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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);
}
}
private void generateSineData(double[] xval, double[] yval, double xnoise, double ynoise) {
double dx = 2 * JdkMath.PI / xval.length;
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();
UnivariateSolver solver = new MullerSolver();
double min;
double max;
double max;
double expected;
double result;
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 {
@Test(expected=NotStrictlyPositiveException.class)
public void testIterationCheckPrecondition() {
new SimplePointChecker<PointValuePair>(1e-1, 1e-2, 0);
new SimplePointChecker<>(1e-1, 1e-2, 0);
}
@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(),
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
= optimizer.optimize(new MaxEval(600),
@ -133,7 +133,7 @@ public class MultivariateFunctionPenaltyAdapterTest {
biQuadratic.getUpper(),
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
= 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("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");
@ -260,10 +260,10 @@ public final class FrequencyTest {
Assert.assertEquals(1, f.getCount(ONE_LONG));
Assert.assertEquals(0, f.getCount(TWO_LONG));
Frequency<Long> g = new Frequency<>();
Frequency<Long> g = new Frequency<Long>();
g.addValue(TWO_LONG);
Frequency<Long> h = new Frequency<>();
Frequency<Long> h = new Frequency<Long>();
h.addValue(THREE_LONG);
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)
public void testInitial() {
PSquarePercentile.newMarkers(new ArrayList<Double>(), 0.5);
PSquarePercentile.newMarkers(new ArrayList<>(), 0.5);
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()));
linkMap.put(id, new HashSet<Long>());
linkMap.put(id, new HashSet<>());
if (id > nextId.get()) {
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:
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.
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">
Added logDensity(double) to RealDistribution and logProbability(int)
to IntegerDistribution interface. The implementations have already been
updated in release 3.3.
updated in release 3.3.
</action>
<action dev="tn" type="update" issue="MATH-1155">
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
case polynomials are generated from multiple threads. Furthermore, the
synchronization is now performed on the coefficient list instead of the class.
</action>
</action>
<action dev="psteitz" type="update" issue="MATH-1246">
Added bootstrap method to KolmogorovSmirnov test.
</action>
@ -599,7 +599,7 @@ Users are encouraged to upgrade to this version as this release not
</action>
<action dev="tn" type="fix" issue="MATH-1283" due-to="Jean Noel Delavalade">
Fixed "Gamma#gamma(double)" for negative values smaller than -20.
</action>
</action>
<action dev="tn" type="fix" issue="MATH-1237" due-to="Ken Williams">
Fixed javadoc of methods {floorDiv,floorMod} in class "FastMath".
</action>
@ -697,7 +697,7 @@ Users are encouraged to upgrade to this version as this release not
</action>
<action dev="psteitz" type="fix" issue="MATH-1245">
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).
</action>
<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">
Improved performance to calculate the two-sample Kolmogorov-Smirnov test
via monte carlo simulation ("KolmogorovSmirnovTets#monteCarloP(...)").
</action>
</action>
<action dev="tn" type="fix" issue="MATH-1241" due-to="Aleksei Dievskii">
A "StackOverflowException" was thrown when passing Double.NaN or infinity
to "Gamma#digamma(double)" or "Gamma#trigamma(double)". Now the input value
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">
Improved performance of calculating the two-sample Kolmogorov-Smirnov
test statistic.
</action>
</action>
<action dev="luc" type="fix" issue="MATH-1232">
Fixed error message for unknown parameter name in ODE.
</action>
@ -738,7 +738,7 @@ Users are encouraged to upgrade to this version as this release not
<action dev="tn" type="fix">
Fix potential branching errors in "FastMath#pow(double, double)" when
passing special values, i.e. infinity, due to erroneous JIT optimization.
</action>
</action>
<action dev="luc" type="fix" issue="MATH-1118" >
Fixed equals/hashcode contract failure for Dfp.
</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">
Computation of 2-sample Kolmogorov-Smirnov statistic in case of ties
was not correct.
</action>
</action>
</release>
<release version="3.5" date="2015-04-17" description="
This is a minor release: It combines bug fixes and new features.
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.
Most notable among the new features are:
getQuadraticMean method added to DescriptiveStatistics SummaryStatistics
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
numerous bug fixes. See below for a full list.
The minimum version of the Java platform required to compile and use
Apache Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0).
Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha
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">
Moved FastMathTestPerformance out of the main test tree, as is is
a benchmark rather than a test.
</action>
</action>
<action dev="luc" type="add">
Added a way to build polyhedrons sets from a list of vertices and
facets specified using vertices indices.
</action>
</action>
<action dev="psteitz" type="update" issue="MATH-1213">
Added Laguerre complex solve methods taking maxEval parameters.
</action>
<action dev="luc" type="fix" issue="MATH-1191">
Fixed ignored method parameters in QRDecomposition protected methods.
</action>
</action>
<action dev="luc" type="fix" issue="MATH-1211" due-to="Mike Zimmerman">
Fixed wrong selection of line/polyhedron intersection point.
</action>
</action>
<action dev="luc" type="fix" issue="MATH-1162">
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">
Fixed link to algorithm description in "PoissonDistribution#sample()".
</action>
</action>
<action dev="psteitz" type="fix" issue="MATH-1208">
EmpiricalDistribution cumulativeProbability can return NaN when evaluated within a constant bin.
</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.
Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.3 JAR file.
Most notable among the new features are:
new distributions (Gumbel, Laplace, Logistic, Nakagami), and
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
several improvements on performances or robustness. See below
for a full list.
The minimum version of the Java platform required to compile and use
Apache Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0).
Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha
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 dev="psteitz" type="fix" issue="MATH-1181">
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.
</action>
<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 dev="luc" type="update" >
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 dev="luc" type="fix" issue="MATH-1174" >
Fixed a problem with too thin polygons considered to have infinite size.
</action>
<action dev="luc" type="add" >
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 dev="luc" type="fix" issue="MATH-1162" >
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")
to avoid evaluating the model when the optimization algorithm does not actually
require it.
</action>
</action>
<action dev="luc" type="fix" issue="MATH-1127">
Fixed overflow in Precision.equals with ulps (both double and float versions).
</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.
Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.x JAR file.
Most notable among the new features are:
Framework for creating artificial neural nets, self organizing feature maps,
computational geometry algorithms (convex hull, enclosing ball), performance
improvements of the linear simplex solver, refactoring of curve fitters,
low-discrepancy random generators (sobol, halton), least-squares fitting.
The minimum version of the Java platform required to compile and use
Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0).
Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha
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
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
used for other purposes.
used for other purposes.
">
<action dev="tn" type="add" issue="MATH-1110" due-to="Edward Segall">
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").
</action>
<action dev="erans" type="update" issue="MATH-995">
Documented limitation of "IterativeLegendreGaussIntegrator" (added
Documented limitation of "IterativeLegendreGaussIntegrator" (added
warning about potential wrong usage).
</action>
<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.
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.
Most notable among the new features are: Framework for automatic
differentiation, Lévy distribution, prime numbers, enumerated
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
coordinates with gradients and Hessians, reorganized clustering
package with different distance implementations.
The minimum version of the Java platform required to compile and use
Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0).
Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha
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 dev="luc" type="add" >
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 dev="luc" type="add" >
Added partial derivatives computation for 3D vectors and rotations.
Added partial derivatives computation for 3D vectors and rotations.
</action>
<action dev="luc" type="fix" issue="MATH-935" >
Fixed DerivativeStructure.atan2 for special cases when both arguments are +/-0.
</action>
<action dev="luc" type="add" >
Added accurate linear combination of DerivativeStructure instances,
avoiding cancellation.
avoiding cancellation.
</action>
<action dev="erans" type="update" issue="MATH-933">
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".
</action>
<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
values in class "SimplexSolver".
</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.
Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.0 JAR file.
Most notable among the new features are: Framework for automatic
differentiation, multivariate mixture model distribution, quaternions,
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
the implementation of the Gamma and Beta special functions, optimizers
API, deprecation of the sparse vector implementation.
The minimal version of the Java platform required to compile and use
Commons Math is Java 5.
Users are encouraged to upgrade to this version as this release not
only includes bug fixes but also deprecates numerous classes and
methods that will be deleted from the next major release (4.0).
Caveat:
1. The implementation of the BOBYQA optimization algorithm is in alpha
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
and to extend the unit tests suite.
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">
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="EqualsHashCode" />
<!-- Method parameters and local variables should not hide fields, except in constructors and setters -->
<!--
<!--
<module name="HiddenField">
<property name="ignoreConstructorParameter" value="true" />
<property name="ignoreSetter" value="true" />

