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
Univariate interface, in which getN() returned a double. The attached patch
inserts the necessary casts to avoid the rounding/truncation errors that were
causing the EmpiricalDistribution and ValueServer unit tests to fail.
The patch also adds a RandomData member variable so that getNext() does not
instantiate a new RandomData instance for each activation
PR: Bugzilla #20149
Obtained from: Issue Patch
Submitted by: Phil Steitz
Reviewed by: Tim O'Brien
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140853 13f79535-47bb-0310-9956-ffa450edef68
contains contributions from Mark Diggory.
* This patch introduces Product and GeometricMean into the Univariate
implementation.
* Discarding the contribution of a discarded element in a rolling
UnivariateImpl requires that the product be calculated explicitly each
time a value is discarded. This is necessary because not all values will
have non-zero values.
* Errors in rolling logic for ListUimpl, and UnivariateImpl were corrected,
and more test cases were added to the JUnit tests for the Univariate
implementations. More rigorous test cases are needed for the entire
suite of Univariate implementations
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140851 13f79535-47bb-0310-9956-ffa450edef68
unit test, 2. The date/time and version are included in the xdoc
transformation, 3. JUnit is now forker, and 4. We use checkstyle.properties
to customize the behavior of Checkstyle.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140849 13f79535-47bb-0310-9956-ffa450edef68
EmpiricalDistribution -- represents an empirical probability distribution and
supports generation of data values that are "like" values in an input file
without making any assumptions about the functional form of the probability
distribution that the data come from. This is useful in simulation
applications where historical data about component performance are
available but do not follow standard distributions (or any application that
requires random data generation from an empirical distribution). Also
generates data for grouped frequency histograms based on the input file.
ValueServer -- a wrapper for RandomData and EmpiricalDistribution that
generates values in each of the following modes:
* DIGEST_MODE -- uses an empirical distribution
* REPLAY_MODE -- replays data from an input file
* UNIFORM_MODE -- generates uniformly distributed random values
* EXPONENTIAL_MODE -- generates exponentially distributed random
values
* GAUSSIAN_MODE -- generates Gaussian distributed random values
* CONSTANT_MODE -- returns the same value every time.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140848 13f79535-47bb-0310-9956-ffa450edef68
* One should be able to use a DoubleArray in a similar way to a
regular double[], to this effect methods for accessing element
values will no longer throw NoSuchElementExceptions when an
index is outside of the element set. These method all throw
ArrayIndexOutOfBoundException if a bad index is supplied.
* Filled out javadoc in FixedDoubleArray.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140845 13f79535-47bb-0310-9956-ffa450edef68
This commit contains the suite of random data generation utilities that
I originally
proposed as extensions to lang.math. There is some functional overlap
with lang.math, but the contract and intention of this implementation is
different in several significant ways.
* the lang implementation maintains "immutability" of the underlying
random number generator (emulating Math). The RandomData
implementation allows users to reseed the random number generator(s)
(this is in effect possible in the recent extensions to lang.math by
passing in a user-supplied random as an actual parameter to the
next() methods) Users can also reset the PRNG algorithm and provider
used by the "secure" methods.
* RandomData includes "secure" methods (delegating to SecureRandom)
* RandomData will generate random deviates from exponential and poisson,
as well as Gaussian and Uniform distributions. These are useful in
simulation applications.
* Overlapping somewhat with lang.StringUtils, RandomData will generate
random hex strings. There is a nextSecureHexString method that will
(I claim :-) generate cryptographically secure string identifiers. I
would appreciate feedback on this algorithm, which I have seen used
elsewhere (similar to what tomcat does to generate session ids); but
not documented as a standard.
PR: Bugzilla 20013
Obtained from: Phil S.
Submitted by: Phil S.
Reviewed by: Tim O.
git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140838 13f79535-47bb-0310-9956-ffa450edef68
Mark R. Diggory
Tim O'Brien
Robert Burrell Donkin