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Moved all links to estimation.py

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master
Per A Brodtkorb 9 years ago
parent 59186edfd6
commit f8d1afb93b
2 changed files with 104 additions and 143 deletions
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139
wafo/stats/_continuous_distns.py
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@ -1120,15 +1120,6 @@ class expon_gen(rv_continuous):
%(example)s %(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): def _rvs(self):
return self._random_state.standard_exponential(self._size) return self._random_state.standard_exponential(self._size)
@ -1593,17 +1584,6 @@ class frechet_r_gen(rv_continuous):
%(example)s %(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): def _pdf(self, x, c):
return c*pow(x, c-1)*exp(-pow(x, c)) return c*pow(x, c-1)*exp(-pow(x, c))
@ -1762,33 +1742,6 @@ class genpareto_gen(rv_continuous):
%(example)s %(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): def _argcheck(self, c):
c = asarray(c) c = asarray(c)
self.b = _lazywhere(c < 0, (c,), self.b = _lazywhere(c < 0, (c,),
@ -1832,57 +1785,6 @@ class genpareto_gen(rv_continuous):
scale = m * ((m / s) ** 2 + 1) / 2 scale = m * ((m / s) ** 2 + 1) / 2
return shape, loc, scale 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): def __stats(self, c):
# return None,None,None,None # return None,None,None,None
k = -c k = -c
@ -1950,24 +1852,8 @@ class genexpon_gen(rv_continuous):
%(example)s %(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): def _pdf(self, x, a, b, c):
return (a + b*(-special.expm1(-c*x)))*exp((-a-b)*x + return (a + b*(-special.expm1(-c*x))) * exp((-a-b)*x +
b*(-special.expm1(-c*x))/c) b*(-special.expm1(-c*x))/c)
def _cdf(self, x, a, b, c): def _cdf(self, x, a, b, c):
@ -4248,15 +4134,6 @@ class rayleigh_gen(rv_continuous):
%(example)s %(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): def _rvs(self):
return chi.rvs(2, size=self._size, random_state=self._random_state) 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): def _argcheck(self, c):
return (c >= 0) 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): def _fitstart(self, data, args=None):
if args is None: if args is None:
args = (0.0,) * self.numargs args = (0.0,) * self.numargs
@ -5167,7 +5032,7 @@ _distn_names, _distn_gen_names = get_distribution_names(pairs, rv_continuous)
__all__ = _distn_names + _distn_gen_names __all__ = _distn_names + _distn_gen_names
if __name__=='__main__': if __name__ == '__main__':
v = genextreme.logpdf(np.inf, 0) v = genextreme.logpdf(np.inf, 0)
v2 = genextreme.logpdf(-np.inf, 0) v2 = genextreme.logpdf(-np.inf, 0)
v2 = genextreme.logpdf(-100, 0) v2 = genextreme.logpdf(-100, 0)

106
wafo/stats/estimation.py
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@ -21,19 +21,115 @@ from scipy.linalg import pinv2
from scipy import optimize from scipy import optimize
import numpy as np 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) atleast_1d, any, asarray, nan, pi, isfinite)
from numpy import flatnonzero as nonzero from numpy import flatnonzero as nonzero
__all__ = ['Profile', 'FitDistribution'] __all__ = ['Profile', 'FitDistribution']
floatinfo = np.finfo(float) floatinfo = np.finfo(float)
arr = asarray arr = asarray
all = alltrue # @ReservedAssignment 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): def chi2isf(p, df):
return special.chdtri(df, p) return special.chdtri(df, p)
@ -195,7 +291,7 @@ class Profile(object):
self.profile_par = not (self.profile_x or self.profile_logSF) self.profile_par = not (self.profile_x or self.profile_logSF)
if self.link is None: if self.link is None:
self.link = self.fit_dist.dist.link self.link = LINKS[self.fit_dist.dist.name]
if self.profile_par: if self.profile_par:
self._local_link = self._par_link self._local_link = self._par_link
self.xlabel = 'phat(%d)' % self.i_fixed self.xlabel = 'phat(%d)' % self.i_fixed
@ -604,13 +700,13 @@ class FitDistribution(rv_frozen):
self.dist = dist self.dist = dist
numargs = dist.numargs numargs = dist.numargs
self.method = method self.method = method.lower()
self.alpha = alpha self.alpha = alpha
self.par_fix = par_fix self.par_fix = par_fix
self.search = search self.search = search
self.copydata = copydata self.copydata = copydata
if self.method.lower()[:].startswith('mps'): if self.method.startswith('mps'):
self._fitfun = self._nlogps self._fitfun = self._nlogps
else: else:
self._fitfun = self._nnlf self._fitfun = self._nnlf
@ -797,7 +893,7 @@ class FitDistribution(rv_frozen):
return np.inf return np.inf
dist = self.dist dist = self.dist
x = asarray((x - loc) / scale) x = asarray((x - loc) / scale)
cond0 = (x <= dist.a) | (dist.b <= x) cond0 = (x < dist.a) | (dist.b < x)
Nbad = np.sum(cond0) Nbad = np.sum(cond0)
if Nbad > 0: if Nbad > 0:
x = argsreduce(~cond0, x)[0] x = argsreduce(~cond0, x)[0]

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