danh
/
pywafo
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Updated distributions.py

Browse Source
master
Per.Andreas.Brodtkorb 12 years ago
parent cf2b303821
commit d540b8d078
2 changed files with 410 additions and 248 deletions
Show Stats Download Patch File Download Diff File

60
pywafo/src/wafo/integrate.py
Unescape Escape View File

@ -1136,7 +1136,7 @@ def richardson(Q, k):
R = Q[k] + (Q[k] - Q[k - 1]) / c
return R
def quadgr(fun, a, b, abseps=1e-5, maxiter=17):
def quadgr(fun, a, b, abseps=1e-5, max_iter=17):
'''
Gauss-Legendre quadrature with Richardson extrapolation.
@ -1446,35 +1446,35 @@ def qdemo(f, a, b):
def main():
val, err = clencurt(np.exp, 0, 2)
valt = np.exp(2) - np.exp(0)
[Q, err] = quadgr(lambda x: x ** 2, 1, 4, 1e-9)
[Q, err] = quadgr(humps, 1, 4, 1e-9)
[x, w] = h_roots(11, 'newton')
sum(w)
[x2, w2] = la_roots(11, 1, 't')
from scitools import numpyutils as npu #@UnresolvedImport
fun = npu.wrap2callable('x**2')
p0 = fun(0)
A = [0, 1, 1]; B = [2, 4, 3]
area, err = gaussq(fun, A, B)
fun = npu.wrap2callable('x**2')
[val1, err1] = gaussq(fun, A, B)
#Integration of x^2*exp(-x) from zero to infinity:
fun2 = npu.wrap2callable('1')
[val2, err2] = gaussq(fun2, 0, np.inf, wfun=3, alpha=2)
[val2, err2] = gaussq(lambda x: x ** 2, 0, np.inf, wfun=3, alpha=0)
#Integrate humps from 0 to 2 and from 1 to 4
[val3, err3] = gaussq(humps, A, B)
[x, w] = p_roots(11, 'newton', 1, 3)
y = np.sum(x ** 2 * w)
# val, err = clencurt(np.exp, 0, 2)
# valt = np.exp(2) - np.exp(0)
# [Q, err] = quadgr(lambda x: x ** 2, 1, 4, 1e-9)
# [Q, err] = quadgr(humps, 1, 4, 1e-9)
#
# [x, w] = h_roots(11, 'newton')
# sum(w)
# [x2, w2] = la_roots(11, 1, 't')
#
# from scitools import numpyutils as npu #@UnresolvedImport
# fun = npu.wrap2callable('x**2')
# p0 = fun(0)
# A = [0, 1, 1]; B = [2, 4, 3]
# area, err = gaussq(fun, A, B)
#
# fun = npu.wrap2callable('x**2')
# [val1, err1] = gaussq(fun, A, B)
#
#
# #Integration of x^2*exp(-x) from zero to infinity:
# fun2 = npu.wrap2callable('1')
# [val2, err2] = gaussq(fun2, 0, np.inf, wfun=3, alpha=2)
# [val2, err2] = gaussq(lambda x: x ** 2, 0, np.inf, wfun=3, alpha=0)
#
# #Integrate humps from 0 to 2 and from 1 to 4
# [val3, err3] = gaussq(humps, A, B)
#
# [x, w] = p_roots(11, 'newton', 1, 3)
# y = np.sum(x ** 2 * w)
x = np.linspace(0, np.pi / 2)
q0 = np.trapz(humps(x), x)

594
pywafo/src/wafo/stats/distributions.py
Unescape Escape View File

@ -58,10 +58,9 @@ __all__ = [
'rdist', 'rayleigh', 'truncrayleigh','reciprocal', 'rice', 'recipinvgauss',
'semicircular', 'triang', 'truncexpon', 'truncnorm',
'tukeylambda', 'uniform', 'vonmises', 'wald', 'wrapcauchy',
'entropy', 'rv_discrete',
'binom', 'bernoulli', 'nbinom', 'geom', 'hypergeom', 'logser',
'poisson', 'planck', 'boltzmann', 'randint', 'zipf', 'dlaplace',
'skellam'
'entropy', 'rv_discrete', 'binom', 'bernoulli', 'nbinom', 'geom',
'hypergeom', 'logser', 'poisson', 'planck', 'boltzmann', 'randint',
'zipf', 'dlaplace', 'skellam'
]
floatinfo = numpy.finfo(float)
@ -241,6 +240,8 @@ Display frozen pdf
>>> x = np.linspace(0, np.minimum(rv.dist.b, 3))
>>> h = plt.plot(x, rv.pdf(x))
Here, ``rv.dist.b`` is the right endpoint of the support of ``rv.dist``.