4
src/site/site.xml
View File

@ -50,9 +50,9 @@
<item name="Javadoc (2.2 release)"
href="http://commons.apache.org/math/javadocs/api-2.2/index.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"/>
<item name="Wiki"
<item name="Wiki"
href="http://wiki.apache.org/commons/Math"/>
<item name="Developers Guide" href="/developers.html"/>
<item name="Proposal" href="/proposal.html"/>

32
src/site/xdoc/developers.xml
View File

@ -15,7 +15,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<document>
<properties>
<title>Developers Guide</title>
@ -43,7 +43,7 @@
<p>
<ol>
<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.
</li>
<li>Download the Commons Math source code. Follow the instructions
@ -57,8 +57,8 @@
read-write access.
</li>
<li>Like most commons components, Commons Math uses Apache Maven as our
build tool. The sources can also be built using Ant (a working
Ant build.xml is included in the top level project directory).
build tool. The sources can also be built using Ant (a working
Ant build.xml is included in the top level project directory).
To build Commons Math using Maven, you can follow the instructions for
<a href="http://maven.apache.org/run-maven/index.html">Building a
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">
Math Wish List Wiki Page.</a>
</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">
email lists</a> and use them appropriately (make sure the string
"[math]" starts the Subject line of all your postings).
@ -81,15 +81,15 @@
<li>
<a href="https://issues.apache.org/jira/secure/Signup!default.jspa">
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">
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>
See the <a href="http://commons.apache.org/math/issue-tracking.html">
Commons Math Issue Tracking Page</a> for more information on how to
search for or submit bugs or enhancement requests.
</p>
</p>
<li>
Generating patches: The requested format for generating patches is
the Unified Diff format, which can be easily generated using the git
@ -105,7 +105,7 @@
<p>
Follow the steps below when making suggestions for additions or
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!
<ol>
<li>Start with a post to the commons-dev mailing list, with [math] at
@ -122,7 +122,7 @@
useful</li>
</ul></li>
<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
List</a> corresponding to the idea. Include a reference to the
discussion thread. </li>
@ -139,7 +139,7 @@
ticket if a patch goes more than a few days with no comment or commit.
</li>
</ol>
</p>
</p>
</subsection>
<subsection name='Coding Style'>
<p>
@ -191,7 +191,7 @@
formulas and escape special characters.</li>
<li>
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>
<li>
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.
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
advertised preconditions</i> it is not appropriate to throw
advertised preconditions</i> it is not appropriate to throw
MathIllegalArgumentException.</li>
<li>
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>
</ul>
</subsection>
@ -259,7 +259,7 @@
All new source file submissions <i>must</i> include the Apache Software
License in a comment that begins the file </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
Agreement (CLA)</a></li>
<li>
@ -271,7 +271,7 @@
<li>
References to source materials covered by restrictive proprietary
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>.
Any questions about copyright or patent issues should be raised on
the commons-dev mailing list before contributing or committing code.

4
src/site/xdoc/index.xml
View File

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

12
src/site/xdoc/mail-lists.xml
View File

@ -48,7 +48,7 @@ limitations under the License.
<section name="Overview">
<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>.
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,
@ -68,8 +68,8 @@ limitations under the License.
</p>
<p>
<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.
Otherwise, please upload the file to a public server and include the URL in the mail.
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.
</p>
</section>
@ -185,10 +185,10 @@ limitations under the License.
General announcements of Apache project releases.
<br /><br />
</td>
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30
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<document>
<properties>
@ -29,40 +29,40 @@
<section name='Proposal for math Package'>
<subsection name='(0) Rationale'>
<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
basic statistics or solving a system of linear equations require components not available in java
<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
basic statistics or solving a system of linear equations require components not available in java
or commons-lang.</p>
<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
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
<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
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
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>
</subsection>
<subsection name='(1) Scope of the Package'>
<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
<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
the Java programming language or commons-lang. The guiding principles for commons-math will be:
<ol>
<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>
<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>
<li>Limited dependencies. No external dependencies beyond Commons components and the JDK</li>
</ol>
</p>
</subsection>
<subsection name='(1.5) Interaction With Other Packages'>
<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 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:
<ul>
<li>Simple univariate statistics (mean, standard deviation, n, confidence intervals)</li>

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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="analysis.html">
<properties>
@ -215,7 +215,7 @@
This may again result in roots way off the true value, without
indication. There is not much a generic algorithm can do if
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
requires knowledge of the analytical properties of the function under
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
much larger in absolute value than 100, the default absolute
accuracy may never be reached because the given relative
accuracy is reached first.
accuracy is reached first.
</td>
</tr>
<tr>
@ -375,7 +375,7 @@ double c = UnivariateSolverUtils.forceSide(100, function,
<p>
A <a href="../apidocs/org/apache/commons/math4/analysis/interpolation/UnivariateInterpolator.html">
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>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
@ -387,7 +387,7 @@ double c = UnivariateSolverUtils.forceSide(100, function,
interpolation algorithm is more determined by the kind of the interpolated function
rather than the set of points to interpolate.
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>Typical usage:</p>
<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
derivative is continuous but not smooth. The x values passed to the
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>.
</p>
<p>
@ -525,7 +525,7 @@ System.out.println("interpolation polynomial: " + interpolator.getPolynomials()[
trapezoid method</a></li>
<li><a href="../apidocs/org/apache/commons/math4/analysis/integration/LegendreGaussIntegrator.html">
Legendre-Gauss method</a></li>
</ul>
</ul>
</p>
</subsection>
<subsection name="4.6 Polynomials" href="polynomials">

