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fixed interface change in optimization.py

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This commit is contained in:
Nicolo Fusi 2012-12-14 14:00:50 +00:00
parent 11dacb378a
commit b4190f907e
2 changed files with 5 additions and 5 deletions
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6
GPy/inference/optimization.py
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@ -170,12 +170,12 @@ class opt_rasm(Optimizer):
Optimizer.__init__(self, *args, **kwargs) Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Rasmussen's Conjugate Gradient" self.opt_name = "Rasmussen's Conjugate Gradient"
def opt(self): def opt(self, f_fp = None, f = None, fp = None):
""" """
Run Rasmussen's Conjugate Gradient optimizer Run Rasmussen's Conjugate Gradient optimizer
""" """
assert self.f_fp != None, "Rasmussen's minimizer requires f_fp" assert f_fp != None, "Rasmussen's minimizer requires f_fp"
statuses = ['Converged', 'Line search failed', 'Maximum number of f evaluations reached', statuses = ['Converged', 'Line search failed', 'Maximum number of f evaluations reached',
'NaNs in optimization'] 'NaNs in optimization']
@ -187,7 +187,7 @@ class opt_rasm(Optimizer):
if self.gtol is not None: if self.gtol is not None:
print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it" print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it"
opt_result = rasm.minimize(self.x_init, self.f_fp, (), messages = self.messages, opt_result = rasm.minimize(self.x_init, f_fp, (), messages = self.messages,
maxnumfuneval = self.max_f_eval) maxnumfuneval = self.max_f_eval)
self.x_opt = opt_result[0] self.x_opt = opt_result[0]
self.f_opt = opt_result[1][-1] self.f_opt = opt_result[1][-1]

2
GPy/models/warped_GP.py
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@ -22,7 +22,7 @@ class warpedGP(GP_regression):
if warping_function == None: if warping_function == None:
self.warping_function = TanhWarpingFunction(warping_terms) self.warping_function = TanhWarpingFunction(warping_terms)
# self.warping_params = np.random.randn(self.warping_function.n_terms, 3) # self.warping_params = np.random.randn(self.warping_function.n_terms, 3)
self.warping_params = np.ones((self.warping_function.n_terms, 3))*1.0 # TODO better init self.warping_params = np.ones((self.warping_function.n_terms, 3))*0.0 # TODO better init
self.warp_params_shape = (self.warping_function.n_terms, 3) # todo get this from the subclass self.warp_params_shape = (self.warping_function.n_terms, 3) # todo get this from the subclass
self.Z = Y.copy() self.Z = Y.copy()