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

Moved all links to estimation.py

Browse Source
master
Per A Brodtkorb 9 years ago
parent 59186edfd6
commit f8d1afb93b
2 changed files with 104 additions and 143 deletions
Show Stats Download Patch File Download Diff File

135
wafo/stats/_continuous_distns.py
Unescape Escape View File

@ -1120,15 +1120,6 @@ class expon_gen(rv_continuous):
%(example)s
"""
def _link(self, x, logSF, phat, ix):
if ix == 1:
return - (x - phat[0]) / logSF
elif ix == 0:
return x + phat[1] * logSF
else:
raise IndexError('Index to the fixed parameter is out of bounds')
def _rvs(self):
return self._random_state.standard_exponential(self._size)
@ -1593,17 +1584,6 @@ class frechet_r_gen(rv_continuous):
%(example)s
"""
def _link(self, x, logSF, phat, ix):
if ix == 0:
phati = log(-logSF) / log((x - phat[1]) / phat[2])
elif ix == 1:
phati = x - phat[2] * (-logSF) ** (1. / phat[0])
elif ix == 2:
phati = (x - phat[1]) / (-logSF) ** (1. / phat[0])
else:
raise IndexError('Index to the fixed parameter is out of bounds')
return phati
def _pdf(self, x, c):
return c*pow(x, c-1)*exp(-pow(x, c))
@ -1762,33 +1742,6 @@ class genpareto_gen(rv_continuous):
%(example)s
"""
def _link(self, x, logSF, phat, ix):
# Reference
# Stuart Coles (2004)
# "An introduction to statistical modelling of extreme values".
# Springer series in statistics
c, loc, scale = phat
if ix == 2:
# Reorganizing w.r.t.scale, Eq. 4.13 and 4.14, pp 81 in
# Coles (2004) gives
# link = -(x-loc)*c/expm1(-c*logSF)
if c != 0.0:
phati = (x - loc) * c / expm1(-c * logSF)
else:
phati = -(x - loc) / logSF
elif ix == 1:
if c != 0:
phati = x + scale * expm1(c * logSF) / c
else:
phati = x + scale * logSF
elif ix == 0:
raise NotImplementedError(
'link(x,logSF,phat,i) where i=0 is not implemented!')
else:
raise IndexError('Index to the fixed parameter is out of bounds')
return phati
def _argcheck(self, c):
c = asarray(c)
self.b = _lazywhere(c < 0, (c,),
@ -1832,57 +1785,6 @@ class genpareto_gen(rv_continuous):
scale = m * ((m / s) ** 2 + 1) / 2
return shape, loc, scale
def hessian_nnlf(self, theta, x, eps=None):
try:
loc = theta[-2]
scale = theta[-1]
args = tuple(theta[:-2])
except IndexError:
raise ValueError("Not enough input arguments.")
if not self._argcheck(*args) or scale <= 0:
return inf
x = asarray((x - loc) / scale)
cond0 = (x <= self.a) | (x >= self.b)
if any(cond0):
np = self.numargs + 2
return valarray((np, np), value=nan)
eps = _EPS
c = args[0]
n = len(x)
if abs(c) > eps:
cx = c * x
sumlog1pcx = sum(log1p(cx))
# LL = n*log(scale) + (1-1/k)*sumlog1mkxn
r = x / (1.0 + cx)
sumix = sum(1.0 / (1.0 + cx) ** 2.0)
sumr = sum(r)
sumr2 = sum(r ** 2.0)
H11 = -2 * sumlog1pcx / c ** 3 + 2 * \
sumr / c ** 2 + (1.0 + 1.0 / c) * sumr2
H22 = c * (c + 1) * sumix / scale ** 2.0
H33 = (n - 2 * (c + 1) * sumr +
c * (c + 1) * sumr2) / scale ** 2.0
H12 = -sum((1 - x) / ((1 + cx) ** 2.0)) / scale
H23 = -(c + 1) * sumix / scale ** 2.0
H13 = -(sumr - (c + 1) * sumr2) / scale
else: # c == 0
sumx = sum(x)
# LL = n*log(scale) + sumx;
sumx2 = sum(x ** 2.0)
H11 = -(2 / 3) * sum(x ** 3.0) + sumx2
H22 = 0.0
H12 = -(n - sum(x)) / scale
H23 = -n * 1.0 / scale ** 2.0
H33 = (n - 2 * sumx) / scale ** 2.0
H13 = -(sumx - sumx2) / scale
# Hessian matrix
H = [[H11, H12, H13], [H12, H22, H23], [H13, H23, H33]]
return asarray(H)
def __stats(self, c):
# return None,None,None,None
k = -c
@ -1950,22 +1852,6 @@ class genexpon_gen(rv_continuous):
%(example)s
"""
def _link(self, x, logSF, phat, ix):
_a, b, c, loc, scale = phat
xn = (x - loc) / scale
fact1 = (xn + expm1(-c * xn) / c)
if ix == 0:
phati = b * fact1 + logSF
elif ix == 1:
phati = (phat[0] - logSF) / fact1
elif ix in [2, 3, 4]:
raise NotImplementedError('Only implemented for index in [0,1]!')
else:
raise IndexError('Index to the fixed parameter is out of bounds')
return phati
def _pdf(self, x, a, b, c):
return (a + b*(-special.expm1(-c*x))) * exp((-a-b)*x +
b*(-special.expm1(-c*x))/c)
@ -4248,15 +4134,6 @@ class rayleigh_gen(rv_continuous):
%(example)s
"""
def _link(self, x, logSF, phat, ix):
if ix == 1:
return x - phat[0] / sqrt(-2.0 * logSF)
elif ix == 0:
return x - phat[1] * sqrt(-2.0 * logSF)
else:
raise IndexError('Index to the fixed parameter is out of bounds')
def _rvs(self):
return chi.rvs(2, size=self._size, random_state=self._random_state)
@ -4314,18 +4191,6 @@ class truncrayleigh_gen(rv_continuous):
def _argcheck(self, c):
return (c >= 0)
def _link(self, x, logSF, phat, ix):
c, loc, scale = phat
if ix == 2:
return x - loc / (sqrt(c * c - 2 * logSF) - c)
elif ix == 1:
return x - scale * (sqrt(c * c - 2 * logSF) - c)
elif ix == 0:
xn = (x - loc) / scale
return - 2 * logSF / xn - xn / 2.0
else:
raise IndexError('Index to the fixed parameter is out of bounds')
def _fitstart(self, data, args=None):
if args is None:
args = (0.0,) * self.numargs