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

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

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@ -7,16 +7,16 @@
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 obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="filter.html">
<properties>
@ -34,11 +34,11 @@
<a href="../apidocs/org/apache/commons/math4/filter/KalmanFilter.html">
KalmanFilter</a> provides a discrete-time filter to estimate
a stochastic linear process.</p>
<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">
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
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
in the mathematical literature:
<ul>
<li>A - state transition matrix</li>
@ -52,7 +52,7 @@
<p>
<dl>
<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
process and measurement noises, simply implement your own models.
<source>
@ -74,7 +74,7 @@ KalmanFilter filter = new KalmanFilter(pm, mm);
</source>
</dd>
<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>
for (;;) {
// predict the state estimate one time-step ahead
@ -86,15 +86,15 @@ for (;;) {
// correct the state estimate with the latest measurement
filter.correct(z);
double[] stateEstimate = filter.getStateEstimation();
// do something with it
}
</source>
</dd>
<dt>Constant Voltage Example</dt>
<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
<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
imposed measurement noise of 0.1V and assume an internal process noise of 1e-5V.
<source>
double constantVoltage = 10d;
@ -118,7 +118,7 @@ RealMatrix R = new Array2DRowRealMatrix(new double[] { measurementNoise });
ProcessModel pm = new DefaultProcessModel(A, B, Q, x, P0);
MeasurementModel mm = new DefaultMeasurementModel(H, R);
KalmanFilter filter = new KalmanFilter(pm, mm);
KalmanFilter filter = new KalmanFilter(pm, mm);
// process and measurement noise vectors
RealVector pNoise = new ArrayRealVector(1);
@ -148,7 +148,7 @@ for (int i = 0; i &lt; 60; i++) {
</source>
</dd>
<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
state is modeled as (position, velocity) and we only observe the position. A 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];
}
</source>
</dd>
</dd>
</dl>
</p>
</p>
</subsection>
</section>
</body>

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@ -7,16 +7,16 @@
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 obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="fraction.html">
<properties>

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@ -7,16 +7,16 @@
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 obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="genetics.html">
<properties>
@ -26,17 +26,17 @@
<section name="16 Genetic Algorithms">
<subsection name="16.1 Overview" href="overview">
<p>
The genetics package provides a framework and implementations for
The genetics package provides a framework and implementations for
genetic algorithms.
</p>
</subsection>
<subsection name="16.2 GA Framework">
<p>
<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">
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">
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">
@ -62,10 +62,10 @@
<li>Loop until new generation is filled:</li>
<ul><li>Apply configured <code>SelectionPolicy</code> to select a pair of parents
from <code>current</code></li>
<li>With probability =
<li>With probability =
<a href="../apidocs/org/apache/commons/math4/genetics/GeneticAlgorithm.html#getCrossoverRate()">
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()">
getMutationRate()</a>,
apply configured <code>MutationPolicy</code> to each of the offspring</li>
@ -73,7 +73,7 @@
space permitting</li>
</ul>
<li>Return nextGeneration</li>
</ol>
</ol>
</p>
</subsection>
<subsection name="16.3 Implementation">
@ -88,16 +88,16 @@ GeneticAlgorithm ga = new GeneticAlgorithm(
0.10,
new TournamentSelection(TOURNAMENT_ARITY)
);
// initial population
Population initial = getInitialPopulation();
// stopping condition
StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
// run the algorithm
Population finalPopulation = ga.evolve(initial, stopCond);
// best chromosome from the final population
Chromosome bestFinal = finalPopulation.getFittestChromosome();
</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
n random chromosomes without replacement from the population, and then selecting the fittest chromosome among them.</td></tr>
</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>
is reached. In the example above, a
<a href="../apidocs/org/apache/commons/math4/genetics/FixedGenerationCount.html">FixedGenerationCount</a>

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

36
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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="index.html">
<properties>
@ -58,24 +58,24 @@
<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#a3.3_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#a3.6_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>
<ul>
<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#a4.3_Root-finding">4.3 Root-finding</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#a4.4_Interpolation">4.4 Interpolation</a></li>
<li><a href="analysis.html#a4.5_Integration">4.5 Integration</a></li>
<li><a href="analysis.html#a4.6_Polynomials">4.6 Polynomials</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>
<ul>
<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>
<ul>
<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#a6.6_fast_math">6.6 Fast mathematical functions</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>
<ul>
<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>
<ul>
<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>
</ul></li>
</ul></li>
<li><a href="fraction.html">9. Fractions</a>
<ul>
<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="geometry.html">11. Geometry</a>
<ul>
<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>
<ul>
<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#a12.4_Direct_Methods">12.4 Direct Methods</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>
<ul>
<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#a15.5_Derivatives">15.5 Derivatives</a></li>
</ul></li>
<li><a href="genetics.html">16. Genetic Algorithms</a>
<li><a href="genetics.html">16. Genetic Algorithms</a>
<ul>
<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#a16.3_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>
<li><a href="filter.html">17. Filters</a>
<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#implementation">18.3 Implementation</a></li>
</ul>
</li>
</li>
<li><a href="exceptions.html">19. Exceptions</a>
<ul>
<li><a href="exceptions.html#a19.1_Overview">19.1 Overview</a></li>
@ -168,7 +168,7 @@
</li>
</ul>
</section>
</body>
</document>

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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="linear.html">
@ -37,7 +37,7 @@
<subsection name="3.2 Real matrices" href="real_matrices">
<p>
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:
<ul>
<li>Matrix addition, subtraction, multiplication</li>
@ -45,7 +45,7 @@
<li>transpose</li>
<li>Norm and Trace</li>
<li>Operation on a vector</li>
</ul>
</ul>
</p>
<p>
Example:
@ -85,7 +85,7 @@ RealMatrix pInverse = new LUDecomposition(p).getSolver().getInverse();
<subsection name="3.3 Real vectors" href="real_vectors">
<p>
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:
<ul>
<li>Vector addition, subtraction</li>
@ -136,7 +136,7 @@ RealVector constants = new ArrayRealVector(new double[] { 1, -2, 1 }, false);
RealVector solution = solver.solve(constants);
</source>
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.
</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.
</p>
<p>
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
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
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.
</p>

