apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-05 17:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'devel' into minibatch

Browse source
This commit is contained in:
mzwiessele 2016-03-07 13:42:24 +00:00
commit 7d2ffcde20
50 changed files with 124 additions and 73 deletions
Download patch file Download diff file Expand all files Collapse all files

2
GPy/core/__init__.py
View file

@ -43,4 +43,4 @@ def randomize(self, rand_gen=None, *args, **kwargs):
Model.randomize = randomize
Param.randomize = randomize
Parameterized.randomize = randomize
Parameterized.randomize = randomize

2
GPy/core/model.py
View file

@ -45,4 +45,4 @@ class Model(ParamzModel, Priorizable):
(including the MAP prior), so we return it here. If your model is not
probabilistic, just return your *negative* gradient here!
"""
return -(self._log_likelihood_gradients() + self._log_prior_gradients())
return -(self._log_likelihood_gradients() + self._log_prior_gradients())

2
GPy/core/parameterization/__init__.py
View file

@ -6,4 +6,4 @@ from .parameterized import Parameterized
from paramz import transformations
from paramz.core import lists_and_dicts, index_operations, observable_array, observable
from paramz import ties_and_remappings, ObsAr
from paramz import ties_and_remappings, ObsAr

2
GPy/core/parameterization/param.py
View file

@ -7,4 +7,4 @@ from paramz.transformations import __fixed__
import logging, numpy as np
class Param(Param, Priorizable):
pass
pass

2
GPy/core/parameterization/parameterized.py
View file

@ -49,4 +49,4 @@ class Parameterized(Parameterized, Priorizable):
If you want to operate on all parameters use m[''] to wildcard select all paramters
and concatenate them. Printing m[''] will result in printing of all parameters in detail.
"""
pass
pass

2
GPy/core/parameterization/transformations.py
View file

@ -1,4 +1,4 @@
# Copyright (c) 2014, Max Zwiessele, James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from paramz.transformations import *
from paramz.transformations import *

2
GPy/core/sparse_gp.py
View file

@ -44,7 +44,7 @@ class SparseGP(GP):
#pick a sensible inference method
if inference_method is None:
if isinstance(likelihood, likelihoods.Gaussian):
inference_method = var_dtc.VarDTC(limit=1)
inference_method = var_dtc.VarDTC(limit=3)
else:
#inference_method = ??
raise NotImplementedError("what to do what to do?")

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

@ -22,7 +22,7 @@ class VarDTC(LatentFunctionInference):
"""
const_jitter = 1e-8
def __init__(self, limit=1):
def __init__(self, limit=3):
from paramz.caching import Cacher
self.limit = limit
self.get_trYYT = Cacher(self._get_trYYT, limit)

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

@ -21,7 +21,7 @@ class VarDTC_minibatch(LatentFunctionInference):
"""
const_jitter = 1e-8
def __init__(self, batchsize=None, limit=1, mpi_comm=None):
def __init__(self, batchsize=None, limit=3, mpi_comm=None):
self.batchsize = batchsize
self.mpi_comm = mpi_comm

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

@ -2,4 +2,4 @@ from paramz.optimization import stochastics, Optimizer
from paramz.optimization import *
import sys
sys.modules['GPy.inference.optimization.stochastics'] = stochastics
sys.modules['GPy.inference.optimization.Optimizer'] = Optimizer
sys.modules['GPy.inference.optimization.Optimizer'] = Optimizer

4
GPy/kern/__init__.py
View file

@ -10,7 +10,7 @@ from .src.add import Add
from .src.prod import Prod
from .src.rbf import RBF
from .src.linear import Linear, LinearFull
from .src.static import Bias, White, Fixed
from .src.static import Bias, White, Fixed, WhiteHeteroscedastic
from .src.brownian import Brownian
from .src.stationary import Exponential, OU, Matern32, Matern52, ExpQuad, RatQuad, Cosine
from .src.mlp import MLP
@ -28,4 +28,4 @@ from .src.trunclinear import TruncLinear,TruncLinear_inf
from .src.splitKern import SplitKern,DEtime
from .src.splitKern import DEtime as DiffGenomeKern
from .src.spline import Spline
from .src.basis_funcs import LogisticBasisFuncKernel, LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel
from .src.basis_funcs import LogisticBasisFuncKernel, LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel

