determined by the kind of the interpolated function
rather than the set of points to interpolate. rather than the set of points to interpolate.
There aren't currently any accuracy controls either, as interpolation There aren't currently any accuracy controls either, as interpolation
accuracy is in general determined by the algorithm. accuracy is in general determined by the algorithm.
</p> </p>
<p>Typical usage:</p> <p>Typical usage:</p>
<source>double x[] = { 0.0, 1.0, 2.0 }; <source>double x[] = { 0.0, 1.0, 2.0 };
@ -405,7 +405,7 @@ System.out println("f(" + interpolationX + ") = " + interpolatedY);</source>
is continuous, smooth and can be differentiated twice. The second is continuous, smooth and can be differentiated twice. The second
derivative is continuous but not smooth. The x values passed to the derivative is continuous but not smooth. The x values passed to the
interpolator must be ordered in ascending order. It is not valid to interpolator must be ordered in ascending order. It is not valid to
evaluate the function for values outside the range evaluate the function for values outside the range
<code>x<sub>0</sub></code>..<code>x<sub>N</sub></code>. <code>x<sub>0</sub></code>..<code>x<sub>N</sub></code>.
</p> </p>
<p> <p>
@ -525,7 +525,7 @@ System.out.println("interpolation polynomial: " + interpolator.getPolynomials()[
trapezoid method</a></li> trapezoid method</a></li>
<li><a href="../apidocs/org/apache/commons/math4/analysis/integration/LegendreGaussIntegrator.html"> <li><a href="../apidocs/org/apache/commons/math4/analysis/integration/LegendreGaussIntegrator.html">
Legendre-Gauss method</a></li> Legendre-Gauss method</a></li>
</ul> </ul>
</p> </p>
</subsection> </subsection>
<subsection name="4.6 Polynomials" href="polynomials"> <subsection name="4.6 Polynomials" href="polynomials">

2
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="stat.html"> <document url="stat.html">
<properties> <properties>

2
src/site/xdoc/userguide/exceptions.xml
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="exceptions.html"> <document url="exceptions.html">
<properties> <properties>

30
src/site/xdoc/userguide/filter.xml
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@ -7,16 +7,16 @@
The ASF licenses this file to You under the Apache License, Version 2.0 The ASF licenses this file to You under the Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0 http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="filter.html"> <document url="filter.html">
<properties> <properties>
@ -34,11 +34,11 @@
<a href="../apidocs/org/apache/commons/math4/filter/KalmanFilter.html"> <a href="../apidocs/org/apache/commons/math4/filter/KalmanFilter.html">
KalmanFilter</a> provides a discrete-time filter to estimate KalmanFilter</a> provides a discrete-time filter to estimate
a stochastic linear process.</p> a stochastic linear process.</p>
<p>A Kalman filter is initialized with a <a href="../apidocs/org/apache/commons/math4/filter/ProcessModel.html"> <p>A Kalman filter is initialized with a <a href="../apidocs/org/apache/commons/math4/filter/ProcessModel.html">
ProcessModel</a> and a <a href="../apidocs/org/apache/commons/math4/filter/MeasurementModel.html"> ProcessModel</a> and a <a href="../apidocs/org/apache/commons/math4/filter/MeasurementModel.html">
MeasurementModel</a>, which contain the corresponding transformation and noise covariance matrices. MeasurementModel</a>, which contain the corresponding transformation and noise covariance matrices.
The parameter names used in the respective models correspond to the following names commonly used The parameter names used in the respective models correspond to the following names commonly used
in the mathematical literature: in the mathematical literature:
<ul> <ul>
<li>A - state transition matrix</li> <li>A - state transition matrix</li>
@ -52,7 +52,7 @@
<p> <p>
<dl> <dl>
<dt>Initialization</dt> <dt>Initialization</dt>
<dd> The following code will create a Kalman filter using the provided <dd> The following code will create a Kalman filter using the provided
DefaultMeasurementModel and DefaultProcessModel classes. To support dynamically changing DefaultMeasurementModel and DefaultProcessModel classes. To support dynamically changing
process and measurement noises, simply implement your own models. process and measurement noises, simply implement your own models.
<source> <source>
@ -74,7 +74,7 @@ KalmanFilter filter = new KalmanFilter(pm, mm);
</source> </source>
</dd> </dd>
<dt>Iteration</dt> <dt>Iteration</dt>
<dd>The following code illustrates how to perform the predict/correct cycle: <dd>The following code illustrates how to perform the predict/correct cycle:
<source> <source>
for (;;) { for (;;) {
// predict the state estimate one time-step ahead // predict the state estimate one time-step ahead
@ -86,15 +86,15 @@ for (;;) {
// correct the state estimate with the latest measurement // correct the state estimate with the latest measurement
filter.correct(z); filter.correct(z);
double[] stateEstimate = filter.getStateEstimation(); double[] stateEstimate = filter.getStateEstimation();
// do something with it // do something with it
} }
</source> </source>
</dd> </dd>
<dt>Constant Voltage Example</dt> <dt>Constant Voltage Example</dt>
<dd>The following example creates a Kalman filter for a static process: a system with a <dd>The following example creates a Kalman filter for a static process: a system with a
constant voltage as internal state. We observe this process with an artificially constant voltage as internal state. We observe this process with an artificially
imposed measurement noise of 0.1V and assume an internal process noise of 1e-5V. imposed measurement noise of 0.1V and assume an internal process noise of 1e-5V.
<source> <source>
double constantVoltage = 10d; double constantVoltage = 10d;
@ -118,7 +118,7 @@ RealMatrix R = new Array2DRowRealMatrix(new double[] { measurementNoise });
ProcessModel pm = new DefaultProcessModel(A, B, Q, x, P0); ProcessModel pm = new DefaultProcessModel(A, B, Q, x, P0);
MeasurementModel mm = new DefaultMeasurementModel(H, R); MeasurementModel mm = new DefaultMeasurementModel(H, R);
KalmanFilter filter = new KalmanFilter(pm, mm); KalmanFilter filter = new KalmanFilter(pm, mm);
// process and measurement noise vectors // process and measurement noise vectors
RealVector pNoise = new ArrayRealVector(1); RealVector pNoise = new ArrayRealVector(1);
@ -148,7 +148,7 @@ for (int i = 0; i &lt; 60; i++) {
</source> </source>
</dd> </dd>
<dt>Increasing Speed Vehicle Example</dt> <dt>Increasing Speed Vehicle Example</dt>
<dd>The following example creates a Kalman filter for a simple linear process: a <dd>The following example creates a Kalman filter for a simple linear process: a
vehicle driving along a street with a velocity increasing at a constant rate. The process vehicle driving along a street with a velocity increasing at a constant rate. The process
state is modeled as (position, velocity) and we only observe the position. A measurement state is modeled as (position, velocity) and we only observe the position. A measurement
noise of 10m is imposed on the simulated measurement. noise of 10m is imposed on the simulated measurement.
@ -217,9 +217,9 @@ for (int i = 0; i &lt; 60; i++) {
double velocity = filter.getStateEstimation()[1]; double velocity = filter.getStateEstimation()[1];
} }
</source> </source>
</dd> </dd>
</dl> </dl>
</p> </p>
</subsection> </subsection>
</section> </section>
</body> </body>

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@ -7,16 +7,16 @@
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(the "License"); you may not use this file except in compliance with (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0 http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
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@ -7,16 +7,16 @@
The ASF licenses this file to You under the Apache License, Version 2.0 The ASF licenses this file to You under the Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0 http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="genetics.html"> <document url="genetics.html">
<properties> <properties>
@ -26,17 +26,17 @@
<section name="16 Genetic Algorithms"> <section name="16 Genetic Algorithms">
<subsection name="16.1 Overview" href="overview"> <subsection name="16.1 Overview" href="overview">
<p> <p>
The genetics package provides a framework and implementations for The genetics package provides a framework and implementations for
genetic algorithms. genetic algorithms.
</p> </p>
</subsection> </subsection>
<subsection name="16.2 GA Framework"> <subsection name="16.2 GA Framework">
<p> <p>
<a href="../apidocs/org/apache/commons/math4/genetics/GeneticAlgorithm.html"> <a href="../apidocs/org/apache/commons/math4/genetics/GeneticAlgorithm.html">
GeneticAlgorithm</a> provides an execution framework for Genetic Algorithms (GA). GeneticAlgorithm</a> provides an execution framework for Genetic Algorithms (GA).
<a href="../apidocs/org/apache/commons/math4/genetics/Population.html"> <a href="../apidocs/org/apache/commons/math4/genetics/Population.html">
Populations,</a> consisting of <a href="../apidocs/org/apache/commons/math4/genetics/Chromosome.html"> Populations,</a> consisting of <a href="../apidocs/org/apache/commons/math4/genetics/Chromosome.html">
Chromosomes</a> are evolved by the <code>GeneticAlgorithm</code> until a Chromosomes</a> are evolved by the <code>GeneticAlgorithm</code> until a
<a href="../apidocs/org/apache/commons/math4/genetics/StoppingCondition.html"> <a href="../apidocs/org/apache/commons/math4/genetics/StoppingCondition.html">
StoppingCondition</a> is reached. Evolution is determined by <a href="../apidocs/org/apache/commons/math4/genetics/SelectionPolicy.html"> StoppingCondition</a> is reached. Evolution is determined by <a href="../apidocs/org/apache/commons/math4/genetics/SelectionPolicy.html">
SelectionPolicy</a>, <a href="../apidocs/org/apache/commons/math4/genetics/MutationPolicy.html"> SelectionPolicy</a>, <a href="../apidocs/org/apache/commons/math4/genetics/MutationPolicy.html">
@ -62,10 +62,10 @@
<li>Loop until new generation is filled:</li> <li>Loop until new generation is filled:</li>
<ul><li>Apply configured <code>SelectionPolicy</code> to select a pair of parents <ul><li>Apply configured <code>SelectionPolicy</code> to select a pair of parents
from <code>current</code></li> from <code>current</code></li>
<li>With probability = <li>With probability =
<a href="../apidocs/org/apache/commons/math4/genetics/GeneticAlgorithm.html#getCrossoverRate()"> <a href="../apidocs/org/apache/commons/math4/genetics/GeneticAlgorithm.html#getCrossoverRate()">
getCrossoverRate()</a>, apply configured <code>CrossoverPolicy</code> to parents</li> getCrossoverRate()</a>, apply configured <code>CrossoverPolicy</code> to parents</li>
<li>With probability = <li>With probability =
<a href="../apidocs/org/apache/commons/math4/genetics/GeneticAlgorithm.html#getMutationRate()"> <a href="../apidocs/org/apache/commons/math4/genetics/GeneticAlgorithm.html#getMutationRate()">
getMutationRate()</a>, getMutationRate()</a>,
apply configured <code>MutationPolicy</code> to each of the offspring</li> apply configured <code>MutationPolicy</code> to each of the offspring</li>
@ -73,7 +73,7 @@
space permitting</li> space permitting</li>
</ul> </ul>
<li>Return nextGeneration</li> <li>Return nextGeneration</li>
</ol> </ol>
</p> </p>
</subsection> </subsection>
<subsection name="16.3 Implementation"> <subsection name="16.3 Implementation">
@ -88,16 +88,16 @@ GeneticAlgorithm ga = new GeneticAlgorithm(
0.10, 0.10,
new TournamentSelection(TOURNAMENT_ARITY) new TournamentSelection(TOURNAMENT_ARITY)
); );
// initial population // initial population
Population initial = getInitialPopulation(); Population initial = getInitialPopulation();
// stopping condition // stopping condition
StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS); StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
// run the algorithm // run the algorithm
Population finalPopulation = ga.evolve(initial, stopCond); Population finalPopulation = ga.evolve(initial, stopCond);
// best chromosome from the final population // best chromosome from the final population
Chromosome bestFinal = finalPopulation.getFittestChromosome(); Chromosome bestFinal = finalPopulation.getFittestChromosome();
</source> </source>
@ -122,7 +122,7 @@ Chromosome bestFinal = finalPopulation.getFittestChromosome();
<td>Each of the two selected chromosomes is selected based on an n-ary tournament -- this is done by drawing <td>Each of the two selected chromosomes is selected based on an n-ary tournament -- this is done by drawing
n random chromosomes without replacement from the population, and then selecting the fittest chromosome among them.</td></tr> n random chromosomes without replacement from the population, and then selecting the fittest chromosome among them.</td></tr>
</table><br/> </table><br/>
The algorithm starts with an <code>initial</code> population of <code>Chromosomes.</code> and executes until The algorithm starts with an <code>initial</code> population of <code>Chromosomes.</code> and executes until
the specified <a href="../apidocs/org/apache/commons/math4/genetics/StoppingCondition.html">StoppingCondition</a> the specified <a href="../apidocs/org/apache/commons/math4/genetics/StoppingCondition.html">StoppingCondition</a>
is reached. In the example above, a is reached. In the example above, a
<a href="../apidocs/org/apache/commons/math4/genetics/FixedGenerationCount.html">FixedGenerationCount</a> <a href="../apidocs/org/apache/commons/math4/genetics/FixedGenerationCount.html">FixedGenerationCount</a>

2
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="geometry.html"> <document url="geometry.html">