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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="ml.html">
@ -39,10 +39,10 @@
The following algorithms are available:
<ul>
<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
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
into k clusters in such that each point belongs to the cluster with the nearest center.
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
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.
</li>
<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
@ -52,7 +52,7 @@
slower than the original kMeans algorithm.
</li>
<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
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.
@ -114,18 +114,18 @@ public static class LocationWrapper implements Clusterable {
}
}
</source>
Now we will create a list of these wrapper objects (one for each location),
which serves as input to our clustering algorithm.
Now we will create a list of these wrapper objects (one for each location),
which serves as input to our clustering algorithm.
<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;LocationWrapper&gt; clusterInput = new ArrayList&lt;LocationWrapper&gt;(locations.size());
for (Location location : locations)
clusterInput.add(new LocationWrapper(location));
</source>
Finally, we can apply our clustering algorithm and output the found clusters.
<source>
// initialize a new clustering algorithm.
<source>
// initialize a new clustering algorithm.
// we use KMeans++ with 10 clusters and 10000 iterations maximum.
// 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);
@ -143,4 +143,4 @@ for (int i=0; i&lt;clusterResults.size(); i++) {
</subsection>
</section>
</body>
</document>
</document>

4
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View File

@ -16,7 +16,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<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() {
public void init(double t0, double[] y0, double t) {
}
public void handleStep(StepInterpolator interpolator, boolean isLast) {
double t = interpolator.getCurrentTime();
double[] y = interpolator.getInterpolatedState();

2
src/site/xdoc/userguide/optimization.xml
View File

@ -261,7 +261,7 @@
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>.
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.
<table cellspacing="0" cellpadding="3">

30
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View File

@ -16,13 +16,13 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document>
<properties>
<title>User Guide - Overview</title>
</properties>
<body>
<section name="Overview">
@ -32,22 +32,22 @@
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,
providing a little more explanation of the mathematical objects and functions included
in the package.
in the package.
</p>
</subsection>
<subsection name="0.2 What's in commons-math" href="summary">
<p>
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,
it's just meant to give a feel for the kinds of things that Commons Math provides.
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,
it's just meant to give a feel for the kinds of things that Commons Math provides.
<ul>
<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 curve to a set of data points</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>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 Ordinary Differential Equations</li>
<li>Minimizing multi-dimensional functions</li>
@ -57,13 +57,13 @@
<li>Performing statistical significance tests</li>
<li>Miscellaneous mathematical functions such as factorials, binomial
coefficients and "special functions" (e.g. gamma, beta functions)</li>
</ul></p>
</ul></p>
<p>
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,
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,
self-contained math/stat components useful for solving common programming problems.
Suggestions for new components or enhancements to existing functionality are always welcome!
All feedback/suggestions for improvement should be sent to the
Suggestions for new components or enhancements to existing functionality are always welcome!
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
[math] at the beginning of the subject line.
</p>
@ -96,15 +96,15 @@
<subsection name="0.4 How interface contracts are specified in commons-math javadoc" href="contracts">
<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
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
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
values/objects or state changes.</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
<a href="../apidocs/org/apache/commons/math4/exception/MathIllegalArgumentException.html">
MathIllegalArgumentException</a> or

4
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View File

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

78
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@ -118,7 +118,7 @@
</table>
</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">
AggregateSummaryStatistics.</a> This class can be used to concurrently
gather statistics for multiple datasets as well as for a combined sample
@ -135,11 +135,11 @@
is thread-safe.
<a href="../apidocs/org/apache/commons/math4/stat/descriptive/SynchronizedDescriptiveStatistics.html">
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
versions for applications that require concurrent access to statistical
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
<code>MultivariateSummaryStatistics.</code>
</p>
@ -239,14 +239,14 @@ DescriptiveStatistics stats = new SynchronizedDescriptiveStatistics();
</dd>
<dt>Compute statistics for multiple samples and overall statistics concurrently</dt>
<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
to accumulate overall statistics contributed by <code>SummaryStatistics</code>
instances created using
<a href="../apidocs/org/apache/commons/math4/stat/descriptive/AggregateSummaryStatistics.html#createContributingStatistics()">
AggregateSummaryStatistics.createContributingStatistics()</a>:
<source>
// Create a AggregateSummaryStatistics instance to accumulate the overall statistics
// Create a AggregateSummaryStatistics instance to accumulate the overall statistics
// and AggregatingSummaryStatistics for the subsamples
AggregateSummaryStatistics aggregate = new AggregateSummaryStatistics();
SummaryStatistics setOneStats = aggregate.createContributingStatistics();
@ -501,7 +501,7 @@ System.out.println(regression.getSlopeStdErr());
System.out.println(regression.getInterceptStdErr() );
// will return Double.NaN, since we constrained the parameter to zero
</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.
</dd>
@ -520,12 +520,12 @@ System.out.println(regression.getInterceptStdErr() );
</p>
<p>
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>.
</p>
<p>
<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">
GLSMultipleLinearRegression</a> implements Generalized Least Squares. See the javadoc for these
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
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)">
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[][])">
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[][])">
GLSMultipleLinearRegression#newSampleData(double[],double[][],double[][])</a> for details.
</p>
@ -545,7 +545,7 @@ System.out.println(regression.getInterceptStdErr() );
<strong>Usage Notes</strong>: <ul>
<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
or do not contain sufficient data to estimate the model.
or do not contain sufficient data to estimate the model.
</li>
<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
@ -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[3] = new double[]{0, 0, 4.0, 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);
</source>
</dd>
<dd>Get regression parameters and diagnostics:
<source>
double[] beta = regression.estimateRegressionParameters();
double[] beta = regression.estimateRegressionParameters();
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[3] = new double[]{0, 0, 4.0, 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][];
omega[0] = new double[]{1.1, 0, 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[4] = new double[]{0, 0, 0, 0, 5.5, 0};
omega[5] = new double[]{0, 0, 0, 0, 0, 6.6};
regression.newSampleData(y, x, omega);
regression.newSampleData(y, x, omega);
</source>
</dd>
</dl>
</p>
</subsection>
</subsection>
<subsection name="1.6 Rank transformations">
<p>
Some statistical algorithms require that input data be replaced by ranks.
The <a href="../apidocs/org/apache/commons/math4/stat/ranking/package-summary.html">
org.apache.commons.math4.stat.ranking</a> package provides rank transformation.
<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">
NaturalRanking</a> provides an implementation that has two configuration options.
<ul>
@ -640,7 +640,7 @@ double[] ranks = ranking.rank(exampleData);
</source>
results in <code>ranks</code> containing <code>{6, 5, 7, 8, 5, 9, 2, 2, 5}.</code>
<source>
new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData);
new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData);
</source>
returns <code>{5, 2, 6, 7, 3, 8, 1, 4}.</code>
</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>
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
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">
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>
for details on these configuration options.
</p>
</subsection>
</subsection>
<subsection name="1.7 Covariance and correlation">
<p>
The <a href="../apidocs/org/apache/commons/math4/stat/correlation/package-summary.html">
org.apache.commons.math4.stat.correlation</a> package computes covariances
and correlations for pairs of arrays or columns of a matrix.
<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">
PearsonsCorrelation</a> provides Pearson's Product-Moment correlation coefficients,
<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/>
<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>
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
bias-corrected is determined by the optional parameter, "biasCorrected," which
defaults to <code>true.</code>
defaults to <code>true.</code>
</li>
<li>
<a href="../apidocs/org/apache/commons/math4/stat/correlation/PearsonsCorrelation.html">
@ -691,7 +691,7 @@ new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData
<li>
<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
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">
NaturalRanking</a> with default strategies for handling ties and NaN values is used.
</li>
@ -699,7 +699,7 @@ new NaturalRanking(NaNStrategy.REMOVED,TiesStrategy.SEQUENTIAL).rank(exampleData
<a href="../apidocs/org/apache/commons/math4/stat/correlation/KendallsCorrelation.html">
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.
</li>
</li>
</ul>
</p>
<p>
@ -727,7 +727,7 @@ new Covariance().computeCovarianceMatrix(data)
</source>
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,
use
use
<source>
computeCovarianceMatrix(data, false)
</source>
@ -750,7 +750,7 @@ new PearsonsCorrelation().correlation(x, y)
new PearsonsCorrelation().computeCorrelationMatrix(data)
</source>
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>
<br/>
<dt><strong>Pearson's correlation significance and standard errors</strong></dt>
@ -768,7 +768,7 @@ correlation.getCorrelationStandardErrors();
</source>
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/>
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/>
<strong>p-values</strong> for the (2-sided) null hypotheses that elements of
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/>
<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
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/>
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>
new PearsonsCorrelation(data).getCorrelationPValues().getEntry(0,1)
</source>
@ -798,13 +798,13 @@ new PearsonsCorrelation(data).getCorrelationPValues().getEntry(0,1)
<source>
new SpearmansCorrelation().correlation(x, y)
</source>
This is equivalent to
This is equivalent to
<source>
RankingAlgorithm ranking = new NaturalRanking();
new PearsonsCorrelation().correlation(ranking.rank(x), ranking.rank(y))
</source>
</dd>
<br/>
<br/>
<dt><strong>Kendalls's tau rank correlation coefficient</strong></dt>
<br/>
<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">
Student's t</a>,
<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://www.itl.nist.gov/div898/handbook/prc/section4/prc43.htm">
One-Way ANOVA</a>,
@ -853,7 +853,7 @@ new KendallsCorrelation().correlation(x, y)
<a href="../apidocs/org/apache/commons/math4/stat/inference/BinomialTest.html">
BinomialTest</a> and
<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">
TestUtils</a> class provides static methods to get test instances or
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:
<code>gTest(expected, observed)</code>, which is the tail probability beyond
<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
probability computed using a ChiSquare distribution with one less degeree
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
one-sample tests against a <code>RealDistribution</code> and two-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
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
<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
@ -1163,7 +1163,7 @@ System.out.println(TestUtils.gTestDataSetsComparison(obs1, obs2)); // p-value
<source>
new GTest().rootLogLikelihoodRatio(5, 1995, 0, 100000);
</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.
</dd>
<br/>
@ -1219,7 +1219,7 @@ TestUtils.kolmogorovSmirnovStatistic(unitNormal, sample)
<source>
TestUtils.kolmogorovSmirnovStatistic(x, y)
</source>
to compute the D-statistic and
to compute the D-statistic and
<source>
TestUtils.kolmogorovSmirnovTest(x, y)
</source>