2
GPy/kern/src/ODE_t.py
View file

@ -162,4 +162,4 @@ class ODE_t(Kern):
self.lengthscale_Yt.gradient = np.sum(dkYdlent*(-0.5*self.lengthscale_Yt**(-2)) * dL_dK)
self.ubias.gradient = np.sum(dkdubias * dL_dK)
self.ubias.gradient = np.sum(dkdubias * dL_dK)

2
GPy/kern/src/__init__.py
View file

@ -1 +1 @@
from . import psi_comp
from . import psi_comp

12
GPy/kern/src/add.py
View file

@ -37,7 +37,7 @@ class Add(CombinationKernel):
else:
return False
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def K(self, X, X2=None, which_parts=None):
"""
Add all kernels together.
@ -51,7 +51,7 @@ class Add(CombinationKernel):
which_parts = [which_parts]
return reduce(np.add, (p.K(X, X2) for p in which_parts))
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def Kdiag(self, X, which_parts=None):
if which_parts is None:
which_parts = self.parts
@ -98,17 +98,17 @@ class Add(CombinationKernel):
[target.__iadd__(p.gradients_XX_diag(dL_dKdiag, X)) for p in self.parts]
return target
@Cache_this(limit=1, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def psi0(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi0(self,Z,variational_posterior)
return reduce(np.add, (p.psi0(Z, variational_posterior) for p in self.parts))
@Cache_this(limit=1, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def psi1(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi1(self,Z,variational_posterior)
return reduce(np.add, (p.psi1(Z, variational_posterior) for p in self.parts))
@Cache_this(limit=1, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def psi2(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi2(self,Z,variational_posterior)
psi2 = reduce(np.add, (p.psi2(Z, variational_posterior) for p in self.parts))
@ -144,7 +144,7 @@ class Add(CombinationKernel):
raise NotImplementedError("psi2 cannot be computed for this kernel")
return psi2
@Cache_this(limit=1, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def psi2n(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi2n(self, Z, variational_posterior)
psi2 = reduce(np.add, (p.psi2n(Z, variational_posterior) for p in self.parts))

2
GPy/kern/src/eq_ode2.py
View file

@ -64,7 +64,7 @@ class EQ_ODE2(Kern):
self.W = Param('W', W)
self.link_parameters(self.lengthscale, self.C, self.B, self.W)
@Cache_this(limit=2)
@Cache_this(limit=3)
def K(self, X, X2=None):
#This way is not working, indexes are lost after using k._slice_X
#index = np.asarray(X, dtype=np.int)

2
GPy/kern/src/kern.py
View file

@ -68,7 +68,7 @@ class Kern(Parameterized):
def _effective_input_dim(self):
return np.size(self._all_dims_active)
@Cache_this(limit=20)
@Cache_this(limit=3)
def _slice_X(self, X):
try:
return X[:, self._all_dims_active].astype('float')

4
GPy/kern/src/linear.py
View file

@ -51,7 +51,7 @@ class Linear(Kern):
self.link_parameter(self.variances)
self.psicomp = PSICOMP_Linear()
@Cache_this(limit=2)
@Cache_this(limit=3)
def K(self, X, X2=None):
if self.ARD:
if X2 is None:
@ -62,7 +62,7 @@ class Linear(Kern):
else:
return self._dot_product(X, X2) * self.variances
@Cache_this(limit=1, ignore_args=(0,))
@Cache_this(limit=3, ignore_args=(0,))
def _dot_product(self, X, X2=None):
if X2 is None:
return tdot(X)