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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="index.html"> <document url="index.html">
<properties> <properties>
@ -58,24 +58,24 @@
<li><a href="linear.html#a3.1_Overview">3.1 Overview</a></li> <li><a href="linear.html#a3.1_Overview">3.1 Overview</a></li>
<li><a href="linear.html#a3.2_Real_matrices">3.2 Real matrices</a></li> <li><a href="linear.html#a3.2_Real_matrices">3.2 Real matrices</a></li>
<li><a href="linear.html#a3.3_Real_vectors">3.3 Real vectors</a></li> <li><a href="linear.html#a3.3_Real_vectors">3.3 Real vectors</a></li>
<li><a href="linear.html#a3.4_Solving_linear_systems">3.4 Solving linear systems</a></li> <li><a href="linear.html#a3.4_Solving_linear_systems">3.4 Solving linear systems</a></li>
<li><a href="linear.html#a3.5_Eigenvalueseigenvectors_and_singular_valuessingular_vectors">3.5 Eigenvalues/eigenvectors and singular values/singular vectors</a></li> <li><a href="linear.html#a3.5_Eigenvalueseigenvectors_and_singular_valuessingular_vectors">3.5 Eigenvalues/eigenvectors and singular values/singular vectors</a></li>
<li><a href="linear.html#a3.6_Non-real_fields_complex_fractions_...">3.6 Non-real fields (complex, fractions ...)</a></li> <li><a href="linear.html#a3.6_Non-real_fields_complex_fractions_...">3.6 Non-real fields (complex, fractions ...)</a></li>
</ul></li> </ul></li>
<li><a href="analysis.html">4. Numerical Analysis</a> <li><a href="analysis.html">4. Numerical Analysis</a>
<ul> <ul>
<li><a href="analysis.html#a4.1_Overview">4.1 Overview</a></li> <li><a href="analysis.html#a4.1_Overview">4.1 Overview</a></li>
<li><a href="analysis.html#a4.2_Error-handling">4.2 Error handling</a></li> <li><a href="analysis.html#a4.2_Error-handling">4.2 Error handling</a></li>
<li><a href="analysis.html#a4.3_Root-finding">4.3 Root-finding</a></li> <li><a href="analysis.html#a4.3_Root-finding">4.3 Root-finding</a></li>
<li><a href="analysis.html#a4.4_Interpolation">4.4 Interpolation</a></li> <li><a href="analysis.html#a4.4_Interpolation">4.4 Interpolation</a></li>
<li><a href="analysis.html#a4.5_Integration">4.5 Integration</a></li> <li><a href="analysis.html#a4.5_Integration">4.5 Integration</a></li>
<li><a href="analysis.html#a4.6_Polynomials">4.6 Polynomials</a></li> <li><a href="analysis.html#a4.6_Polynomials">4.6 Polynomials</a></li>
<li><a href="analysis.html#a4.7_Differentiation">4.7 Differentiation</a></li> <li><a href="analysis.html#a4.7_Differentiation">4.7 Differentiation</a></li>
</ul></li> </ul></li>
<li><a href="special.html">5. Special Functions</a> <li><a href="special.html">5. Special Functions</a>
<ul> <ul>
<li><a href="special.html#a5.1_Overview">5.1 Overview</a></li> <li><a href="special.html#a5.1_Overview">5.1 Overview</a></li>
</ul></li> </ul></li>
<li><a href="utilities.html">6. Utilities</a> <li><a href="utilities.html">6. Utilities</a>
<ul> <ul>
<li><a href="utilities.html#a6.1_Overview">6.1 Overview</a></li> <li><a href="utilities.html#a6.1_Overview">6.1 Overview</a></li>
@ -85,25 +85,25 @@
<li><a href="utilities.html#a6.5_binomial_coefficients_factorials_Stirling_numbers_and_other_common_math_functions">6.5 Binomial coefficients, factorials, Stirling numbers and other common math functions</a></li> <li><a href="utilities.html#a6.5_binomial_coefficients_factorials_Stirling_numbers_and_other_common_math_functions">6.5 Binomial coefficients, factorials, Stirling numbers and other common math functions</a></li>
<li><a href="utilities.html#a6.6_fast_math">6.6 Fast mathematical functions</a></li> <li><a href="utilities.html#a6.6_fast_math">6.6 Fast mathematical functions</a></li>
<li><a href="utilities.html#a6.7_miscellaneous">6.7 Miscellaneous</a></li> <li><a href="utilities.html#a6.7_miscellaneous">6.7 Miscellaneous</a></li>
</ul></li> </ul></li>
<li><a href="complex.html">7. Complex Numbers</a> <li><a href="complex.html">7. Complex Numbers</a>
<ul> <ul>
<li><a href="complex.html#a7.1_Overview">7.1 Overview</a></li> <li><a href="complex.html#a7.1_Overview">7.1 Overview</a></li>
</ul></li> </ul></li>
<li><a href="distribution.html">8. Probability Distributions</a> <li><a href="distribution.html">8. Probability Distributions</a>
<ul> <ul>
<li><a href="distribution.html#a8.1_Overview">8.1 Overview</a></li> <li><a href="distribution.html#a8.1_Overview">8.1 Overview</a></li>
<li><a href="distribution.html#a8.2_Generating_data_like_an_input_file">8.2 Generating data 'like' an input file</a></li> <li><a href="distribution.html#a8.2_Generating_data_like_an_input_file">8.2 Generating data 'like' an input file</a></li>
</ul></li> </ul></li>
<li><a href="fraction.html">9. Fractions</a> <li><a href="fraction.html">9. Fractions</a>
<ul> <ul>
<li><a href="fraction.html#a9.1_Overview">9.1 Overview</a></li> <li><a href="fraction.html#a9.1_Overview">9.1 Overview</a></li>
</ul></li> </ul></li>
<li><a href="transform.html">10. Transforms</a></li> <li><a href="transform.html">10. Transforms</a></li>
<li><a href="geometry.html">11. Geometry</a> <li><a href="geometry.html">11. Geometry</a>
<ul> <ul>
<li><a href="geometry.html#a11.1_Overview">11.1 Overview</a></li> <li><a href="geometry.html#a11.1_Overview">11.1 Overview</a></li>
</ul></li> </ul></li>
<li><a href="optimization.html">12. Optimization</a> <li><a href="optimization.html">12. Optimization</a>
<ul> <ul>
<li><a href="optimization.html#a12.1_Overview">12.1 Overview</a></li> <li><a href="optimization.html#a12.1_Overview">12.1 Overview</a></li>
@ -111,7 +111,7 @@
<li><a href="optimization.html#a12.3_Linear_Programming">12.3 Linear Programming</a></li> <li><a href="optimization.html#a12.3_Linear_Programming">12.3 Linear Programming</a></li>
<li><a href="optimization.html#a12.4_Direct_Methods">12.4 Direct Methods</a></li> <li><a href="optimization.html#a12.4_Direct_Methods">12.4 Direct Methods</a></li>
<li><a href="optimization.html#a12.5_General_Case">12.5 General Case</a></li> <li><a href="optimization.html#a12.5_General_Case">12.5 General Case</a></li>
</ul></li> </ul></li>
<li><a href="fitting.html">13. Curve Fitting</a> <li><a href="fitting.html">13. Curve Fitting</a>
<ul> <ul>
<li><a href="fitting.html#a13.1_Overview">13.1 Overview</a></li> <li><a href="fitting.html#a13.1_Overview">13.1 Overview</a></li>
@ -139,11 +139,11 @@
<li><a href="ode.html#a15.4_Available_Integrators">15.4 Available Integrators</a></li> <li><a href="ode.html#a15.4_Available_Integrators">15.4 Available Integrators</a></li>
<li><a href="ode.html#a15.5_Derivatives">15.5 Derivatives</a></li> <li><a href="ode.html#a15.5_Derivatives">15.5 Derivatives</a></li>
</ul></li> </ul></li>
<li><a href="genetics.html">16. Genetic Algorithms</a> <li><a href="genetics.html">16. Genetic Algorithms</a>
<ul> <ul>
<li><a href="genetics.html#a16.1_Overview">16.1 Overview</a></li> <li><a href="genetics.html#a16.1_Overview">16.1 Overview</a></li>
<li><a href="genetics.html#a16.2_GA_Framework">16.2 GA Framework</a></li> <li><a href="genetics.html#a16.2_GA_Framework">16.2 GA Framework</a></li>
<li><a href="genetics.html#a16.3_Implementation">16.3 Implementation and Examples</a></li> <li><a href="genetics.html#a16.3_Implementation">16.3 Implementation and Examples</a></li>
</ul></li> </ul></li>
<li><a href="filter.html">17. Filters</a> <li><a href="filter.html">17. Filters</a>
<ul> <ul>
@ -157,7 +157,7 @@
<li><a href="ml.html#clustering">18.2 Clustering algorithms and distance measures</a></li> <li><a href="ml.html#clustering">18.2 Clustering algorithms and distance measures</a></li>
<li><a href="ml.html#implementation">18.3 Implementation</a></li> <li><a href="ml.html#implementation">18.3 Implementation</a></li>
</ul> </ul>
</li> </li>
<li><a href="exceptions.html">19. Exceptions</a> <li><a href="exceptions.html">19. Exceptions</a>
<ul> <ul>
<li><a href="exceptions.html#a19.1_Overview">19.1 Overview</a></li> <li><a href="exceptions.html#a19.1_Overview">19.1 Overview</a></li>
@ -168,7 +168,7 @@
</li> </li>
</ul> </ul>
</section> </section>
</body> </body>
</document> </document>

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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="linear.html"> <document url="linear.html">
@ -37,7 +37,7 @@
<subsection name="3.2 Real matrices" href="real_matrices"> <subsection name="3.2 Real matrices" href="real_matrices">
<p> <p>
The <a href="../apidocs/org/apache/commons/math4/linear/RealMatrix.html"> The <a href="../apidocs/org/apache/commons/math4/linear/RealMatrix.html">
RealMatrix</a> interface represents a matrix with real numbers as RealMatrix</a> interface represents a matrix with real numbers as
entries. The following basic matrix operations are supported: entries. The following basic matrix operations are supported:
<ul> <ul>
<li>Matrix addition, subtraction, multiplication</li> <li>Matrix addition, subtraction, multiplication</li>
@ -45,7 +45,7 @@
<li>transpose</li> <li>transpose</li>
<li>Norm and Trace</li> <li>Norm and Trace</li>
<li>Operation on a vector</li> <li>Operation on a vector</li>
</ul> </ul>
</p> </p>
<p> <p>
Example: Example:
@ -85,7 +85,7 @@ RealMatrix pInverse = new LUDecomposition(p).getSolver().getInverse();
<subsection name="3.3 Real vectors" href="real_vectors"> <subsection name="3.3 Real vectors" href="real_vectors">
<p> <p>
The <a href="../apidocs/org/apache/commons/math4/linear/RealVector.html"> The <a href="../apidocs/org/apache/commons/math4/linear/RealVector.html">
RealVector</a> interface represents a vector with real numbers as RealVector</a> interface represents a vector with real numbers as
entries. The following basic matrix operations are supported: entries. The following basic matrix operations are supported:
<ul> <ul>
<li>Vector addition, subtraction</li> <li>Vector addition, subtraction</li>
@ -136,7 +136,7 @@ RealVector constants = new ArrayRealVector(new double[] { 1, -2, 1 }, false);
RealVector solution = solver.solve(constants); RealVector solution = solver.solve(constants);
</source> </source>
The <code>solution</code> vector will contain values for x The <code>solution</code> vector will contain values for x
(<code>solution.getEntry(0)</code>), y (<code>solution.getEntry(1)</code>), (<code>solution.getEntry(0)</code>), y (<code>solution.getEntry(1)</code>),
and z (<code>solution.getEntry(2)</code>) that solve the system. and z (<code>solution.getEntry(2)</code>) that solve the system.
</p> </p>
<p> <p>
@ -170,8 +170,8 @@ RealVector solution = solver.solve(constants);
In this case, the solution will be provided also as an array of double. In this case, the solution will be provided also as an array of double.
</p> </p>
<p> <p>
It is possible to solve multiple systems with the same coefficient matrix It is possible to solve multiple systems with the same coefficient matrix
in one method call. To do this, create a matrix whose column vectors correspond in one method call. To do this, create a matrix whose column vectors correspond
to the constant vectors for the systems to be solved and use <code>solve(RealMatrix),</code> to the constant vectors for the systems to be solved and use <code>solve(RealMatrix),</code>
which returns a matrix with column vectors representing the solutions. which returns a matrix with column vectors representing the solutions.
</p> </p>

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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="ml.html"> <document url="ml.html">
@ -39,10 +39,10 @@
The following algorithms are available: The following algorithms are available:
<ul> <ul>
<li><a href="../apidocs/org/apache/commons/math4/ml/clustering/KMeansPlusPlusClusterer.html">KMeans++</a>: <li><a href="../apidocs/org/apache/commons/math4/ml/clustering/KMeansPlusPlusClusterer.html">KMeans++</a>:
It is based on the well-known kMeans algorithm, but uses a different method for It is based on the well-known kMeans algorithm, but uses a different method for
choosing the initial values (or "seeds") and thus avoids cases where KMeans sometimes choosing the initial values (or "seeds") and thus avoids cases where KMeans sometimes
results in poor clusterings. KMeans/KMeans++ clustering aims to partition n observations results in poor clusterings. KMeans/KMeans++ clustering aims to partition n observations
into k clusters in such that each point belongs to the cluster with the nearest center. into k clusters in such that each point belongs to the cluster with the nearest center.
</li> </li>
<li><a href="../apidocs/org/apache/commons/math4/ml/clustering/FuzzyKMeansClusterer.html">Fuzzy-KMeans</a>: <li><a href="../apidocs/org/apache/commons/math4/ml/clustering/FuzzyKMeansClusterer.html">Fuzzy-KMeans</a>:
A variation of the classical K-Means algorithm, with the major difference that a single A variation of the classical K-Means algorithm, with the major difference that a single
@ -52,7 +52,7 @@
slower than the original kMeans algorithm. slower than the original kMeans algorithm.
</li> </li>
<li><a href="../apidocs/org/apache/commons/math4/ml/clustering/DBSCANClusterer.html">DBSCAN</a>: <li><a href="../apidocs/org/apache/commons/math4/ml/clustering/DBSCANClusterer.html">DBSCAN</a>:
Density-based spatial clustering of applications with noise (DBSCAN) finds a number of Density-based spatial clustering of applications with noise (DBSCAN) finds a number of
clusters starting from the estimated density distribution of corresponding nodes. The clusters starting from the estimated density distribution of corresponding nodes. The
main advantages over KMeans/KMeans++ are that DBSCAN does not require the specification main advantages over KMeans/KMeans++ are that DBSCAN does not require the specification
of an initial number of clusters and can find arbitrarily shaped clusters. of an initial number of clusters and can find arbitrarily shaped clusters.
@ -114,18 +114,18 @@ public static class LocationWrapper implements Clusterable {
} }
} }
</source> </source>
Now we will create a list of these wrapper objects (one for each location), Now we will create a list of these wrapper objects (one for each location),
which serves as input to our clustering algorithm. which serves as input to our clustering algorithm.
<source> <source>
// we have a list of our locations we want to cluster. create a // we have a list of our locations we want to cluster. create a
List&lt;Location&gt; locations = ...; List&lt;Location&gt; locations = ...;
List&lt;LocationWrapper&gt; clusterInput = new ArrayList&lt;LocationWrapper&gt;(locations.size()); List&lt;LocationWrapper&gt; clusterInput = new ArrayList&lt;LocationWrapper&gt;(locations.size());
for (Location location : locations) for (Location location : locations)
clusterInput.add(new LocationWrapper(location)); clusterInput.add(new LocationWrapper(location));
</source> </source>
Finally, we can apply our clustering algorithm and output the found clusters. Finally, we can apply our clustering algorithm and output the found clusters.
<source> <source>
// initialize a new clustering algorithm. // initialize a new clustering algorithm.
// we use KMeans++ with 10 clusters and 10000 iterations maximum. // we use KMeans++ with 10 clusters and 10000 iterations maximum.
// we did not specify a distance measure; the default (euclidean distance) is used. // we did not specify a distance measure; the default (euclidean distance) is used.
KMeansPlusPlusClusterer&lt;LocationWrapper&gt; clusterer = new KMeansPlusPlusClusterer&lt;LocationWrapper&gt;(10, 10000); KMeansPlusPlusClusterer&lt;LocationWrapper&gt; clusterer = new KMeansPlusPlusClusterer&lt;LocationWrapper&gt;(10, 10000);
@ -143,4 +143,4 @@ for (int i=0; i&lt;clusterResults.size(); i++) {
</subsection> </subsection>
</section> </section>
</body> </body>
</document> </document>

