[math] Distributions tests

[Phil Steitz]

To close out BZ #28829, I need to add some degenerate test cases to 
BinomialDistributionTest.  As I started to do this, I realized that adding 
more test points would be easier and we could eliminate duplication of 
code across test classes in the distributions package if we defined 
abstract test classes for discrete and continuous distributions that 
provided generic verification methods. The setup for the discrete case 
looks like this:

Abstract base class for [EMAIL PROTECTED] DiscreteDistribution} tests.
<p>
To create a concrete test class for a discrete distribution implementation,
implement makeDistribution() to return a distribution instance to use in
tests and each of the test data generation methods below.  In each case, 
the test points and test values arrays returned represent parallel arrays 
of inputs and expected values for the distribution returned by 
makeDistribution().
<p>
makeDensityTestPoints() -- arguments used to test probability density 
calculation
makeDensityTestValues() -- expected probability densities
makeCumulativeTestPoints() -- arguments used to test cumulative probabilities
makeCumulativeTestValues() -- expected cumulative probabilites
makeInverseCumulativeTestPoints() -- arguments used to test inverse cdf 
evaluation
makeInverseCumulativeTestValues() -- expected inverse cdf values

Then one default method for each type of test runs through the test points 
/ expected values and checks the test instance.  To support tests using 
multiple instances, setXxx methods would also be provided for the instance 
and test data arrays.  Similar setup for continuous. This will make it 
easier to add more test cases (including adding missing tests for discrete 
pdf calculations) and simplify development of new distribution test 
classes. Any objections?

Phil



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