10
GPy/kern/src/mlp.py
View file

@ -45,7 +45,7 @@ class MLP(Kern):
self.link_parameters(self.variance, self.weight_variance, self.bias_variance)
@Cache_this(limit=20, ignore_args=())
@Cache_this(limit=3, ignore_args=())
def K(self, X, X2=None):
if X2 is None:
X_denom = np.sqrt(self._comp_prod(X)+1.)
@ -57,7 +57,7 @@ class MLP(Kern):
XTX = self._comp_prod(X,X2)/X_denom[:,None]/X2_denom[None,:]
return self.variance*four_over_tau*np.arcsin(XTX)
@Cache_this(limit=20, ignore_args=())
@Cache_this(limit=3, ignore_args=())
def Kdiag(self, X):
"""Compute the diagonal of the covariance matrix for X."""
X_prod = self._comp_prod(X)
@ -88,14 +88,14 @@ class MLP(Kern):
"""Gradient of diagonal of covariance with respect to X"""
return self._comp_grads_diag(dL_dKdiag, X)[3]
@Cache_this(limit=50, ignore_args=())
@Cache_this(limit=3, ignore_args=())
def _comp_prod(self, X, X2=None):
if X2 is None:
return (np.square(X)*self.weight_variance).sum(axis=1)+self.bias_variance
else:
return (X*self.weight_variance).dot(X2.T)+self.bias_variance
@Cache_this(limit=20, ignore_args=(1,))
@Cache_this(limit=3, ignore_args=(1,))
def _comp_grads(self, dL_dK, X, X2=None):
var,w,b = self.variance, self.weight_variance, self.bias_variance
K = self.K(X, X2)
@ -130,7 +130,7 @@ class MLP(Kern):
dX2 = common.T.dot(X)*w-((common*XTX).sum(axis=0)/(X2_prod+1.))[:,None]*X2*w
return dvar, dw, db, dX, dX2
@Cache_this(limit=20, ignore_args=(1,))
@Cache_this(limit=3, ignore_args=(1,))
def _comp_grads_diag(self, dL_dKdiag, X):
var,w,b = self.variance, self.weight_variance, self.bias_variance
K = self.Kdiag(X)

2
GPy/kern/src/poly.py
View file

@ -27,7 +27,7 @@ class Poly(Kern):
_, _, B = self._AB(X, X2)
return B * self.variance
@Cache_this(limit=2)
@Cache_this(limit=3)
def _AB(self, X, X2=None):
if X2 is None:
dot_prod = np.dot(X, X.T)

4
GPy/kern/src/prod.py
View file

@ -39,7 +39,7 @@ class Prod(CombinationKernel):
kernels.insert(i, part)
super(Prod, self).__init__(kernels, name)
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def K(self, X, X2=None, which_parts=None):
if which_parts is None:
which_parts = self.parts
@ -48,7 +48,7 @@ class Prod(CombinationKernel):
which_parts = [which_parts]
return reduce(np.multiply, (p.K(X, X2) for p in which_parts))
@Cache_this(limit=2, force_kwargs=['which_parts'])
@Cache_this(limit=3, force_kwargs=['which_parts'])
def Kdiag(self, X, which_parts=None):
if which_parts is None:
which_parts = self.parts

8
GPy/kern/src/psi_comp/__init__.py
View file

@ -21,7 +21,7 @@ from .gaussherm import PSICOMP_GH
from . import rbf_psi_comp, linear_psi_comp, ssrbf_psi_comp, sslinear_psi_comp
class PSICOMP_RBF(PSICOMP):
@Cache_this(limit=10, ignore_args=(0,))
@Cache_this(limit=3, ignore_args=(0,))
def psicomputations(self, kern, Z, variational_posterior, return_psi2_n=False):
variance, lengthscale = kern.variance, kern.lengthscale
if isinstance(variational_posterior, variational.NormalPosterior):
@ -31,7 +31,7 @@ class PSICOMP_RBF(PSICOMP):
else:
raise ValueError("unknown distriubtion received for psi-statistics")
@Cache_this(limit=10, ignore_args=(0,2,3,4))
@Cache_this(limit=3, ignore_args=(0,2,3,4))
def psiDerivativecomputations(self, kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
variance, lengthscale = kern.variance, kern.lengthscale
if isinstance(variational_posterior, variational.NormalPosterior):
@ -43,7 +43,7 @@ class PSICOMP_RBF(PSICOMP):
class PSICOMP_Linear(PSICOMP):
@Cache_this(limit=10, ignore_args=(0,))
@Cache_this(limit=3, ignore_args=(0,))
def psicomputations(self, kern, Z, variational_posterior, return_psi2_n=False):
variances = kern.variances
if isinstance(variational_posterior, variational.NormalPosterior):
@ -53,7 +53,7 @@ class PSICOMP_Linear(PSICOMP):
else:
raise ValueError("unknown distriubtion received for psi-statistics")
@Cache_this(limit=10, ignore_args=(0,2,3,4))
@Cache_this(limit=3, ignore_args=(0,2,3,4))
def psiDerivativecomputations(self, kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
variances = kern.variances
if isinstance(variational_posterior, variational.NormalPosterior):