4
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="ode.html"> <document url="ode.html">
@ -152,7 +152,7 @@ dp853.integrate(ode, 0.0, y, 16.0, y); // now y contains final state at time t=1
StepHandler stepHandler = new StepHandler() { StepHandler stepHandler = new StepHandler() {
public void init(double t0, double[] y0, double t) { public void init(double t0, double[] y0, double t) {
} }
public void handleStep(StepInterpolator interpolator, boolean isLast) { public void handleStep(StepInterpolator interpolator, boolean isLast) {
double t = interpolator.getCurrentTime(); double t = interpolator.getCurrentTime();
double[] y = interpolator.getInterpolatedState(); double[] y = interpolator.getInterpolatedState();

2
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@ -261,7 +261,7 @@
We are looking to find the best parameters [a, b, c] for the quadratic function We are looking to find the best parameters [a, b, c] for the quadratic function
<b><code>f(x) = a x<sup>2</sup> + b x + c</code></b>. <b><code>f(x) = a x<sup>2</sup> + b x + c</code></b>.
The data set below was generated using [a = 8, b = 10, c = 16]. The data set below was generated using [a = 8, b = 10, c = 16].
A random number between zero and one was added to each y value calculated. A random number between zero and one was added to each y value calculated.
<table cellspacing="0" cellpadding="3"> <table cellspacing="0" cellpadding="3">

30
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@ -16,13 +16,13 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document> <document>
<properties> <properties>
<title>User Guide - Overview</title> <title>User Guide - Overview</title>
</properties> </properties>
<body> <body>
<section name="Overview"> <section name="Overview">
@ -32,22 +32,22 @@
This guide is intended to help programmers quickly find what they need to develop This guide is intended to help programmers quickly find what they need to develop
solutions using Commons Math. It also provides a supplement to the javadoc API documentation, solutions using Commons Math. It also provides a supplement to the javadoc API documentation,
providing a little more explanation of the mathematical objects and functions included providing a little more explanation of the mathematical objects and functions included
in the package. in the package.
</p> </p>
</subsection> </subsection>
<subsection name="0.2 What's in commons-math" href="summary"> <subsection name="0.2 What's in commons-math" href="summary">
<p> <p>
Commons Math is made up of a small set of math/stat utilities addressing Commons Math is made up of a small set of math/stat utilities addressing
programming problems like the ones in the list below. This list is not exhaustive, programming problems like the ones in the list below. This list is not exhaustive,
it's just meant to give a feel for the kinds of things that Commons Math provides. it's just meant to give a feel for the kinds of things that Commons Math provides.
<ul> <ul>
<li>Computing means, variances and other summary statistics for a list of numbers</li> <li>Computing means, variances and other summary statistics for a list of numbers</li>
<li>Fitting a line to a set of data points using linear regression</li> <li>Fitting a line to a set of data points using linear regression</li>
<li>Fitting a curve to a set of data points</li> <li>Fitting a curve to a set of data points</li>
<li>Finding a smooth curve that passes through a collection of points (interpolation)</li> <li>Finding a smooth curve that passes through a collection of points (interpolation)</li>
<li>Fitting a parametric model to a set of measurements using least-squares methods</li> <li>Fitting a parametric model to a set of measurements using least-squares methods</li>
<li>Solving equations involving real-valued functions (i.e. root-finding)</li> <li>Solving equations involving real-valued functions (i.e. root-finding)</li>
<li>Solving systems of linear equations</li> <li>Solving systems of linear equations</li>
<li>Solving Ordinary Differential Equations</li> <li>Solving Ordinary Differential Equations</li>
<li>Minimizing multi-dimensional functions</li> <li>Minimizing multi-dimensional functions</li>
@ -57,13 +57,13 @@
<li>Performing statistical significance tests</li> <li>Performing statistical significance tests</li>
<li>Miscellaneous mathematical functions such as factorials, binomial <li>Miscellaneous mathematical functions such as factorials, binomial
coefficients and "special functions" (e.g. gamma, beta functions)</li> coefficients and "special functions" (e.g. gamma, beta functions)</li>
</ul></p> </ul></p>
<p> <p>
We are actively seeking ideas for additional components that fit into the We are actively seeking ideas for additional components that fit into the
<a href="../index.html#summary">Commons Math vision</a> of a set of lightweight, <a href="../index.html#summary">Commons Math vision</a> of a set of lightweight,
self-contained math/stat components useful for solving common programming problems. self-contained math/stat components useful for solving common programming problems.
Suggestions for new components or enhancements to existing functionality are always welcome! Suggestions for new components or enhancements to existing functionality are always welcome!
All feedback/suggestions for improvement should be sent to the All feedback/suggestions for improvement should be sent to the
<a href="http://commons.apache.org/mail-lists.html">commons-dev mailing list</a> with <a href="http://commons.apache.org/mail-lists.html">commons-dev mailing list</a> with
[math] at the beginning of the subject line. [math] at the beginning of the subject line.
</p> </p>
@ -96,15 +96,15 @@
<subsection name="0.4 How interface contracts are specified in commons-math javadoc" href="contracts"> <subsection name="0.4 How interface contracts are specified in commons-math javadoc" href="contracts">
<p> <p>
You should always read the javadoc class and method comments carefully when using You should always read the javadoc class and method comments carefully when using
Commons Math components in your programs. The javadoc provides references to the algorithms Commons Math components in your programs. The javadoc provides references to the algorithms
that are used, usage notes about limitations, performance, etc. as well as interface contracts. that are used, usage notes about limitations, performance, etc. as well as interface contracts.
Interface contracts are specified in terms of preconditions (what has to be true in order Interface contracts are specified in terms of preconditions (what has to be true in order
for the method to return valid results), special values returned (e.g. Double.NaN) for the method to return valid results), special values returned (e.g. Double.NaN)
or exceptions that may be thrown if the preconditions are not met, and definitions for returned or exceptions that may be thrown if the preconditions are not met, and definitions for returned
values/objects or state changes.</p> values/objects or state changes.</p>
<p> <p>
When the actual parameters provided to a method or the internal state of an object When the actual parameters provided to a method or the internal state of an object
make a computation meaningless, a make a computation meaningless, a
<a href="../apidocs/org/apache/commons/math4/exception/MathIllegalArgumentException.html"> <a href="../apidocs/org/apache/commons/math4/exception/MathIllegalArgumentException.html">
MathIllegalArgumentException</a> or MathIllegalArgumentException</a> or

4
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="random.html"> <document url="random.html">
@ -74,7 +74,7 @@ import org.apache.commons.rng.RandomSource;
// Import common PRNG interface and factory class that instantiates the PRNG. // Import common PRNG interface and factory class that instantiates the PRNG.
// Create (and possibly seed) a PRNG. // Create (and possibly seed) a PRNG.
long seed = 17399225432L; // Fixed seed means same results every time long seed = 17399225432L; // Fixed seed means same results every time
UniformRandomProvider rng = RandomSource.create(RandomSource.MT, seed); UniformRandomProvider rng = RandomSource.create(RandomSource.MT, seed);
// Create a a factory of correlated vectors. // Create a a factory of correlated vectors.

6
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View File

@ -7,16 +7,16 @@
The ASF licenses this file to You under the Apache License, Version 2.0 The ASF licenses this file to You under the Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0 http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="fraction.html"> <document url="fraction.html">
<properties> <properties>