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@ -16,7 +16,7 @@
See the License for the specific language governing permissions and
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<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
limitations under the License.
-->
<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="utilities.html">
@ -39,14 +39,14 @@
<subsection name="6.2 Double array utilities" href="arrays">
<p>
To maintain statistics based on a "rolling" window of values, a resizable
array implementation was developed and is provided for reuse in the
To maintain statistics based on a "rolling" window of values, a resizable
array implementation was developed and is provided for reuse in the
<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">
DoubleArray</a>. This interface adds one method,
<code>addElementRolling(double)</code> to basic list accessors.
The <code>addElementRolling</code> method adds an element
<code>addElementRolling(double)</code> to basic list accessors.
The <code>addElementRolling</code> method adds an element
(the actual parameter) to the end of the list and removes the first element
in the list.
</p>
@ -64,10 +64,10 @@
the end needs to be created. Both of these operations are handled
automatically, with frequency / effect driven by the configuration
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">
ResizableDoubleArray</a>
for details.
for details.
</p>
</subsection>
@ -103,7 +103,7 @@
default: return 6.0;
}
}
public double getB(int n, double x) {
double y = (2.0 * n) - 1.0;
return y * y;
@ -129,7 +129,7 @@
return n;
}
}
public double getB(int n, double x) {
if (n % 2 == 0) {
return -x;
@ -151,17 +151,17 @@
<a href="../apidocs/org/apache/commons/math4/util/ArithmeticUtils.html">ArithmeticUtils</a>
utility class. ArithmeticUtils currently includes methods to compute the following: <ul>
<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>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>
<li>
Stirling numbers of the second kind -- S(n,k) as an exact long value
<code>stirlingS2(int, int)</code> for small n, k.</li>
<li>
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>
<li>
Least common multiple and greatest common denominator functions.
@ -186,7 +186,7 @@
<p>
FastMath speed is achieved by relying heavily on optimizing compilers to
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
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