Check accuracy of cdf and ppf
>>> prb = %(name)s.cdf(x, %(shapes)s)
@ -1165,8 +1166,8 @@ class rv_generic(object):
arg1, arg2, ... : array_like
The shape parameter(s) for the distribution (see docstring of the instance
object for more information)
loc: array_like, optioal
location parameter (deafult = 0)
loc: array_like, optional
location parameter (default = 0)
scale : array_like, optional
scale paramter (default = 1)
@ -1204,9 +1205,9 @@ class rv_continuous(rv_generic):
Upper bound of the support of the distribution, default is plus
infinity.
xa : float, optional
Lower bound for fixed point calculation for generic ppf.
DEPRECATED
xb : float, optional
Upper bound for fixed point calculation for generic ppf.
DEPRECATED
xtol : float, optional
The tolerance for fixed point calculation for generic ppf.
badvalue : object, optional
@ -1438,7 +1439,7 @@ class rv_continuous(rv_generic):
"""
def __init__(self, momtype=1, a=None, b=None, xa=-10.0, xb=10.0,
def __init__(self, momtype=1, a=None, b=None, xa=None, xb=None,
xtol=1e-14, badvalue=None, name=None, longname=None,
shapes=None, extradoc=None):
@ -1457,6 +1458,12 @@ class rv_continuous(rv_generic):
self.a = -inf
if b is None:
self.b = inf
if xa is not None:
warnings.warn("The `xa` parameter is deprecated and will be "
"removed in scipy 0.12", DeprecationWarning)
if xb is not None:
warnings.warn("The `xb` parameter is deprecated and will be "
"removed in scipy 0.12", DeprecationWarning)
self.xa = xa
self.xb = xb
self.xtol = xtol
@ -1539,7 +1546,28 @@ class rv_continuous(rv_generic):
return apply(self.cdf, (x, )+args)-q
def _ppf_single_call(self, q, *args):
return optimize.brentq(self._ppf_to_solve, self.xa, self.xb, args=(q,)+args, xtol=self.xtol)
left = right = None
if self.a > -np.inf:
left = self.a
if self.b < np.inf:
right = self.b
factor = 10.
if not left: # i.e. self.a = -inf
left = -1.*factor
while self._ppf_to_solve(left, q,*args) > 0.:
right = left
left *= factor
# left is now such that cdf(left) < q
if not right: # i.e. self.b = inf
right = factor
while self._ppf_to_solve(right, q,*args) < 0.:
left = right
right *= factor
# right is now such that cdf(right) > q
return optimize.brentq(self._ppf_to_solve, \
left, right, args=(q,)+args, xtol=self.xtol)
# moment from definition
def _mom_integ0(self, x,m,*args):
@ -1727,7 +1755,6 @@ class rv_continuous(rv_generic):
cond2 = (x >= self.b) & cond0
cond = cond0 & cond1
output = zeros(shape(cond),'d')
#place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0)+np.isnan(x),self.badvalue)
place(output,cond2,1.0)
if any(cond): #call only if at least 1 entry
@ -2398,12 +2425,13 @@ class rv_continuous(rv_generic):
fixedn = []
index = range(Nargs)
names = ['f%d' % n for n in range(Nargs - 2)] + ['floc', 'fscale']
x0 = args[:]
for n, key in zip(index[::-1], names[::-1]):
x0 = []
for n, key in zip(index, names):
if kwds.has_key(key):
fixedn.append(n)
args[n] = kwds[key]
del x0[n]
else:
x0.append(args[n])
method = kwds.get('method', 'ml').lower()
if method.startswith('mps'):
fitfun = self.nlogps
@ -2737,12 +2765,7 @@ def _norm_logpdf(x):
def _norm_cdf(x):
return special.ndtr(x)
def _norm_logcdf(x):
logcdf = log(special.ndtr(x))
large_x = (5<x)
if np.any(large_x):
logcdf[large_x] = log1p(-special.ndtr(-x[large_x]))
return logcdf
#return log(special.ndtr(x))
return special.log_ndtr(x)
def _norm_ppf(q):
return special.ndtri(q)
class norm_gen(rv_continuous):
@ -3362,35 +3385,39 @@ class erlang_gen(rv_continuous):
%(before_notes)s
See Also
--------
gamma
Notes
-----
The Erlang distribution is a special case of the Gamma distribution, with
the shape parameter an integer.
