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Changed the examples (started boston data) and increased tolerance of

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finding fhat
This commit is contained in:
Alan Saul 2013-09-18 16:51:28 +01:00
parent ebfff6c832
commit ca09051a56
2 changed files with 85 additions and 21 deletions
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8
GPy/likelihoods/laplace.py
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@ -291,7 +291,7 @@ class Laplace(likelihood):
f_hat = sp.optimize.fmin_ncg(obj, f, fprime=obj_grad, fhess=obj_hess, disp=False)
return f_hat[:, None]
def rasm_mode(self, K, MAX_ITER=200, MAX_RESTART=10):
def rasm_mode(self, K, MAX_ITER=100, MAX_RESTART=10):
"""
Rasmussen's numerically stable mode finding
For nomenclature see Rasmussen & Williams 2006
@ -320,7 +320,7 @@ class Laplace(likelihood):
return -0.5*np.dot(a.T, f) + self.likelihood_function.link_function(self.data, f, extra_data=self.extra_data)
difference = np.inf
epsilon = 1e-10
epsilon = 1e-6
step_size = 1
rs = 0
i = 0
@ -330,7 +330,7 @@ class Laplace(likelihood):
#W = np.maximum(W, 0)
if not self.likelihood_function.log_concave:
#print "Under 1e-10: {}".format(np.sum(W < 1e-10))
W[W < 1e-10] = 1e-10 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
W[W < 1e-6] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
# If the likelihood is non-log-concave. We wan't to say that there is a negative variance
# To cause the posterior to become less certain than the prior and likelihood,
# This is a property only held by non-log-concave likelihoods
@ -355,7 +355,7 @@ class Laplace(likelihood):
i_o = partial(inner_obj, old_a=old_a, da=da, K=K)
#new_obj = sp.optimize.brent(i_o, tol=1e-4, maxiter=20)
new_obj = sp.optimize.minimize_scalar(i_o, method='brent', tol=1e-6, options={'maxiter':20, 'disp':True}).fun
new_obj = sp.optimize.minimize_scalar(i_o, method='brent', tol=1e-4, options={'maxiter':20}).fun
f = self.f.copy()
a = self.a.copy()