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refactored parts of kreg_demo2 into kreg_demo3 and _get_data

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master
Per.Andreas.Brodtkorb 13 years ago
parent 4c15fd22b9
commit 99aa5e1b9e
1 changed files with 70 additions and 72 deletions
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130
pywafo/src/wafo/kdetools.py
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@ -3471,13 +3471,13 @@ def _logitinv(x):
return 1.0/(np.exp(-x)+1)
def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0):
import scipy.stats as st
from sg_filter import SavitzkyGolay
#from sg_filter import SavitzkyGolay
dist = st.norm
scale1 = 0.5
loc1= 1.5
norm1 = dist.pdf(-loc1, loc=-loc1, scale=scale1) + dist.pdf(-loc1, loc=loc1, scale=scale1)
norm1 = scale2*(dist.pdf(-loc1, loc=-loc1, scale=scale1) + dist.pdf(-loc1, loc=loc1, scale=scale1))
fun1 = lambda x : (dist.pdf(x, loc=-loc1, scale=scale1) + dist.pdf(x, loc=loc1, scale=scale1))/norm1
x = np.sort(6*np.random.rand(n,1)-3, axis=0)
@ -3486,19 +3486,39 @@ def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
#y = (np.cos(x)>2*np.random.rand(n, 1)-1).ravel()
x = x.ravel()
alpha=0.05
z0 = -_invnorm(alpha/2)
if symmetric:
xi = np.hstack((x.ravel(),-x.ravel()))
yi = np.hstack((y, y))
i = np.argsort(xi)
x = xi[i]
y = yi[i]
hisj = Kernel('gauss', fun='hisj').get_smoothing(x)
hste = Kernel('gauss', fun='hste').get_smoothing(x)
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=0.25
z0 = -_invnorm(alpha/2)
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
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
else:
hs2 = 4*hs1
hy = 4*hx
#hy2 = Kernel('gauss', fun=fun).get_smoothing(y)
#kernel = Kernel('gauss',fun=fun)
hopt = (hste+hisj)/2 #kernel.get_smoothing(x)
hopt = (hs1+2*hs2)/3 #kernel.get_smoothing(x)
#hopt = sqrt(hs1*hs2)
#hopt=sqrt(hx*hy);
if hs is None:
hs = hopt
@ -3508,7 +3528,7 @@ def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
#reverse = np.exp
xmin, xmax = x.min(), x.max()
ni = 2*int((xmax-xmin)/hopt)+1
ni = max(2*int((xmax-xmin)/hopt)+1,5)
print(ni)
print(xmin, xmax)
@ -3516,39 +3536,33 @@ def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
xiii = np.linspace(xmin-hopt,xmax+hopt, 4*ni+1)
from wafo.interpolate import stineman_interp
fact = stineman_interp([x.size], [0, 2000], [0.25, 1.0], yp=[0.75/2000,0.75/2000]).clip(min=0.25,max=1.3)
fact = stineman_interp([x.size], [0, 2000], [0.25, 1.0], yp=[0.75/2000,0.75/2000]).clip(min=0.9,max=1.0)
print("fact=%g" % (fact))
kreg = KRegression(x, y, hs=hs*fact, p=0)
fi = kreg(xi)
f = kreg(xiii,output='plotobj', title='Kernel regression n=%d, hste=%g, hisj=%g' % (n,hste,hisj), plotflag=1)
#fi = kreg(xi)
f = kreg(xiii,output='plotobj', title='KRegr %s f=%g, n=%d, hs1=%g, hs2=%g' % (fun,fact,n,hs1,hs2), plotflag=1)
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)
#yi[yi==0] = 1.0/(c[c!=0].min()+4)#
#yi[yi==1] = 1-1.0/(c[c!=0].min()+4) #
#yi[yi==0] = 1.0/(c[c!=0].min()+4)
#yi[yi==1] = 1-1.0/(c[c!=0].min()+4)
#yi[yi==0] = fi[yi==0]
#yi[yi==0] = np.exp(stineman_interp(xi[yi==0], xi[yi>0],np.log(yi[yi>0])))
#yi[yi==0] = fun1(xi[yi==0])
try:
yi[yi==0] = yi[yi>0].min()/sqrt(n)
except:
yi[yi==0] = 1./n
yi[yi==1] =1-(1-yi[yi<1].max())/sqrt(n)
logity = forward(yi)