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) {
throw new IllegalArgumentException();
}
ContinuousDistribution.Sampler dist = new NormalDistribution(0.0, noise).createSampler(rng);
List<Vector2D> points = new ArrayList<>();
@ -77,11 +77,11 @@ public class ClusterAlgorithmComparison {
for (double angle = 0; angle < range; angle += step) {
Vector2D outerCircle = Vector2D.of(FastMath.cos(angle), FastMath.sin(angle));
Vector2D innerCircle = outerCircle.multiply(factor);
points.add(outerCircle.add(generateNoiseVector(dist)));
points.add(innerCircle.add(generateNoiseVector(dist)));
}
if (shuffle) {
ListSampler.shuffle(rng, points);
}
@ -97,7 +97,7 @@ public class ClusterAlgorithmComparison {
int nSamplesOut = samples / 2;
int nSamplesIn = samples - nSamplesOut;
List<Vector2D> points = new ArrayList<>();
double range = FastMath.PI;
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);
points.add(innerCircle.add(generateNoiseVector(dist)));
}
if (shuffle) {
ListSampler.shuffle(rng, points);
}
@ -133,29 +133,29 @@ public class ClusterAlgorithmComparison {
for (int i = 0; i < centers; i++) {
centerPoints[i] = Vector2D.of(uniform.sample(), uniform.sample());
}
int[] nSamplesPerCenter = new int[centers];
int count = samples / centers;
Arrays.fill(nSamplesPerCenter, count);
for (int i = 0; i < samples % centers; i++) {
nSamplesPerCenter[i]++;
}
List<Vector2D> points = new ArrayList<>();
for (int i = 0; i < centers; i++) {
for (int j = 0; j < nSamplesPerCenter[i]; j++) {
points.add(centerPoints[i].add(generateNoiseVector(gauss)));
}
}
if (shuffle) {
ListSampler.shuffle(rng, points);
}
return points;
}
public static List<Vector2D> makeRandom(int samples) {
SobolSequenceGenerator generator = new SobolSequenceGenerator(2);
generator.skipTo(999999);
@ -167,14 +167,14 @@ public class ClusterAlgorithmComparison {
Vector2D point = Vector2D.of(vector);
points.add(point);
}
return points;
}
public static Vector2D generateNoiseVector(ContinuousDistribution.Sampler distribution) {
return Vector2D.of(distribution.sample(), distribution.sample());
}
public static List<DoublePoint> normalize(final List<Vector2D> input,
double minX,
double maxX,
@ -191,16 +191,16 @@ public class ClusterAlgorithmComparison {
}
return points;
}
@SuppressWarnings("serial")
public static class Display extends ExampleFrame {
public Display() {
setTitle("Commons-Math: Cluster algorithm comparison");
setSize(800, 800);
setLayout(new GridBagLayout());
int nSamples = 1500;
final long seed = RandomSource.createLong(); // Random seed.
@ -213,7 +213,7 @@ public class ClusterAlgorithmComparison {
datasets.add(normalize(makeRandom(nSamples), -1, 1, -1, 1));
List<Pair<String, Clusterer<DoublePoint>>> algorithms = new ArrayList<>();
algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=2)",
new KMeansPlusPlusClusterer<>(2)));
algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=3)",
@ -224,7 +224,7 @@ public class ClusterAlgorithmComparison {
new FuzzyKMeansClusterer<>(3, 10)));
algorithms.add(new Pair<String, Clusterer<DoublePoint>>("DBSCAN\n(eps=.1, min=3)",
new DBSCANClusterer<>(0.1, 3)));
GridBagConstraints c = new GridBagConstraints();
c.fill = GridBagConstraints.VERTICAL;
c.gridx = 0;
@ -248,7 +248,7 @@ public class ClusterAlgorithmComparison {
c.gridx++;
}
c.gridy++;
}
}
}
}
@ -264,7 +264,7 @@ public class ClusterAlgorithmComparison {
this.clusters = clusters;
this.duration = duration;
}
@Override
protected void paintComponent(Graphics g) {
super.paintComponent(g);
@ -276,10 +276,10 @@ public class ClusterAlgorithmComparison {
int h = getHeight();
g2.clearRect(0, 0, w, h);
g2.setPaint(Color.black);
g2.drawRect(0, 0, w - 1, h - 1);
int index = 0;
Color[] colors = new Color[] { Color.red, Color.blue, Color.green.darker() };
for (Cluster<DoublePoint> cluster : clusters) {
@ -289,7 +289,7 @@ public class ClusterAlgorithmComparison {
double[] arr = p.getPoint();
g2.fill(new Ellipse2D.Double(arr[0] - 1, arr[1] - 1, 3, 3));
}
Clusterable p = transform(cluster.centroid(), w, h);
double[] arr = p.getPoint();
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.draw(s);
}
g2.setPaint(Color.black);
g2.drawString(String.format("%.2f s", duration / 1e3), w - 40, h - 5);
}
}
@Override
public Dimension getPreferredSize() {
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")
public static class ExampleFrame extends JFrame {
/**
* Returns the main panel which should be printed by the screenshot action.
* <p>
* 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
*/
@ -73,14 +73,14 @@ public class ExampleUtils {
}
}
});
JMenuItem exit = new JMenuItem("Exit");
exit.addActionListener(new ActionListener() {
public void actionPerformed(ActionEvent e) {
System.exit(0);
}
});
JMenu menu = new JMenu("File");
menu.add(screenshot);
menu.add(exit);
@ -102,7 +102,7 @@ public class ExampleUtils {
component.paint(image.getGraphics());
return image;
}
public static BufferedImage resizeImage(BufferedImage originalImage, int width, int height, int type) throws IOException {
BufferedImage resizedImage = new BufferedImage(width, height, type);
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.
*
*
*/
public class FastMathTestPerformance {
private static final int RUNS = Integer.parseInt(System.getProperty("testRuns","10000000"));
@ -288,7 +288,7 @@ public class FastMathTestPerformance {
report("cos",strictTime,fastTime,mathTime);
assertTrue(!Double.isNaN(x));
}
private static void testAcos() {
double x = 0;
long time = System.nanoTime();
@ -594,7 +594,7 @@ public class FastMathTestPerformance {
report("tanh",strictTime,fastTime,mathTime);
assertTrue(!Double.isNaN(x));
}
private static void testExpm1() {
double x = 0;
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
points = normalize(points);
// now test if the sample is within the unit circle
List<Vector2D> circlePoints = new ArrayList<>();
for (Vector2D p : points) {
@ -80,7 +80,7 @@ public class LowDiscrepancyGeneratorComparison {
Vector2D point = Vector2D.of(vector);
points.add(point);
}
return normalize(points);
}
@ -92,16 +92,16 @@ public class LowDiscrepancyGeneratorComparison {
minX = FastMath.min(minX, p.getX());
maxX = FastMath.max(maxX, p.getX());
}
double minY, maxY;
// 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) {
minX = minY = 0.0;
maxX = maxY = 1.0;
} else {
minX = minY = -FastMath.sqrt(3);
maxX = maxY = FastMath.sqrt(3);
maxX = maxY = FastMath.sqrt(3);
}
double rangeX = maxX - minX;
@ -116,16 +116,16 @@ public class LowDiscrepancyGeneratorComparison {
}
return points;
}
@SuppressWarnings("serial")
public static class Display extends ExampleFrame {
public Display() {
setTitle("Commons-Math: Pseudo/Quasi-random examples");
setSize(800, 800);
setLayout(new GridBagLayout());
int[] datasets = new int[] { 256, 1000, 2500, 1000 };
