mirror of
https://github.com/saitohirga/WSJT-X.git
synced 2024-11-18 01:52:05 -05:00
230 lines
9.0 KiB
Plaintext
230 lines
9.0 KiB
Plaintext
[section:hypergeometric_dist Hypergeometric Distribution]
|
|
|
|
``#include <boost/math/distributions/hypergeometric.hpp>``
|
|
|
|
namespace boost{ namespace math{
|
|
|
|
template <class RealType = double,
|
|
class ``__Policy`` = ``__policy_class`` >
|
|
class hypergeometric_distribution;
|
|
|
|
template <class RealType, class Policy>
|
|
class hypergeometric_distribution
|
|
{
|
|
public:
|
|
typedef RealType value_type;
|
|
typedef Policy policy_type;
|
|
// Construct:
|
|
hypergeometric_distribution(unsigned r, unsigned n, unsigned N);
|
|
// Accessors:
|
|
unsigned total()const;
|
|
unsigned defective()const;
|
|
unsigned sample_count()const;
|
|
};
|
|
|
|
typedef hypergeometric_distribution<> hypergeometric;
|
|
|
|
}} // namespaces
|
|
|
|
The hypergeometric distribution describes the number of "events" /k/
|
|
from a sample /n/ drawn from a total population /N/ ['without replacement].
|
|
|
|
Imagine we have a sample of /N/ objects of which /r/ are "defective"
|
|
and N-r are "not defective"
|
|
(the terms "success\/failure" or "red\/blue" are also used). If we sample /n/
|
|
items /without replacement/ then what is the probability that exactly
|
|
/k/ items in the sample are defective? The answer is given by the pdf of the
|
|
hypergeometric distribution `f(k; r, n, N)`, whilst the probability of
|
|
/k/ defectives or fewer is given by F(k; r, n, N), where F(k) is the
|
|
CDF of the hypergeometric distribution.
|
|
|
|
[note Unlike almost all of the other distributions in this library,
|
|
the hypergeometric distribution is strictly discrete: it can not be
|
|
extended to real valued arguments of its parameters or random variable.]
|
|
|
|
The following graph shows how the distribution changes as the proportion
|
|
of "defective" items changes, while keeping the population and sample sizes
|
|
constant:
|
|
|
|
[graph hypergeometric_pdf_1]
|
|
|
|
Note that since the distribution is symmetrical in parameters /n/ and /r/, if we
|
|
change the sample size and keep the population and proportion "defective" the same
|
|
then we obtain basically the same graphs:
|
|
|
|
[graph hypergeometric_pdf_2]
|
|
|
|
[h4 Member Functions]
|
|
|
|
hypergeometric_distribution(unsigned r, unsigned n, unsigned N);
|
|
|
|
Constructs a hypergeometric distribution with a population of /N/ objects,
|
|
of which /r/ are defective, and from which /n/ are sampled.
|
|
|
|
unsigned total()const;
|
|
|
|
Returns the total number of objects /N/.
|
|
|
|
unsigned defective()const;
|
|
|
|
Returns the number of objects /r/ in population /N/ which are defective.
|
|
|
|
unsigned sample_count()const;
|
|
|
|
Returns the number of objects /n/ which are sampled from the population /N/.
|
|
|
|
[h4 Non-member Accessors]
|
|
|
|
All the [link math_toolkit.dist_ref.nmp usual non-member accessor functions]
|
|
that are generic to all distributions are supported: __usual_accessors.
|
|
|
|
The domain of the random variable is the unsigned integers in the range
|
|
\[max(0, n + r - N), min(n, r)\]. A __domain_error is raised if the
|
|
random variable is outside this range, or is not an integral value.
|
|
|
|
[caution
|
|
The quantile function will by default return an integer result that has been
|
|
/rounded outwards/. That is to say lower quantiles (where the probability is
|
|
less than 0.5) are rounded downward, and upper quantiles (where the probability
|
|
is greater than 0.5) are rounded upwards. This behaviour
|
|
ensures that if an X% quantile is requested, then /at least/ the requested
|
|
coverage will be present in the central region, and /no more than/
|
|
the requested coverage will be present in the tails.
|
|
|
|
This behaviour can be changed so that the quantile functions are rounded
|
|
differently using
|
|
[link math_toolkit.pol_overview Policies]. It is strongly
|
|
recommended that you read the tutorial
|
|
[link math_toolkit.pol_tutorial.understand_dis_quant
|
|
Understanding Quantiles of Discrete Distributions] before
|
|
using the quantile function on the Hypergeometric distribution. The
|
|
[link math_toolkit.pol_ref.discrete_quant_ref reference docs]
|
|
describe how to change the rounding policy
|
|
for these distributions.
|
|
|
|
However, note that the implementation method of the quantile function
|
|
always returns an integral value, therefore attempting to use a __Policy
|
|
that requires (or produces) a real valued result will result in a
|
|
compile time error.
|
|
] [/ caution]
|
|
|
|
|
|
[h4 Accuracy]
|
|
|
|
For small N such that
|
|
`N < boost::math::max_factorial<RealType>::value` then table based
|
|
lookup of the results gives an accuracy to a few epsilon.
|
|
`boost::math::max_factorial<RealType>::value` is 170 at double or long double
|
|
precision.