The Erlang distribution is a special case of the Gamma
distribution, with the shape parameter ``a`` an integer. Refer to
the ``gamma`` distribution for further examples.
%(example)s
"""
def _rvs(self, n):
return gamma.rvs(n,size=self._size)
def _arg_check(self, n):
return (n > 0) & (floor(n)==n)
def _pdf(self, x, n):
Px = (x)**(n-1.0)*exp(-x)/special.gamma(n)
def _rvs(self, a):
return gamma.rvs(a, size=self._size)
def _arg_check(self, a):
return (a > 0) & (floor(a)==a)
def _pdf(self, x, a):
Px = (x)**(a-1.0)*exp(-x)/special.gamma(a)
return Px
def _logpdf(self, x, n):
return (n-1.0)*log(x) - x - gamln(n)
def _cdf(self, x, n):
return special.gdtr(1.0,n,x)
def _sf(self, x, n):
return special.gdtrc(1.0,n,x)
def _ppf(self, q, n):
return special.gdtrix(1.0, n, q)
def _stats(self, n):
n = n*1.0
return n, n, 2/sqrt(n), 6/n
def _entropy(self, n):
return special.psi(n)*(1-n) + 1 + gamln(n)
erlang = erlang_gen(a=0.0, name='erlang', shapes='n')
def _logpdf(self, x, a):
return (a-1.0)*log(x) - x - gamln(a)
def _cdf(self, x, a):
return special.gdtr(1.0,a,x)
def _sf(self, x, a):
return special.gdtrc(1.0,a,x)
def _ppf(self, q, a):
return special.gdtrix(1.0, a, q)
def _stats(self, a):
a = a*1.0
return a, a, 2/sqrt(a), 6/a
def _entropy(self, a):
return special.psi(a)*(1-a) + 1 + gamln(a)
erlang = erlang_gen(a=0.0, name='erlang', shapes='a')
## Exponential (gamma distributed with a=1.0, loc=loc and scale=scale)
@ -3405,12 +3432,14 @@ class expon_gen(rv_continuous):
-----
The probability density function for `expon` is::
expon.pdf(x) = exp(-x)
expon.pdf(x) = lambda * exp(- lambda*x)
for ``x >= 0``.
The scale parameter is equal to ``scale = 1.0 / lambda``.
`expon` does not have shape parameters.
%(example)s
"""
@ -3601,8 +3630,7 @@ class foldcauchy_gen(rv_continuous):
return 1.0/pi*(arctan(x-c) + arctan(x+c))
def _stats(self, c):
return inf, inf, nan, nan
# setting xb=1000 allows to calculate ppf for up to q=0.9993
foldcauchy = foldcauchy_gen(a=0.0, name='foldcauchy', xb=1000, shapes='c')
foldcauchy = foldcauchy_gen(a=0.0, name='foldcauchy', shapes='c')
## F
@ -4218,14 +4246,25 @@ class gamma_gen(rv_continuous):
Notes
-----
When ``a`` is an integer, this is the Erlang distribution, and for ``a=1``
it is the exponential distribution.
The probability density function for `gamma` is::
gamma.pdf(x, a) = x**(a-1) * exp(-x) / gamma(a)
gamma.pdf(x, a) = (lambda*x)**(a-1) * exp(-lambda*x) / gamma(a)
for ``x >= 0``, ``a > 0``. Here ``gamma(a)`` refers to the gamma function.
The scale parameter is equal to ``scale = 1.0 / lambda``.
`gamma` has a shape parameter `a` which needs to be set explicitly. For instance:
for ``x >= 0``, ``a > 0``.
>>> from scipy.stats import gamma
>>> rv = gamma(3., loc = 0., scale = 2.)
produces a frozen form of `gamma` with shape ``a = 3.``, ``loc =
0.`` and ``lambda = 1./scale = 1./2.``.
When ``a`` is an integer, `gamma` reduces to the Erlang
distribution, and when ``a=1`` to the exponential distribution.