6
GPy/kern/src/psi_comp/gaussherm.py
View file

@ -27,7 +27,7 @@ class PSICOMP_GH(PSICOMP):
def _setup_observers(self):
pass
@Cache_this(limit=10, ignore_args=(0,))
@Cache_this(limit=3, ignore_args=(0,))
def comp_K(self, Z, qX):
if self.Xs is None or self.Xs.shape != qX.mean.shape:
from paramz import ObsAr
@ -38,7 +38,7 @@ class PSICOMP_GH(PSICOMP):
self.Xs[i] = self.locs[i]*S_sq+mu
return self.Xs
@Cache_this(limit=10, ignore_args=(0,))
@Cache_this(limit=3, ignore_args=(0,))
def psicomputations(self, kern, Z, qX, return_psi2_n=False):
mu, S = qX.mean.values, qX.variance.values
N,M,Q = mu.shape[0],Z.shape[0],mu.shape[1]
@ -62,7 +62,7 @@ class PSICOMP_GH(PSICOMP):
psi2 += self.weights[i]* tdot(Kfu.T)
return psi0, psi1, psi2
@Cache_this(limit=10, ignore_args=(0, 2,3,4))
@Cache_this(limit=3, ignore_args=(0, 2,3,4))
def psiDerivativecomputations(self, kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, qX):
mu, S = qX.mean.values, qX.variance.values
if self.cache_K: Xs = self.comp_K(Z, qX)

4
GPy/kern/src/psi_comp/rbf_psi_comp.py
View file

@ -132,5 +132,5 @@ def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S):
return _dL_dvar, _dL_dl, _dL_dZ, _dL_dmu, _dL_dS
_psi1computations = Cacher(__psi1computations, limit=5)
_psi2computations = Cacher(__psi2computations, limit=5)
_psi1computations = Cacher(__psi1computations, limit=3)
_psi2computations = Cacher(__psi2computations, limit=3)

4
GPy/kern/src/psi_comp/rbf_psi_gpucomp.py
View file

@ -326,7 +326,7 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
except:
return self.fall_back.psicomputations(kern, Z, variational_posterior, return_psi2_n)
@Cache_this(limit=10, ignore_args=(0,))
@Cache_this(limit=3, ignore_args=(0,))
def _psicomputations(self, kern, Z, variational_posterior, return_psi2_n=False):
"""
Z - MxQ
@ -371,7 +371,7 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
except:
return self.fall_back.psiDerivativecomputations(kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
@Cache_this(limit=10, ignore_args=(0,2,3,4))
@Cache_this(limit=3, ignore_args=(0,2,3,4))
def _psiDerivativecomputations(self, kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
# resolve the requirement of dL_dpsi2 to be symmetric
if len(dL_dpsi2.shape)==2: dL_dpsi2 = (dL_dpsi2+dL_dpsi2.T)/2

2
GPy/kern/src/psi_comp/ssrbf_psi_comp.py
View file

@ -88,7 +88,7 @@ try:
return psi0,psi1,psi2,psi2n
from GPy.util.caching import Cacher
psicomputations = Cacher(_psicomputations, limit=1)
psicomputations = Cacher(_psicomputations, limit=3)
def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
ARD = (len(lengthscale)!=1)

4
GPy/kern/src/psi_comp/ssrbf_psi_gpucomp.py
View file

@ -375,7 +375,7 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
def get_dimensions(self, Z, variational_posterior):
return variational_posterior.mean.shape[0], Z.shape[0], Z.shape[1]
@Cache_this(limit=1, ignore_args=(0,))
@Cache_this(limit=3, ignore_args=(0,))
def psicomputations(self, kern, Z, variational_posterior, return_psi2_n=False):
"""
Z - MxQ
@ -409,7 +409,7 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
else:
return psi0, psi1_gpu.get(), psi2_gpu.get()
@Cache_this(limit=1, ignore_args=(0,2,3,4))
@Cache_this(limit=3, ignore_args=(0,2,3,4))
def psiDerivativecomputations(self, kern, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
variance, lengthscale = kern.variance, kern.lengthscale
from ....util.linalg_gpu import sum_axis

