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Various Py3 fixes

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This commit is contained in:
Mike Croucher 2015-03-04 03:22:44 +00:00
parent 273beca272
commit 6d2393ae90
4 changed files with 14 additions and 14 deletions
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18
GPy/core/parameterization/priors.py
View file

@ -414,7 +414,7 @@ class DGPLVM_KFDA(Prior):
def compute_cls(self, x):
cls = {}
# Appending each data point to its proper class
for j in xrange(self.datanum):
for j in range(self.datanum):
class_label = self.get_class_label(self.lbl[j])
if class_label not in cls:
cls[class_label] = []
@ -553,7 +553,7 @@ class DGPLVM(Prior):
def compute_cls(self, x):
cls = {}
# Appending each data point to its proper class
for j in xrange(self.datanum):
for j in range(self.datanum):
class_label = self.get_class_label(self.lbl[j])
if class_label not in cls:
cls[class_label] = []
@ -572,7 +572,7 @@ class DGPLVM(Prior):
# Adding data points as tuple to the dictionary so that we can access indices
def compute_indices(self, x):
data_idx = {}
for j in xrange(self.datanum):
for j in range(self.datanum):
class_label = self.get_class_label(self.lbl[j])
if class_label not in data_idx:
data_idx[class_label] = []
@ -591,7 +591,7 @@ class DGPLVM(Prior):
else:
lst_idx = []
# Here we put indices of each class in to the list called lst_idx_all
for m in xrange(len(data_idx[i])):
for m in range(len(data_idx[i])):
lst_idx.append(data_idx[i][m][0])
lst_idx_all.append(lst_idx)
return lst_idx_all
@ -627,7 +627,7 @@ class DGPLVM(Prior):
# pdb.set_trace()
# Calculating Bi
B_i[i] = (M_i[i] - M_0).reshape(1, self.dim)
for k in xrange(self.datanum):
for k in range(self.datanum):
for i in data_idx:
N_i = float(len(data_idx[i]))
if k in lst_idx_all[i]:
@ -772,7 +772,7 @@ class DGPLVM_T(Prior):
def compute_cls(self, x):
cls = {}
# Appending each data point to its proper class
for j in xrange(self.datanum):
for j in range(self.datanum):
class_label = self.get_class_label(self.lbl[j])
if class_label not in cls:
cls[class_label] = []
@ -791,7 +791,7 @@ class DGPLVM_T(Prior):
# Adding data points as tuple to the dictionary so that we can access indices
def compute_indices(self, x):
data_idx = {}
for j in xrange(self.datanum):
for j in range(self.datanum):
class_label = self.get_class_label(self.lbl[j])
if class_label not in data_idx:
data_idx[class_label] = []
@ -810,7 +810,7 @@ class DGPLVM_T(Prior):
else:
lst_idx = []
# Here we put indices of each class in to the list called lst_idx_all
for m in xrange(len(data_idx[i])):
for m in range(len(data_idx[i])):
lst_idx.append(data_idx[i][m][0])
lst_idx_all.append(lst_idx)
return lst_idx_all
@ -846,7 +846,7 @@ class DGPLVM_T(Prior):
# pdb.set_trace()
# Calculating Bi
B_i[i] = (M_i[i] - M_0).reshape(1, self.dim)
for k in xrange(self.datanum):
for k in range(self.datanum):
for i in data_idx:
N_i = float(len(data_idx[i]))
if k in lst_idx_all[i]:

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

@ -30,7 +30,7 @@ class SparseGPMissing(StochasticStorage):
Thus, we can just make sure the loop goes over self.d every
time.
"""
self.d = xrange(model.Y_normalized.shape[1])
self.d = range(model.Y_normalized.shape[1])
class SparseGPStochastics(StochasticStorage):
"""

2
GPy/kern/_src/add.py
View file

@ -165,7 +165,7 @@ class Add(CombinationKernel):
else:
eff_dL_dpsi1 += dL_dpsi2.sum(0) * p2.psi1(Z, variational_posterior) * 2.
grads = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
[np.add(target_grads[i],grads[i],target_grads[i]) for i in xrange(len(grads))]
[np.add(target_grads[i],grads[i],target_grads[i]) for i in range(len(grads))]
return target_grads
def add(self, other):

6
GPy/models/sparse_gp_minibatch.py
View file

@ -82,7 +82,7 @@ class SparseGPMiniBatch(SparseGP):
m_f = lambda i: "Precomputing Y for missing data: {: >7.2%}".format(float(i+1)/overall)
message = m_f(-1)
print(message, end=' ')
for d in xrange(overall):
for d in range(overall):
self.Ylist.append(self.Y_normalized[self.ninan[:, d], d][:, None])
print(' '*(len(message)+1) + '\r', end=' ')
message = m_f(d)
@ -182,11 +182,11 @@ class SparseGPMiniBatch(SparseGP):
full_values[key][value_indices[key]] += current_values[key]
"""
for key in current_values.keys():
if value_indices is not None and value_indices.has_key(key):
if value_indices is not None and key in value_indices:
index = value_indices[key]
else:
index = slice(None)
if full_values.has_key(key):
if key in full_values:
full_values[key][index] += current_values[key]
else:
full_values[key] = current_values[key]