78
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@ -118,7 +118,7 @@
</table> </table>
</p> </p>
<p> <p>
<code>SummaryStatistics</code> can be aggregated using <code>SummaryStatistics</code> can be aggregated using
<a href="../apidocs/org/apache/commons/math4/stat/descriptive/AggregateSummaryStatistics.html"> <a href="../apidocs/org/apache/commons/math4/stat/descriptive/AggregateSummaryStatistics.html">
AggregateSummaryStatistics.</a> This class can be used to concurrently AggregateSummaryStatistics.</a> This class can be used to concurrently
gather statistics for multiple datasets as well as for a combined sample gather statistics for multiple datasets as well as for a combined sample
@ -135,11 +135,11 @@
is thread-safe. is thread-safe.
<a href="../apidocs/org/apache/commons/math4/stat/descriptive/SynchronizedDescriptiveStatistics.html"> <a href="../apidocs/org/apache/commons/math4/stat/descriptive/SynchronizedDescriptiveStatistics.html">
SynchronizedDescriptiveStatistics</a> and SynchronizedDescriptiveStatistics</a> and
<a href="../apidocs/org/apache/commons/math4/stat/descriptive/SynchronizedSummaryStatistics.html"> <a href="../apidocs/org/apache/commons/math4/stat/descriptive/SynchronizedSummaryStatistics.html">
SynchronizedSummaryStatistics</a>, respectively, provide thread-safe SynchronizedSummaryStatistics</a>, respectively, provide thread-safe
versions for applications that require concurrent access to statistical versions for applications that require concurrent access to statistical
aggregates by multiple threads. aggregates by multiple threads.
<a href="../apidocs/org/apache/commons/math4/stat/descriptive/SynchronizedMultiVariateSummaryStatistics.html"> <a href="../apidocs/org/apache/commons/math4/stat/descriptive/SynchronizedMultiVariateSummaryStatistics.html">
SynchronizedMultivariateSummaryStatistics</a> provides thread-safe SynchronizedMultivariateSummaryStatistics</a> provides thread-safe
<code>MultivariateSummaryStatistics.</code> <code>MultivariateSummaryStatistics.</code>
</p> </p>
@ -239,14 +239,14 @@ DescriptiveStatistics stats = new SynchronizedDescriptiveStatistics();
</dd> </dd>
<dt>Compute statistics for multiple samples and overall statistics concurrently</dt> <dt>Compute statistics for multiple samples and overall statistics concurrently</dt>
<br/> <br/>
<dd>There are two ways to do this using <code>AggregateSummaryStatistics.</code> <dd>There are two ways to do this using <code>AggregateSummaryStatistics.</code>
The first is to use an <code>AggregateSummaryStatistics</code> instance The first is to use an <code>AggregateSummaryStatistics</code> instance
to accumulate overall statistics contributed by <code>SummaryStatistics</code> to accumulate overall statistics contributed by <code>SummaryStatistics</code>
instances created using instances created using
<a href="../apidocs/org/apache/commons/math4/stat/descriptive/AggregateSummaryStatistics.html#createContributingStatistics()"> <a href="../apidocs/org/apache/commons/math4/stat/descriptive/AggregateSummaryStatistics.html#createContributingStatistics()">
AggregateSummaryStatistics.createContributingStatistics()</a>: AggregateSummaryStatistics.createContributingStatistics()</a>:
<source> <source>
// Create a AggregateSummaryStatistics instance to accumulate the overall statistics // Create a AggregateSummaryStatistics instance to accumulate the overall statistics
// and AggregatingSummaryStatistics for the subsamples // and AggregatingSummaryStatistics for the subsamples
AggregateSummaryStatistics aggregate = new AggregateSummaryStatistics(); AggregateSummaryStatistics aggregate = new AggregateSummaryStatistics();
SummaryStatistics setOneStats = aggregate.createContributingStatistics(); SummaryStatistics setOneStats = aggregate.createContributingStatistics();
@ -501,7 +501,7 @@ System.out.println(regression.getSlopeStdErr());
System.out.println(regression.getInterceptStdErr() ); System.out.println(regression.getInterceptStdErr() );
// will return Double.NaN, since we constrained the parameter to zero // will return Double.NaN, since we constrained the parameter to zero
</source> </source>
Caution must be exercised when interpreting the slope when no constant is being estimated. The slope Caution must be exercised when interpreting the slope when no constant is being estimated. The slope
may be biased. may be biased.
</dd> </dd>
@ -520,12 +520,12 @@ System.out.println(regression.getInterceptStdErr() );
</p> </p>
<p> <p>
where Y is an n-vector <b>regressand</b>, X is a [n,k] matrix whose k columns are called where Y is an n-vector <b>regressand</b>, X is a [n,k] matrix whose k columns are called
<b>regressors</b>, b is k-vector of <b>regression parameters</b> and u is an n-vector <b>regressors</b>, b is k-vector of <b>regression parameters</b> and u is an n-vector
of <b>error terms</b> or <b>residuals</b>. of <b>error terms</b> or <b>residuals</b>.
</p> </p>
<p> <p>
<a href="../apidocs/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.html"> <a href="../apidocs/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.html">
OLSMultipleLinearRegression</a> provides Ordinary Least Squares Regression, and OLSMultipleLinearRegression</a> provides Ordinary Least Squares Regression, and
<a href="../apidocs/org/apache/commons/math4/stat/regression/GLSMultipleLinearRegression.html"> <a href="../apidocs/org/apache/commons/math4/stat/regression/GLSMultipleLinearRegression.html">
GLSMultipleLinearRegression</a> implements Generalized Least Squares. See the javadoc for these GLSMultipleLinearRegression</a> implements Generalized Least Squares. See the javadoc for these
classes for details on the algorithms and formulas used. classes for details on the algorithms and formulas used.
@ -535,9 +535,9 @@ System.out.println(regression.getInterceptStdErr() );
the regressand (Y) value, followed by regressor values; or using a double[][] array with rows corresponding to the regressand (Y) value, followed by regressor values; or using a double[][] array with rows corresponding to
observations. GLS models also require a double[][] array representing the covariance matrix of the error terms. See observations. GLS models also require a double[][] array representing the covariance matrix of the error terms. See
<a href="../apidocs/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.html#newSampleData(double[], int, int)"> <a href="../apidocs/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.html#newSampleData(double[], int, int)">
AbstractMultipleLinearRegression#newSampleData(double[],int,int)</a>, AbstractMultipleLinearRegression#newSampleData(double[],int,int)</a>,
<a href="../apidocs/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.html#newSampleData(double[], double[][])"> <a href="../apidocs/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.html#newSampleData(double[], double[][])">
OLSMultipleLinearRegression#newSampleData(double[], double[][])</a> and OLSMultipleLinearRegression#newSampleData(double[], double[][])</a> and
<a href="../apidocs/org/apache/commons/math4/stat/regression/GLSMultipleLinearRegression.html#newSampleData(double[], double[][], double[][])"> <a href="../apidocs/org/apache/commons/math4/stat/regression/GLSMultipleLinearRegression.html#newSampleData(double[], double[][], double[][])">
GLSMultipleLinearRegression#newSampleData(double[],double[][],double[][])</a> for details. GLSMultipleLinearRegression#newSampleData(double[],double[][],double[][])</a> for details.
</p> </p>
@ -545,7 +545,7 @@ System.out.println(regression.getInterceptStdErr() );
<strong>Usage Notes</strong>: <ul> <strong>Usage Notes</strong>: <ul>
<li> Data are validated when invoking any of the newSample, newX, newY or newCovariance methods and <li> Data are validated when invoking any of the newSample, newX, newY or newCovariance methods and
<code>IllegalArgumentException</code> is thrown when input data arrays do not have matching dimensions <code>IllegalArgumentException</code> is thrown when input data arrays do not have matching dimensions
or do not contain sufficient data to estimate the model. or do not contain sufficient data to estimate the model.
</li> </li>
<li> By default, regression models are estimated with intercept terms. In the notation above, this implies that the <li> By default, regression models are estimated with intercept terms. In the notation above, this implies that the
X matrix contains an initial row identically equal to 1. X data supplied to the newX or newSample methods should not X matrix contains an initial row identically equal to 1. X data supplied to the newX or newSample methods should not
@ -568,13 +568,13 @@ x[1] = new double[]{2.0, 0, 0, 0, 0};
x[2] = new double[]{0, 3.0, 0, 0, 0}; x[2] = new double[]{0, 3.0, 0, 0, 0};
x[3] = new double[]{0, 0, 4.0, 0, 0}; x[3] = new double[]{0, 0, 4.0, 0, 0};
x[4] = new double[]{0, 0, 0, 5.0, 0}; x[4] = new double[]{0, 0, 0, 5.0, 0};
x[5] = new double[]{0, 0, 0, 0, 6.0}; x[5] = new double[]{0, 0, 0, 0, 6.0};
regression.newSampleData(y, x); regression.newSampleData(y, x);
</source> </source>
</dd> </dd>
<dd>Get regression parameters and diagnostics: <dd>Get regression parameters and diagnostics:
<source> <source>
double[] beta = regression.estimateRegressionParameters(); double[] beta = regression.estimateRegressionParameters();
double[] residuals = regression.estimateResiduals(); double[] residuals = regression.estimateResiduals();
@ -599,7 +599,7 @@ x[1] = new double[]{2.0, 0, 0, 0, 0};
x[2] = new double[]{0, 3.0, 0, 0, 0}; x[2] = new double[]{0, 3.0, 0, 0, 0};
x[3] = new double[]{0, 0, 4.0, 0, 0}; x[3] = new double[]{0, 0, 4.0, 0, 0};
x[4] = new double[]{0, 0, 0, 5.0, 0}; x[4] = new double[]{0, 0, 0, 5.0, 0};
x[5] = new double[]{0, 0, 0, 0, 6.0}; x[5] = new double[]{0, 0, 0, 0, 6.0};
double[][] omega = new double[6][]; double[][] omega = new double[6][];
omega[0] = new double[]{1.1, 0, 0, 0, 0, 0}; omega[0] = new double[]{1.1, 0, 0, 0, 0, 0};
omega[1] = new double[]{0, 2.2, 0, 0, 0, 0}; omega[1] = new double[]{0, 2.2, 0, 0, 0, 0};
@ -607,19 +607,19 @@ omega[2] = new double[]{0, 0, 3.3, 0, 0, 0};
omega[3] = new double[]{0, 0, 0, 4.4, 0, 0}; omega[3] = new double[]{0, 0, 0, 4.4, 0, 0};
omega[4] = new double[]{0, 0, 0, 0, 5.5, 0}; omega[4] = new double[]{0, 0, 0, 0, 5.5, 0};
omega[5] = new double[]{0, 0, 0, 0, 0, 6.6}; omega[5] = new double[]{0, 0, 0, 0, 0, 6.6};
regression.newSampleData(y, x, omega); regression.newSampleData(y, x, omega);
</source> </source>
</dd> </dd>
</dl> </dl>
</p> </p>
</subsection> </subsection>
<subsection name="1.6 Rank transformations"> <subsection name="1.6 Rank transformations">
<p> <p>
Some statistical algorithms require that input data be replaced by ranks. Some statistical algorithms require that input data be replaced by ranks.
The <a href="../apidocs/org/apache/commons/math4/stat/ranking/package-summary.html"> The <a href="../apidocs/org/apache/commons/math4/stat/ranking/package-summary.html">
org.apache.commons.math4.stat.ranking</a> package provides rank transformation. org.apache.commons.math4.stat.ranking</a> package provides rank transformation.
<a href="../apidocs/org/apache/commons/math4/stat/ranking/RankingAlgorithm.html"> <a href="../apidocs/org/apache/commons/math4/stat/ranking/RankingAlgorithm.html">
RankingAlgorithm</a> defines the interface for ranking. RankingAlgorithm</a> defines the interface for ranking.
<a href="../apidocs/org/apache/commons/math4/stat/ranking/NaturalRanking.html"> <a href="../apidocs/org/apache/commons/math4/stat/ranking/NaturalRanking.html">
NaturalRanking</a> provides an implementation that has two configuration options. NaturalRanking</a> provides an implementation that has two configuration options.
<ul> <ul>
@ -640,7 +640,7 @@ double[] ranks = ranking.rank(exampleData);
</source> </source>
results in <code>ranks</code> containing <code>{6, 5, 7, 8, 5, 9, 2, 2, 5}.</code> results in <code>ranks</code> containing <code>{6, 5, 7, 8, 5, 9, 2, 2, 5}.</code>
<source> <source>
new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData); new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData);
</source> </source>
returns <code>{5, 2, 6, 7, 3, 8, 1, 4}.</code> returns <code>{5, 2, 6, 7, 3, 8, 1, 4}.</code>
</p> </p>
@ -648,20 +648,20 @@ new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData
The default <code>NaNStrategy</code> is NaNStrategy.MAXIMAL. This makes <code>NaN</code> The default <code>NaNStrategy</code> is NaNStrategy.MAXIMAL. This makes <code>NaN</code>
values larger than any other value (including <code>Double.POSITIVE_INFINITY</code>). The values larger than any other value (including <code>Double.POSITIVE_INFINITY</code>). The
default <code>TiesStrategy</code> is <code>TiesStrategy.AVERAGE,</code> which assigns tied default <code>TiesStrategy</code> is <code>TiesStrategy.AVERAGE,</code> which assigns tied
values the average of the ranks applicable to the sequence of ties. See the values the average of the ranks applicable to the sequence of ties. See the
<a href="../apidocs/org/apache/commons/math4/stat/ranking/NaturalRanking.html"> <a href="../apidocs/org/apache/commons/math4/stat/ranking/NaturalRanking.html">
NaturalRanking</a> for more examples and <a href="../apidocs/org/apache/commons/math4/stat/ranking/TiesStrategy.html"> NaturalRanking</a> for more examples and <a href="../apidocs/org/apache/commons/math4/stat/ranking/TiesStrategy.html">
TiesStrategy</a> and <a href="../apidocs/org/apache/commons/math4/stat/ranking/NaNStrategy.html">NaNStrategy</a> TiesStrategy</a> and <a href="../apidocs/org/apache/commons/math4/stat/ranking/NaNStrategy.html">NaNStrategy</a>
for details on these configuration options. for details on these configuration options.
</p> </p>
</subsection> </subsection>
<subsection name="1.7 Covariance and correlation"> <subsection name="1.7 Covariance and correlation">
<p> <p>
The <a href="../apidocs/org/apache/commons/math4/stat/correlation/package-summary.html"> The <a href="../apidocs/org/apache/commons/math4/stat/correlation/package-summary.html">
org.apache.commons.math4.stat.correlation</a> package computes covariances org.apache.commons.math4.stat.correlation</a> package computes covariances
and correlations for pairs of arrays or columns of a matrix. and correlations for pairs of arrays or columns of a matrix.
<a href="../apidocs/org/apache/commons/math4/stat/correlation/Covariance.html"> <a href="../apidocs/org/apache/commons/math4/stat/correlation/Covariance.html">
Covariance</a> computes covariances, Covariance</a> computes covariances,
<a href="../apidocs/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.html"> <a href="../apidocs/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.html">
PearsonsCorrelation</a> provides Pearson's Product-Moment correlation coefficients, PearsonsCorrelation</a> provides Pearson's Product-Moment correlation coefficients,
<a href="../apidocs/org/apache/commons/math4/stat/correlation/SpearmansCorrelation.html"> <a href="../apidocs/org/apache/commons/math4/stat/correlation/SpearmansCorrelation.html">
@ -676,10 +676,10 @@ new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData
Unbiased covariances are given by the formula <br/> Unbiased covariances are given by the formula <br/>
<code>cov(X, Y) = sum [(x<sub>i</sub> - E(X))(y<sub>i</sub> - E(Y))] / (n - 1)</code> <code>cov(X, Y) = sum [(x<sub>i</sub> - E(X))(y<sub>i</sub> - E(Y))] / (n - 1)</code>
where <code>E(X)</code> is the mean of <code>X</code> and <code>E(Y)</code> where <code>E(X)</code> is the mean of <code>X</code> and <code>E(Y)</code>
is the mean of the <code>Y</code> values. Non-bias-corrected estimates use is the mean of the <code>Y</code> values. Non-bias-corrected estimates use
<code>n</code> in place of <code>n - 1.</code> Whether or not covariances are <code>n</code> in place of <code>n - 1.</code> Whether or not covariances are
bias-corrected is determined by the optional parameter, "biasCorrected," which bias-corrected is determined by the optional parameter, "biasCorrected," which
defaults to <code>true.</code> defaults to <code>true.</code>
</li> </li>
<li> <li>
<a href="../apidocs/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.html"> <a href="../apidocs/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.html">
@ -691,7 +691,7 @@ new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData
<li> <li>
<a href="../apidocs/org/apache/commons/math4/stat/correlation/SpearmansCorrelation.html"> <a href="../apidocs/org/apache/commons/math4/stat/correlation/SpearmansCorrelation.html">
SpearmansCorrelation</a> applies a rank transformation to the input data and computes Pearson's SpearmansCorrelation</a> applies a rank transformation to the input data and computes Pearson's
correlation on the ranked data. The ranking algorithm is configurable. By default, correlation on the ranked data. The ranking algorithm is configurable. By default,
<a href="../apidocs/org/apache/commons/math4/stat/ranking/NaturalRanking.html"> <a href="../apidocs/org/apache/commons/math4/stat/ranking/NaturalRanking.html">
NaturalRanking</a> with default strategies for handling ties and NaN values is used. NaturalRanking</a> with default strategies for handling ties and NaN values is used.
</li> </li>
@ -699,7 +699,7 @@ new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData
<a href="../apidocs/org/apache/commons/math4/stat/correlation/KendallsCorrelation.html"> <a href="../apidocs/org/apache/commons/math4/stat/correlation/KendallsCorrelation.html">
KendallsCorrelation</a> computes the association between two measured quantities. A tau test KendallsCorrelation</a> computes the association between two measured quantities. A tau test
is a non-parametric hypothesis test for statistical dependence based on the tau coefficient. is a non-parametric hypothesis test for statistical dependence based on the tau coefficient.
</li> </li>
</ul> </ul>
</p> </p>
<p> <p>
@ -727,7 +727,7 @@ new Covariance().computeCovarianceMatrix(data)
</source> </source>
The i-jth entry of the returned matrix is the unbiased covariance of the ith and jth The i-jth entry of the returned matrix is the unbiased covariance of the ith and jth
columns of <code>data.</code> As above, to get non-bias-corrected covariances, columns of <code>data.</code> As above, to get non-bias-corrected covariances,
use use
<source> <source>
computeCovarianceMatrix(data, false) computeCovarianceMatrix(data, false)
</source> </source>
@ -750,7 +750,7 @@ new PearsonsCorrelation().correlation(x, y)
new PearsonsCorrelation().computeCorrelationMatrix(data) new PearsonsCorrelation().computeCorrelationMatrix(data)
</source> </source>
The i-jth entry of the returned matrix is the Pearson's product-moment correlation between the The i-jth entry of the returned matrix is the Pearson's product-moment correlation between the
ith and jth columns of <code>data.</code> ith and jth columns of <code>data.</code>
</dd> </dd>
<br/> <br/>
<dt><strong>Pearson's correlation significance and standard errors</strong></dt> <dt><strong>Pearson's correlation significance and standard errors</strong></dt>
@ -768,7 +768,7 @@ correlation.getCorrelationStandardErrors();
</source> </source>
The formula used to compute the standard error is <br/> The formula used to compute the standard error is <br/>
<code>SE<sub>r</sub> = ((1 - r<sup>2</sup>) / (n - 2))<sup>1/2</sup></code><br/> <code>SE<sub>r</sub> = ((1 - r<sup>2</sup>) / (n - 2))<sup>1/2</sup></code><br/>
where <code>r</code> is the estimated correlation coefficient and where <code>r</code> is the estimated correlation coefficient and
<code>n</code> is the number of observations in the source dataset.<br/><br/> <code>n</code> is the number of observations in the source dataset.<br/><br/>
<strong>p-values</strong> for the (2-sided) null hypotheses that elements of <strong>p-values</strong> for the (2-sided) null hypotheses that elements of
a correlation matrix are zero populate the RealMatrix returned by a correlation matrix are zero populate the RealMatrix returned by
@ -780,9 +780,9 @@ correlation.getCorrelationPValues()
a value with absolute value greater than or equal to <br/> a value with absolute value greater than or equal to <br/>
<code>|r<sub>ij</sub>|((n - 2) / (1 - r<sub>ij</sub><sup>2</sup>))<sup>1/2</sup></code>, <code>|r<sub>ij</sub>|((n - 2) / (1 - r<sub>ij</sub><sup>2</sup>))<sup>1/2</sup></code>,
where <code>r<sub>ij</sub></code> is the estimated correlation between the ith and jth where <code>r<sub>ij</sub></code> is the estimated correlation between the ith and jth
columns of the source array or RealMatrix. This is sometimes referred to as the columns of the source array or RealMatrix. This is sometimes referred to as the
<i>significance</i> of the coefficient.<br/><br/> <i>significance</i> of the coefficient.<br/><br/>
For example, if <code>data</code> is a RealMatrix with 2 columns and 10 rows, then For example, if <code>data</code> is a RealMatrix with 2 columns and 10 rows, then
<source> <source>
new PearsonsCorrelation(data).getCorrelationPValues().getEntry(0,1) new PearsonsCorrelation(data).getCorrelationPValues().getEntry(0,1)
</source> </source>
@ -798,13 +798,13 @@ new PearsonsCorrelation(data).getCorrelationPValues().getEntry(0,1)
<source> <source>
new SpearmansCorrelation().correlation(x, y) new SpearmansCorrelation().correlation(x, y)
</source> </source>
This is equivalent to This is equivalent to
<source> <source>
RankingAlgorithm ranking = new NaturalRanking(); RankingAlgorithm ranking = new NaturalRanking();
new PearsonsCorrelation().correlation(ranking.rank(x), ranking.rank(y)) new PearsonsCorrelation().correlation(ranking.rank(x), ranking.rank(y))
</source> </source>
</dd> </dd>
<br/> <br/>
<dt><strong>Kendalls's tau rank correlation coefficient</strong></dt> <dt><strong>Kendalls's tau rank correlation coefficient</strong></dt>
<br/> <br/>
<dd>To compute the Kendall's tau rank correlation between two double arrays <dd>To compute the Kendall's tau rank correlation between two double arrays
@ -823,7 +823,7 @@ new KendallsCorrelation().correlation(x, y)
<a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc22.htm"> <a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc22.htm">
Student's t</a>, Student's t</a>,
<a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm"> <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
Chi-Square</a>, Chi-Square</a>,
<a href="http://en.wikipedia.org/wiki/G-test">G Test</a>, <a href="http://en.wikipedia.org/wiki/G-test">G Test</a>,
<a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc43.htm"> <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc43.htm">
One-Way ANOVA</a>, One-Way ANOVA</a>,
@ -853,7 +853,7 @@ new KendallsCorrelation().correlation(x, y)
<a href="../apidocs/org/apache/commons/math4/stat/inference/BinomialTest.html"> <a href="../apidocs/org/apache/commons/math4/stat/inference/BinomialTest.html">
BinomialTest</a> and BinomialTest</a> and
<a href="../apidocs/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.html"> <a href="../apidocs/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.html">
KolmogorovSmirnovTest</a>. KolmogorovSmirnovTest</a>.
The <a href="../apidocs/org/apache/commons/math4/stat/inference/TestUtils.html"> The <a href="../apidocs/org/apache/commons/math4/stat/inference/TestUtils.html">
TestUtils</a> class provides static methods to get test instances or TestUtils</a> class provides static methods to get test instances or
to compute test statistics directly. The examples below all use the to compute test statistics directly. The examples below all use the
@ -891,7 +891,7 @@ new KendallsCorrelation().correlation(x, y)
<li>The G test implementation provides two p-values: <li>The G test implementation provides two p-values:
<code>gTest(expected, observed)</code>, which is the tail probability beyond <code>gTest(expected, observed)</code>, which is the tail probability beyond
<code>g(expected, observed)</code> in the ChiSquare distribution with degrees <code>g(expected, observed)</code> in the ChiSquare distribution with degrees
of freedom one less than the common length of input arrays and of freedom one less than the common length of input arrays and
<code>gTestIntrinsic(expected, observed)</code> which is the same tail <code>gTestIntrinsic(expected, observed)</code> which is the same tail
probability computed using a ChiSquare distribution with one less degeree probability computed using a ChiSquare distribution with one less degeree
of freedom. </li> of freedom. </li>
@ -908,9 +908,9 @@ new KendallsCorrelation().correlation(x, y)
\(F_n\) is the empirical distribution of the \(n\) sample data points. Both \(F_n\) is the empirical distribution of the \(n\) sample data points. Both
one-sample tests against a <code>RealDistribution</code> and two-sample tests one-sample tests against a <code>RealDistribution</code> and two-sample tests
(comparing two empirical distributions) are supported. For one-sample tests, (comparing two empirical distributions) are supported. For one-sample tests,
the distribution of \(D_n\) is estimated using the method in the distribution of \(D_n\) is estimated using the method in
<a href="http://www.jstatsoft.org/v08/i18/">Evaluating Kolmogorov's Distribution</a> by <a href="http://www.jstatsoft.org/v08/i18/">Evaluating Kolmogorov's Distribution</a> by
George Marsaglia, Wai Wan Tsang, and Jingbo Wang, with quick decisions in some cases George Marsaglia, Wai Wan Tsang, and Jingbo Wang, with quick decisions in some cases
for extreme values using the method described in for extreme values using the method described in
<a href="http://www.jstatsoft.org/v39/i11/"> Computing the Two-Sided Kolmogorov-Smirnov <a href="http://www.jstatsoft.org/v39/i11/"> Computing the Two-Sided Kolmogorov-Smirnov
Distribution</a> by Richard Simard and Pierre L'Ecuyer. In the 2-sample case, estimation Distribution</a> by Richard Simard and Pierre L'Ecuyer. In the 2-sample case, estimation
@ -1163,7 +1163,7 @@ System.out.println(TestUtils.gTestDataSetsComparison(obs1, obs2)); // p-value
<source> <source>
new GTest().rootLogLikelihoodRatio(5, 1995, 0, 100000); new GTest().rootLogLikelihoodRatio(5, 1995, 0, 100000);
</source> </source>
returns the root log likelihood associated with the null hypothesis that A returns the root log likelihood associated with the null hypothesis that A
and B are independent. and B are independent.
</dd> </dd>
<br/> <br/>
@ -1219,7 +1219,7 @@ TestUtils.kolmogorovSmirnovStatistic(unitNormal, sample)
<source> <source>
TestUtils.kolmogorovSmirnovStatistic(x, y) TestUtils.kolmogorovSmirnovStatistic(x, y)
</source> </source>
to compute the D-statistic and to compute the D-statistic and
<source> <source>
TestUtils.kolmogorovSmirnovTest(x, y) TestUtils.kolmogorovSmirnovTest(x, y)
</source> </source>

