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Per.Andreas.Brodtkorb 13 years ago
parent c69f54dc0c
commit 8c5754e30c
2 changed files with 82 additions and 99 deletions
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125
pywafo/src/wafo/kdetools.py
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@ -1450,7 +1450,7 @@ class Kernel(object):
break
else:
ai = bi
y = np.asarray([fun(j) for j in x])
#y = np.asarray([fun(j) for j in x])
#pylab.figure(1)
#pylab.plot(x,y)
#pylab.show()
@ -1459,9 +1459,6 @@ class Kernel(object):
try:
t_star = optimize.brentq(fun, a=ai, b=bi)
except:
# try:
# t_star = optimize.bisect(fun, a=ai, b=bi+1)
# except:
t_star = 0.28*N**(-2./5)
warnings.warn('Failure in obtaining smoothing parameter')
@ -2635,68 +2632,19 @@ def kde_demo3():
pylab.figure(0)
def kde_demo4(hs=None, fast=False):
'''
'''
N = 100
#ei = np.random.normal(loc=0, scale=0.075, size=(N,))
ei = np.array([-0.08508516, 0.10462496, 0.07694448, -0.03080661, 0.05777525,
0.06096313, -0.16572389, 0.01838912, -0.06251845, -0.09186784,
-0.04304887, -0.13365788, -0.0185279 , -0.07289167, 0.02319097,
0.06887854, -0.08938374, -0.15181813, 0.03307712, 0.08523183,
-0.0378058 , -0.06312874, 0.01485772, 0.06307944, -0.0632959 ,
0.18963205, 0.0369126 , -0.01485447, 0.04037722, 0.0085057 ,
-0.06912903, 0.02073998, 0.1174351 , 0.17599277, -0.06842139,
0.12587608, 0.07698113, -0.0032394 , -0.12045792, -0.03132877,
0.05047314, 0.02013453, 0.04080741, 0.00158392, 0.10237899,
-0.09069682, 0.09242174, -0.15445323, 0.09190278, 0.07138498,
0.03002497, 0.02495252, 0.01286942, 0.06449978, 0.03031802,
0.11754861, -0.02322272, 0.00455867, -0.02132251, 0.09119446,
-0.03210086, -0.06509545, 0.07306443, 0.04330647, 0.078111 ,
-0.04146907, 0.05705476, 0.02492201, -0.03200572, -0.02859788,
-0.05893749, 0.00089538, 0.0432551 , 0.04001474, 0.04888828,
-0.17708392, 0.16478644, 0.1171006 , 0.11664846, 0.01410477,
-0.12458953, -0.11692081, 0.0413047 , -0.09292439, -0.07042327,
0.14119701, -0.05114335, 0.04994696, -0.09520663, 0.04829406,
-0.01603065, -0.1933216 , 0.19352763, 0.11819496, 0.04567619,
-0.08348306, 0.00812816, -0.00908206, 0.14528945, 0.02901065])
x = np.linspace(0, 1, N)
y0 = 2*np.exp(-x**2/(2*0.3**2))+3*np.exp(-(x-1)**2/(2*0.7**2))
y = y0 + ei
kreg = KRegression(x, y, p=0, hs=hs)
kreg.tkde.kernel.get_smooting = kreg.tkde.kernel.hste
if fast:
kreg.__call__ = kreg.eval_grid_fast
f = kreg(output='plot', title='Kernel regression', plotflag=1)
pylab.figure(0)
f.plot(label='p=0')
kreg.p=1
f1 = kreg(output='plot', title='Kernel regression', plotflag=1)
f1.plot(label='p=1')
pylab.plot(x,y,'.', x,y0, 'k')
pylab.legend()
pylab.show()
print(kreg.tkde.tkde.inv_hs)
print(kreg.tkde.tkde.hs)
def kde_demo5(N=50):
def kde_demo4(N=50):
'''Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior
for multimodal distributions
for 1D multimodal distributions
KDEDEMO5 shows that the improved Sheather-Jones plug-in smoothing is a better
KDEDEMO4 shows that the improved Sheather-Jones plug-in smoothing is a better
compared to normal reference rules (in this case the hns)
'''
import scipy.stats as st
data = np.hstack((st.norm.rvs(loc=5, scale=1, size=(N,)), st.norm.rvs(loc=-5, scale=1, size=(N,))))
data = np.hstack((st.norm.rvs(loc=5, scale=1, size=(N,)),
st.norm.rvs(loc=-5, scale=1, size=(N,))))
#x = np.linspace(1.5e-3, 5, 55)
@ -2717,11 +2665,11 @@ def kde_demo5(N=50):
pylab.plot(x + loc, st.norm.pdf(x, 0, scale=1)/2, 'k:', label='True density')
pylab.legend()
def kde_demo6(N=500):
def kde_demo5(N=500):
'''Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior
for multimodal distributions
for 2D multimodal distributions
KDEDEMO5 shows that the improved Sheather-Jones plug-in smoothing is a better
KDEDEMO5 shows that the improved Sheather-Jones plug-in smoothing is better
compared to normal reference rules (in this case the hns)
'''
import scipy.stats as st
@ -2743,10 +2691,63 @@ def kde_demo6(N=500):
f1.plot()
pylab.plot(data[0], data[1], '.')
