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Small updates

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per.andreas.brodtkorb 14 years ago
parent 4d8ba7aefc
commit 51387dcb5d
6 changed files with 89 additions and 65 deletions
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2
pywafo/src/Wafo.egg-info/PKG-INFO
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@ -1,7 +1,7 @@
Metadata-Version: 1.0
Name: wafo
Version: 0.1.2
Summary: UNKNOWN
Summary: Statistical analysis and simulation of random waves and random loads
Home-page: http://code.google.com/p/pywafo/
Author: WAFO-group
Author-email: wafo@maths.lth.se

3
pywafo/src/Wafo.egg-info/SOURCES.txt
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@ -262,14 +262,11 @@ src/wafo/spectrum/models.py
src/wafo/stats/__init__.py
src/wafo/stats/core.py
src/wafo/stats/distributions.py
src/wafo/stats/distributions_juli2010.py
src/wafo/stats/estimation.py
src/wafo/stats/misc.py
src/wafo/stats/twolumps.py
src/wafo/test/__init__.py
src/wafo/test/test_gaussian.py
src/wafo/test/test_misc.py
src/wafo/test/test_objects.py
src/wafo/transform/__init__.py
src/wafo/transform/core.py
src/wafo/transform/models.py

129
pywafo/src/wafo/kdetools.py
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@ -18,14 +18,14 @@ from scipy.special import gamma
from misc import tranproc, trangood
from itertools import product
_stats_epan=(1. / 5, 3. / 5, np.inf)
_stats_biwe=(1. / 7, 5. / 7, 45. / 2),
_stats_triw=(1. / 9, 350. / 429, np.inf),
_stats_rect=(1. / 3, 1. / 2, np.inf),
_stats_tria=(1. / 6, 2. / 3, np.inf),
_stats_lapl=(2, 1. / 4, np.inf),
_stats_logi=(pi ** 2 / 3, 1. / 6, 1 / 42),
_stats_gaus=(1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi)))
_stats_epan = (1. / 5, 3. / 5, np.inf)
_stats_biwe = (1. / 7, 5. / 7, 45. / 2),
_stats_triw = (1. / 9, 350. / 429, np.inf),
_stats_rect = (1. / 3, 1. / 2, np.inf),
_stats_tria = (1. / 6, 2. / 3, np.inf),
_stats_lapl = (2, 1. / 4, np.inf),
_stats_logi = (pi ** 2 / 3, 1. / 6, 1 / 42),
_stats_gaus = (1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi)))
@ -100,7 +100,7 @@ class KDE(object):
"""
def __init__(self, dataset, hs=None, kernel=None,L2=None,alpha=0.0):
def __init__(self, dataset, hs=None, kernel=None, L2=None, alpha=0.0):
self.kernel = kernel if kernel else Kernel('gauss')
self.hs = hs
self.L2 = L2
@ -121,21 +121,21 @@ class KDE(object):
h = get_smoothing(self.dataset)
hsiz = h.shape
if (min(hsiz)==1) or (self.d==1):
if max(hsiz)==1:
h = h*np.ones(self.d)
if (min(hsiz) == 1) or (self.d == 1):
if max(hsiz) == 1:
h = h * np.ones(self.d)
else:
h.shape = (self.d,) # make sure it has the correct dimension
# If h negative calculate automatic values
ind, = np.where(h<=0)
ind, = np.where(h <= 0)
for i in ind.tolist(): #
h[i] = get_smoothing(self.dataset[i])
deth = h.prod()
self.inv_hs = linalg.diag(1.0/h)
self.inv_hs = linalg.diag(1.0 / h)
else: #fully general smoothing matrix
deth = linalg.det(h)
if deth<=0:
if deth <= 0:
raise ValueError('bandwidth matrix h must be positive definit!')
self.inv_hs = linalg.inv(h)
self.hs = h
@ -188,9 +188,9 @@ class KDE(object):
diff = self.dataset - points[:, i, np.newaxis]
tdiff = np.dot(self.inv_hs, diff)
tmp = self.kernel(tdiff)
result[i] = tmp.sum(axis=-1)
result[i] = tmp.sum(axis= -1)
result /= (self._norm_factor*self.kernel.norm_factor(d,self.n))
result /= (self._norm_factor * self.kernel.norm_factor(d, self.n))
return result
@ -481,7 +481,7 @@ class _Kernel(object):
return 1.0
def norm_kernel(self, x):
X = np.atleast_2d(x)
return self._kernel(X)/self.norm_factor(*X.shape)
return self._kernel(X) / self.norm_factor(*X.shape)
def kernel(self, x):
return self._kernel(np.atleast_2d(x))
__call__ = kernel
@ -538,7 +538,7 @@ class _KernelRectangular(_Kernel):
return np.where(np.all(np.abs(x) <= self.r, axis=0), 1, 0.0)
def norm_factor(self, d=1, n=None):
r = self.r
return (2*r) ** d
return (2 * r) ** d
mkernel_rectangular = _KernelRectangular(stats=_stats_rect)
class _KernelTriangular(_Kernel):
@ -565,7 +565,7 @@ class _KernelLaplace(_Kernel):
absX = np.abs(x)
return exp(-absX.sum(axis=0))
def norm_factor(self, d=1, n=None):
return 2**d
return 2 ** d
mkernel_laplace = _KernelLaplace(stats=_stats_lapl)
class _KernelLogistic(_Kernel):
@ -825,8 +825,8 @@ class Kernel(object):
return a * linalg.sqrtm(covA) * n * (-1. / (d + 4))
def norm_factor(self, d=1,n=None):
return self.kernel.norm_factor(n,d)
def norm_factor(self, d=1, n=None):