2
src/site/xdoc/userguide/transform.xml
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="transform.html"> <document url="transform.html">

28
src/site/xdoc/userguide/utilities.xml
View File

@ -16,7 +16,7 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="utilities.html"> <document url="utilities.html">
@ -39,14 +39,14 @@
<subsection name="6.2 Double array utilities" href="arrays"> <subsection name="6.2 Double array utilities" href="arrays">
<p> <p>
To maintain statistics based on a "rolling" window of values, a resizable To maintain statistics based on a "rolling" window of values, a resizable
array implementation was developed and is provided for reuse in the array implementation was developed and is provided for reuse in the
<code>util</code> package. The core functionality provided is described in <code>util</code> package. The core functionality provided is described in
the documentation for the interface, the documentation for the interface,
<a href="../apidocs/org/apache/commons/math4/util/DoubleArray.html"> <a href="../apidocs/org/apache/commons/math4/util/DoubleArray.html">
DoubleArray</a>. This interface adds one method, DoubleArray</a>. This interface adds one method,
<code>addElementRolling(double)</code> to basic list accessors. <code>addElementRolling(double)</code> to basic list accessors.
The <code>addElementRolling</code> method adds an element The <code>addElementRolling</code> method adds an element
(the actual parameter) to the end of the list and removes the first element (the actual parameter) to the end of the list and removes the first element
in the list. in the list.
</p> </p>
@ -64,10 +64,10 @@
the end needs to be created. Both of these operations are handled the end needs to be created. Both of these operations are handled
automatically, with frequency / effect driven by the configuration automatically, with frequency / effect driven by the configuration
properties <code>expansionMode</code>, <code>expansionFactor</code> and properties <code>expansionMode</code>, <code>expansionFactor</code> and
<code>contractionCriteria.</code> See <code>contractionCriteria.</code> See
<a href="../apidocs/org/apache/commons/math4/util/ResizableDoubleArray.html"> <a href="../apidocs/org/apache/commons/math4/util/ResizableDoubleArray.html">
ResizableDoubleArray</a> ResizableDoubleArray</a>
for details. for details.
</p> </p>
</subsection> </subsection>
@ -103,7 +103,7 @@
default: return 6.0; default: return 6.0;
} }
} }
public double getB(int n, double x) { public double getB(int n, double x) {
double y = (2.0 * n) - 1.0; double y = (2.0 * n) - 1.0;
return y * y; return y * y;
@ -129,7 +129,7 @@
return n; return n;
} }
} }
public double getB(int n, double x) { public double getB(int n, double x) {
if (n % 2 == 0) { if (n % 2 == 0) {
return -x; return -x;
@ -151,17 +151,17 @@
<a href="../apidocs/org/apache/commons/math4/util/ArithmeticUtils.html">ArithmeticUtils</a> <a href="../apidocs/org/apache/commons/math4/util/ArithmeticUtils.html">ArithmeticUtils</a>
utility class. ArithmeticUtils currently includes methods to compute the following: <ul> utility class. ArithmeticUtils currently includes methods to compute the following: <ul>
<li> <li>
Binomial coefficients -- "n choose k" available as an (exact) long value, Binomial coefficients -- "n choose k" available as an (exact) long value,
<code>binomialCoefficient(int, int)</code> for small n, k; as a double, <code>binomialCoefficient(int, int)</code> for small n, k; as a double,
<code>binomialCoefficientDouble(int, int)</code> for larger values; and in <code>binomialCoefficientDouble(int, int)</code> for larger values; and in
a "super-sized" version, <code>binomialCoefficientLog(int, int)</code> a "super-sized" version, <code>binomialCoefficientLog(int, int)</code>
that returns the natural logarithm of the value.</li> that returns the natural logarithm of the value.</li>
<li> <li>
Stirling numbers of the second kind -- S(n,k) as an exact long value Stirling numbers of the second kind -- S(n,k) as an exact long value
<code>stirlingS2(int, int)</code> for small n, k.</li> <code>stirlingS2(int, int)</code> for small n, k.</li>
<li> <li>
Factorials -- like binomial coefficients, these are available as exact long Factorials -- like binomial coefficients, these are available as exact long
values, <code>factorial(int)</code>; doubles, values, <code>factorial(int)</code>; doubles,
<code>factorialDouble(int)</code>; or logs, <code>factorialLog(int)</code>. </li> <code>factorialDouble(int)</code>; or logs, <code>factorialLog(int)</code>. </li>
<li> <li>
Least common multiple and greatest common denominator functions. Least common multiple and greatest common denominator functions.
@ -186,7 +186,7 @@
<p> <p>
FastMath speed is achieved by relying heavily on optimizing compilers to FastMath speed is achieved by relying heavily on optimizing compilers to
native code present in many JVM todays and use of large tables. Precomputed native code present in many JVM todays and use of large tables. Precomputed
literal arrays are provided in this class to speed up load time. These literal arrays are provided in this class to speed up load time. These
precomputed tables are used in the default configuration, to improve speed precomputed tables are used in the default configuration, to improve speed
even at first use of the class. If users prefer to compute the tables even at first use of the class. If users prefer to compute the tables
automatically at load time, they can change a compile-time constant. This will automatically at load time, they can change a compile-time constant. This will

52
src/userguide/java/org/apache/commons/math4/userguide/ClusterAlgorithmComparison.java
View File