%(example)s
@ -5590,9 +5629,10 @@ class powerlaw_gen(rv_continuous):
def _ppf(self, q, a):
return pow(q, 1.0/a)
def _stats(self, a):
return a/(a+1.0), a*(a+2.0)/(a+1.0)**2, \
2*(1.0-a)*sqrt((a+2.0)/(a*(a+3.0))), \
6*polyval([1,-1,-6,2],a)/(a*(a+3.0)*(a+4))
return (a / (a + 1.0),
a / (a + 2.0) / (a + 1.0) ** 2,
-2.0 * ((a - 1.0) / (a + 3.0)) * sqrt((a + 2.0) / a),
6 * polyval([1, -1, -6, 2], a) / (a * (a + 3.0) * (a + 4)))
def _entropy(self, a):
return 1 - 1.0/a - log(a)
powerlaw = powerlaw_gen(a=0.0, b=1.0, name="powerlaw", shapes="a")
@ -5894,9 +5934,8 @@ class recipinvgauss_gen(rv_continuous):
trm2 = 1.0/mu + x
isqx = 1.0/sqrt(x)
return 1.0-_norm_cdf(isqx*trm1)-exp(2.0/mu)*_norm_cdf(-isqx*trm2)
# xb=50 or something large is necessary for stats to converge without exception
recipinvgauss = recipinvgauss_gen(a=0.0, xb=50, name='recipinvgauss',
shapes="mu")
recipinvgauss = recipinvgauss_gen(a=0.0, name='recipinvgauss', shapes="mu")
# Semicircular
@ -6096,16 +6135,8 @@ class tukeylambda_gen(rv_continuous):
vals2 = log(q/(1-q))
return where((lam == 0)&(q==q), vals2, vals1)
def _stats(self, lam):
mu2 = 2*gam(lam+1.5)-lam*pow(4,-lam)*sqrt(pi)*gam(lam)*(1-2*lam)
mu2 /= lam*lam*(1+2*lam)*gam(1+1.5)
mu4 = 3*gam(lam)*gam(lam+0.5)*pow(2,-2*lam) / lam**3 / gam(2*lam+1.5)
mu4 += 2.0/lam**4 / (1+4*lam)
mu4 -= 2*sqrt(3)*gam(lam)*pow(2,-6*lam)*pow(3,3*lam) * \
gam(lam+1.0/3)*gam(lam+2.0/3) / (lam**3.0 * gam(2*lam+1.5) * \
gam(lam+0.5))
g2 = mu4 / mu2 / mu2 - 3.0
return 0, mu2, 0, g2
return 0, _tlvar(lam), 0, _tlkurt(lam)
def _entropy(self, lam):
def integ(p):
return log(pow(p,lam-1)+pow(1-p,lam-1))
@ -6118,7 +6149,7 @@ tukeylambda = tukeylambda_gen(name='tukeylambda', shapes="lam")
class uniform_gen(rv_continuous):
"""A uniform continuous random variable.
This distribution is constant between `loc` and ``loc = scale``.
This distribution is constant between `loc` and ``loc + scale``.
%(before_notes)s
@ -6263,32 +6294,52 @@ wrapcauchy = wrapcauchy_gen(a=0.0, b=2*pi, name='wrapcauchy', shapes="c")
### DISCRETE DISTRIBUTIONS
###
def entropy(pk,qk=None):
"""S = entropy(pk,qk=None)
def entropy(pk, qk=None, base=None):
""" Calculate the entropy of a distribution for given probability values.
If only probabilities `pk` are given, the entropy is calculated as
``S = -sum(pk * log(pk), axis=0)``.
If `qk` is not None, then compute a relative entropy
``S = sum(pk * log(pk / qk), axis=0)``.
calculate the entropy of a distribution given the p_k values
S = -sum(pk * log(pk), axis=0)
This routine will normalize `pk` and `qk` if they don't sum to 1.
If qk is not None, then compute a relative entropy
S = sum(pk * log(pk / qk), axis=0)
Parameters
----------
pk : sequence
Defines the (discrete) distribution. ``pk[i]`` is the (possibly
unnormalized) probability of event ``i``.
qk : sequence, optional
Sequence against which the relative entropy is computed. Should be in
the same format as `pk`.
base : float, optional
The logarithmic base to use, defaults to ``e`` (natural logarithm).
Returns
-------
S : float
The calculated entropy.