def kreg_demo1(hs=None, fast=False, fun='hisj'):
'''
'''
N = 100
#ei = np.random.normal(loc=0, scale=0.075, size=(N,))
ei = np.array([-0.08508516, 0.10462496, 0.07694448, -0.03080661, 0.05777525,
0.06096313, -0.16572389, 0.01838912, -0.06251845, -0.09186784,
-0.04304887, -0.13365788, -0.0185279 , -0.07289167, 0.02319097,
0.06887854, -0.08938374, -0.15181813, 0.03307712, 0.08523183,
-0.0378058 , -0.06312874, 0.01485772, 0.06307944, -0.0632959 ,
0.18963205, 0.0369126 , -0.01485447, 0.04037722, 0.0085057 ,
-0.06912903, 0.02073998, 0.1174351 , 0.17599277, -0.06842139,
0.12587608, 0.07698113, -0.0032394 , -0.12045792, -0.03132877,
0.05047314, 0.02013453, 0.04080741, 0.00158392, 0.10237899,
-0.09069682, 0.09242174, -0.15445323, 0.09190278, 0.07138498,
0.03002497, 0.02495252, 0.01286942, 0.06449978, 0.03031802,
0.11754861, -0.02322272, 0.00455867, -0.02132251, 0.09119446,
-0.03210086, -0.06509545, 0.07306443, 0.04330647, 0.078111 ,
-0.04146907, 0.05705476, 0.02492201, -0.03200572, -0.02859788,
-0.05893749, 0.00089538, 0.0432551 , 0.04001474, 0.04888828,
-0.17708392, 0.16478644, 0.1171006 , 0.11664846, 0.01410477,
-0.12458953, -0.11692081, 0.0413047 , -0.09292439, -0.07042327,
0.14119701, -0.05114335, 0.04994696, -0.09520663, 0.04829406,
-0.01603065, -0.1933216 , 0.19352763, 0.11819496, 0.04567619,
-0.08348306, 0.00812816, -0.00908206, 0.14528945, 0.02901065])
x = np.linspace(0, 1, N)
y0 = 2*np.exp(-x**2/(2*0.3**2))+3*np.exp(-(x-1)**2/(2*0.7**2))
y = y0 + ei
kernel = Kernel('gauss',fun=fun)
kreg = KRegression(x, y, p=0, hs=hs, kernel=kernel)
if fast:
kreg.__call__ = kreg.eval_grid_fast
f = kreg(output='plot', title='Kernel regression', plotflag=1)
pylab.figure(0)
f.plot(label='p=0')
kreg.p=1
f1 = kreg(output='plot', title='Kernel regression', plotflag=1)
f1.plot(label='p=1')
pylab.plot(x,y,'.', x,y0, 'k')
pylab.legend()
pylab.show()
print(kreg.tkde.tkde.inv_hs)
print(kreg.tkde.tkde.hs)
def test_docstrings():
import doctest
doctest.testmod()
if __name__ == '__main__':
#test_docstrings()
kde_demo2()
#kde_demo2()
kreg_demo1()

52
pywafo/src/wafo/stats/core.py
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@ -735,33 +735,13 @@ class RegLogit(object):
See also regglm, reglm, regnonlm
'''
#% Copyright (C) 1995, 1996, 1997, 1998, 1999, 2000, 2002, 2005, 2007
#% Kurt Hornik
#%
#% Reglogit is free software; you can redistribute it and/or modify it
#% under the terms of the GNU General Public License as published by
#% the Free Software Foundation; either version 3 of the License, or (at
#% your option) any later version.
