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#! /usr/bin/env python
# Last Change: Mon Jul 23 09:00 PM 2007 J
# Copyright (c) 2001, 2002 Enthought, Inc.
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"""Some more special functions which may be useful for multivariate statistical
analysis."""
import numpy as N
from scipy.special import gammaln as loggam
def multigammaln(a, d):
"""returns the log of multivariate gamma, also sometimes called the
generalized gamma.
:Parameters:
a : ndarray
the multivariate gamma is computed for each item of a
d : int
the dimension of the space of integration.
:Returns:
res : ndarray
the values of the log multivariate gamma at the given points a.
Note
----
The formal definition of the multivariate gamma of dimension d for a real a
is :
\Gamma_d(a) = \int_{A>0}{e^{-tr(A)\cdot{|A|}^{a - (m+1)/2}dA}}
with the condition a > (d-1)/2, and A>0 being the set of all the positive
definite matrices of dimension s. Note that a is a scalar: the integration
is multivariate, the argument is not.
This can be proven to be equal to the much friendler equation:
\Gamma_d(a) = \pi^{d(d-1)/4}\prod_{i=1}^{d}{\Gamma(a - (i-1)/2)}.
Reference:
----------
R. J. Muirhead, Aspects of multivariate statistical theory (Wiley Series in
probability and mathematical statistics). """
a = N.asarray(a)
if not N.isscalar(d) or (N.floor(d) != d):
raise ValueError("d should be a positive integer (dimension)")
if N.any(a <= 0.5 * (d - 1)):
raise ValueError("condition a (%f) > 0.5 * (d-1) (%f) not met" \
% (a, 0.5 * (d-1)))
res = (d * (d-1) * 0.25) * N.log(N.pi)
if a.size == 1:
axis = -1
else:
axis = 0
res += N.sum(loggam([(a - (j - 1.)/2) for j in range(1, d+1)]), axis)
return res
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