100% test path coverage.
* Used distribution framework TDistribution to implement
getSlopeConfidenceInterval and getSignificance methods.
PR: Issue #20657
Obtained from: Bugzilla
Submitted by: Phil Steitz
Reviewed by: Tim O'Brien
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140900 13f79535-47bb-0310-9956-ffa450edef68
Gamma.java. Gamma.java explicitly stated that it was "based on" an
implementation from Numerical Recipes in C. Please see
http://www.nr.com/infotop.html - these implementations (and derivatives
of these works) are covered under restrictive licensing terms. This
code will be removed from the repository until these issues can be
addressed.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140875 13f79535-47bb-0310-9956-ffa450edef68
included in javadoc recently, plus a few more generic references.
* update the task list based on accomplishments and discussion
PR: Issue 20357
Obtained from: Bugzilla
Submitted by: Phil Steitz
Reviewed by: Tim O'Brien
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140872 13f79535-47bb-0310-9956-ffa450edef68
List of JavaBeans. This implementation extends the ListUnivariateImpl, and uses the
PropertyUtils classes from commons-beanutils. With this implementation, the following
is possible:
List persons = new ArrayList();
// populate array with 100 persons
Univariate ageU = new BeanListUnivariateImpl( persons, "age" );
Univariate incomeU = new BeanListUnivariateImpl( persons, "jobInfo.income" );
System.out.println( "Mean age is: " + ageU.getMean() );
System.out.println( "Mean income is: " + incomeU.getMean() );
Better error handling is needed near our use of PropertyUtils.
This patch introduces a dependency on commons-beanutils, which in turn depends
on commons-collections and commons-logging.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140863 13f79535-47bb-0310-9956-ffa450edef68
* a distribution factory following the abstract factory pattern.
The value being, if users want to swap out the default distribution
implementations with their own, why stop them. The factory is responsible for
create the distribution instances (only continuous ones for now).
* Each distribution has a cummulativeProbabilty method which returns the value of
the CDF for a given value. By default, via the abstract distribution base
class, an inverseCummulativeProbability method is all provided for all
distributions. It's implementation is generically handled by applying root
finding techniques to the cummulativeProbability method. That way, when new
distributions are added, authors only need to create the CDF method and the
inverse is provided automatically.
* Currenty, the only distributions are Chi-Squared and Gamma (one actually since
Chi-Squared is a specialized Gamma) but more are planned.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140861 13f79535-47bb-0310-9956-ffa450edef68
variable. The implementation uses running sums and does not require the data
to be stored in memory. Since I could not conceive of any significantly
different implementation strategies that did not amount to just improving
efficiency or numerical accuracy of what I am submitting, I did not abstract
the interface.
The test cases validate the computations against NIST reference data and
verified computations. The slope, intercept, their standard errors and
r-square estimates are accurate to within 10E-12 against the reference data
set. MSE and other ANOVA stats are good at least to within 10E-8. -- Phil S.
PR: Issue #20224
Obtained from: Bugzilla
Submitted by: Phil Steitz
Reviewed by: Tim O'Brien
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140858 13f79535-47bb-0310-9956-ffa450edef68
The attached patch includes the following improvements to Univariate and
UnivariateImpl:
* Improved efficiency of min, max and product maintenance when windowSize is
limited by incorporating suggestion posted to commons-dev by Brend Worden
(added author credit). Thanks, Brent!
* Added javadoc specifying NaN contracts for all statistics, definitions for
geometric and arithmetic means.
* Made some slight modifications to UnivariateImpl to make it consistent with
NaN contracts
* All interface documentation moved to Univariate. The interface specification
includes the NaN semantics and a first attempt at clealy defining exactly
what "rolling" means and how this effects what statistics are defined when.
* Added test cases to verify that min, max, product are correctly maintained
when "rolling" and to verify that NaN contracts are satisfied.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140857 13f79535-47bb-0310-9956-ffa450edef68
Mark R. Diggory
Tim O'Brien