@ -68,7 +68,7 @@ public class ClusterAlgorithmComparison {
if (factor < 0 || factor > 1) { if (factor < 0 || factor > 1) {
throw new IllegalArgumentException(); throw new IllegalArgumentException();
} }
ContinuousDistribution.Sampler dist = new NormalDistribution(0.0, noise).createSampler(rng); ContinuousDistribution.Sampler dist = new NormalDistribution(0.0, noise).createSampler(rng);
List<Vector2D> points = new ArrayList<>(); List<Vector2D> points = new ArrayList<>();
@ -77,11 +77,11 @@ public class ClusterAlgorithmComparison {
for (double angle = 0; angle < range; angle += step) { for (double angle = 0; angle < range; angle += step) {
Vector2D outerCircle = Vector2D.of(FastMath.cos(angle), FastMath.sin(angle)); Vector2D outerCircle = Vector2D.of(FastMath.cos(angle), FastMath.sin(angle));
Vector2D innerCircle = outerCircle.multiply(factor); Vector2D innerCircle = outerCircle.multiply(factor);
points.add(outerCircle.add(generateNoiseVector(dist))); points.add(outerCircle.add(generateNoiseVector(dist)));
points.add(innerCircle.add(generateNoiseVector(dist))); points.add(innerCircle.add(generateNoiseVector(dist)));
} }
if (shuffle) { if (shuffle) {
ListSampler.shuffle(rng, points); ListSampler.shuffle(rng, points);
} }
@ -97,7 +97,7 @@ public class ClusterAlgorithmComparison {
int nSamplesOut = samples / 2; int nSamplesOut = samples / 2;
int nSamplesIn = samples - nSamplesOut; int nSamplesIn = samples - nSamplesOut;
List<Vector2D> points = new ArrayList<>(); List<Vector2D> points = new ArrayList<>();
double range = FastMath.PI; double range = FastMath.PI;
double step = range / (nSamplesOut / 2.0); double step = range / (nSamplesOut / 2.0);
@ -111,7 +111,7 @@ public class ClusterAlgorithmComparison {
Vector2D innerCircle = Vector2D.of(1 - FastMath.cos(angle), 1 - FastMath.sin(angle) - 0.5); Vector2D innerCircle = Vector2D.of(1 - FastMath.cos(angle), 1 - FastMath.sin(angle) - 0.5);
points.add(innerCircle.add(generateNoiseVector(dist))); points.add(innerCircle.add(generateNoiseVector(dist)));
} }
if (shuffle) { if (shuffle) {
ListSampler.shuffle(rng, points); ListSampler.shuffle(rng, points);
} }
@ -133,29 +133,29 @@ public class ClusterAlgorithmComparison {
for (int i = 0; i < centers; i++) { for (int i = 0; i < centers; i++) {
centerPoints[i] = Vector2D.of(uniform.sample(), uniform.sample()); centerPoints[i] = Vector2D.of(uniform.sample(), uniform.sample());
} }
int[] nSamplesPerCenter = new int[centers]; int[] nSamplesPerCenter = new int[centers];
int count = samples / centers; int count = samples / centers;
Arrays.fill(nSamplesPerCenter, count); Arrays.fill(nSamplesPerCenter, count);
for (int i = 0; i < samples % centers; i++) { for (int i = 0; i < samples % centers; i++) {
nSamplesPerCenter[i]++; nSamplesPerCenter[i]++;
} }
List<Vector2D> points = new ArrayList<>(); List<Vector2D> points = new ArrayList<>();
for (int i = 0; i < centers; i++) { for (int i = 0; i < centers; i++) {
for (int j = 0; j < nSamplesPerCenter[i]; j++) { for (int j = 0; j < nSamplesPerCenter[i]; j++) {
points.add(centerPoints[i].add(generateNoiseVector(gauss))); points.add(centerPoints[i].add(generateNoiseVector(gauss)));
} }
} }
if (shuffle) { if (shuffle) {
ListSampler.shuffle(rng, points); ListSampler.shuffle(rng, points);
} }
return points; return points;
} }
public static List<Vector2D> makeRandom(int samples) { public static List<Vector2D> makeRandom(int samples) {
SobolSequenceGenerator generator = new SobolSequenceGenerator(2); SobolSequenceGenerator generator = new SobolSequenceGenerator(2);
generator.skipTo(999999); generator.skipTo(999999);
@ -167,14 +167,14 @@ public class ClusterAlgorithmComparison {
Vector2D point = Vector2D.of(vector); Vector2D point = Vector2D.of(vector);
points.add(point); points.add(point);
} }
return points; return points;
} }
public static Vector2D generateNoiseVector(ContinuousDistribution.Sampler distribution) { public static Vector2D generateNoiseVector(ContinuousDistribution.Sampler distribution) {
return Vector2D.of(distribution.sample(), distribution.sample()); return Vector2D.of(distribution.sample(), distribution.sample());
} }
public static List<DoublePoint> normalize(final List<Vector2D> input, public static List<DoublePoint> normalize(final List<Vector2D> input,
double minX, double minX,
double maxX, double maxX,
@ -191,16 +191,16 @@ public class ClusterAlgorithmComparison {
} }
return points; return points;
} }
@SuppressWarnings("serial") @SuppressWarnings("serial")
public static class Display extends ExampleFrame { public static class Display extends ExampleFrame {
public Display() { public Display() {
setTitle("Commons-Math: Cluster algorithm comparison"); setTitle("Commons-Math: Cluster algorithm comparison");
setSize(800, 800); setSize(800, 800);
setLayout(new GridBagLayout()); setLayout(new GridBagLayout());
int nSamples = 1500; int nSamples = 1500;
final long seed = RandomSource.createLong(); // Random seed. final long seed = RandomSource.createLong(); // Random seed.
@ -213,7 +213,7 @@ public class ClusterAlgorithmComparison {
datasets.add(normalize(makeRandom(nSamples), -1, 1, -1, 1)); datasets.add(normalize(makeRandom(nSamples), -1, 1, -1, 1));
List<Pair<String, Clusterer<DoublePoint>>> algorithms = new ArrayList<>(); List<Pair<String, Clusterer<DoublePoint>>> algorithms = new ArrayList<>();
algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=2)", algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=2)",
new KMeansPlusPlusClusterer<>(2))); new KMeansPlusPlusClusterer<>(2)));
algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=3)", algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=3)",
@ -224,7 +224,7 @@ public class ClusterAlgorithmComparison {
new FuzzyKMeansClusterer<>(3, 10))); new FuzzyKMeansClusterer<>(3, 10)));
algorithms.add(new Pair<String, Clusterer<DoublePoint>>("DBSCAN\n(eps=.1, min=3)", algorithms.add(new Pair<String, Clusterer<DoublePoint>>("DBSCAN\n(eps=.1, min=3)",
new DBSCANClusterer<>(0.1, 3))); new DBSCANClusterer<>(0.1, 3)));
GridBagConstraints c = new GridBagConstraints(); GridBagConstraints c = new GridBagConstraints();
c.fill = GridBagConstraints.VERTICAL; c.fill = GridBagConstraints.VERTICAL;
c.gridx = 0; c.gridx = 0;
@ -248,7 +248,7 @@ public class ClusterAlgorithmComparison {
c.gridx++; c.gridx++;
} }
c.gridy++; c.gridy++;
} }
} }
} }
@ -264,7 +264,7 @@ public class ClusterAlgorithmComparison {
this.clusters = clusters; this.clusters = clusters;
this.duration = duration; this.duration = duration;
} }
@Override @Override
protected void paintComponent(Graphics g) { protected void paintComponent(Graphics g) {
super.paintComponent(g); super.paintComponent(g);
@ -276,10 +276,10 @@ public class ClusterAlgorithmComparison {
int h = getHeight(); int h = getHeight();
g2.clearRect(0, 0, w, h); g2.clearRect(0, 0, w, h);
g2.setPaint(Color.black); g2.setPaint(Color.black);
g2.drawRect(0, 0, w - 1, h - 1); g2.drawRect(0, 0, w - 1, h - 1);
int index = 0; int index = 0;
Color[] colors = new Color[] { Color.red, Color.blue, Color.green.darker() }; Color[] colors = new Color[] { Color.red, Color.blue, Color.green.darker() };
for (Cluster<DoublePoint> cluster : clusters) { for (Cluster<DoublePoint> cluster : clusters) {
@ -289,7 +289,7 @@ public class ClusterAlgorithmComparison {
double[] arr = p.getPoint(); double[] arr = p.getPoint();
g2.fill(new Ellipse2D.Double(arr[0] - 1, arr[1] - 1, 3, 3)); g2.fill(new Ellipse2D.Double(arr[0] - 1, arr[1] - 1, 3, 3));
} }
Clusterable p = transform(cluster.centroid(), w, h); Clusterable p = transform(cluster.centroid(), w, h);
double[] arr = p.getPoint(); double[] arr = p.getPoint();
Shape s = new Ellipse2D.Double(arr[0] - 4, arr[1] - 4, 8, 8); Shape s = new Ellipse2D.Double(arr[0] - 4, arr[1] - 4, 8, 8);
@ -297,11 +297,11 @@ public class ClusterAlgorithmComparison {
g2.setPaint(Color.black); g2.setPaint(Color.black);
g2.draw(s); g2.draw(s);
} }
g2.setPaint(Color.black); g2.setPaint(Color.black);
g2.drawString(String.format("%.2f s", duration / 1e3), w - 40, h - 5); g2.drawString(String.format("%.2f s", duration / 1e3), w - 40, h - 5);
} }
@Override @Override
public Dimension getPreferredSize() { public Dimension getPreferredSize() {
return new Dimension(150, 150); return new Dimension(150, 150);

10
src/userguide/java/org/apache/commons/math4/userguide/ExampleUtils.java
View File

@ -39,12 +39,12 @@ public class ExampleUtils {
@SuppressWarnings("serial") @SuppressWarnings("serial")
public static class ExampleFrame extends JFrame { public static class ExampleFrame extends JFrame {
/** /**
* Returns the main panel which should be printed by the screenshot action. * Returns the main panel which should be printed by the screenshot action.
* <p> * <p>
* By default, it returns the content pane of this frame, but can be overridden * By default, it returns the content pane of this frame, but can be overridden
* in case the frame has a global scroll pane which would cut off any offscreen content. * in case the frame has a global scroll pane which would cut off any offscreen content.
* *
* @return the main panel to print * @return the main panel to print
*/ */
@ -73,14 +73,14 @@ public class ExampleUtils {
} }
} }
}); });
JMenuItem exit = new JMenuItem("Exit"); JMenuItem exit = new JMenuItem("Exit");
exit.addActionListener(new ActionListener() { exit.addActionListener(new ActionListener() {
public void actionPerformed(ActionEvent e) { public void actionPerformed(ActionEvent e) {
System.exit(0); System.exit(0);
} }
}); });
JMenu menu = new JMenu("File"); JMenu menu = new JMenu("File");
menu.add(screenshot); menu.add(screenshot);
menu.add(exit); menu.add(exit);
@ -102,7 +102,7 @@ public class ExampleUtils {
component.paint(image.getGraphics()); component.paint(image.getGraphics());
return image; return image;
} }
public static BufferedImage resizeImage(BufferedImage originalImage, int width, int height, int type) throws IOException { public static BufferedImage resizeImage(BufferedImage originalImage, int width, int height, int type) throws IOException {
BufferedImage resizedImage = new BufferedImage(width, height, type); BufferedImage resizedImage = new BufferedImage(width, height, type);
Graphics2D g = resizedImage.createGraphics(); Graphics2D g = resizedImage.createGraphics();

6
src/userguide/java/org/apache/commons/math4/userguide/FastMathTestPerformance.java
View File

@ -21,7 +21,7 @@ import org.apache.commons.math4.util.FastMath;
/** /**
* Performance benchmark for FastMath. * Performance benchmark for FastMath.
* *
*/ */
public class FastMathTestPerformance { public class FastMathTestPerformance {
private static final int RUNS = Integer.parseInt(System.getProperty("testRuns","10000000")); private static final int RUNS = Integer.parseInt(System.getProperty("testRuns","10000000"));
@ -288,7 +288,7 @@ public class FastMathTestPerformance {
report("cos",strictTime,fastTime,mathTime); report("cos",strictTime,fastTime,mathTime);
assertTrue(!Double.isNaN(x)); assertTrue(!Double.isNaN(x));
} }
private static void testAcos() { private static void testAcos() {
double x = 0; double x = 0;
long time = System.nanoTime(); long time = System.nanoTime();
@ -594,7 +594,7 @@ public class FastMathTestPerformance {
report("tanh",strictTime,fastTime,mathTime); report("tanh",strictTime,fastTime,mathTime);
assertTrue(!Double.isNaN(x)); assertTrue(!Double.isNaN(x));
} }
private static void testExpm1() { private static void testExpm1() {
double x = 0; double x = 0;
long time = System.nanoTime(); long time = System.nanoTime();

44
src/userguide/java/org/apache/commons/math4/userguide/LowDiscrepancyGeneratorComparison.java
View File

@ -60,7 +60,7 @@ public class LowDiscrepancyGeneratorComparison {
// normalize points first // normalize points first
points = normalize(points); points = normalize(points);
// now test if the sample is within the unit circle // now test if the sample is within the unit circle
List<Vector2D> circlePoints = new ArrayList<>(); List<Vector2D> circlePoints = new ArrayList<>();
for (Vector2D p : points) { for (Vector2D p : points) {
@ -80,7 +80,7 @@ public class LowDiscrepancyGeneratorComparison {
Vector2D point = Vector2D.of(vector); Vector2D point = Vector2D.of(vector);
points.add(point); points.add(point);
} }
return normalize(points); return normalize(points);
} }
@ -92,16 +92,16 @@ public class LowDiscrepancyGeneratorComparison {
minX = FastMath.min(minX, p.getX()); minX = FastMath.min(minX, p.getX());
maxX = FastMath.max(maxX, p.getX()); maxX = FastMath.max(maxX, p.getX());
} }
double minY, maxY; double minY, maxY;
// use the minimum to detect if we either have input values in the range [0, 1] or [-sqrt(3), sqrt(3)] // use the minimum to detect if we either have input values in the range [0, 1] or [-sqrt(3), sqrt(3)]
if (FastMath.abs(minX) < 0.1) { if (FastMath.abs(minX) < 0.1) {
minX = minY = 0.0; minX = minY = 0.0;
maxX = maxY = 1.0; maxX = maxY = 1.0;
} else { } else {
minX = minY = -FastMath.sqrt(3); minX = minY = -FastMath.sqrt(3);
maxX = maxY = FastMath.sqrt(3); maxX = maxY = FastMath.sqrt(3);
} }
double rangeX = maxX - minX; double rangeX = maxX - minX;
@ -116,16 +116,16 @@ public class LowDiscrepancyGeneratorComparison {
} }
return points; return points;
} }
@SuppressWarnings("serial") @SuppressWarnings("serial")
public static class Display extends ExampleFrame { public static class Display extends ExampleFrame {
public Display() { public Display() {
setTitle("Commons-Math: Pseudo/Quasi-random examples"); setTitle("Commons-Math: Pseudo/Quasi-random examples");
setSize(800, 800); setSize(800, 800);
setLayout(new GridBagLayout()); setLayout(new GridBagLayout());
int[] datasets = new int[] { 256, 1000, 2500, 1000 }; int[] datasets = new int[] { 256, 1000, 2500, 1000 };
List<Pair<String, RandomVectorGenerator>> generators = new ArrayList<Pair<String, RandomVectorGenerator>>(); List<Pair<String, RandomVectorGenerator>> generators = new ArrayList<Pair<String, RandomVectorGenerator>>();
@ -137,18 +137,18 @@ public class LowDiscrepancyGeneratorComparison {
RandomSource.create(RandomSource.MT, 123456789), RandomSource.create(RandomSource.MT, 123456789),
RandomSource.create(RandomSource.MT, 987654321) RandomSource.create(RandomSource.MT, 987654321)
}; };
public double[] nextVector() { public double[] nextVector() {
final double[] vector = new double[2]; final double[] vector = new double[2];
vector[0] = rngs[0].nextDouble(); vector[0] = rngs[0].nextDouble();
vector[1] = rngs[1].nextDouble(); vector[1] = rngs[1].nextDouble();
return vector; return vector;
} }
})); }));
generators.add(new Pair<>("HaltonSequence", new HaltonSequenceGenerator(2))); generators.add(new Pair<>("HaltonSequence", new HaltonSequenceGenerator(2)));
generators.add(new Pair<>("SobolSequence", new SobolSequenceGenerator(2))); generators.add(new Pair<>("SobolSequence", new SobolSequenceGenerator(2)));
GridBagConstraints c = new GridBagConstraints(); GridBagConstraints c = new GridBagConstraints();
c.fill = GridBagConstraints.VERTICAL; c.fill = GridBagConstraints.VERTICAL;
c.gridx = 1; c.gridx = 1;
@ -163,7 +163,7 @@ public class LowDiscrepancyGeneratorComparison {
c.gridx++; c.gridx++;
} }
int saveY = ++c.gridy; int saveY = ++c.gridy;
c.gridx = 0; c.gridx = 0;
for (int type = 0; type < 4; type++) { for (int type = 0; type < 4; type++) {
JLabel text = new JLabel("n=" + String.valueOf(datasets[type])); JLabel text = new JLabel("n=" + String.valueOf(datasets[type]));
@ -198,7 +198,7 @@ public class LowDiscrepancyGeneratorComparison {
} }
c.gridy++; c.gridy++;
} }
} }
} }
@ -212,7 +212,7 @@ public class LowDiscrepancyGeneratorComparison {
public Plot(final List<Vector2D> points) { public Plot(final List<Vector2D> points) {
this.points = points; this.points = points;
} }
@Override @Override
protected void paintComponent(Graphics g) { protected void paintComponent(Graphics g) {
super.paintComponent(g); super.paintComponent(g);
@ -224,17 +224,17 @@ public class LowDiscrepancyGeneratorComparison {
int h = getHeight(); int h = getHeight();
g2.clearRect(0, 0, w, h); g2.clearRect(0, 0, w, h);
g2.setPaint(Color.black); g2.setPaint(Color.black);
g2.drawRect(0, 0, w - 1, h - 1); g2.drawRect(0, 0, w - 1, h - 1);
for (Vector2D point : points) { for (Vector2D point : points) {
Vector2D p = transform(point, w, h); Vector2D p = transform(point, w, h);
double[] arr = p.toArray(); double[] arr = p.toArray();
g2.draw(new Rectangle2D.Double(arr[0] - 1, arr[1] - 1, 2, 2)); g2.draw(new Rectangle2D.Double(arr[0] - 1, arr[1] - 1, 2, 2));
} }
} }
@Override @Override
public Dimension getPreferredSize() { public Dimension getPreferredSize() {
return new Dimension(140, 140); return new Dimension(140, 140);