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, 987654321)
};
public double[] nextVector() {
final double[] vector = new double[2];
vector[0] = rngs[0].nextDouble();
vector[1] = rngs[1].nextDouble();
return vector;
}
}));
generators.add(new Pair<>("HaltonSequence", new HaltonSequenceGenerator(2)));
generators.add(new Pair<>("SobolSequence", new SobolSequenceGenerator(2)));
}));
generators.add(new Pair<>("HaltonSequence", new HaltonSequenceGenerator(2)));
generators.add(new Pair<>("SobolSequence", new SobolSequenceGenerator(2)));
GridBagConstraints c = new GridBagConstraints();
c.fill = GridBagConstraints.VERTICAL;
c.gridx = 1;
@ -163,7 +163,7 @@ public class LowDiscrepancyGeneratorComparison {
c.gridx++;
}
int saveY = ++c.gridy;
c.gridx = 0;
for (int type = 0; type < 4; type++) {
JLabel text = new JLabel("n=" + String.valueOf(datasets[type]));
@ -198,7 +198,7 @@ public class LowDiscrepancyGeneratorComparison {
}
c.gridy++;
}
}
}
}
@ -212,7 +212,7 @@ public class LowDiscrepancyGeneratorComparison {
public Plot(final List<Vector2D> points) {
this.points = points;
}
@Override
protected void paintComponent(Graphics g) {
super.paintComponent(g);
@ -224,17 +224,17 @@ public class LowDiscrepancyGeneratorComparison {
int h = getHeight();
g2.clearRect(0, 0, w, h);
g2.setPaint(Color.black);
g2.drawRect(0, 0, w - 1, h - 1);
for (Vector2D point : points) {
Vector2D p = transform(point, w, h);
double[] arr = p.toArray();
g2.draw(new Rectangle2D.Double(arr[0] - 1, arr[1] - 1, 2, 2));
}
}
}
@Override
public Dimension getPreferredSize() {
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 static class Cannonball {
private final double[] gravity = { 0, -9.81 };
private final double[] velocity;
private final double[] location;
private final double timeslice;
private final double measurementNoise;
private final GaussianRandomGenerator rng;
public Cannonball(double timeslice, double angle, double initialVelocity, double measurementNoise, int seed) {
this.timeslice = timeslice;
final double angleInRadians = FastMath.toRadians(angle);
this.velocity = new double[] {
initialVelocity * FastMath.cos(angleInRadians),
initialVelocity * FastMath.sin(angleInRadians)
};
this.location = new double[] { 0, 0 };
this.measurementNoise = measurementNoise;
this.rng = new GaussianRandomGenerator(RandomSource.create(RandomSource.WELL_19937_C, seed));
}
public double getX() {
return location[0];
}
public double getY() {
return location[1];
}
@ -97,18 +97,18 @@ public class CannonballExample {
public double getXVelocity() {
return velocity[0];
}
public double getYVelocity() {
return velocity[1];
}
public void step() {
// Break gravitational force into a smaller time slice.
double[] slicedGravity = gravity.clone();
for ( int i = 0; i < slicedGravity.length; i++ ) {
slicedGravity[i] *= timeslice;
}
// Apply the acceleration to velocity.
double[] slicedVelocity = velocity.clone();
for ( int i = 0; i < velocity.length; i++ ) {
@ -123,9 +123,9 @@ public class CannonballExample {
}
}
}
public static void cannonballTest(Chart chart) {
// time interval for each iteration
final double dt = 0.1;
// the number of iterations to run
@ -139,7 +139,7 @@ public class CannonballExample {
// the cannonball itself
final Cannonball cannonball = new Cannonball(dt, angle, initialVelocity, measurementNoise, 1000);
// A = [ 1, dt, 0, 0 ] => x(n+1) = x(n) + vx(n)
// [ 0, 1, 0, 0 ] => vx(n+1) = vx(n)
// [ 0, 0, 1, dt ] => y(n+1) = y(n) + vy(n)
@ -148,7 +148,7 @@ public class CannonballExample {
{ 1, dt, 0, 0 },
{ 0, 1, 0, 0 },
{ 0, 0, 1, dt },
{ 0, 0, 0, 1 }
{ 0, 0, 0, 1 }
});
// The control vector, which adds acceleration to the kinematic equations.
@ -183,7 +183,7 @@ public class CannonballExample {
{ 0, 0, 1, 0 },
{ 0, 0, 0, 0 }
});
// 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.
final double speedX = cannonball.getXVelocity();
@ -201,7 +201,7 @@ public class CannonballExample {
// we assume no process noise -> zero matrix
final RealMatrix Q = MatrixUtils.createRealMatrix(4, 4);
// the measurement covariance matrix
final RealMatrix R = MatrixUtils.createRealMatrix(new double[][] {
{ var, 0, 0, 0 },
@ -220,7 +220,7 @@ public class CannonballExample {
final List<Number> measuredY = new ArrayList<>();
final List<Number> kalmanX = new ArrayList<>();
final List<Number> kalmanY = new ArrayList<>();
for (int i = 0; i < iterations; i++) {
// get real location
@ -251,7 +251,7 @@ public class CannonballExample {
Series dataset = chart.addSeries("true", realX, realY);
dataset.setMarker(SeriesMarker.NONE);
dataset = chart.addSeries("measured", measuredX, measuredY);
dataset.setLineStyle(SeriesLineStyle.DOT_DOT);
dataset.setMarker(SeriesMarker.NONE);
@ -275,10 +275,10 @@ public class CannonballExample {
chart.getStyleManager().setLegendPadding(6);
chart.getStyleManager().setLegendSeriesLineLength(10);
chart.getStyleManager().setAxisTickLabelsFont(new Font("Arial", Font.PLAIN, 10));
chart.getStyleManager().setChartBackgroundColor(Color.white);
chart.getStyleManager().setChartPadding(4);
chart.getStyleManager().setChartType(ChartType.Line);
return chart;
}
@ -290,20 +290,20 @@ public class CannonballExample {
Chart chart = createChart("Cannonball", LegendPosition.InsideNE);
cannonballTest(chart);
container.add(new XChartPanel(chart));
container.setBorder(BorderFactory.createLineBorder(Color.black, 1));
return container;
}
@SuppressWarnings("serial")
public static class Display extends ExampleFrame {
private JComponent container;
public Display() {
setTitle("Commons Math: Kalman Filter - Cannonball");
setSize(800, 600);
container = new JPanel();
JComponent comp = createComponent();
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 static class VoltMeter {
private final double initialVoltage;
private final double processNoise;
private final double measurementNoise;
@ -68,20 +68,20 @@ public class ConstantVoltageExample {
this.measurementNoise = measurementNoise;
rng = new GaussianRandomGenerator(RandomSource.create(RandomSource.WELL_19937_C, seed));
}
/**
* Returns the real voltage without any measurement noise.
*
*
* @return the real voltage
*/
public double getVoltage() {
return voltage;
}
public double getMeasuredVoltage() {
return getVoltage() + rng.nextNormalizedDouble() * measurementNoise;
return getVoltage() + rng.nextNormalizedDouble() * measurementNoise;
}
public void step() {
// we apply only the process noise
voltage = initialVoltage + rng.nextNormalizedDouble() * processNoise;
@ -96,7 +96,7 @@ public class ConstantVoltageExample {
final double processNoise = 1e-5d;
final VoltMeter voltMeter = new VoltMeter(voltage, processNoise, measurementNoise, 2);
// the state transition matrix -> constant
final RealMatrix A = new Array2DRowRealMatrix(new double[] { 1d });
@ -108,7 +108,7 @@ public class ConstantVoltageExample {