return self.kernel.norm_factor(n, d)
def evaluate(self, X):
return self.kernel(np.atleast_2d(X))
__call__ = evaluate
@ -981,7 +981,17 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None):
out[s] = func(vals[s])
return out
def bitget(int_type, offset):
'''
Returns the value of the bit at the offset position in int_type.
Example
-------
>>> bitget(5, np.r_[0:4])
array([1, 0, 1, 0])
'''
mask = (1 << offset)
return (int_type & mask) != 0
def gridcount(data, X):
'''
GRIDCOUNT D-dimensional histogram using linear binning.
@ -1016,11 +1026,13 @@ def gridcount(data, X):
>>> import numpy as np
>>> import wafo.kdetools as wk
>>> import pylab as plb
>>> N = 500;
>>> N = 20;
>>> data = np.random.rayleigh(1,N)
>>> x = np.linspace(0,max(data)+1,50)
>>> dx = x[1]-x[0]
>>> c = wk.gridcount(data,x)
>>> h = plb.plot(x,c,'.') # 1D histogram
>>> h1 = plb.plot(x,c/dx/N) # 1D probability density plot
>>> np.trapz(x,c/dx/N)
@ -1043,7 +1055,6 @@ def gridcount(data, X):
if d != d1:
raise ValueError('Dimension 0 of data and X do not match.')
dx = np.diff(x[:, :2], axis=1)
xlo = x[:, 0]
xup = x[:, -1]
@ -1061,47 +1072,46 @@ def gridcount(data, X):
abs = np.abs
if d == 1:
x.shape = (-1,)
c = (accum(binx, (x[binx + 1] - data), size=[inc, ]) +
accum(binx, (data - x[binx]), size=[inc, ])) / w
c = (accum(binx, (x[binx + 1] - dat), size=[inc, ]) +
accum(binx, (dat - x[binx]), size=[inc, ])) / w
elif d == 2:
b2 = binx[1]
b1 = binx[0]
c_ = np.c_
stk = np.vstack
c = (accum(c_[b1, b2] , abs(np.prod(stk([X[0, b1 + 1], X[1, b2 + 1]]) - data, axis=0)), size=[inc, inc]) +
accum(c_[b1 + 1, b2] , abs(np.prod(stk([X[0, b1], X[1, b2 + 1]]) - data, axis=0)), size=[inc, inc]) +
accum(c_[b1 , b2 + 1], abs(np.prod(stk([X[0, b1 + 1], X[1, b2]]) - data, axis=0)), size=[inc, inc]) +
accum(c_[b1 + 1, b2 + 1], abs(np.prod(stk([X[0, b1], X[1, b2]]) - data, axis=0)), size=[inc, inc])) / w
c = (accum(c_[b1, b2] , abs(np.prod(stk([X[0, b1 + 1], X[1, b2 + 1]]) - dat, axis=0)), size=[inc, inc]) +
accum(c_[b1 + 1, b2] , abs(np.prod(stk([X[0, b1], X[1, b2 + 1]]) - dat, axis=0)), size=[inc, inc]) +
accum(c_[b1 , b2 + 1], abs(np.prod(stk([X[0, b1 + 1], X[1, b2]]) - dat, axis=0)), size=[inc, inc]) +
accum(c_[b1 + 1, b2 + 1], abs(np.prod(stk([X[0, b1], X[1, b2]]) - dat, axis=0)), size=[inc, inc])) / w
else: # % d>2
raise ValueError('Not implemented for d>2')
Nc = csiz.prod()
c = np.zeros((Nc, 1))
c = np.zeros((Nc,))
fact2 = inc * np.arange(d)
fact1 = csiz.cumprod() / inc
fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int)
fact1 = np.asarray(np.reshape(csiz.cumprod() / inc, (d, -1)), dtype=int)
#fact1 = fact1(ones(n,1),:);
# for ir in xrange(2**(d-1)):
# bt0[:,:,1] = bitget(ir,1:d)
# bt0[:,:,2] = 1-bt0[:,:,1]
# for ix in range(2):
# one = mod(ix,2)+1;
# two = mod(ix+1,2)+1;
# # Convert to linear index (faster than sub2ind)
# b1 = sum((binx + bt0(ones(n,1),:,one)-1).*fact1,2)+1; #%linear index to c
# bt2 = bt0(:,:,two) + fact2;
# b2 = binx + bt2(ones(n,1),:); #% linear index to X
#
# c = c + accum(b1,abs(prod(X(b2)-data,2)),[Nc,1]);
# #c = c + accum([b1,ones(n,1)],abs(prod(X(b2)-data,2)),[Nc,1]);
# #[len,bin,val] = bincount(b1,abs(prod(X(b2)-data,2)));
# #c(bin) = c(bin)+val;
#
# #end
# #end
# c = reshape(c/w,csiz);
#end
if d == 2: #% make sure c is stored in the same way as meshgrid
bt0 = [0, 0]
X1 = X.ravel()
for ir in xrange(2 ** (d - 1)):
bt0[0] = np.reshape(bitget(ir, np.arange(d)), (d, -1))
bt0[1] = 1 - bt0[0]
for ix in xrange(2):
one = np.mod(ix, 2)
two = np.mod(ix + 1, 2)
# Convert to linear index
b1 = np.sum((binx + bt0[one]) * fact1, axis=0) #linear index to c
bt2 = bt0[two] + fact2
b2 = binx + bt2 # linear index to X
c += accum(b1, abs(np.prod(X1[b2] - dat, axis=0)), size=(Nc,))
c = np.reshape(c / w, csiz)
# TODO: check that the flipping of axis is correct
T = range(d); T[-2],T[-1] = T[-1], T[-2]
c = c.transpose(*T)
if d == 2: # make sure c is stored in the same way as meshgrid
c = c.T
elif d == 3:
c = c.transpose(1, 0, 2)
@ -1119,7 +1129,7 @@ def test_kde():
f = kde(x)
#plb.hist(data.ravel())
plb.plot(x,f)
plb.plot(x, f)
plb.show()
def test_gridcount():
@ -1129,7 +1139,8 @@ def test_gridcount():
N = 500;
data = np.random.rayleigh(1, size=(2, N))
x = np.linspace(0, max(data.ravel()) + 1, 10)
X = np.vstack((x, x))
X = np.vstack((x, x))
dx = x[1] - x[0]
c = wk.gridcount(data, X)
h = plb.contourf(x, x, c)
@ -1147,4 +1158,4 @@ def main():
if __name__ == '__main__':
#main()
#test_gridcount()
test_kde()
test_kde()