#%
#% Reglogit is distributed in the hope that it will be useful, but
#% WITHOUT ANY WARRANTY; without even the implied warranty of
#% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
#% General Public License for more details.
#%
#% You should have received a copy of the GNU General Public License
#% along with Reglogit; see the file COPYING. If not, see
#% <http://www.gnu.org/licenses/>.
#% Original for MATLAB written by Gordon K Smyth <gks@maths.uq.oz.au>,
#% U of Queensland, Australia, on Nov 19, 1990. Last revision Aug 3,
#% 1992.
#
#% Author: Gordon K Smyth <gks@maths.uq.oz.au>,
#% Adapted-By: KH <Kurt.Hornik@wu-wien.ac.at>
#% Revised by: pab
#% -renamed from logistic_regression to reglogit
#% -renamed from oridinal to reglogit
#% -added predict, summary and compare
#% Description: Ordinal logistic regression
#
@ -773,7 +753,7 @@ class RegLogit(object):
self.maxiter =maxiter
self.accuracy = accuracy
self.alpha = alpha
self.deletcolinear = deletecolinear
self.deletecolinear = deletecolinear
self.verbose = False
self.family = None
self.link = None
@ -825,8 +805,8 @@ class RegLogit(object):
if len(ix):
X = X[:, ix]
txt = [' %d,' % i for i in iy]
txt[-1] = ' %d' % iy[-1]
warnings.warn('Covariate matrix is singular. Removing column(s):%s',txt)
#txt[-1] = ' %d' % iy[-1]
warnings.warn('Covariate matrix is singular. Removing column(s):%s' % txt)
mx = X.shape[0]
if (mx != my):
raise ValueError('x and y must have the same number of observations');
@ -872,7 +852,7 @@ class RegLogit(object):
[mx, nx] = X.shape
[my, ny] = y.shape
g = z.sum(axis=0).cumsum() / my
g = (z.sum(axis=0).cumsum() / my).reshape(-1,1)
theta00 = np.log(g / (1 - g))
beta00 = np.zeros((nx, 1))
# starting values
@ -943,7 +923,7 @@ class RegLogit(object):
else:
eta = (y * 0 + 1) * theta.T;
#end
gammai = np.diff(np.hstack(((y * 0), self.logitinv(eta), (y * 0 + 1))),1,2)
gammai = np.diff(np.hstack(((y * 0), self.logitinv(eta), (y * 0 + 1))),n=1,axis=1)
k0 = min(y)
mu = (k0-1)+np.dot(gammai,np.arange(1,nz+2).T); #% E(Y|X)
r = np.corrcoef(np.hstack((y,mu)))
@ -1234,13 +1214,13 @@ class RegLogit(object):
# first derivative
v = g * (1 - g) / p;
v1 = g1 * (1 - g1) / p;
dlogp = np.hstack(((dmult(v, z) - dmult(v1, z1)), (dmult(v - v1, x))))
dlogp = np.hstack((((v*z) - (v1*z1)), ((v - v1)*x)))
dl = np.sum(dlogp, axis=0).T
# second derivative
w = v * (1 - 2 * g)
w1 = v1 * (1 - 2 * g1)
d2l = zx.T * dmult (w, zx) - z1x.T * dmult(w1, z1x) - dlogp.T * dlogp;
d2l = np.dot(zx.T, (w*zx)) - np.dot(z1x.T, (w1*z1x)) - np.dot(dlogp.T, dlogp)
return dev, p, dl, d2l
#end %function
@ -1301,21 +1281,23 @@ def _test_reslife():
mrl.plot()
def test_reglogit():
y=np.array([1, 1, 2, 1, 3, 2, 3, 2, 3, 3])
x = np.arange(10).T
b = reglogit(y,x)
b.display() % members and methods
y=np.array([1, 1, 2, 1, 3, 2, 3, 2, 3, 3]).reshape(-1,1)
x = np.arange(10).reshape(-1,1)
b = RegLogit()
b.fit(y,x)
#b.display() #% members and methods
b.summary()
[mu,plo,pup] = b.predict();
plot(x,mu,'g',x,plo,'r:',x,pup,'r:')
def main():
def test_doctstrings():
#_test_dispersion_idx()
import doctest
doctest.testmod()
if __name__ == '__main__':
pass
main()
test_reglogit()
#test_doctstrings()(

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