# plt.plot(xi, np.log(yi/(1-yi)), xi,logity,'.')
# plt.show()
# return
# ymin = np.log(yi[yi>0].min())-1
# logyi = np.log(yi).clip(min=ymin)
# plt.scatter(xi,logyi)
# plt.show()
# return
#print(logyi)
gkreg = KRegression(xi, logity, hs=hs*fact, xmin=xmin-hopt,xmax=xmax+hopt)
fg = gkreg.eval_grid(xi,output='plotobj', title='Kernel regression', plotflag=1)
sa = (fg.data-logity).std()
@ -3565,24 +3579,19 @@ def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
fg = gkreg.eval_grid(xiii,output='plotobj', title='Kernel regression', plotflag=1)
pi = reverse(fg.data)
dx = xi[1]-xi[0]
ckreg = KDE(x,hs=hs)
#ci = ckreg.eval_grid_fast(xi)*n*dx
ciii = ckreg.eval_grid_fast(xiii)*dx*n*(1+symmetric)
ciii = ckreg.eval_grid_fast(xiii)*dx* x.size #n*(1+symmetric)
pi = reverse(fg.data)
sa1 = np.sqrt(1./(ciii*pi*(1-pi)))
plo3 = reverse(fg.data-z0*sa)
pup3 = reverse(fg.data+z0*sa)
# sa1 = np.sqrt(1./(ciii*pi*(1-pi)))
# plo3 = reverse(fg.data-z0*sa)
# pup3 = reverse(fg.data+z0*sa)
fg.data = pi
pi = f.data
#pi = f.data
# ref Casella and Berger (1990) "Statistical inference" pp444
a = 2*pi + z0**2/(ciii+1e-16)
@ -3592,12 +3601,11 @@ def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
# Jeffreys intervall a=b=0.5
#st.beta.isf(alpha/2, x+a, n-x+b)
ab = 0.05
ab = 0.03
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))
#pup = (pi + z0*np.sqrt(pi*(1-pi)/ciii)).clip(min=0,max=1)
#plo = (pi - z0*np.sqrt(pi*(1-pi)/ciii)).clip(min=0,max=1)
# Wilson score
den = 1+(z0**2./ciii);
@ -3605,31 +3613,14 @@ def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False):
halfwidth=(z0*sqrt((pi1*(1-pi1)/ciii)+(z0**2/(4*(ciii**2)))))/den
plo = (xc-halfwidth).clip(min=0) # wilson score
pup = (xc+halfwidth).clip(max=1.0) # wilson score
#pup = (pi + z0*np.sqrt(pi*(1-pi)/ciii)).clip(min=0,max=1) # dont use
#plo = (pi - z0*np.sqrt(pi*(1-pi)/ciii)).clip(min=0,max=1)
logity[logity==-40] = np.nan
#saz = (logity-forward(stineman_interp(xi, xiii, plo)))/z0
# slogity = smoothn(logity, robust=False) #, weight=1./saz)
# slogity2 = SavitzkyGolay(n=2, degree=2).smooth(logity)
# sa1 = sqrt(evar(logity))
# sa = (slogity-logity).std()
# #print('estd = %g %g' % (sa,sa1))
#
#
# plo3 = reverse(slogity-z0*sa)
# pup3 = reverse(slogity+z0*sa)
# syi = reverse(slogity)
# syi2 = reverse(slogity2)
#print(fg.data)
#fg.data = np.exp(fg.data)
#plt.figure(2)
fg.plot(label='KReg grid')
f.plot(label='KRegression')
labtxt = '%d CI' % (int(100*(1-alpha)))
#plt.plot(xi, syi, 'k',xi, syi2,'k--', label='smoothn')
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))))
#plt.plot(xiii, 0.5*np.cos(xiii)+.5, 'r', label='True model')
plt.plot(xiii, fun1(xiii), 'r', label='True model')
plt.scatter(xi,yi, label='data')
print('maxp = %g' % (np.nanmax(f.data)))
@ -3701,16 +3692,23 @@ def test_docstrings():
if __name__ == '__main__':
import wafo.fig as fig
plt.ioff()
plt.ion()
#test_docstrings()
#kde_demo2()
#kreg_demo1(fast=True)
#kde_gauss_demo()
#kreg_demo2(n=120,symmetric=True,fun='hste', plotlog=True)
for i, n in enumerate([10,100,1000,2000,4000]):
plt.figure(i)
kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False)
fig.tile(range(5))
k = 0
for i, n in enumerate([50, 100,300,600, 4000]):
x,y, fun1 = _get_data(n, symmetric=True,loc1=1.2, scale1=0.3, scale2=1.25)
k0 = k
for j, fun in enumerate(['hste', 'hisj', 'hstt', 'hldpi']):
plt.figure(k)
k +=1
kreg_demo3(x,y,fun1, hs=None, 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()

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