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Small updates

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per.andreas.brodtkorb 13 years ago
parent bb13a4188c
commit 0a0cbc2fe2
2 changed files with 170 additions and 59 deletions
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209
pywafo/src/wafo/kdetools.py
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@ -417,7 +417,8 @@ class _KDE(object):
else:
titlestr = 'Kernel density estimate (%s)' % self.kernel.name
kwds2 = dict(title=titlestr)
kwds2['plot_kwds'] = dict(plotflag=1)
kwds2['plot_kwds'] = kwds.pop('plot_kwds',dict(plotflag=1))
kwds2.update(**kwds)
args = self.args
if self.d == 1:
@ -1030,7 +1031,7 @@ class KRegression(_KDE):
"""
def __init__(self, data, y, p=0, hs=None, kernel=None, alpha=0.0, xmin=None, xmax=None, inc=128, L2=None):
self.tkde = TKDE(data, hs, kernel, alpha, xmin, xmax, inc, L2)
self.tkde = TKDE(data, hs=hs, kernel=kernel, alpha=alpha, xmin=xmin,xmax=xmax, inc=inc, L2=L2)
self.y = y
self.p = p
@ -3492,25 +3493,16 @@ def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0):
x = xi[i]
y = yi[i]
return x, y, fun1
def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
x,y, fun1 = _get_data(n, symmetric)
kreg_demo3(x,y,fun1, hs=None, fun='hisj', plotlog=False)
def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
import scipy.stats as st
alpha=0.1
z0 = -_invnorm(alpha/2)
def _get_regression_smooting(x,y,fun='hste'):
hs1 = Kernel('gauss', fun=fun).get_smoothing(x)
n = x.size
hx = np.median(np.abs(x-np.median(x)))/0.6745*(4.0/(3*n))**0.2
#hx = np.median(np.abs(x-np.median(x)))/0.6745*(4.0/(3*n))**0.2
if (y==True).any():
hs2 = Kernel('gauss', fun=fun).get_smoothing(x[y==True])
hy = np.median(np.abs(y-np.mean(y)))/0.6745*(4.0/(3*n))**0.2
#hy = np.median(np.abs(y-np.mean(y)))/0.6745*(4.0/(3*n))**0.2
else:
hs2 = 4*hs1
hy = 4*hx
#hy = 4*hx
#hy2 = Kernel('gauss', fun=fun).get_smoothing(y)
#kernel = Kernel('gauss',fun=fun)
@ -3518,7 +3510,20 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
#hopt = (hs1+4*hs2)/5 #kernel.get_smoothing(x)
#hopt = hs2
hopt = sqrt(hs1*hs2)
#hopt=sqrt(hx*hy);
return hopt, hs1, hs2
def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
x,y, fun1 = _get_data(n, symmetric)
kreg_demo3(x,y,fun1, hs=None, fun='hisj', plotlog=False)
def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
import scipy.stats as st
alpha=0.1
z0 = -_invnorm(alpha/2)
n = x.size
hopt, hs1, hs2 =_get_regression_smooting(x,y,fun='hste')
if hs is None:
hs = hopt
@ -3528,12 +3533,12 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
#reverse = np.exp
xmin, xmax = x.min(), x.max()
ni = max(2*int((xmax-xmin)/hopt)+1,5)
ni = max(2*int((xmax-xmin)/hopt)+3,5)
print(ni)
print(xmin, xmax)
xi = np.linspace(xmin-hopt,xmax+hopt, ni)
xiii = np.linspace(xmin-hopt,xmax+hopt, 4*ni+1)
sml = hopt
xi = np.linspace(xmin-sml,xmax+sml, ni)
xiii = np.linspace(xmin-sml,xmax+sml, 4*ni+1)
c = gridcount(x, xi)
if (y==True).any():
@ -3547,7 +3552,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
print("fact=%g" % (fact))
kreg = KRegression(x, y, hs=hs*fact, p=0)
fiii = kreg(xiii)
yiii = stineman_interp(xiii, x, y)
yiii = stineman_interp(xiii, xi, yi)
fit = fun1(xiii).clip(max=1.0)
df = np.diff(fiii)
eerr = np.abs(yiii-fiii).std()+ 0.5*(df[:-1]*df[1:]<0).sum()/n
@ -3608,12 +3613,14 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
# Jeffreys intervall a=b=0.5
#st.beta.isf(alpha/2, x+a, n-x+b)
