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general changes to bebugging code

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This commit is contained in:
James Hensman 2013-01-16 11:21:55 +00:00
parent a6300fab10
commit 9f05744374
3 changed files with 29 additions and 18 deletions
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4
GPy/models/sparse_GP_regression.py
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@ -269,7 +269,9 @@ class sgp_debugE(sparse_GP_regression):
self.dL_dpsi2 = - 0.5 * self.beta * (self.G)
# Compute dL_dKmm
self.dL_dKmm = np.dot(np.dot(self.G,self.beta*self.psi2) - np.dot(self.LBL_inv, self.psi1VVpsi1), self.Kmmi) + 0.5*self.G # dE
tmp = mdot(self.beta*self.psi2, self.Kmmi, self.psi1VVpsi1)
self.dL_dKmm = -0.5*mdot(self.Kmmi,tmp + tmp.T + self.psi1VVpsi1,self.Kmmi)
#self.dL_dKmm = np.dot(np.dot(self.G,self.beta*self.psi2) - np.dot(self.LBL_inv, self.psi1VVpsi1), self.Kmmi) + 0.5*self.G # dE
def log_likelihood(self):
A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta))

2
GPy/util/linalg.py
View file

@ -63,8 +63,8 @@ def jitchol(A,maxtries=5):
raise linalg.LinAlgError, "not pd: negative diagonal elements"
jitter= diagA.mean()*1e-6
for i in range(1,maxtries+1):
print 'Warning: adding jitter of '+str(jitter)
try:
print 'Warning: adding jitter of '+str(jitter)
return linalg.cholesky(A+np.eye(A.shape[0])*jitter, lower = True)
except:
jitter *= 10

41
grid_parameters.py
View file

@ -8,41 +8,50 @@ pb.close('all')
N = 1000
M = 10
resolution=3
resolution=5
X = np.linspace(0,12,N)[:,None]
Z = np.linspace(0,12,M)[:,None] # inducing points (fixed for now)
Y = np.sin(X) + np.random.randn(*X.shape)/np.sqrt(50.)
k = GPy.kern.rbf(1)
#k = GPy.kern.rbf(1)
k = GPy.kern.Matern32(1) + GPy.kern.white(1)
models = [GPy.models.sparse_GP_regression(X,Y,Z=Z,kernel=k),
GPy.models.sgp_debugB(X,Y,Z=Z,kernel=k),
GPy.models.sgp_debugC(X,Y,Z=Z,kernel=k),
GPy.models.sgp_debugE(X,Y,Z=Z,kernel=k)]
#[m.constrain_fixed('iip') for m in models]
#m.constrain_fixed('white',1e-6)
#[m.constrain_fixed('precision',50) for m in models]
#[m.ensure_default_constraints() for m in models]
GPy.models.sgp_debugC(X,Y,Z=Z,kernel=k)]#,
#GPy.models.sgp_debugE(X,Y,Z=Z,kernel=k)]
[m.constrain_fixed('white',0.001) for m in models]
xx,yy = np.mgrid[1.5:4:0+resolution*1j,-2:2:0+resolution*1j]
#xx,yy = np.mgrid[1.5:4:0+resolution*1j,-2:2:0+resolution*1j]
xx,yy = np.mgrid[3:16:0+resolution*1j,-2:2:0+resolution*1j]
lls = []
cgs = []
grads = []
count = 0
for l,v in zip(xx.flatten(),yy.flatten()):
count += 1
print count, 'of', resolution**2
sys.stdout.flush()
[m.set('lengthscale',l) for m in models]
[m.set('rbf_variance',10.**v) for m in models]
lls.append(models[0].log_likelihood())
[m.set('_variance',10.**v) for m in models]
lls.append([m.log_likelihood() for m in models])
grads.append([m.log_likelihood_gradients() for m in models])
cgs.append([m.checkgrad(verbose=0,return_ratio=True) for m in models])
lls = np.array(lls).reshape(resolution,resolution)
cgs = np.array(zip(*cgs),dtype=np.float64).reshape(-1,resolution,resolution)
lls = np.array(zip(*lls)).reshape(-1,resolution,resolution)
cgs = np.array(zip(*cgs)).reshape(-1,resolution,resolution)
for cg in cgs:
for ll,cg in zip(lls,cgs):
pb.figure()
pb.contourf(xx,yy,lls,50,cmap=pb.cm.gray)
pb.contourf(xx,yy,ll,100,cmap=pb.cm.gray)
pb.colorbar()
try:
pb.contour(xx,yy,np.exp(ll),colors='k')
except:
pass
pb.scatter(xx.flatten(),yy.flatten(),20,np.log(np.abs(cg.flatten())),cmap=pb.cm.jet,linewidth=0)
pb.colorbar()