Routine will normalize pk and qk if they don't sum to 1
"""
pk = arr(pk)
pk = 1.0* pk / sum(pk,axis=0)
pk = 1.0* pk / sum(pk, axis=0)
if qk is None:
vec = where(pk == 0, 0.0, pk*log(pk))
else:
qk = arr(qk)
if len(qk) != len(pk):
raise ValueError("qk and pk must have same length.")
qk = 1.0*qk / sum(qk,axis=0)
qk = 1.0*qk / sum(qk, axis=0)
# If qk is zero anywhere, then unless pk is zero at those places
# too, the relative entropy is infinite.
if any(take(pk,nonzero(qk==0.0),axis=0)!=0.0, 0):
if any(take(pk, nonzero(qk == 0.0), axis=0) != 0.0, 0):
return inf
vec = where (pk == 0, 0.0, -pk*log(pk / qk))
return -sum(vec,axis=0)
S = -sum(vec, axis=0)
if base is not None:
S /= log(base)
return S
## Handlers for generic case where xk and pk are given
@ -6568,6 +6619,8 @@ class rv_discrete(rv_generic):
>>> x = np.arange(0, np.min(rv.dist.b, 3)+1)
>>> h = plt.plot(x, rv.pmf(x))
Here, ``rv.dist.b`` is the right endpoint of the support of ``rv.dist``.
Check accuracy of cdf and ppf:
>>> prb = generic.cdf(x, <shape(s)>)
@ -6588,7 +6641,7 @@ class rv_discrete(rv_generic):
moment_tol=1e-8,values=None,inc=1,longname=None,
shapes=None, extradoc=None):
super(rv_generic, self).__init__()
super(rv_generic,self).__init__()
self.fix_loc = self._fix_loc
@ -7517,87 +7570,96 @@ class nbinom_gen(rv_discrete):
Notes
-----
Probability mass function, given by
``np.choose(k+n-1, n-1) * p**n * (1-p)**k`` for ``k >= 0``.
The probability mass function for `nbinom` is::
nbinom.pmf(k) = choose(k+n-1, n-1) * p**n * (1-p)**k
for ``k >= 0``.
`nbinom` takes ``n`` and ``p`` as shape parameters.
%(example)s
"""
def _rvs(self, n, pr):
return mtrand.negative_binomial(n, pr, self._size)
def _argcheck(self, n, pr):
return (n >= 0) & (pr >= 0) & (pr <= 1)
def _pmf(self, x, n, pr):
def _rvs(self, n, p):
return mtrand.negative_binomial(n, p, self._size)
def _argcheck(self, n, p):
return (n >= 0) & (p >= 0) & (p <= 1)
def _pmf(self, x, n, p):
coeff = exp(gamln(n+x) - gamln(x+1) - gamln(n))
return coeff * power(pr,n) * power(1-pr,x)
def _logpmf(self, x, n, pr):
return coeff * power(p,n) * power(1-p,x)
def _logpmf(self, x, n, p):
coeff = gamln(n+x) - gamln(x+1) - gamln(n)
return coeff + n*log(pr) + x*log1p(-pr)
def _cdf(self, x, n, pr):
return coeff + n*log(p) + x*log1p(-p)
def _cdf(self, x, n, p):
k = floor(x)
return special.betainc(n, k+1, pr)
def _sf_skip(self, x, n, pr):
return special.betainc(n, k+1, p)
def _sf_skip(self, x, n, p):
#skip because special.nbdtrc doesn't work for 0<n<1
k = floor(x)
return special.nbdtrc(k,n,pr)
def _ppf(self, q, n, pr):
vals = ceil(special.nbdtrik(q,n,pr))
return special.nbdtrc(k,n,p)
def _ppf(self, q, n, p):
vals = ceil(special.nbdtrik(q,n,p))
vals1 = (vals-1).clip(0.0, np.inf)
temp = self._cdf(vals1,n,pr)
temp = self._cdf(vals1,n,p)
return where(temp >= q, vals1, vals)
def _stats(self, n, pr):
Q = 1.0 / pr
def _stats(self, n, p):
Q = 1.0 / p
P = Q - 1.0
mu = n*P
var = n*P*Q
g1 = (Q+P)/sqrt(n*P*Q)
g2 = (1.0 + 6*P*Q) / (n*P*Q)
return mu, var, g1, g2
nbinom = nbinom_gen(name='nbinom', shapes="n, pr", extradoc="""
Negative binomial distribution
nbinom.pmf(k,n,p) = choose(k+n-1,n-1) * p**n * (1-p)**k
for k >= 0.