50
src/userguide/java/org/apache/commons/math4/userguide/filter/CannonballExample.java
View File

@ -53,35 +53,35 @@ import com.xeiam.xchart.StyleManager.LegendPosition;
public class CannonballExample { public class CannonballExample {
public static class Cannonball { public static class Cannonball {
private final double[] gravity = { 0, -9.81 }; private final double[] gravity = { 0, -9.81 };
private final double[] velocity; private final double[] velocity;
private final double[] location; private final double[] location;
private final double timeslice; private final double timeslice;
private final double measurementNoise; private final double measurementNoise;
private final GaussianRandomGenerator rng; private final GaussianRandomGenerator rng;
public Cannonball(double timeslice, double angle, double initialVelocity, double measurementNoise, int seed) { public Cannonball(double timeslice, double angle, double initialVelocity, double measurementNoise, int seed) {
this.timeslice = timeslice; this.timeslice = timeslice;
final double angleInRadians = FastMath.toRadians(angle); final double angleInRadians = FastMath.toRadians(angle);
this.velocity = new double[] { this.velocity = new double[] {
initialVelocity * FastMath.cos(angleInRadians), initialVelocity * FastMath.cos(angleInRadians),
initialVelocity * FastMath.sin(angleInRadians) initialVelocity * FastMath.sin(angleInRadians)
}; };
this.location = new double[] { 0, 0 }; this.location = new double[] { 0, 0 };
this.measurementNoise = measurementNoise; this.measurementNoise = measurementNoise;
this.rng = new GaussianRandomGenerator(RandomSource.create(RandomSource.WELL_19937_C, seed)); this.rng = new GaussianRandomGenerator(RandomSource.create(RandomSource.WELL_19937_C, seed));
} }
public double getX() { public double getX() {
return location[0]; return location[0];
} }
public double getY() { public double getY() {
return location[1]; return location[1];
} }
@ -97,18 +97,18 @@ public class CannonballExample {
public double getXVelocity() { public double getXVelocity() {
return velocity[0]; return velocity[0];
} }
public double getYVelocity() { public double getYVelocity() {
return velocity[1]; return velocity[1];
} }
public void step() { public void step() {
// Break gravitational force into a smaller time slice. // Break gravitational force into a smaller time slice.
double[] slicedGravity = gravity.clone(); double[] slicedGravity = gravity.clone();
for ( int i = 0; i < slicedGravity.length; i++ ) { for ( int i = 0; i < slicedGravity.length; i++ ) {
slicedGravity[i] *= timeslice; slicedGravity[i] *= timeslice;
} }
// Apply the acceleration to velocity. // Apply the acceleration to velocity.
double[] slicedVelocity = velocity.clone(); double[] slicedVelocity = velocity.clone();
for ( int i = 0; i < velocity.length; i++ ) { for ( int i = 0; i < velocity.length; i++ ) {
@ -123,9 +123,9 @@ public class CannonballExample {
} }
} }
} }
public static void cannonballTest(Chart chart) { public static void cannonballTest(Chart chart) {
// time interval for each iteration // time interval for each iteration
final double dt = 0.1; final double dt = 0.1;
// the number of iterations to run // the number of iterations to run
@ -139,7 +139,7 @@ public class CannonballExample {
// the cannonball itself // the cannonball itself
final Cannonball cannonball = new Cannonball(dt, angle, initialVelocity, measurementNoise, 1000); final Cannonball cannonball = new Cannonball(dt, angle, initialVelocity, measurementNoise, 1000);
// A = [ 1, dt, 0, 0 ] => x(n+1) = x(n) + vx(n) // A = [ 1, dt, 0, 0 ] => x(n+1) = x(n) + vx(n)
// [ 0, 1, 0, 0 ] => vx(n+1) = vx(n) // [ 0, 1, 0, 0 ] => vx(n+1) = vx(n)
// [ 0, 0, 1, dt ] => y(n+1) = y(n) + vy(n) // [ 0, 0, 1, dt ] => y(n+1) = y(n) + vy(n)
@ -148,7 +148,7 @@ public class CannonballExample {
{ 1, dt, 0, 0 }, { 1, dt, 0, 0 },
{ 0, 1, 0, 0 }, { 0, 1, 0, 0 },
{ 0, 0, 1, dt }, { 0, 0, 1, dt },
{ 0, 0, 0, 1 } { 0, 0, 0, 1 }
}); });
// The control vector, which adds acceleration to the kinematic equations. // The control vector, which adds acceleration to the kinematic equations.
@ -183,7 +183,7 @@ public class CannonballExample {
{ 0, 0, 1, 0 }, { 0, 0, 1, 0 },
{ 0, 0, 0, 0 } { 0, 0, 0, 0 }
}); });
// This is our guess of the initial state. I intentionally set the Y value // This is our guess of the initial state. I intentionally set the Y value
// wrong to illustrate how fast the Kalman filter will pick up on that. // wrong to illustrate how fast the Kalman filter will pick up on that.
final double speedX = cannonball.getXVelocity(); final double speedX = cannonball.getXVelocity();
@ -201,7 +201,7 @@ public class CannonballExample {
// we assume no process noise -> zero matrix // we assume no process noise -> zero matrix
final RealMatrix Q = MatrixUtils.createRealMatrix(4, 4); final RealMatrix Q = MatrixUtils.createRealMatrix(4, 4);
// the measurement covariance matrix // the measurement covariance matrix
final RealMatrix R = MatrixUtils.createRealMatrix(new double[][] { final RealMatrix R = MatrixUtils.createRealMatrix(new double[][] {
{ var, 0, 0, 0 }, { var, 0, 0, 0 },
@ -220,7 +220,7 @@ public class CannonballExample {
final List<Number> measuredY = new ArrayList<>(); final List<Number> measuredY = new ArrayList<>();
final List<Number> kalmanX = new ArrayList<>(); final List<Number> kalmanX = new ArrayList<>();
final List<Number> kalmanY = new ArrayList<>(); final List<Number> kalmanY = new ArrayList<>();
for (int i = 0; i < iterations; i++) { for (int i = 0; i < iterations; i++) {
// get real location // get real location
@ -251,7 +251,7 @@ public class CannonballExample {
Series dataset = chart.addSeries("true", realX, realY); Series dataset = chart.addSeries("true", realX, realY);
dataset.setMarker(SeriesMarker.NONE); dataset.setMarker(SeriesMarker.NONE);
dataset = chart.addSeries("measured", measuredX, measuredY); dataset = chart.addSeries("measured", measuredX, measuredY);
dataset.setLineStyle(SeriesLineStyle.DOT_DOT); dataset.setLineStyle(SeriesLineStyle.DOT_DOT);
dataset.setMarker(SeriesMarker.NONE); dataset.setMarker(SeriesMarker.NONE);
@ -275,10 +275,10 @@ public class CannonballExample {
chart.getStyleManager().setLegendPadding(6); chart.getStyleManager().setLegendPadding(6);
chart.getStyleManager().setLegendSeriesLineLength(10); chart.getStyleManager().setLegendSeriesLineLength(10);
chart.getStyleManager().setAxisTickLabelsFont(new Font("Arial", Font.PLAIN, 10)); chart.getStyleManager().setAxisTickLabelsFont(new Font("Arial", Font.PLAIN, 10));
chart.getStyleManager().setChartBackgroundColor(Color.white); chart.getStyleManager().setChartBackgroundColor(Color.white);
chart.getStyleManager().setChartPadding(4); chart.getStyleManager().setChartPadding(4);
chart.getStyleManager().setChartType(ChartType.Line); chart.getStyleManager().setChartType(ChartType.Line);
return chart; return chart;
} }
@ -290,20 +290,20 @@ public class CannonballExample {
Chart chart = createChart("Cannonball", LegendPosition.InsideNE); Chart chart = createChart("Cannonball", LegendPosition.InsideNE);
cannonballTest(chart); cannonballTest(chart);
container.add(new XChartPanel(chart)); container.add(new XChartPanel(chart));
container.setBorder(BorderFactory.createLineBorder(Color.black, 1)); container.setBorder(BorderFactory.createLineBorder(Color.black, 1));
return container; return container;
} }
@SuppressWarnings("serial") @SuppressWarnings("serial")
public static class Display extends ExampleFrame { public static class Display extends ExampleFrame {
private JComponent container; private JComponent container;
public Display() { public Display() {
setTitle("Commons Math: Kalman Filter - Cannonball"); setTitle("Commons Math: Kalman Filter - Cannonball");
setSize(800, 600); setSize(800, 600);
container = new JPanel(); container = new JPanel();
JComponent comp = createComponent(); JComponent comp = createComponent();
container.add(comp); container.add(comp);

36
src/userguide/java/org/apache/commons/math4/userguide/filter/ConstantVoltageExample.java
View File

@ -53,7 +53,7 @@ import com.xeiam.xchart.StyleManager.LegendPosition;
public class ConstantVoltageExample { public class ConstantVoltageExample {
public static class VoltMeter { public static class VoltMeter {
private final double initialVoltage; private final double initialVoltage;
private final double processNoise; private final double processNoise;
private final double measurementNoise; private final double measurementNoise;
@ -68,20 +68,20 @@ public class ConstantVoltageExample {
this.measurementNoise = measurementNoise; this.measurementNoise = measurementNoise;
rng = new GaussianRandomGenerator(RandomSource.create(RandomSource.WELL_19937_C, seed)); rng = new GaussianRandomGenerator(RandomSource.create(RandomSource.WELL_19937_C, seed));
} }
/** /**
* Returns the real voltage without any measurement noise. * Returns the real voltage without any measurement noise.
* *
* @return the real voltage * @return the real voltage
*/ */
public double getVoltage() { public double getVoltage() {
return voltage; return voltage;
} }
public double getMeasuredVoltage() { public double getMeasuredVoltage() {
return getVoltage() + rng.nextNormalizedDouble() * measurementNoise; return getVoltage() + rng.nextNormalizedDouble() * measurementNoise;
} }
public void step() { public void step() {
// we apply only the process noise // we apply only the process noise
voltage = initialVoltage + rng.nextNormalizedDouble() * processNoise; voltage = initialVoltage + rng.nextNormalizedDouble() * processNoise;
@ -96,7 +96,7 @@ public class ConstantVoltageExample {
final double processNoise = 1e-5d; final double processNoise = 1e-5d;
final VoltMeter voltMeter = new VoltMeter(voltage, processNoise, measurementNoise, 2); final VoltMeter voltMeter = new VoltMeter(voltage, processNoise, measurementNoise, 2);
// the state transition matrix -> constant // the state transition matrix -> constant
final RealMatrix A = new Array2DRowRealMatrix(new double[] { 1d }); final RealMatrix A = new Array2DRowRealMatrix(new double[] { 1d });
@ -108,7 +108,7 @@ public class ConstantVoltageExample {
// the initial state vector -> slightly wrong // the initial state vector -> slightly wrong
final RealVector x0 = new ArrayRealVector(new double[] { 1.45 }); final RealVector x0 = new ArrayRealVector(new double[] { 1.45 });
// the process covariance matrix // the process covariance matrix
final RealMatrix Q = new Array2DRowRealMatrix(new double[] { processNoise * processNoise }); final RealMatrix Q = new Array2DRowRealMatrix(new double[] { processNoise * processNoise });
@ -128,7 +128,7 @@ public class ConstantVoltageExample {
final List<Number> kalmanVoltageSeries = new ArrayList<>(); final List<Number> kalmanVoltageSeries = new ArrayList<>();
final List<Number> covSeries = new ArrayList<>(); final List<Number> covSeries = new ArrayList<>();
for (int i = 0; i < 300; i++) { for (int i = 0; i < 300; i++) {
xAxis.add(i); xAxis.add(i);
@ -152,7 +152,7 @@ public class ConstantVoltageExample {
Series dataset = chart1.addSeries("real", xAxis, realVoltageSeries); Series dataset = chart1.addSeries("real", xAxis, realVoltageSeries);
dataset.setMarker(SeriesMarker.NONE); dataset.setMarker(SeriesMarker.NONE);
dataset = chart1.addSeries("measured", xAxis, measuredVoltageSeries); dataset = chart1.addSeries("measured", xAxis, measuredVoltageSeries);
dataset.setLineStyle(SeriesLineStyle.DOT_DOT); dataset.setLineStyle(SeriesLineStyle.DOT_DOT);
dataset.setMarker(SeriesMarker.NONE); dataset.setMarker(SeriesMarker.NONE);
@ -166,7 +166,7 @@ public class ConstantVoltageExample {
chart2.setYAxisTitle("(Voltage)²"); chart2.setYAxisTitle("(Voltage)²");
chart2.setXAxisTitle("Iteration"); chart2.setXAxisTitle("Iteration");
dataset = chart2.addSeries("cov", xAxis, covSeries); dataset = chart2.addSeries("cov", xAxis, covSeries);
dataset.setLineColor(Color.black); dataset.setLineColor(Color.black);
dataset.setLineStyle(SeriesLineStyle.SOLID); dataset.setLineStyle(SeriesLineStyle.SOLID);
@ -187,10 +187,10 @@ public class ConstantVoltageExample {
chart.getStyleManager().setLegendPadding(6); chart.getStyleManager().setLegendPadding(6);
chart.getStyleManager().setLegendSeriesLineLength(10); chart.getStyleManager().setLegendSeriesLineLength(10);
chart.getStyleManager().setAxisTickLabelsFont(new Font("Arial", Font.PLAIN, 10)); chart.getStyleManager().setAxisTickLabelsFont(new Font("Arial", Font.PLAIN, 10));
chart.getStyleManager().setChartBackgroundColor(Color.white); chart.getStyleManager().setChartBackgroundColor(Color.white);
chart.getStyleManager().setChartPadding(4); chart.getStyleManager().setChartPadding(4);
chart.getStyleManager().setChartType(ChartType.Line); chart.getStyleManager().setChartType(ChartType.Line);
return chart; return chart;
} }
@ -198,28 +198,28 @@ public class ConstantVoltageExample {
public static JComponent createComponent() { public static JComponent createComponent() {
JComponent container = new JPanel(); JComponent container = new JPanel();
container.setLayout(new BoxLayout(container, BoxLayout.LINE_AXIS)); container.setLayout(new BoxLayout(container, BoxLayout.LINE_AXIS));
Chart chart1 = createChart("Voltage", 550, 450, LegendPosition.InsideNE, true); Chart chart1 = createChart("Voltage", 550, 450, LegendPosition.InsideNE, true);
Chart chart2 = createChart("Error Covariance", 450, 450, LegendPosition.InsideNE, false); Chart chart2 = createChart("Error Covariance", 450, 450, LegendPosition.InsideNE, false);
constantVoltageTest(chart1, chart2); constantVoltageTest(chart1, chart2);
container.add(new XChartPanel(chart1)); container.add(new XChartPanel(chart1));
container.add(new XChartPanel(chart2)); container.add(new XChartPanel(chart2));
container.setBorder(BorderFactory.createLineBorder(Color.black, 1)); container.setBorder(BorderFactory.createLineBorder(Color.black, 1));
return container; return container;
} }
@SuppressWarnings("serial") @SuppressWarnings("serial")
public static class Display extends ExampleFrame { public static class Display extends ExampleFrame {
private JComponent container; private JComponent container;
public Display() { public Display() {
setTitle("Commons-Math: Kalman Filter example"); setTitle("Commons-Math: Kalman Filter example");
setSize(1100, 700); setSize(1100, 700);
container = new JPanel(); container = new JPanel();
JComponent comp = createComponent(); JComponent comp = createComponent();