|
|
|
|
For larger N such that `N < boost::math::prime(boost::math::max_prime)`
|
|
then only basic arithmetic is required for the calculation
|
|
and the accuracy is typically < 20 epsilon. This takes care of N
|
|
up to 104729.
|
|
|
|
For `N > boost::math::prime(boost::math::max_prime)` then accuracy quickly
|
|
degrades, with 5 or 6 decimal digits being lost for N = 110000.
|
|
|
|
In general for very large N, the user should expect to lose log[sub 10]N
|
|
decimal digits of precision during the calculation, with the results
|
|
becoming meaningless for N >= 10[super 15].
|
|
|
|
[h4 Testing]
|
|
|
|
There are three sets of tests: our implementation is tested against a table of values
|
|
produced by Mathematica's implementation of this distribution. We also sanity check
|
|
our implementation against some spot values computed using the online calculator
|
|
here [@http://stattrek.com/Tables/Hypergeometric.aspx http://stattrek.com/Tables/Hypergeometric.aspx].
|
|
Finally we test accuracy against some high precision test data using
|
|
this implementation and NTL::RR.
|
|
|
|
[h4 Implementation]
|
|
|
|
The PDF can be calculated directly using the formula:
|
|
|
|
[equation hypergeometric1]
|
|
|
|
However, this can only be used directly when the largest of the factorials
|
|
is guaranteed not to overflow the floating point representation used.
|
|
This formula is used directly when `N < max_factorial<RealType>::value`
|
|
in which case table lookup of the factorials gives a rapid and accurate
|
|
implementation method.
|
|
|
|
For larger /N/ the method described in
|
|
"An Accurate Computation of the Hypergeometric Distribution Function",
|
|
Trong Wu, ACM Transactions on Mathematical Software, Vol. 19, No. 1,
|
|
March 1993, Pages 33-43 is used. The method relies on the fact that
|
|
there is an easy method for factorising a factorial into the product
|
|
of prime numbers:
|
|
|
|
[equation hypergeometric2]
|
|
|
|
Where p[sub i] is the i'th prime number, and e[sub i] is a small
|
|
positive integer or zero, which can be calculated via:
|
|
|
|
[equation hypergeometric3]
|
|
|
|
Further we can combine the factorials in the expression for the PDF
|
|
to yield the PDF directly as the product of prime numbers:
|
|
|
|
[equation hypergeometric4]
|
|
|
|
With this time the exponents e[sub i] being either positive, negative
|
|
or zero. Indeed such a degree of cancellation occurs in the calculation
|
|
of the e[sub i] that many are zero, and typically most have a magnitude
|
|
or no more than 1 or 2.
|
|
|
|
Calculation of the product of the primes requires some care to prevent
|
|
numerical overflow, we use a novel recursive method which splits the
|
|
calculation into a series of sub-products, with a new sub-product
|
|
started each time the next multiplication would cause either overflow
|
|
or underflow. The sub-products are stored in a linked list on the
|
|
program stack, and combined in an order that will guarantee no overflow
|
|
or unnecessary-underflow once the last sub-product has been calculated.
|
|
|
|
This method can be used as long as N is smaller than the largest prime
|
|
number we have stored in our table of primes (currently 104729). The method
|
|
is relatively slow (calculating the exponents requires the most time), but
|
|
requires only a small number of arithmetic operations to
|
|
calculate the result (indeed there is no shorter method involving only basic
|
|
arithmetic once the exponents have been found), the method is therefore
|
|
much more accurate than the alternatives.
|
|
|
|
For much larger N, we can calculate the PDF from the factorials using
|
|
either lgamma, or by directly combining lanczos approximations to avoid
|
|
calculating via logarithms. We use the latter method, as it is usually
|
|
1 or 2 decimal digits more accurate than computing via logarithms with
|
|
lgamma. However, in this area where N > 104729, the user should expect
|
|
to lose around log[sub 10]N decimal digits during the calculation in
|
|
the worst case.
|
|
|
|
The CDF and its complement is calculated by directly summing the PDF's.
|
|
We start by deciding whether the CDF, or its complement, is likely to be
|
|
the smaller of the two and then calculate the PDF at /k/ (or /k+1/ if we're
|
|
calculating the complement) and calculate successive PDF values via the
|
|
recurrence relations:
|
|
|
|
[equation hypergeometric5]
|
|
|
|
Until we either reach the end of the distributions domain, or the next
|
|
PDF value to be summed would be too small to affect the result.
|
|
|
|
The quantile is calculated in a similar manner to the CDF: we first guess
|
|
which end of the distribution we're nearer to, and then sum PDFs starting
|
|
from the end of the distribution this time, until we have some value /k/ that
|
|
gives the required CDF.
|
|
|
|
The median is simply the quantile at 0.5, and the remaining properties are
|
|
calculated via:
|
|
|
|
[equation hypergeometric6]
|
|
|
|
[endsect]
|
|
|
|
[/ hypergeometric.qbk
|
|
Copyright 2008 John Maddock.
|
|
Distributed under the Boost Software License, Version 1.0.
|
|
(See accompanying file LICENSE_1_0.txt or copy at
|
|
http://www.boost.org/LICENSE_1_0.txt).
|
|
]
|