"""
)
nbinom = nbinom_gen(name='nbinom', shapes="n, p")
## Geometric distribution
class geom_gen(rv_discrete):
def _rvs(self, pr):
return mtrand.geometric(pr,size=self._size)
def _argcheck(self, pr):
return (pr<=1) & (pr >= 0)
def _pmf(self, k, pr):
return (1-pr)**(k-1) * pr
def _logpmf(self, k, pr):
return (k-1)*log1p(-pr) + pr
def _cdf(self, x, pr):
"""A geometric discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `geom` is::
geom.pmf(k) = (1-p)**(k-1)*p
for ``k >= 1``.
`geom` takes ``p`` as shape parameter.
%(example)s
"""
def _rvs(self, p):
return mtrand.geometric(p,size=self._size)
def _argcheck(self, p):
return (p<=1) & (p >= 0)
def _pmf(self, k, p):
return (1-p)**(k-1) * p
def _logpmf(self, k, p):
return (k-1)*log1p(-p) + p
def _cdf(self, x, p):
k = floor(x)
return (1.0-(1.0-pr)**k)
def _sf(self, x, pr):
return (1.0-(1.0-p)**k)
def _sf(self, x, p):
k = floor(x)
return (1.0-pr)**k
def _ppf(self, q, pr):
vals = ceil(log1p(-q)/log1p(-pr))
temp = 1.0-(1.0-pr)**(vals-1)
return (1.0-p)**k
def _ppf(self, q, p):
vals = ceil(log1p(-q)/log1p(-p))
temp = 1.0-(1.0-p)**(vals-1)
return where((temp >= q) & (vals > 0), vals-1, vals)
def _stats(self, pr):
mu = 1.0/pr
qr = 1.0-pr
var = qr / pr / pr
g1 = (2.0-pr) / sqrt(qr)
g2 = numpy.polyval([1,-6,6],pr)/(1.0-pr)
def _stats(self, p):
mu = 1.0/p
qr = 1.0-p
var = qr / p / p
g1 = (2.0-p) / sqrt(qr)
g2 = numpy.polyval([1,-6,6],p)/(1.0-p)
return mu, var, g1, g2
geom = geom_gen(a=1,name='geom', longname="A geometric",
shapes="pr", extradoc="""
Geometric distribution
shapes="p")
geom.pmf(k,p) = (1-p)**(k-1)*p
for k >= 1
"""
)
## Hypergeometric distribution
@ -7618,7 +7680,37 @@ class hypergeom_gen(rv_discrete):
pmf(k, M, n, N) = choose(n, k) * choose(M - n, N - k) / choose(M, N),
for N - (M-n) <= k <= min(m,N)
%(example)s
Examples
--------
>>> from scipy.stats import hypergeom
Suppose we have a collection of 20 animals, of which 7 are dogs. Then if
we want to know the probability of finding a given number of dogs if we
choose at random 12 of the 20 animals, we can initialize a frozen
distribution and plot the probability mass function:
>>> [M, n, N] = [20, 7, 12]
>>> rv = hypergeom(M, n, N)
>>> x = np.arange(0, n+1)
>>> pmf_dogs = rv.pmf(x)
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.plot(x, pmf_dogs, 'bo')
>>> ax.vlines(x, 0, pmf_dogs, lw=2)
>>> ax.set_xlabel('# of dogs in our group of chosen animals')
>>> ax.set_ylabel('hypergeom PMF')
>>> plt.show()
Instead of using a frozen distribution we can also use `hypergeom`
methods directly. To for example obtain the cumulative distribution
function, use:
>>> prb = hypergeom.cdf(x, M, n, N)
And to generate random numbers:
>>> R = hypergeom.rvs(M, n, N, size=10)
"""
def _rvs(self, M, n, N):
@ -7681,6 +7773,23 @@ hypergeom = hypergeom_gen(name='hypergeom', shapes="M, n, N")
## Logarithmic (Log-Series), (Series) distribution
# FIXME: Fails _cdfvec
class logser_gen(rv_discrete):
"""A Logarithmic (Log-Series, Series) discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `logser` is::
logser.pmf(k) = - p**k / (k*log(1-p))
for ``k >= 1``.
`logser` takes ``p`` as shape parameter.
