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Exception fixes for Python 3 compat

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This commit is contained in:
Mike Croucher 2015-02-26 13:36:45 +00:00
parent c4fb58176d
commit 7c6ff2982f
6 changed files with 9 additions and 9 deletions
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4
GPy/inference/latent_function_inference/dtc.py
View file

@ -29,7 +29,7 @@ class DTC(LatentFunctionInference):
#make sure the noise is not hetero #make sure the noise is not hetero
beta = 1./likelihood.gaussian_variance(Y_metadata) beta = 1./likelihood.gaussian_variance(Y_metadata)
if beta.size > 1: if beta.size > 1:
raise NotImplementedError, "no hetero noise with this implementation of DTC" raise NotImplementedError("no hetero noise with this implementation of DTC")
Kmm = kern.K(Z) Kmm = kern.K(Z)
Knn = kern.Kdiag(X) Knn = kern.Kdiag(X)
@ -97,7 +97,7 @@ class vDTC(object):
#make sure the noise is not hetero #make sure the noise is not hetero
beta = 1./likelihood.gaussian_variance(Y_metadata) beta = 1./likelihood.gaussian_variance(Y_metadata)
if beta.size > 1: if beta.size > 1:
raise NotImplementedError, "no hetero noise with this implementation of DTC" raise NotImplementedError("no hetero noise with this implementation of DTC")
Kmm = kern.K(Z) Kmm = kern.K(Z)
Knn = kern.Kdiag(X) Knn = kern.Kdiag(X)

2
GPy/inference/latent_function_inference/expectation_propagation_dtc.py
View file

@ -314,7 +314,7 @@ def _compute_dL_dR(likelihood, het_noise, uncertain_inputs, LB, _LBi_Lmi_psi1Vf,
dL_dR = None dL_dR = None
elif het_noise: elif het_noise:
if uncertain_inputs: if uncertain_inputs:
raise NotImplementedError, "heteroscedatic derivates with uncertain inputs not implemented" raise NotImplementedError("heteroscedatic derivates with uncertain inputs not implemented")
else: else:
#from ...util.linalg import chol_inv #from ...util.linalg import chol_inv
#LBi = chol_inv(LB) #LBi = chol_inv(LB)

2
GPy/inference/latent_function_inference/fitc.py
View file

@ -26,7 +26,7 @@ class FITC(LatentFunctionInference):
#make sure the noise is not hetero #make sure the noise is not hetero
sigma_n = likelihood.gaussian_variance(Y_metadata) sigma_n = likelihood.gaussian_variance(Y_metadata)
if sigma_n.size >1: if sigma_n.size >1:
raise NotImplementedError, "no hetero noise with this implementation of FITC" raise NotImplementedError("no hetero noise with this implementation of FITC")
Kmm = kern.K(Z) Kmm = kern.K(Z)
Knn = kern.Kdiag(X) Knn = kern.Kdiag(X)

6
GPy/inference/latent_function_inference/posterior.py
View file

@ -52,7 +52,7 @@ class Posterior(object):
or ((mean is not None) and (cov is not None)): or ((mean is not None) and (cov is not None)):
pass # we have sufficient to compute the posterior pass # we have sufficient to compute the posterior
else: else:
raise ValueError, "insufficient information to compute the posterior" raise ValueError("insufficient information to compute the posterior")
self._K_chol = K_chol self._K_chol = K_chol
self._K = K self._K = K
@ -134,13 +134,13 @@ class Posterior(object):
#self._woodbury_chol = jitchol(W) #self._woodbury_chol = jitchol(W)
#try computing woodbury chol from cov #try computing woodbury chol from cov
elif self._covariance is not None: elif self._covariance is not None:
raise NotImplementedError, "TODO: check code here" raise NotImplementedError("TODO: check code here")
B = self._K - self._covariance B = self._K - self._covariance
tmp, _ = dpotrs(self.K_chol, B) tmp, _ = dpotrs(self.K_chol, B)
self._woodbury_inv, _ = dpotrs(self.K_chol, tmp.T) self._woodbury_inv, _ = dpotrs(self.K_chol, tmp.T)
_, _, self._woodbury_chol, _ = pdinv(self._woodbury_inv) _, _, self._woodbury_chol, _ = pdinv(self._woodbury_inv)
else: else:
raise ValueError, "insufficient information to compute posterior" raise ValueError("insufficient information to compute posterior")
return self._woodbury_chol return self._woodbury_chol
@property @property

2
GPy/inference/latent_function_inference/var_dtc.py
View file

@ -213,7 +213,7 @@ def _compute_dL_dR(likelihood, het_noise, uncertain_inputs, LB, _LBi_Lmi_psi1Vf,
dL_dR = None dL_dR = None
elif het_noise: elif het_noise:
if uncertain_inputs: if uncertain_inputs:
raise NotImplementedError, "heteroscedatic derivates with uncertain inputs not implemented" raise NotImplementedError("heteroscedatic derivates with uncertain inputs not implemented")
else: else:
#from ...util.linalg import chol_inv #from ...util.linalg import chol_inv
#LBi = chol_inv(LB) #LBi = chol_inv(LB)

2
GPy/inference/optimization/optimization.py
View file

@ -54,7 +54,7 @@ class Optimizer():
self.time = str(end - start) self.time = str(end - start)
def opt(self, f_fp=None, f=None, fp=None): def opt(self, f_fp=None, f=None, fp=None):
raise NotImplementedError, "this needs to be implemented to use the optimizer class" raise NotImplementedError("this needs to be implemented to use the optimizer class")
def plot(self): def plot(self):
""" """