16
pywafo/src/wafo/stats/distributions.py
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@ -216,6 +216,22 @@ Check accuracy of cdf and ppf
Random number generation
>>> R = %(name)s.rvs(%(shapes)s, size=100)
Compare ML and MPS method
>>> phat = %(name)s.fit2(R, method='ml');
>>> phat.plotfitsummary(); plt.figure(plt.gcf().number+1)
>>> phat2 = %(name)s.fit2(R, method='mps')
>>> phat2.plotfitsummary(); plt.figure(plt.gcf().number+1)
Fix loc=0 and estimate shapes and scale
>>> phat3 = %(name)s.fit2(R, scale=1, floc=0, method='mps')
>>> phat3.plotfitsummary(); plt.figure(plt.gcf().number+1)
Accurate confidence interval with profile loglikelihood
>>> lp = phat3.profile()
>>> lp.plot()
>>> pci = lp.get_bounds()
"""
_doc_default = ''.join([_doc_default_longsummary,

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pywafo/src/wafo/stats/estimation.py
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@ -713,7 +713,7 @@ class FitDistribution(rv_frozen):
'''Compute covariance
'''
somefixed = (self.par_fix != None) and any(isfinite(self.par_fix))
H1 = numpy.asmatrix(self.dist.hessian_nnlf(self.par, self.data))
#H1 = numpy.asmatrix(self.dist.hessian_nnlf(self.par, self.data))
H = numpy.asmatrix(self.dist.hessian_nlogps(self.par, self.data))
self.H = H
try:

2
pywafo/src/wafo/stats/tests/test_estimation.py
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@ -7,7 +7,7 @@ Created on 19. nov. 2010
import wafo.stats as ws
from wafo.stats.estimation import Profile, FitDistribution
from numpy import log, array
def test_profile():
def test_fit_and_profile():
'''
# MLE
import wafo.stats as ws

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