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Fixed a bug in KRegression

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master
Per.Andreas.Brodtkorb 13 years ago
parent a664f15e78
commit 663636eece
2 changed files with 126 additions and 20 deletions
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40
pywafo/src/wafo/kdetools.py
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@ -211,8 +211,6 @@ class KDEgauss(object):
at = dctn(c) * kw
z = idctn(at)*at.size/np.prod(R)
return z*(z>0.0)
#ix = (slice(0, inc),)*d
#return z[ix] * (z[ix] > 0.0)
def _eval_grid_fun(self, eval_grd, *args, **kwds):
output = kwds.pop('output', 'value')
@ -655,7 +653,11 @@ class TKDE(_KDE):
fill_value=0.0)
#fi.shape = shape0i
self.args = args
return fi*(fi>0)
r = kwds.get('r', 0)
if r==0:
return fi*(fi>0)
else:
return fi
return f
def _eval_grid(self, *args, **kwds):
if self.L2 is None:
@ -869,13 +871,13 @@ class KDE(_KDE):
norm_fact0 = (kw.sum()*dx.prod()*self.n)
norm_fact = (self._norm_factor * self.kernel.norm_factor(d, self.n))
if np.abs(norm_fact0-norm_fact)>0.05*norm_fact:
warnings.warn('Numerical inaccuracy due to too low discretization. Increase the discretization (inc=%d)!' % self.inc)
warnings.warn('Numerical inaccuracy due to too low discretization. Increase the discretization of the evaluation grid (inc=%d)!' % inc)
norm_fact = norm_fact0
kw = kw/norm_fact
r = kwds.get('r', 0)
if r!=0:
kw *= np.vstack(Xnc) ** r
kw *= np.vstack(Xnc) ** r if d>1 else Xnc[0]
kw.shape = shape0
kw = np.fft.ifftshift(kw)
fftn = np.fft.fftn
@ -891,8 +893,10 @@ class KDE(_KDE):
z = np.real(ifftn(fftn(c, s=nfft) * fftn(kw)))
ix = (slice(0, inc),)*d
return z[ix] * (z[ix] > 0.0)
if r==0:
return z[ix] * (z[ix] > 0.0)
else:
return z[ix]
def _eval_grid(self, *args, **kwds):
grd = meshgrid(*args) if len(args) > 1 else list(args)
@ -2957,7 +2961,8 @@ def kreg_demo1(hs=None, fast=False, fun='hisj'):
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)
hopt = kernel.hisj(x)
kreg = KRegression(x, y, p=0, hs=hs, kernel=kernel, xmin=-2*hopt, xmax=1+2*hopt)
if fast:
kreg.__call__ = kreg.eval_grid_fast
@ -2969,14 +2974,26 @@ def kreg_demo1(hs=None, fast=False, fun='hisj'):
kreg.p=1
f1 = kreg(output='plot', title='Kernel regression', plotflag=1)
f1.plot(label='p=1')
plt.plot(x,y,'.', x,y0, 'k')
print(f1.data)
plt.plot(x, y, '.', label='data')
plt.plot(x, y0, 'k', label='True model')
plt.legend()
plt.show()
print(kreg.tkde.tkde.inv_hs)
print(kreg.tkde.tkde.hs)
def kreg_demo2(n=100):
x = np.sort(5*np.random.rand(n,1)-2.5, axis=0)
y = (np.cos(x)>2*np.random.rand(n, 1)-1).ravel()
kreg = KRegression(x.ravel(),y)
f = kreg(output='plotobj', title='Kernel regression', plotflag=1)
f.plot()
plt.show()
def kde_gauss_demo(n=50):
'''
KDEDEMO Demonstrate the KDEgauss
@ -3029,6 +3046,8 @@ def kde_gauss_demo(n=50):
format_ = 'hs0=%s hs1=%s hs2=%s' % (format_, format_, format_)
print(format_ % tuple(kde0.hs.tolist()+kde1.tkde.hs.tolist()+kde2.hs.tolist()))
print('inc0 = %d, inc1 = %d, inc2 = %d' % (kde0.inc, kde1.inc,kde2.inc))
def test_docstrings():
import doctest
doctest.testmod()
@ -3038,4 +3057,5 @@ if __name__ == '__main__':
#test_docstrings()
#kde_demo2()
#kreg_demo1(fast=True)
kde_gauss_demo()
#kde_gauss_demo()
kreg_demo1()

