From bb13a4188ca6d5c9768c92a4527979c0ed7ac2ba Mon Sep 17 00:00:00 2001 From: "Per.Andreas.Brodtkorb" Date: Fri, 30 Dec 2011 15:40:12 +0000 Subject: [PATCH] Updated kreg_demo3 --- pywafo/src/wafo/kdetools.py | 78 ++++++++++++++++++++++--------------- 1 file changed, 47 insertions(+), 31 deletions(-) diff --git a/pywafo/src/wafo/kdetools.py b/pywafo/src/wafo/kdetools.py index 6a7aa89..8180e4e 100644 --- a/pywafo/src/wafo/kdetools.py +++ b/pywafo/src/wafo/kdetools.py @@ -1608,7 +1608,7 @@ class Kernel(object): h[dim] = h1 #end % for dim loop return h - def hisj(self, data, inc=512, L=5): + def hisj(self, data, inc=512, L=7): ''' HISJ Improved Sheather-Jones estimate of smoothing parameter. @@ -1620,7 +1620,7 @@ class Kernel(object): Parameters ---------- data - a vector of data from which the density estimate is constructed; - n - the number of mesh points used in the uniform discretization + inc - the number of mesh points used in the uniform discretization Returns ------- @@ -1637,7 +1637,7 @@ class Kernel(object): # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() - STEconstant = R / (mu2 ** (2) * n) + STEconstant = R / (n * mu2 ** 2) amin = A.min(axis=1) # Find the minimum value of A. amax = A.max(axis=1) # Find the maximum value of A. @@ -1679,7 +1679,7 @@ class Kernel(object): #a = dct(c/c.sum(), norm=None) a = dct(c/len(A[dim]), norm=None) - #% now compute the optimal bandwidth^2 using the referenced method + # now compute the optimal bandwidth^2 using the referenced method I = np.asfarray(np.arange(1, inc))**2 a2 = (a[1:]/2)**2 fun = lambda t: fixed_point(t, N, I, a2) @@ -1825,7 +1825,7 @@ class Kernel(object): return h - def hscv(self, data, hvec=None, inc=128, maxit=100): + def hscv(self, data, hvec=None, inc=128, maxit=100, fulloutput=False): ''' HSCV Smoothed cross-validation estimate of smoothing parameter. @@ -1954,7 +1954,10 @@ class Kernel(object): warnings.warn('Optimum is probably higher than hs=%g for dim=%d' % (h[dim] * s, dim)) hvec = hvec * (STEconstant / STEconstant2) ** (1 / 5) - return h * sigmaA + if fulloutput: + return h * sigmaA, score, hvec, sigmaA + else: + return h * sigmaA def hldpi(self, data, L=2, inc=128): '''HLDPI L-stage Direct Plug-In estimate of smoothing parameter. @@ -1992,8 +1995,8 @@ class Kernel(object): # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() - AMISEconstant = (8 * sqrt(pi) * R / (3 * mu2 ** 2 * n)) ** (1. / 5) - STEconstant = R / (mu2 ** (2) * n) + AMISEconstant = (8 * sqrt(pi) * R / (3 * n * mu2 ** 2)) ** (1. / 5) + STEconstant = R / (n * mu2 ** 2) sigmaA = self.hns(A) / AMISEconstant @@ -2055,11 +2058,7 @@ class Kernel(object): #end #end h[dim] = (STEconstant / PSI) ** (1. / 5) - return h - - - def norm_factor(self, d=1, n=None): return self.kernel.norm_factor(d, n) def eval_points(self, points): @@ -3478,7 +3477,7 @@ def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0): 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 + fun1 = lambda x : ((dist.pdf(x, loc=-loc1, scale=scale1) + dist.pdf(x, loc=loc1, scale=scale1))/norm1).clip(max=1.0) x = np.sort(6*np.random.rand(n,1)-3, axis=0) @@ -3499,9 +3498,8 @@ def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False): def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): import scipy.stats as st - - alpha=0.25 + alpha=0.1 z0 = -_invnorm(alpha/2) hs1 = Kernel('gauss', fun=fun).get_smoothing(x) @@ -3516,8 +3514,10 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): #hy2 = Kernel('gauss', fun=fun).get_smoothing(y) #kernel = Kernel('gauss',fun=fun) - hopt = (hs1+2*hs2)/3 #kernel.get_smoothing(x) - #hopt = sqrt(hs1*hs2) + #hopt = (hs1+2*hs2)/3 + #hopt = (hs1+4*hs2)/5 #kernel.get_smoothing(x) + #hopt = hs2 + hopt = sqrt(hs1*hs2) #hopt=sqrt(hx*hy); if hs is None: hs = hopt @@ -3535,19 +3535,26 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): xi = np.linspace(xmin-hopt,xmax+hopt, ni) 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=0.9,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='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) + + 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) + fiii = kreg(xiii) + yiii = stineman_interp(xiii, x, y) + 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 + err = (fiii-fit).std() + f = kreg(xiii,output='plotobj', title='%s err=%1.3f,eerr=%1.3f, n=%d, hs=%1.3f, hs1=%1.3f, hs2=%1.3f' % (fun,err,eerr,n,hs, hs1,hs2), plotflag=1) + + #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] @@ -3601,7 +3608,7 @@ 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 = 0.03 + ab = 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)) @@ -3630,7 +3637,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): if plotlog: plt.setp(h,yscale='log') #plt.show() - + return hs1, hs2 def kde_gauss_demo(n=50): ''' KDEDEMO Demonstrate the KDEgauss @@ -3700,12 +3707,21 @@ if __name__ == '__main__': #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=1.2, scale1=0.3, scale2=1.25) + x,y, fun1 = _get_data(n, symmetric=True,loc1=0.5, scale1=0.3, scale2=.75) 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) + + 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()