ab = 0.055
ab = 0.07 #0.055
pi1 = pi #fun1(xiii)
pup2 = np.where(pi==1, 1, st.beta.isf(alpha/2, ciii*pi1+ab, ciii*(1-pi1)+ab))
plo2 = np.where(pi==0, 0, st.beta.isf(1-alpha/2, ciii*pi1+ab, ciii*(1-pi1)+ab))
averr = np.trapz(pup2-plo2, xiii)/(xiii[-1]-xiii[0]) + 0.5*(df[:-1]*df[1:]<0).sum()/n
#f2 = kreg_demo4(x, y, hs, hopt)
# Wilson score
den = 1+(z0**2./ciii);
xc=(pi1+(z0**2)/(2*ciii))/den;
@ -3624,7 +3631,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
#plo = (pi - z0*np.sqrt(pi*(1-pi)/ciii)).clip(min=0,max=1)
fg.plot(label='KReg grid' )
f.plot(label='KRegression')
f.plot(label='KReg averr=%2.3f ' %(averr))
labtxt = '%d CI' % (int(100*(1-alpha)))
plt.fill_between(xiii, pup, plo, alpha=0.20,color='r', linestyle='--', label=labtxt)
plt.fill_between(xiii, pup2, plo2,alpha = 0.20, color='b', linestyle=':',label='%d CI2' % (int(100*(1-alpha))))
@ -3638,6 +3645,131 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
plt.setp(h,yscale='log')
#plt.show()
return hs1, hs2
def check_kreg_demo3():
import wafo.fig as fig
plt.ion()
k = 0
for i, n in enumerate([50, 100,300,600, 4000]):
x,y, fun1 = _get_data(n, symmetric=True,loc1=1.0, scale1=0.6, scale2=1.25)
k0 = k
for j, fun in enumerate(['hste']):
hsmax, hs1, hs2 =_get_regression_smooting(x,y,fun=fun)
for hi in np.linspace(hsmax*0.25,hsmax,9):
plt.figure(k)
k +=1
unused = kreg_demo3(x,y,fun1, hs=hi, fun=fun, plotlog=False)
#kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False)
fig.tile(range(k0,k))
plt.ioff()
plt.show()
def check_kreg_demo4():
import wafo.fig as fig
plt.ion()
#test_docstrings()
#kde_demo2()
#kreg_demo1(fast=True)
#kde_gauss_demo()
#kreg_demo2(n=120,symmetric=True,fun='hste', plotlog=True)
k = 0
for i, n in enumerate([100,300,600]):
x,y, fun1 = _get_data(n, symmetric=True,loc1=0.6, scale1=0.3, scale2=1.25)
k0 = k
hopt1, h1,h2 = _get_regression_smooting(x,y,fun='hns')
hopt2, h1,h2 = _get_regression_smooting(x,y,fun='hste')
hopt = sqrt(hopt1*hopt2)
for j, fun in enumerate(['hste', 'hisj', 'hns', 'hstt']):
hsmax, hs1, hs2 =_get_regression_smooting(x,y,fun=fun)
fmax = kreg_demo4(x, y, hsmax+0.1, hopt)
for hi in np.linspace(hsmax*0.25,hsmax,25):
f = kreg_demo4(x, y, hi, hopt)
if f.prediction_error_avg<=fmax.prediction_error_avg:
fmax = f
plt.figure(k)
k +=1
fmax.plot()
xi = fmax.args[::4]
c = gridcount(x, xi)
if (y==True).any():
c0 = gridcount(x[y==True],xi)
else:
c0 = np.zeros(xi.shape)
yi = np.where(c==0, 0, c0/c)
plt.plot(xi, yi, 'b.', xi, fun1(xi),'r')
#kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False)
fig.tile(range(k0,k))
plt.ioff()
plt.show()
def kreg_demo4(x,y, hs, hopt):
import scipy.stats as st
n = x.size
alpha=0.1
z0 = -_invnorm(alpha/2)
xmin, xmax = x.min(), x.max()
ni = max(2*int((xmax-xmin)/hopt)+3,5)
sml = hopt
xi = np.linspace(xmin-sml,xmax+sml, ni)
xiii = np.linspace(xmin-sml,xmax+sml, 4*ni+1)
from wafo.interpolate import stineman_interp
kreg = KRegression(x, y, hs=hs, p=0)
f = kreg(xiii, output='plotobj', plot_kwds = dict(plotflag=7))
dx = xi[1]-xi[0]
ciiii = kreg.tkde.eval_grid_fast(xiii)*dx* x.size
ckreg = KDE(x,hs=hs)
ciii = ckreg.eval_grid_fast(xiii)*dx* x.size #n*(1+symmetric)
pi = f.data
# Jeffreys intervall a=b=0.5
#st.beta.isf(alpha/2, x+a, n-x+b)
ab = 0.07 #0.055
pi1 = pi #fun1(xiii)
pup = np.where(pi1==1, 1, st.beta.isf(alpha/2, ciii*pi1+ab, ciii*(1-pi1)+ab))
plo = np.where(pi1==0, 0, st.beta.isf(1-alpha/2, ciii*pi1+ab, ciii*(1-pi1)+ab))