106
pywafo/src/wafo/stats/core.py
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@ -873,13 +873,10 @@ class RegLogit(object):
nulldev = self.loglike (tb0, y, X, z, z1)[0]
else:
nulldev = dev
# maximize likelihood using Levenberg modified Newton's method
iter = 0;
stop = False
while not stop:
iter += 1
for i in range(self.maxiter+1):
tbold = tb;
devold = dev;
tb = tbold - np.linalg.lstsq(d2l, dl)[0]
@ -907,7 +904,9 @@ class RegLogit(object):
print(np.linalg.eig(d2l)[0].T);
#end
#end
stop = np.abs(np.dot(dl, np.linalg.lstsq(d2l, dl)[0]) / len(dl)) <= tol or iter>self.maxiter
stop = np.abs(np.dot(dl, np.linalg.lstsq(d2l, dl)[0]) / len(dl)) <= tol
if stop:
break
#end %while
#% tidy up output
@ -975,8 +974,8 @@ class RegLogit(object):
self.dispersion = 1;
self.R2 = R2;
self.R2adj = R2adj;
self.numiter = iter;
self.converged = iter<self.maxiter;
self.numiter = i
self.converged = i<self.maxiter;
self.note = '';
self.date = now()
@ -1270,6 +1269,93 @@ def test_reglogit():
[mu,plo,pup] = b.predict(fulloutput=True);
pass
#plot(x,mu,'g',x,plo,'r:',x,pup,'r:')
def test_reglogit2():
n = 40
x = np.sort(5*np.random.rand(n, 1)-2.5, axis=0)
y = (np.cos(x)>2*np.random.rand(n,1)-1)
b = RegLogit()
b.fit(y,x)
#b.display() #% members and methods
b.summary()
[mu,plo,pup] = b.predict(fulloutput=True);
import matplotlib.pyplot as pl
pl.plot(x,mu,'g',x,plo,'r:',x,pup,'r:')
pl.show()
def test_sklearn0():
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
# FIXME: the iris dataset has only 4 features!
iris = datasets.load_iris()
X = iris.data
y = iris.target
X = np.sort(5*np.random.rand(40, 1)-2.5, axis=0)
y = (2*(np.cos(X)>2*np.random.rand(40, 1)-1)-1).ravel()
score = []
# Set regularization parameter
cvals = np.logspace(-1,1,5)
for C in cvals:
clf_LR = LogisticRegression(C=C, penalty='l2')
clf_LR.fit(X, y)
score.append(clf_LR.score(X,y))
plot(cvals, score)
def test_sklearn():
X = np.sort(5*np.random.rand(40, 1)-2.5, axis=0)
y = (2*(np.cos(X)>2*np.random.rand(40, 1)-1)-1).ravel()
from sklearn.svm import SVR
###############################################################################
# look at the results
import pylab as pl
pl.scatter(X, .5*np.cos(X)+0.5, c='k', label='True model')
pl.hold('on')
cvals= np.logspace(-1,3,20)
score = []
for c in cvals:
svr_rbf = SVR(kernel='rbf', C=c, gamma=0.1, probability=True)
svrf = svr_rbf.fit(X, y)
y_rbf = svrf.predict(X)
score.append(svrf.score(X,y))
pl.plot(X, y_rbf, label='RBF model c=%g' % c)
pl.xlabel('data')
pl.ylabel('target')
pl.title('Support Vector Regression')
pl.legend()
pl.show()
def test_sklearn1():
X = np.sort(5*np.random.rand(40, 1)-2.5, axis=0)
y = (2*(np.cos(X)>2*np.random.rand(40, 1)-1)-1).ravel()
from sklearn.svm import SVR
cvals= np.logspace(-1,4,10)
svr_rbf = SVR(kernel='rbf', C=1e4, gamma=0.1, probability=True)
svr_lin = SVR(kernel='linear', C=1e4, probability=True)
svr_poly = SVR(kernel='poly', C=1e4, degree=2, probability=True)
y_rbf = svr_rbf.fit(X, y).predict(X)
y_lin = svr_lin.fit(X, y).predict(X)
y_poly = svr_poly.fit(X, y).predict(X)
###############################################################################
# look at the results
import pylab as pl
pl.scatter(X, .5*np.cos(X)+0.5, c='k', label='True model')
pl.hold('on')
pl.plot(X, y_rbf, c='g', label='RBF model')
pl.plot(X, y_lin, c='r', label='Linear model')
pl.plot(X, y_poly, c='b', label='Polynomial model')
pl.xlabel('data')
pl.ylabel('target')
pl.title('Support Vector Regression')
pl.legend()
pl.show()
def test_doctstrings():
#_test_dispersion_idx()
@ -1278,6 +1364,6 @@ def test_doctstrings():
if __name__ == '__main__':
test_reglogit()
test_reglogit2()
#test_doctstrings()(

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