%(example)s
"""
def _rvs(self, pr):
# looks wrong for pr>0.5, too few k=1
# trying to use generic is worse, no k=1 at all
@ -7704,23 +7813,35 @@ class logser_gen(rv_discrete):
g2 = mu4 / var**2 - 3.0
return mu, var, g1, g2
logser = logser_gen(a=1,name='logser', longname='A logarithmic',
shapes='pr', extradoc="""
Logarithmic (Log-Series, Series) distribution
logser.pmf(k,p) = - p**k / (k*log(1-p))
for k >= 1
"""
)
shapes='p')
## Poisson distribution
class poisson_gen(rv_discrete):
"""A Poisson discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `poisson` is::
poisson.pmf(k) = exp(-mu) * mu**k / k!
for ``k >= 0``.
`poisson` takes ``mu`` as shape parameter.
%(example)s
"""
def _rvs(self, mu):
return mtrand.poisson(mu, self._size)
def _pmf(self, k, mu):
def _logpmf(self, k, mu):
Pk = k*log(mu)-gamln(k+1) - mu
return exp(Pk)
return Pk
def _pmf(self, k, mu):
return exp(self._logpmf(k, mu))
def _cdf(self, x, mu):
k = floor(x)
return special.pdtr(k,mu)
@ -7737,19 +7858,27 @@ class poisson_gen(rv_discrete):
g1 = 1.0/arr(sqrt(mu))
g2 = 1.0 / arr(mu)
return mu, var, g1, g2
poisson = poisson_gen(name="poisson", longname='A Poisson',
shapes="mu", extradoc="""
poisson = poisson_gen(name="poisson", longname='A Poisson', shapes="mu")
Poisson distribution
## (Planck) Discrete Exponential
class planck_gen(rv_discrete):
"""A Planck discrete exponential random variable.
poisson.pmf(k, mu) = exp(-mu) * mu**k / k!
for k >= 0
"""
)
%(before_notes)s
## (Planck) Discrete Exponential
Notes
-----
The probability mass function for `planck` is::
class planck_gen(rv_discrete):
planck.pmf(k) = (1-exp(-lambda))*exp(-lambda*k)
for ``k*lambda >= 0``.
`planck` takes ``lambda`` as shape parameter.
%(example)s
"""
def _argcheck(self, lambda_):
if (lambda_ > 0):
self.a = 0
@ -7781,18 +7910,28 @@ class planck_gen(rv_discrete):
l = lambda_
C = -expm1(-l)
return l * exp(-l) / C - log(C)
planck = planck_gen(name='planck', longname='A discrete exponential ',
shapes="lamda",
extradoc="""
planck = planck_gen(name='planck',longname='A discrete exponential ',
shapes="lamda")
Planck (Discrete Exponential)
planck.pmf(k,b) = (1-exp(-b))*exp(-b*k)
for k*b >= 0
"""
)
class boltzmann_gen(rv_discrete):
"""A Boltzmann (Truncated Discrete Exponential) random variable.
%(before_notes)s
Notes
-----
The probability mass function for `boltzmann` is::
boltzmann.pmf(k) = (1-exp(-lambda)*exp(-lambda*k)/(1-exp(-lambda*N))
for ``k = 0,...,N-1``.
`boltzmann` takes ``lambda`` and ``N`` as shape parameters.
%(example)s
"""
def _pmf(self, k, lambda_, N):
fact = (expm1(-lambda_))/(expm1(-lambda_*N))
return fact*exp(-lambda_*k)
@ -7819,22 +7958,28 @@ class boltzmann_gen(rv_discrete):
return mu, var, g1, g2
boltzmann = boltzmann_gen(name='boltzmann',longname='A truncated discrete exponential ',
shapes="lamda, N",
extradoc="""
shapes="lamda, N")
Boltzmann (Truncated Discrete Exponential)
## Discrete Uniform
boltzmann.pmf(k,b,N) = (1-exp(-b))*exp(-b*k)/(1-exp(-b*N))
for k=0,..,N-1
"""
)
class randint_gen(rv_discrete):
"""A uniform discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `randint` is::
randint.pmf(k) = 1./(max- min)
for ``k = min,...,max``.
## Discrete Uniform
`randint` takes ``min`` and ``max`` as shape parameters.
class randint_gen(rv_discrete):
%(example)s
"""
def _argcheck(self, min, max):
self.a = min
self.b = max-1
@ -7868,23 +8013,30 @@ class randint_gen(rv_discrete):
def _entropy(self, min, max):
return log(max-min)
randint = randint_gen(name='randint',longname='A discrete uniform '\
'(random integer)', shapes="min, max",
extradoc="""
Discrete Uniform
Random integers >=min and <max.
randint.pmf(k,min, max) = 1/(max-min)
for min <= k < max.
