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Python 3 fixes

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This commit is contained in:
Mike Croucher 2015-06-23 01:26:52 -07:00
parent e58cc51c47
commit e7d1277e62
17 changed files with 52 additions and 48 deletions
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4
GPy/models/dpgplvm.py
View file

@ -3,7 +3,7 @@
import numpy as np
from .. import kern
from bayesian_gplvm import BayesianGPLVM
from .bayesian_gplvm import BayesianGPLVM
from ..core.parameterization.variational import NormalPosterior, NormalPrior
class DPBayesianGPLVM(BayesianGPLVM):
@ -15,5 +15,5 @@ class DPBayesianGPLVM(BayesianGPLVM):
name='bayesian gplvm', mpi_comm=None, normalizer=None,
missing_data=False, stochastic=False, batchsize=1):
super(DPBayesianGPLVM,self).__init__(Y=Y, input_dim=input_dim, X=X, X_variance=X_variance, init=init, num_inducing=num_inducing, Z=Z, kernel=kernel, inference_method=inference_method, likelihood=likelihood, mpi_comm=mpi_comm, normalizer=normalizer, missing_data=missing_data, stochastic=stochastic, batchsize=batchsize, name='dp bayesian gplvm')
self.X.mean.set_prior(X_prior)
self.X.mean.set_prior(X_prior)
self.link_parameter(X_prior)

2
GPy/models/gp_regression.py
View file

@ -31,7 +31,7 @@ class GPRegression(GP):
if kernel is None:
kernel = kern.RBF(X.shape[1])
likelihood = likelihoods.Gaussian(variance=noise_var)
likelihood = likelihoods.Gaussian(variance=noise_var)
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer)

12
GPy/models/gradient_checker.py
View file

@ -228,14 +228,14 @@ class HessianChecker(GradientChecker):
if verbose:
if block_indices:
print "\nBlock {}".format(block_indices)
print("\nBlock {}".format(block_indices))
else:
print "\nAll blocks"
print("\nAll blocks")
header = ['Checked', 'Max-Ratio', 'Min-Ratio', 'Min-Difference', 'Max-Difference']
header_string = map(lambda x: ' | '.join(header), [header])
separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator])
print('\n'.join([header_string[0], separator]))
min_r = '%.6f' % float(numpy.min(ratio))
max_r = '%.6f' % float(numpy.max(ratio))
max_d = '%.6f' % float(numpy.max(difference))
@ -248,7 +248,7 @@ class HessianChecker(GradientChecker):
checked = "\033[91m False \033[0m"
grad_string = "{} | {} | {} | {} | {} ".format(checked, cols[0], cols[1], cols[2], cols[3])
print grad_string
print(grad_string)
if plot:
import pylab as pb
@ -348,7 +348,7 @@ class SkewChecker(HessianChecker):
numeric_hess_partial = nd.Jacobian(self._df, vectorized=True)
numeric_hess = numeric_hess_partial(x)
print "Done making numerical hessian"
print("Done making numerical hessian")
if analytic_hess.dtype is np.dtype('object'):
#Blockify numeric_hess aswell
blocksizes, pagesizes = get_block_shapes_3d(analytic_hess)
@ -365,7 +365,7 @@ class SkewChecker(HessianChecker):
#Unless super_plot is set, just plot the first one
p = True if (plot and block_ind == numeric_hess.shape[2]-1) or super_plot else False
if verbose:
print "Checking derivative of hessian wrt parameter number {}".format(block_ind)
print("Checking derivative of hessian wrt parameter number {}".format(block_ind))
check_passed[block_ind] = self.checkgrad_block(analytic_hess[:,:,block_ind], numeric_hess[:,:,block_ind], verbose=verbose, step=step, tolerance=tolerance, block_indices=block_indices, plot=p)
current_index += current_size