From 412990574f0debd5b1d6213a7cc1ac64c072a9b7 Mon Sep 17 00:00:00 2001 From: "Per.Andreas.Brodtkorb" Date: Mon, 2 Jan 2012 15:43:29 +0000 Subject: [PATCH] small updates --- pywafo/src/wafo/kdetools.py | 62 ++++++++++++++++++++++++------------- 1 file changed, 40 insertions(+), 22 deletions(-) diff --git a/pywafo/src/wafo/kdetools.py b/pywafo/src/wafo/kdetools.py index 1be6a4a..eca93d4 100644 --- a/pywafo/src/wafo/kdetools.py +++ b/pywafo/src/wafo/kdetools.py @@ -3511,6 +3511,7 @@ def _get_regression_smooting(x,y,fun='hste'): #hopt = hs2 hopt = sqrt(hs1*hs2) 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) @@ -3523,7 +3524,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): n = x.size - hopt, hs1, hs2 =_get_regression_smooting(x,y,fun='hste') + hopt, hs1, hs2 =_get_regression_smooting(x,y,fun='hos') if hs is None: hs = hopt @@ -3536,7 +3537,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): ni = max(2*int((xmax-xmin)/hopt)+3,5) print(ni) print(xmin, xmax) - sml = hopt + sml = hopt*0.1 xi = np.linspace(xmin-sml,xmax+sml, ni) xiii = np.linspace(xmin-sml,xmax+sml, 4*ni+1) @@ -3555,7 +3556,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): 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 + 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) @@ -3618,7 +3619,7 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): 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 + averr = np.trapz(pup2-plo2, xiii)/(xiii[-1]-xiii[0]) + 0.5*(df[:-1]*df[1:]<0).sum() #f2 = kreg_demo4(x, y, hs, hopt) # Wilson score @@ -3630,7 +3631,20 @@ def kreg_demo3(x,y, fun1, hs=None, fun='hisj', plotlog=False): #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) - fg.plot(label='KReg grid' ) + #mi = kreg.eval_grid(x) + #sigma = (stineman_interp(x, xiii, pup)-stineman_interp(x, xiii, plo))/4 + #aic = np.abs((y-mi)/sigma).std()+ 0.5*(df[:-1]*df[1:]<0).sum()/n + #aic = np.abs((yiii-fiii)/(pup-plo)).std()+ 0.5*(df[:-1]*df[1:]<0).sum() + ((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() + + k = (df[:-1]*df[1:]<0).sum() # numpeaks + sigmai = (pup-plo) + aic = (((yiii-fiii)/sigmai)**2).sum()+ 2*k*(k+1)/np.maximum(ni-k+1,1) + np.abs((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() + + #aic = (((yiii-fiii)/sigmai)**2).sum()+ 2*k*(k+1)/(ni-k+1) + np.abs((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() + + #aic = averr + ((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() + + fg.plot(label='KReg grid aic=%2.3f' % (aic) ) 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) @@ -3678,19 +3692,19 @@ def check_kreg_demo4(): #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) + for i, n in enumerate([100,300,600,4000]): + x,y, fun1 = _get_data(n, symmetric=True,loc1=0.1, scale1=0.6, scale2=0.75) k0 = k - hopt1, h1,h2 = _get_regression_smooting(x,y,fun='hns') + hopt1, h1,h2 = _get_regression_smooting(x,y,fun='hos') hopt2, h1,h2 = _get_regression_smooting(x,y,fun='hste') hopt = sqrt(hopt1*hopt2) - for j, fun in enumerate(['hste', 'hisj', 'hns', 'hstt']): + 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): + for hi in np.linspace(hsmax*0.1,hsmax,55): f = kreg_demo4(x, y, hi, hopt) - if f.prediction_error_avg<=fmax.prediction_error_avg: + if f.aicc<=fmax.aicc: fmax = f plt.figure(k) k +=1 @@ -3702,10 +3716,10 @@ def check_kreg_demo4(): else: c0 = np.zeros(xi.shape) yi = np.where(c==0, 0, c0/c) - plt.plot(xi, yi, 'b.', xi, fun1(xi),'r') + plt.plot(xi, yi, 'b.', x, fun1(x),'r') #kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False) - fig.tile(range(k0,k)) + fig.tile(range(0,k)) plt.ioff() plt.show() @@ -3719,7 +3733,7 @@ def kreg_demo4(x,y, hs, hopt): xmin, xmax = x.min(), x.max() ni = max(2*int((xmax-xmin)/hopt)+3,5) - sml = hopt + sml = hopt*0.1 xi = np.linspace(xmin-sml,xmax+sml, ni) xiii = np.linspace(xmin-sml,xmax+sml, 4*ni+1) @@ -3747,14 +3761,14 @@ def kreg_demo4(x,y, hs, hopt): # 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 +# 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]) + f.prediction_error_avg = np.trapz(pup-plo, xiii)/(xiii[-1]-xiii[0]) fiii = f.data c = gridcount(x, xi) @@ -3765,8 +3779,12 @@ def kreg_demo4(x,y, hs, hopt): 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) + k = (df[:-1]*df[1:]<0).sum() # numpeaks + sigmai = (pup-plo) + aicc = (((yiii-fiii)/sigmai)**2).sum()+ 2*k*(k+1)/np.maximum(ni-k+1,1) + np.abs((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() + + f.aicc = aicc + f.labels.title='perr=%1.3f,aicc=%1.3f, n=%d, hs=%1.3f' % (f.prediction_error_avg,aicc,n,hs) return f