46
GPy/kern/src/static.py
View file

@ -81,6 +81,52 @@ class White(Static):
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
self.variance.gradient = dL_dpsi0.sum()
class WhiteHeteroscedastic(Static):
def __init__(self, input_dim, num_data, variance=1., active_dims=None, name='white_hetero'):
"""
A heteroscedastic White kernel (nugget/noise).
It defines one variance (nugget) per input sample.
Prediction excludes any noise learnt by this Kernel, so be careful using this kernel.
You can plot the errors learnt by this kernel by something similar as:
plt.errorbar(m.X, m.Y, yerr=2*np.sqrt(m.kern.white.variance))
"""
super(Static, self).__init__(input_dim, active_dims, name)
self.variance = Param('variance', np.ones(num_data) * variance, Logexp())
self.link_parameters(self.variance)
def Kdiag(self, X):
if X.shape[0] == self.variance.shape[0]:
# If the input has the same number of samples as
# the number of variances, we return the variances
return self.variance
return 0.
def K(self, X, X2=None):
if X2 is None:
return np.eye(X.shape[0])*self.variance
else:
return np.zeros((X.shape[0], X2.shape[0]))
def psi2(self, Z, variational_posterior):
return np.zeros((Z.shape[0], Z.shape[0]), dtype=np.float64)
def psi2n(self, Z, variational_posterior):
return np.zeros((1, Z.shape[0], Z.shape[0]), dtype=np.float64)
def update_gradients_full(self, dL_dK, X, X2=None):
if X2 is None:
self.variance.gradient = np.diagonal(dL_dK)
else:
self.variance.gradient = 0.
def update_gradients_diag(self, dL_dKdiag, X):
self.variance.gradient = dL_dKdiag
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
self.variance.gradient = dL_dpsi0
class Bias(Static):
def __init__(self, input_dim, variance=1., active_dims=None, name='bias'):
super(Bias, self).__init__(input_dim, variance, active_dims, name)

4
GPy/kern/src/stationary.py
View file

@ -81,11 +81,11 @@ class Stationary(Kern):
def dK_dr(self, r):
raise NotImplementedError("implement derivative of the covariance function wrt r to use this class")
@Cache_this(limit=20, ignore_args=())
@Cache_this(limit=3, ignore_args=())
def dK2_drdr(self, r):
raise NotImplementedError("implement second derivative of covariance wrt r to use this method")
@Cache_this(limit=5, ignore_args=())
@Cache_this(limit=3, ignore_args=())
def K(self, X, X2=None):
"""
Kernel function applied on inputs X and X2.

8
GPy/kern/src/trunclinear.py
View file

@ -54,12 +54,12 @@ class TruncLinear(Kern):
self.add_parameter(self.variances)
self.add_parameter(self.delta)
@Cache_this(limit=2)
@Cache_this(limit=3)
def K(self, X, X2=None):
XX = self.variances*self._product(X, X2)
return XX.sum(axis=-1)
@Cache_this(limit=2)
@Cache_this(limit=3)
def _product(self, X, X2=None):
if X2 is None:
X2 = X
@ -149,12 +149,12 @@ class TruncLinear_inf(Kern):
self.add_parameter(self.variances)
# @Cache_this(limit=2)
# @Cache_this(limit=3)
def K(self, X, X2=None):
tmp = self._product(X, X2)
return (self.variances*tmp).sum(axis=-1)
# @Cache_this(limit=2)
# @Cache_this(limit=3)
def _product(self, X, X2=None):
if X2 is None:
X2 = X

2
GPy/models/bayesian_gplvm.py
View file

@ -61,7 +61,7 @@ class BayesianGPLVM(SparseGP_MPI):
else:
from ..inference.latent_function_inference.var_dtc import VarDTC
self.logger.debug("creating inference_method var_dtc")
inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1])
inference_method = VarDTC(limit=3 if not missing_data else Y.shape[1])
if isinstance(inference_method,VarDTC_minibatch):
inference_method.mpi_comm = mpi_comm

4
GPy/models/bayesian_gplvm_minibatch.py
View file

@ -61,7 +61,7 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
if inference_method is None:
from ..inference.latent_function_inference.var_dtc import VarDTC
self.logger.debug("creating inference_method var_dtc")
inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1])
inference_method = VarDTC(limit=3 if not missing_data else Y.shape[1])
super(BayesianGPLVMMiniBatch,self).__init__(X, Y, Z, kernel, likelihood=likelihood,
name=name, inference_method=inference_method,
@ -126,4 +126,4 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
d = self.output_dim
self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)*self.stochastics.batchsize/d
self._Xgrad = self.X.gradient.copy()
self._Xgrad = self.X.gradient.copy()

2
GPy/models/gplvm.py
View file

@ -41,4 +41,4 @@ class GPLVM(GP):
def parameters_changed(self):
super(GPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)

2
GPy/models/sparse_gp_minibatch.py
View file

@ -45,7 +45,7 @@ class SparseGPMiniBatch(SparseGP):
# pick a sensible inference method
if inference_method is None:
if isinstance(likelihood, likelihoods.Gaussian):
inference_method = var_dtc.VarDTC(limit=1 if not missing_data else Y.shape[1])
inference_method = var_dtc.VarDTC(limit=3 if not missing_data else Y.shape[1])
else:
#inference_method = ??
raise NotImplementedError("what to do what to do?")