// the initial state vector -> slightly wrong
final RealVector x0 = new ArrayRealVector(new double[] { 1.45 });
// the process covariance matrix
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> covSeries = new ArrayList<>();
for (int i = 0; i < 300; i++) {
xAxis.add(i);
@ -152,7 +152,7 @@ public class ConstantVoltageExample {
Series dataset = chart1.addSeries("real", xAxis, realVoltageSeries);
dataset.setMarker(SeriesMarker.NONE);
dataset = chart1.addSeries("measured", xAxis, measuredVoltageSeries);
dataset.setLineStyle(SeriesLineStyle.DOT_DOT);
dataset.setMarker(SeriesMarker.NONE);
@ -166,7 +166,7 @@ public class ConstantVoltageExample {
chart2.setYAxisTitle("(Voltage)²");
chart2.setXAxisTitle("Iteration");
dataset = chart2.addSeries("cov", xAxis, covSeries);
dataset.setLineColor(Color.black);
dataset.setLineStyle(SeriesLineStyle.SOLID);
@ -187,10 +187,10 @@ public class ConstantVoltageExample {
chart.getStyleManager().setLegendPadding(6);
chart.getStyleManager().setLegendSeriesLineLength(10);
chart.getStyleManager().setAxisTickLabelsFont(new Font("Arial", Font.PLAIN, 10));
chart.getStyleManager().setChartBackgroundColor(Color.white);
chart.getStyleManager().setChartPadding(4);
chart.getStyleManager().setChartType(ChartType.Line);
return chart;
}
@ -198,28 +198,28 @@ public class ConstantVoltageExample {
public static JComponent createComponent() {
JComponent container = new JPanel();
container.setLayout(new BoxLayout(container, BoxLayout.LINE_AXIS));
Chart chart1 = createChart("Voltage", 550, 450, LegendPosition.InsideNE, true);
Chart chart2 = createChart("Error Covariance", 450, 450, LegendPosition.InsideNE, false);
constantVoltageTest(chart1, chart2);
container.add(new XChartPanel(chart1));
container.add(new XChartPanel(chart2));
container.setBorder(BorderFactory.createLineBorder(Color.black, 1));
return container;
}
@SuppressWarnings("serial")
public static class Display extends ExampleFrame {
private JComponent container;
public Display() {
setTitle("Commons-Math: Kalman Filter example");
setSize(1100, 700);
container = new JPanel();
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
StoppingCondition stoppingCondition = new StoppingCondition() {
int generation = 0;
// @Override
public boolean isSatisfied(Population population) {
Chromosome fittestChromosome = population.getFittestChromosome();
if (generation == 1 || generation % 10 == 0) {
System.out.println("Generation " + generation + ": " + fittestChromosome.toString());
}
@ -82,7 +82,7 @@ public class HelloWorldExample {
};
System.out.println("Starting evolution ...");
// run the algorithm
Population finalPopulation = ga.evolve(initial, stoppingCondition);
@ -94,15 +94,15 @@ public class HelloWorldExample {
System.out.println("Generation " + ga.getGenerationsEvolved() + ": " + best.toString());
System.out.println("Total execution time: " + (endTime - startTime) + "ms");
}
private static List<Character> randomRepresentation(int length) {
return asList(RandomStringUtils.randomAscii(length));
}
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() {
List<Chromosome> popList = new LinkedList<>();
@ -173,7 +173,7 @@ public class HelloWorldExample {
StringChromosome strChromosome = (StringChromosome) original;
List<Character> characters = strChromosome.getStringRepresentation();
int mutationIndex = GeneticAlgorithm.getRandomGenerator().nextInt(characters.size());
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.
* <p>
* The fitness function is quite simple yet expensive to compute:
*
*
* - draw the polygons of a chromosome to an image
* - compare each pixel with the corresponding reference image
* <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)
* - allow loading / selection of other images
* - 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>
*/
@SuppressWarnings("serial")
@ -77,7 +77,7 @@ public class ImageEvolutionExample {
public static final int POLYGON_COUNT = 100;
public static class Display extends ExampleFrame {
private GeneticAlgorithm ga;
private Population currentPopulation;
private Chromosome bestFit;
@ -86,16 +86,16 @@ public class ImageEvolutionExample {
private volatile boolean noStopRequested;
private BufferedImage ref;
private BufferedImage referenceImage;
private BufferedImage testImage;
private ImagePainter painter;
public Display() throws Exception {
setTitle("Commons-Math: Image Evolution Example");
setSize(600, 400);
setLayout(new FlowLayout());
Box bar = Box.createHorizontalBox();
@ -143,7 +143,7 @@ public class ImageEvolutionExample {
currentPopulation = getInitialPopulation();
bestFit = currentPopulation.getFittestChromosome();
}
public boolean isAlive() {
return internalThread != null && internalThread.isAlive();
}
@ -175,7 +175,7 @@ public class ImageEvolutionExample {
}
private class ImagePainter extends Component {
private int width;
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 UniformRandomProvider random = GeneticAlgorithm.getRandomGenerator();
Polygon p = new Polygon();
p.data = new float[polygonSize];
@ -61,10 +61,10 @@ public class Polygon {
p.data[1] = random.nextFloat(); // g
p.data[2] = random.nextFloat(); // b
p.data[3] = FastMath.max(0.2f, random.nextFloat() * random.nextFloat()); // a
float px = random.nextFloat();
float py = random.nextFloat();
for (int k = 0; k < length; k++) {
p.data[4 + 2*k] = px + (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.
* In case a component is going to be mutated, its value will be randomly modified in the
* uniform range of [-mutationAmount, +mutationAmount].
*
*
* @param mutationRate the mutation rate
* @param mutationAmount the mutation amount
* @return a new Polygon
@ -91,7 +91,7 @@ public class Polygon {
float val = this.data[i];
if (GeneticAlgorithm.getRandomGenerator().nextFloat() < mutationRate) {
val += GeneticAlgorithm.getRandomGenerator().nextFloat() * mutationAmount * 2 - mutationAmount;
if (val < 0f) {
val = 0f;
} else if (val > 1f) {
@ -101,7 +101,7 @@ public class Polygon {
mutated.data[i] = val;
}
return mutated;
}
}
/**
* Draw the Polygon to the buffer of the given size.
@ -111,7 +111,7 @@ public class Polygon {
GeneralPath path = new GeneralPath();
path.moveTo(data[4] * width, data[5] * height);
int polygonLength = (data.length - 4) / 2;
for (int j = 1; j < polygonLength; j++) {
path.lineTo(data[4 + j * 2] * width, data[5 + j * 2] * height);

4
src/userguide/java/org/apache/commons/math4/userguide/genetics/PolygonChromosome.java
View File

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

4
src/userguide/java/org/apache/commons/math4/userguide/genetics/RandomPolygonMutation.java
View File

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

6
src/userguide/pom.xml
View File

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