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xrange fixes for Python 3

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This commit is contained in:
Mike Croucher 2015-03-07 07:49:59 +00:00
parent 5eeb2f18e9
commit cf1c382acd
14 changed files with 29 additions and 29 deletions
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6
GPy/inference/latent_function_inference/posterior.py
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@ -107,7 +107,7 @@ class Posterior(object):
if self._precision is None:
cov = np.atleast_3d(self.covariance)
self._precision = np.zeros(cov.shape) # if one covariance per dimension
for p in xrange(cov.shape[-1]):
for p in range(cov.shape[-1]):
self._precision[:,:,p] = pdinv(cov[:,:,p])[0]
return self._precision
@ -125,7 +125,7 @@ class Posterior(object):
if self._woodbury_inv is not None:
winv = np.atleast_3d(self._woodbury_inv)
self._woodbury_chol = np.zeros(winv.shape)
for p in xrange(winv.shape[-1]):
for p in range(winv.shape[-1]):
self._woodbury_chol[:,:,p] = pdinv(winv[:,:,p])[2]
#Li = jitchol(self._woodbury_inv)
#self._woodbury_chol, _ = dtrtri(Li)
@ -160,7 +160,7 @@ class Posterior(object):
elif self._covariance is not None:
B = np.atleast_3d(self._K) - np.atleast_3d(self._covariance)
self._woodbury_inv = np.empty_like(B)
for i in xrange(B.shape[-1]):
for i in range(B.shape[-1]):
tmp, _ = dpotrs(self.K_chol, B[:,:,i])
self._woodbury_inv[:,:,i], _ = dpotrs(self.K_chol, tmp.T)
return self._woodbury_inv

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

@ -92,7 +92,7 @@ class VarDTC_minibatch(LatentFunctionInference):
psi0_full = 0.
YRY_full = 0.
for n_start in xrange(0,num_data,batchsize):
for n_start in range(0,num_data,batchsize):
n_end = min(batchsize+n_start, num_data)
if batchsize==num_data:
Y_slice = Y

6
GPy/inference/mcmc/hmc.py
View file

@ -39,7 +39,7 @@ class HMC:
:rtype: numpy.ndarray
"""
params = np.empty((num_samples,self.p.size))
for i in xrange(num_samples):
for i in range(num_samples):
self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)
H_old = self._computeH()
theta_old = self.model.optimizer_array.copy()
@ -59,7 +59,7 @@ class HMC:
return params
def _update(self, hmc_iters):
for i in xrange(hmc_iters):
for i in range(hmc_iters):
self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
self.model.optimizer_array = self.model.optimizer_array + self.stepsize*np.dot(self.Minv, self.p)
self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
@ -82,7 +82,7 @@ class HMC_shortcut:
def sample(self, m_iters=1000, hmc_iters=20):
params = np.empty((m_iters,self.p.size))
for i in xrange(m_iters):
for i in range(m_iters):
# sample a stepsize from the uniform distribution
stepsize = np.exp(np.random.rand()*(self.stepsize_range[1]-self.stepsize_range[0])+self.stepsize_range[0])
self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)