# Wilson score
den = 1+(z0**2./ciii);
xc=(pi1+(z0**2)/(2*ciii))/den;
halfwidth=(z0*sqrt((pi1*(1-pi1)/ciii)+(z0**2/(4*(ciii**2)))))/den
plo2 = (xc-halfwidth).clip(min=0) # wilson score
pup2 = (xc+halfwidth).clip(max=1.0) # wilson score
f.dataCI = np.vstack((plo,pup)).T
f.prediction_error_avg = np.trapz(pup-plo, xiii)/(xiii[-1]-xiii[0])
fiii = f.data
c = gridcount(x, xi)
if (y==True).any():
c0 = gridcount(x[y==True],xi)
else:
c0 = np.zeros(xi.shape)
yi = np.where(c==0, 0, c0/c)
yiii = stineman_interp(xiii, xi, yi)
df = np.diff(fiii)
eerr = np.abs(yiii-fiii).std()+ 0.5*(df[:-1]*df[1:]<0).sum()/n
f.labels.title='perr=%1.3f,eerr=%1.3f, n=%d, hs=%1.3f' % (f.prediction_error_avg,eerr,n,hs)
return f
def kde_gauss_demo(n=50):
'''
KDEDEMO Demonstrate the KDEgauss
@ -3697,35 +3829,14 @@ def test_docstrings():
doctest.testmod()
if __name__ == '__main__':
import wafo.fig as fig
#check_kreg_demo3()
check_kreg_demo4()
#test_smoothn_2d()
#test_smoothn_cardioid()
plt.ion()
#test_docstrings()
#kde_demo2()
#kreg_demo1(fast=True)
#kde_gauss_demo()
#kreg_demo2(n=120,symmetric=True,fun='hste', plotlog=True)
k = 0
for i, n in enumerate([50, 100,300,600, 4000]):
x,y, fun1 = _get_data(n, symmetric=True,loc1=0.5, scale1=0.3, scale2=.75)
k0 = k
for j, fun in enumerate(['hste']):
hs1 = Kernel('gauss', fun=fun).get_smoothing(x)
if (y==True).any():
hs2 = Kernel('gauss', fun=fun).get_smoothing(x[y==True])
else:
hs2 = 4*hs1
hsmax = sqrt(hs1*hs2)
for hi in np.linspace(hsmax*0.25,hsmax,9):
plt.figure(k)
k +=1
unused = kreg_demo3(x,y,fun1, hs=hi, fun=fun, plotlog=False)
#kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False)
fig.tile(range(k0,k))
plt.ioff()
plt.show()
#test_smoothn_2d()
#test_smoothn_cardioid()

14
pywafo/src/wafo/wafodata.py
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@ -86,8 +86,7 @@ class WafoData(object):
plotbackend.hold('on')
tmp = []
child_args = args if len(args) else tuple(self.plot_args_children)
child_kwds = dict()
child_kwds.update(self.plot_kwds_children)
child_kwds = dict(self.plot_kwds_children).copy()
child_kwds.update(**kwds)
for child in self.children:
tmp1 = child.plot(*child_args, **kwds)
@ -96,8 +95,7 @@ class WafoData(object):
if len(tmp) == 0:
tmp = None
main_args = args if len(args) else tuple(self.plot_args)
main_kwds = dict()
main_kwds.update(self.plot_kwds)
main_kwds = dict(self.plot_kwds).copy()
main_kwds.update(kwds)
tmp2 = self.plotter.plot(self, *main_args, **main_kwds)
return tmp2, tmp
@ -203,7 +201,7 @@ class Plotter_1d(object):
def _plot(self, plotflag, wdata, *args, **kwds):
x = wdata.args
data = transformdata(x, wdata.data, plotflag)
dataCI = ()
dataCI = getattr(wdata, 'dataCI', ())
h1 = plot1d(x, data, dataCI, plotflag, *args, **kwds)
return h1
@ -228,6 +226,9 @@ def plot1d(args, data, dataCI, plotflag, *varargin, **kwds):
H = plotbackend.fill_between(args, data, level, *varargin, **kwds);
else:
H = plotbackend.fill_between(args, data, *varargin, **kwds);
elif plottype==7:
H = plotbackend.plot(args, data, *varargin, **kwds)
H = plotbackend.fill_between(args, dataCI[:,0], dataCI[:,1], alpha=0.2, color='r');
scale = plotscale(plotflag)
logXscale = 'x' in scale
@ -255,9 +256,8 @@ def plot1d(args, data, dataCI, plotflag, *varargin, **kwds):
plotbackend.axis(ax)
if dataCI and plottype < 3:
if np.any(dataCI) and plottype < 3:
plotbackend.hold('on')
plot1d(args, dataCI, (), plotflag, 'r--');
return H

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