106
wafo/stats/estimation.py
Unescape Escape View File

@ -21,19 +21,115 @@ from scipy.linalg import pinv2
from scipy import optimize
import numpy as np
from numpy import (alltrue, arange, zeros, log, sqrt, exp,
from numpy import (alltrue, arange, zeros, log, sqrt, exp, expm1,
atleast_1d, any, asarray, nan, pi, isfinite)
from numpy import flatnonzero as nonzero
__all__ = ['Profile', 'FitDistribution']
floatinfo = np.finfo(float)
arr = asarray
all = alltrue # @ReservedAssignment
def _expon_link(x, logSF, phat, ix):
if ix == 1:
return - (x - phat[0]) / logSF
elif ix == 0:
return x + phat[1] * logSF
else:
raise IndexError('Index to the fixed parameter is out of bounds')
def _weibull_min_link(x, logSF, phat, ix):
if ix == 0:
phati = log(-logSF) / log((x - phat[1]) / phat[2])
elif ix == 1:
phati = x - phat[2] * (-logSF) ** (1. / phat[0])
elif ix == 2:
phati = (x - phat[1]) / (-logSF) ** (1. / phat[0])
else:
raise IndexError('Index to the fixed parameter is out of bounds')
return phati
def _genpareto_link(x, logSF, phat, ix):
# Reference
# Stuart Coles (2004)
# "An introduction to statistical modelling of extreme values".
# Springer series in statistics
c, loc, scale = phat
if ix == 2:
# Reorganizing w.r.t.scale, Eq. 4.13 and 4.14, pp 81 in
# Coles (2004) gives
# link = -(x-loc)*c/expm1(-c*logSF)
if c != 0.0:
phati = (x - loc) * c / expm1(-c * logSF)
else:
phati = -(x - loc) / logSF
elif ix == 1:
if c != 0:
phati = x + scale * expm1(c * logSF) / c
else:
phati = x + scale * logSF
elif ix == 0:
raise NotImplementedError(
'link(x,logSF,phat,i) where i=0 is not implemented!')
else:
raise IndexError('Index to the fixed parameter is out of bounds')
return phati
def _genexpon_link(x, logSF, phat, ix):
_a, b, c, loc, scale = phat
xn = (x - loc) / scale
fact1 = (xn + expm1(-c * xn) / c)
if ix == 0:
phati = b * fact1 + logSF
elif ix == 1:
phati = (phat[0] - logSF) / fact1
elif ix in [2, 3, 4]:
raise NotImplementedError('Only implemented for index in [0,1]!')
else:
raise IndexError('Index to the fixed parameter is out of bounds')
return phati
def _rayleigh_link(x, logSF, phat, ix):
if ix == 1:
return x - phat[0] / sqrt(-2.0 * logSF)
elif ix == 0:
return x - phat[1] * sqrt(-2.0 * logSF)
else:
raise IndexError('Index to the fixed parameter is out of bounds')
def _trunclayleigh_link(x, logSF, phat, ix):
c, loc, scale = phat
if ix == 2:
return x - loc / (sqrt(c * c - 2 * logSF) - c)
elif ix == 1:
return x - scale * (sqrt(c * c - 2 * logSF) - c)
elif ix == 0:
xn = (x - loc) / scale
return - 2 * logSF / xn - xn / 2.0
else:
raise IndexError('Index to the fixed parameter is out of bounds')
LINKS = dict(expon=_expon_link,
weibull_min=_weibull_min_link,
frechet_r=_weibull_min_link,
genpareto=_genpareto_link,
genexpon=_genexpon_link,
rayleigh=_rayleigh_link,
trunclayleigh=_trunclayleigh_link)
def chi2isf(p, df):
return special.chdtri(df, p)
@ -195,7 +291,7 @@ class Profile(object):
self.profile_par = not (self.profile_x or self.profile_logSF)
if self.link is None:
self.link = self.fit_dist.dist.link
self.link = LINKS[self.fit_dist.dist.name]
if self.profile_par:
self._local_link = self._par_link
self.xlabel = 'phat(%d)' % self.i_fixed
@ -604,13 +700,13 @@ class FitDistribution(rv_frozen):
self.dist = dist
numargs = dist.numargs
self.method = method
self.method = method.lower()
self.alpha = alpha
self.par_fix = par_fix
self.search = search
self.copydata = copydata
if self.method.lower()[:].startswith('mps'):
if self.method.startswith('mps'):
self._fitfun = self._nlogps
else:
self._fitfun = self._nnlf
@ -797,7 +893,7 @@ class FitDistribution(rv_frozen):
return np.inf
dist = self.dist
x = asarray((x - loc) / scale)
cond0 = (x <= dist.a) | (dist.b <= x)
cond0 = (x < dist.a) | (dist.b < x)
Nbad = np.sum(cond0)
if Nbad > 0:
x = argsreduce(~cond0, x)[0]

Write Preview
Loading…
Cancel
Save