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Small refactoring

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per.andreas.brodtkorb 13 years ago
parent 0c2f6621e4
commit 52fbfae045
1 changed files with 79 additions and 54 deletions
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pywafo/src/wafo/kdetools.py
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@ -10,24 +10,21 @@
#-------------------------------------------------------------------------------
#!/usr/bin/env python
from __future__ import division
from itertools import product
from misc import tranproc #, trangood
from numpy import pi, sqrt, atleast_2d, exp, newaxis #@UnresolvedImport
from scipy import interpolate, linalg, sparse
from scipy.special import gamma
from scipy.ndimage.morphology import distance_transform_edt
import scipy.special as special
import scipy.optimize as optimize
from wafo.misc import meshgrid, nextpow2
from wafo.wafodata import WafoData
from dctpack import dct, dctn, idctn
import copy
import numpy as np
import scipy
import warnings
import matplotlib.pyplot as plt
from itertools import product
from scipy import interpolate, linalg, optimize, sparse, special, stats
from scipy.special import gamma
from scipy.ndimage.morphology import distance_transform_edt
from numpy import pi, sqrt, atleast_2d, exp, newaxis #@UnresolvedImport
from wafo.misc import meshgrid, nextpow2, tranproc #, trangood
from wafo.wafodata import WafoData
from wafo.dctpack import dct, dctn, idctn
from wafo.plotbackend import plotbackend as plt
from wafo import fig
def _invnorm(q):
return special.ndtri(q)
@ -3211,7 +3208,6 @@ def InitialGuess(y,I):
return z
def test_smoothn_1d():
import matplotlib.pyplot as plt
x = np.linspace(0,100,2**8)
y = np.cos(x/10)+(x/50)**2 + np.random.randn(x.size)/10
y[np.r_[70, 75, 80]] = np.array([5.5, 5, 6])
@ -3512,14 +3508,37 @@ def _get_regression_smooting(x,y,fun='hste'):
hopt = sqrt(hs1*hs2)
return hopt, hs1, hs2
def regressionbin(x,y):
'''
Return kernel regression estimate for binomial data
Parameters
----------
x : arraylike
positions
y : arraylike
of 0 and 1
'''
hopt1, h1,h2 = _get_regression_smooting(x,y,fun='hos')
hopt2, h1,h2 = _get_regression_smooting(x,y,fun='hste')
hopt = sqrt(hopt1*hopt2)
fbest = kreg_demo4(x, y, hopt2+0.1, hopt)
for fun in ['hste']: # , 'hisj', 'hns', 'hstt'
hsmax, hs1, hs2 =_get_regression_smooting(x,y,fun=fun)
for hi in np.linspace(hsmax*0.1,hsmax,55):
f = kreg_demo4(x, y, hi, hopt)
if f.aicc<=fbest.aicc:
fbest = f
return fbest
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
st = stats
alpha=0.1
alpha=0.05
z0 = -_invnorm(alpha/2)
@ -3548,12 +3567,9 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
c0 = np.zeros(xi.shape)
yi = np.where(c==0, 0, c0/c)
from wafo.interpolate import stineman_interp
fact = 1.0 #stineman_interp([x.size], [0, 2000], [0.25, 1.0], yp=[0.75/2000,0.75/2000]).clip(min=0.75,max=1.0)
print("fact=%g" % (fact))
kreg = KRegression(x, y, hs=hs*fact, p=0)
kreg = KRegression(x, y, hs=hs, p=0)
fiii = kreg(xiii)
yiii = stineman_interp(xiii, xi, yi)
yiii = interpolate.interp1d(xi, yi)(xiii)
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
@ -3576,7 +3592,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
logity = forward(yi)
gkreg = KRegression(xi, logity, hs=hs*fact, xmin=xmin-hopt,xmax=xmax+hopt)
gkreg = KRegression(xi, logity, hs=hs, xmin=xmin-hopt,xmax=xmax+hopt)
fg = gkreg.eval_grid(xi,output='plotobj', title='Kernel regression', plotflag=1)
sa = (fg.data-logity).std()
sa2 = iqrange(fg.data-logity) / 1.349
@ -3662,7 +3678,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False):
def check_kreg_demo3():
import wafo.fig as fig
plt.ion()
k = 0
@ -3683,8 +3699,6 @@ def check_kreg_demo3():
plt.show()
def check_kreg_demo4():
import wafo.fig as fig
plt.ion()
#test_docstrings()
#kde_demo2()
@ -3698,6 +3712,7 @@ def check_kreg_demo4():
hopt1, h1,h2 = _get_regression_smooting(x,y,fun='hos')
hopt2, h1,h2 = _get_regression_smooting(x,y,fun='hste')
hopt = sqrt(hopt1*hopt2)
#hopt = _get_regression_smooting(x,y,fun='hos')[0]
for j, fun in enumerate(['hste']): # , 'hisj', 'hns', 'hstt'
hsmax, hs1, hs2 =_get_regression_smooting(x,y,fun=fun)
@ -3709,27 +3724,36 @@ def check_kreg_demo4():
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.', x, fun1(x),'r')
plt.plot(x, fun1(x),'r')
#kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False)
fig.tile(range(0,k))
plt.ioff()
plt.show()
def kreg_demo4(x,y, hs, hopt):
import scipy.stats as st
def empirical_bin_prb(x,y, hopt):
'''
Returns empirical binomial probabiltity
'''
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*0.1
xi = np.linspace(xmin-sml,xmax+sml, ni)
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)
return WafoData(yi,xi,plotmethod='scatter', plot_kwds=dict(color='r', s=5))
def kreg_demo4(x,y, hs, hopt, alpha=0.05):
st = stats
n = x.size
xmin, xmax = x.min(), x.max()
ni = max(2*int((xmax-xmin)/hopt)+3,5)
@ -3737,37 +3761,34 @@ def kreg_demo4(x,y, hs, 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)
ciii = kreg.tkde.eval_grid_fast(xiii) * dx * x.size
# ckreg = KDE(x,hs=hs)
# ciiii = ckreg.eval_grid_fast(xiii)*dx* x.size #n*(1+symmetric)
f = kreg(xiii, output='plotobj') #, plot_kwds=dict(plotflag=7))
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)
ab = 0.07 #0.5
pi1 = pi
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
# z0 = -_invnorm(alpha/2)
# 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.dataCI = np.vstack((plo,pup)).T
f.prediction_error_avg = np.trapz(pup-plo, xiii)/(xiii[-1]-xiii[0])
fiii = f.data
@ -3777,7 +3798,11 @@ def kreg_demo4(x,y, hs, hopt):
else:
c0 = np.zeros(xi.shape)
yi = np.where(c==0, 0, c0/c)
yiii = stineman_interp(xiii, xi, yi)
f.children = [WafoData([plo, pup],xiii,plotmethod='fill_between',plot_kwds=dict(alpha=0.2, color='r')),
WafoData(yi,xi,plotmethod='scatter', plot_kwds=dict(color='r', s=5))]
yiii = interpolate.interp1d(xi, yi)(xiii)
df = np.diff(fiii)
k = (df[:-1]*df[1:]<0).sum() # numpeaks
sigmai = (pup-plo)
@ -3797,7 +3822,7 @@ def kde_gauss_demo(n=50):
different values of the smoothing parameter, hs.
'''
import scipy.stats as st
st = stats
#x = np.linspace(-4, 4, 101)
#data = np.random.normal(loc=0, scale=1.0, size=n)
#data = np.random.exponential(scale=1.0, size=n)

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