16
src/userguide/java/org/apache/commons/math4/userguide/genetics/HelloWorldExample.java
View File

@ -60,13 +60,13 @@ public class HelloWorldExample {
// stopping condition // stopping condition
StoppingCondition stoppingCondition = new StoppingCondition() { StoppingCondition stoppingCondition = new StoppingCondition() {
int generation = 0; int generation = 0;
// @Override // @Override
public boolean isSatisfied(Population population) { public boolean isSatisfied(Population population) {
Chromosome fittestChromosome = population.getFittestChromosome(); Chromosome fittestChromosome = population.getFittestChromosome();
if (generation == 1 || generation % 10 == 0) { if (generation == 1 || generation % 10 == 0) {
System.out.println("Generation " + generation + ": " + fittestChromosome.toString()); System.out.println("Generation " + generation + ": " + fittestChromosome.toString());
} }
@ -82,7 +82,7 @@ public class HelloWorldExample {
}; };
System.out.println("Starting evolution ..."); System.out.println("Starting evolution ...");
// run the algorithm // run the algorithm
Population finalPopulation = ga.evolve(initial, stoppingCondition); Population finalPopulation = ga.evolve(initial, stoppingCondition);
@ -94,15 +94,15 @@ public class HelloWorldExample {
System.out.println("Generation " + ga.getGenerationsEvolved() + ": " + best.toString()); System.out.println("Generation " + ga.getGenerationsEvolved() + ": " + best.toString());
System.out.println("Total execution time: " + (endTime - startTime) + "ms"); System.out.println("Total execution time: " + (endTime - startTime) + "ms");
} }
private static List<Character> randomRepresentation(int length) { private static List<Character> randomRepresentation(int length) {
return asList(RandomStringUtils.randomAscii(length)); return asList(RandomStringUtils.randomAscii(length));
} }
private static List<Character> asList(String str) { private static List<Character> asList(String str) {
return Arrays.asList(ArrayUtils.toObject(str.toCharArray())); return Arrays.asList(ArrayUtils.toObject(str.toCharArray()));
} }
private static Population getInitialPopulation() { private static Population getInitialPopulation() {
List<Chromosome> popList = new LinkedList<>(); List<Chromosome> popList = new LinkedList<>();
@ -173,7 +173,7 @@ public class HelloWorldExample {
StringChromosome strChromosome = (StringChromosome) original; StringChromosome strChromosome = (StringChromosome) original;
List<Character> characters = strChromosome.getStringRepresentation(); List<Character> characters = strChromosome.getStringRepresentation();
int mutationIndex = GeneticAlgorithm.getRandomGenerator().nextInt(characters.size()); int mutationIndex = GeneticAlgorithm.getRandomGenerator().nextInt(characters.size());
List<Character> mutatedChromosome = new ArrayList<>(characters); List<Character> mutatedChromosome = new ArrayList<>(characters);

16
src/userguide/java/org/apache/commons/math4/userguide/genetics/ImageEvolutionExample.java
View File

@ -49,7 +49,7 @@ import org.apache.commons.math4.userguide.ExampleUtils.ExampleFrame;
* with ~100 semi-transparent polygons of length 6. * with ~100 semi-transparent polygons of length 6.
* <p> * <p>
* The fitness function is quite simple yet expensive to compute: * The fitness function is quite simple yet expensive to compute:
* *
* - draw the polygons of a chromosome to an image * - draw the polygons of a chromosome to an image
* - compare each pixel with the corresponding reference image * - compare each pixel with the corresponding reference image
* <p> * <p>
@ -62,7 +62,7 @@ import org.apache.commons.math4.userguide.ExampleUtils.ExampleFrame;
* - add a gallery of results after x iterations / minutes (either automatic or based on button click) * - add a gallery of results after x iterations / minutes (either automatic or based on button click)
* - allow loading / selection of other images * - allow loading / selection of other images
* - add logging in the user interface, e.g. number of generations, time spent, ... * - add logging in the user interface, e.g. number of generations, time spent, ...
* *
* @see <a href="http://www.nihilogic.dk/labs/evolving-images/">Evolving Images with JavaScript and canvas (Nihilogic)</a> * @see <a href="http://www.nihilogic.dk/labs/evolving-images/">Evolving Images with JavaScript and canvas (Nihilogic)</a>
*/ */
@SuppressWarnings("serial") @SuppressWarnings("serial")
@ -77,7 +77,7 @@ public class ImageEvolutionExample {
public static final int POLYGON_COUNT = 100; public static final int POLYGON_COUNT = 100;
public static class Display extends ExampleFrame { public static class Display extends ExampleFrame {
private GeneticAlgorithm ga; private GeneticAlgorithm ga;
private Population currentPopulation; private Population currentPopulation;
private Chromosome bestFit; private Chromosome bestFit;
@ -86,16 +86,16 @@ public class ImageEvolutionExample {
private volatile boolean noStopRequested; private volatile boolean noStopRequested;
private BufferedImage ref; private BufferedImage ref;
private BufferedImage referenceImage; private BufferedImage referenceImage;
private BufferedImage testImage; private BufferedImage testImage;
private ImagePainter painter; private ImagePainter painter;
public Display() throws Exception { public Display() throws Exception {
setTitle("Commons-Math: Image Evolution Example"); setTitle("Commons-Math: Image Evolution Example");
setSize(600, 400); setSize(600, 400);
setLayout(new FlowLayout()); setLayout(new FlowLayout());
Box bar = Box.createHorizontalBox(); Box bar = Box.createHorizontalBox();
@ -143,7 +143,7 @@ public class ImageEvolutionExample {
currentPopulation = getInitialPopulation(); currentPopulation = getInitialPopulation();
bestFit = currentPopulation.getFittestChromosome(); bestFit = currentPopulation.getFittestChromosome();
} }
public boolean isAlive() { public boolean isAlive() {
return internalThread != null && internalThread.isAlive(); return internalThread != null && internalThread.isAlive();
} }
@ -175,7 +175,7 @@ public class ImageEvolutionExample {
} }
private class ImagePainter extends Component { private class ImagePainter extends Component {
private int width; private int width;
private int height; private int height;

14
src/userguide/java/org/apache/commons/math4/userguide/genetics/Polygon.java
View File

@ -53,7 +53,7 @@ public class Polygon {
final int polygonSize = 4 + 2 * length; final int polygonSize = 4 + 2 * length;
final UniformRandomProvider random = GeneticAlgorithm.getRandomGenerator(); final UniformRandomProvider random = GeneticAlgorithm.getRandomGenerator();
Polygon p = new Polygon(); Polygon p = new Polygon();
p.data = new float[polygonSize]; p.data = new float[polygonSize];
@ -61,10 +61,10 @@ public class Polygon {
p.data[1] = random.nextFloat(); // g p.data[1] = random.nextFloat(); // g
p.data[2] = random.nextFloat(); // b p.data[2] = random.nextFloat(); // b
p.data[3] = FastMath.max(0.2f, random.nextFloat() * random.nextFloat()); // a p.data[3] = FastMath.max(0.2f, random.nextFloat() * random.nextFloat()); // a
float px = random.nextFloat(); float px = random.nextFloat();
float py = random.nextFloat(); float py = random.nextFloat();
for (int k = 0; k < length; k++) { for (int k = 0; k < length; k++) {
p.data[4 + 2*k] = px + (random.nextFloat() - 0.5f); p.data[4 + 2*k] = px + (random.nextFloat() - 0.5f);
p.data[5 + 2*k] = py + (random.nextFloat() - 0.5f); p.data[5 + 2*k] = py + (random.nextFloat() - 0.5f);
@ -78,7 +78,7 @@ public class Polygon {
* Each component of the Polygon may be mutated according to the specified mutation rate. * Each component of the Polygon may be mutated according to the specified mutation rate.
* In case a component is going to be mutated, its value will be randomly modified in the * In case a component is going to be mutated, its value will be randomly modified in the
* uniform range of [-mutationAmount, +mutationAmount]. * uniform range of [-mutationAmount, +mutationAmount].
* *
* @param mutationRate the mutation rate * @param mutationRate the mutation rate
* @param mutationAmount the mutation amount * @param mutationAmount the mutation amount
* @return a new Polygon * @return a new Polygon
@ -91,7 +91,7 @@ public class Polygon {
float val = this.data[i]; float val = this.data[i];
if (GeneticAlgorithm.getRandomGenerator().nextFloat() < mutationRate) { if (GeneticAlgorithm.getRandomGenerator().nextFloat() < mutationRate) {
val += GeneticAlgorithm.getRandomGenerator().nextFloat() * mutationAmount * 2 - mutationAmount; val += GeneticAlgorithm.getRandomGenerator().nextFloat() * mutationAmount * 2 - mutationAmount;
if (val < 0f) { if (val < 0f) {
val = 0f; val = 0f;
} else if (val > 1f) { } else if (val > 1f) {
@ -101,7 +101,7 @@ public class Polygon {
mutated.data[i] = val; mutated.data[i] = val;
} }
return mutated; return mutated;
} }
/** /**
* Draw the Polygon to the buffer of the given size. * Draw the Polygon to the buffer of the given size.
@ -111,7 +111,7 @@ public class Polygon {
GeneralPath path = new GeneralPath(); GeneralPath path = new GeneralPath();
path.moveTo(data[4] * width, data[5] * height); path.moveTo(data[4] * width, data[5] * height);
int polygonLength = (data.length - 4) / 2; int polygonLength = (data.length - 4) / 2;
for (int j = 1; j < polygonLength; j++) { for (int j = 1; j < polygonLength; j++) {
path.lineTo(data[4 + j * 2] * width, data[5 + j * 2] * height); path.lineTo(data[4 + j * 2] * width, data[5 + j * 2] * height);

4
src/userguide/java/org/apache/commons/math4/userguide/genetics/PolygonChromosome.java
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@ -78,7 +78,7 @@ public class PolygonChromosome extends AbstractListChromosome<Polygon> {
public double fitness() { public double fitness() {
Graphics2D g2 = testImage.createGraphics(); Graphics2D g2 = testImage.createGraphics();
int width = testImage.getWidth(); int width = testImage.getWidth();
int height = testImage.getHeight(); int height = testImage.getHeight();
@ -123,7 +123,7 @@ public class PolygonChromosome extends AbstractListChromosome<Polygon> {
public String toString() { public String toString() {
return String.format("(f=%s)", getFitness()); return String.format("(f=%s)", getFitness());
} }
public static Chromosome randomChromosome(int polygonLength, int polygonCount) { public static Chromosome randomChromosome(int polygonLength, int polygonCount) {
List<Polygon> list = new ArrayList<>(polygonCount); List<Polygon> list = new ArrayList<>(polygonCount);
for (int j = 0; j < polygonCount; j++) { for (int j = 0; j < polygonCount; j++) {

4
src/userguide/java/org/apache/commons/math4/userguide/genetics/RandomPolygonMutation.java
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@ -23,10 +23,10 @@ import org.apache.commons.math4.genetics.Chromosome;
import org.apache.commons.math4.genetics.MutationPolicy; import org.apache.commons.math4.genetics.MutationPolicy;
public class RandomPolygonMutation implements MutationPolicy { public class RandomPolygonMutation implements MutationPolicy {
private float mutationRate; private float mutationRate;
private float mutationAmount; private float mutationAmount;
public RandomPolygonMutation(float mutationRate, float mutationAmount) { public RandomPolygonMutation(float mutationRate, float mutationAmount) {
this.mutationRate = mutationRate; this.mutationRate = mutationRate;
this.mutationAmount = mutationAmount; this.mutationAmount = mutationAmount;

6
src/userguide/pom.xml
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@ -22,7 +22,7 @@
<modelVersion>4.0.0</modelVersion> <modelVersion>4.0.0</modelVersion>
<groupId>org.apache.commons</groupId> <groupId>org.apache.commons</groupId>
<artifactId>commons-math4-examples</artifactId> <artifactId>commons-math4-examples</artifactId>
<version>4.0-SNAPSHOT</version> <version>4.0-SNAPSHOT</version>
<name>Commons Math User Guide</name> <name>Commons Math User Guide</name>
<inceptionYear>2003</inceptionYear> <inceptionYear>2003</inceptionYear>
<description>Examples</description> <description>Examples</description>
@ -41,7 +41,7 @@
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<maven.compiler.source>1.7</maven.compiler.source> <maven.compiler.source>1.7</maven.compiler.source>
<maven.compiler.target>1.7</maven.compiler.target> <maven.compiler.target>1.7</maven.compiler.target>
</properties> </properties>
<build> <build>
<sourceDirectory>java</sourceDirectory> <sourceDirectory>java</sourceDirectory>
@ -105,7 +105,7 @@
<groupId>org.piccolo2d</groupId> <groupId>org.piccolo2d</groupId>
<artifactId>piccolo2d-extras</artifactId> <artifactId>piccolo2d-extras</artifactId>
<version>3.0</version> <version>3.0</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.apache.commons</groupId> <groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId> <artifactId>commons-lang3</artifactId>