"""
)
'(random integer)', shapes="min, max")
# Zipf distribution
# FIXME: problems sampling.
class zipf_gen(rv_discrete):
"""A Zipf discrete random variable.
%(before_notes)s
Notes
-----
The probability mass function for `zipf` is::
zipf.pmf(k) = 1/(zeta(a)*k**a)
for ``k >= 1``.
`zipf` takes ``a`` as shape parameter.
%(example)s
"""
def _rvs(self, a):
return mtrand.zipf(a, size=self._size)
def _argcheck(self, a):
@ -7910,19 +8062,28 @@ class zipf_gen(rv_discrete):
g2 = mu4 / arr(var**2) - 3.0
return mu, var, g1, g2
zipf = zipf_gen(a=1,name='zipf', longname='A Zipf',
shapes="a", extradoc="""
shapes="a")
Zipf distribution
zipf.pmf(k,a) = 1/(zeta(a)*k**a)
for k >= 1
"""
)
# Discrete Laplacian
class dlaplace_gen(rv_discrete):
"""A Laplacian discrete random variable.
%(before_notes)s
# Discrete Laplacian
Notes
-----
The probability mass function for `dlaplace` is::
class dlaplace_gen(rv_discrete):
dlaplace.pmf(k) = tanh(a/2) * exp(-a*abs(k))
for ``a >0``.
`dlaplace` takes ``a`` as shape parameter.
%(example)s
"""
def _pmf(self, k, a):
return tanh(a/2.0)*exp(-a*abs(k))
def _cdf(self, x, a):
@ -7955,17 +8116,35 @@ class dlaplace_gen(rv_discrete):
return a / sinh(a) - log(tanh(a/2.0))
dlaplace = dlaplace_gen(a=-inf,
name='dlaplace', longname='A discrete Laplacian',
shapes="a", extradoc="""
shapes="a")
Discrete Laplacian distribution.
dlaplace.pmf(k,a) = tanh(a/2) * exp(-a*abs(k))
for a > 0.
"""
)
class skellam_gen(rv_discrete):
"""A Skellam discrete random variable.
%(before_notes)s
class skellam_gen(rv_discrete):
Notes
-----
Probability distribution of the difference of two correlated or
uncorrelated Poisson random variables.
Let k1 and k2 be two Poisson-distributed r.v. with expected values
lam1 and lam2. Then, ``k1 - k2`` follows a Skellam distribution with
parameters ``mu1 = lam1 - rho*sqrt(lam1*lam2)`` and
``mu2 = lam2 - rho*sqrt(lam1*lam2)``, where rho is the correlation
coefficient between k1 and k2. If the two Poisson-distributed r.v.
are independent then ``rho = 0``.
Parameters mu1 and mu2 must be strictly positive.
For details see: http://en.wikipedia.org/wiki/Skellam_distribution
`skellam` takes ``mu1`` and ``mu2`` as shape parameters.
%(example)s
"""
def _rvs(self, mu1, mu2):
n = self._size
return np.random.poisson(mu1, n)-np.random.poisson(mu2, n)
@ -7993,26 +8172,9 @@ class skellam_gen(rv_discrete):
g2 = 1 / var
return mean, var, g1, g2
skellam = skellam_gen(a=-np.inf, name="skellam", longname='A Skellam',
shapes="mu1,mu2", extradoc="""
Skellam distribution
Probability distribution of the difference of two correlated or
uncorrelated Poisson random variables.
Let k1 and k2 be two Poisson-distributed r.v. with expected values
lam1 and lam2. Then, k1-k2 follows a Skellam distribution with
parameters mu1 = lam1 - rho*sqrt(lam1*lam2) and
mu2 = lam2 - rho*sqrt(lam1*lam2), where rho is the correlation
coefficient between k1 and k2. If the two Poisson-distributed r.v.
are independent then rho = 0.
Parameters mu1 and mu2 must be strictly positive.
shapes="mu1,mu2")
For details see: http://en.wikipedia.org/wiki/Skellam_distribution
"""
)
def test_lognorm():

Write Preview
Loading…
Cancel
Save