2
GPy/models/sparse_gp_regression.py
View file

@ -62,4 +62,4 @@ class SparseGPRegression(SparseGP_MPI):
if isinstance(self.inference_method,VarDTC_minibatch):
update_gradients_sparsegp(self, mpi_comm=self.mpi_comm)
else:
super(SparseGPRegression, self).parameters_changed()
super(SparseGPRegression, self).parameters_changed()

2
GPy/plotting/Tango.py
View file

@ -104,4 +104,4 @@ cdict_Alu = {'red' :((0./5,colorsRGB['Aluminium1'][0]/256.,colorsRGB['Aluminium1
(2./5,colorsRGB['Aluminium3'][2]/256.,colorsRGB['Aluminium3'][2]/256.),
(3./5,colorsRGB['Aluminium4'][2]/256.,colorsRGB['Aluminium4'][2]/256.),
(4./5,colorsRGB['Aluminium5'][2]/256.,colorsRGB['Aluminium5'][2]/256.),
(5./5,colorsRGB['Aluminium6'][2]/256.,colorsRGB['Aluminium6'][2]/256.))}
(5./5,colorsRGB['Aluminium6'][2]/256.,colorsRGB['Aluminium6'][2]/256.))}

2
GPy/plotting/__init__.py
View file

@ -107,4 +107,4 @@ try:
lib = config.get('plotting', 'library')
change_plotting_library(lib)
except NoOptionError:
print("No plotting library was specified in config file. \n{}".format(error_suggestion))
print("No plotting library was specified in config file. \n{}".format(error_suggestion))

2
GPy/plotting/gpy_plot/gp_plots.py
View file

@ -420,4 +420,4 @@ def _plot(self, canvas, plots, helper_data, helper_prediction, levels, plot_indu
if helper_prediction[2] is not None:
plots.update(_plot_samples(self, canvas, helper_data, helper_prediction, projection, "Samples"))
return plots
return plots

2
GPy/plotting/gpy_plot/kernel_plots.py
View file

@ -140,4 +140,4 @@ def plot_covariance(kernel, x=None, label=None,
return pl().add_to_canvas(canvas, plots)
else:
raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")
raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")

2
GPy/plotting/gpy_plot/plot_util.py
View file

@ -380,4 +380,4 @@ def x_frame2D(X,plot_limits=None,resolution=None):
resolution = resolution or 50
xx, yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
Xnew = np.vstack((xx.flatten(),yy.flatten())).T
return Xnew, xx, yy, xmin, xmax
return Xnew, xx, yy, xmin, xmax

2
GPy/plotting/matplot_dep/__init__.py
View file

@ -18,4 +18,4 @@
from .util import align_subplot_array, align_subplots, fewerXticks, removeRightTicks, removeUpperTicks
from . import controllers, base_plots
from . import controllers, base_plots

2
GPy/plotting/matplot_dep/controllers/__init__.py
View file

@ -1 +1 @@
from .imshow_controller import ImshowController, ImAnnotateController
from .imshow_controller import ImshowController, ImAnnotateController

2
GPy/plotting/matplot_dep/controllers/imshow_controller.py
View file

@ -72,4 +72,4 @@ class ImAnnotateController(ImshowController):
text.set_x(x+xoffset)
text.set_y(y+yoffset)
text.set_text("{}".format(X[1][j, i]))
return view
return view

2
GPy/plotting/matplot_dep/defaults.py
View file

@ -72,4 +72,4 @@ latent = dict(aspect='auto', cmap='Greys', interpolation='bicubic')
gradient = dict(aspect='auto', cmap='RdBu', interpolation='nearest', alpha=.7)
magnification = dict(aspect='auto', cmap='Greys', interpolation='bicubic')
latent_scatter = dict(s=40, linewidth=.2, edgecolor='k', alpha=.9)
annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)
annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

2
GPy/plotting/matplot_dep/util.py
View file

@ -116,4 +116,4 @@ def align_subplot_array(axes,xlim=None, ylim=None):
if i<(M*(N-1)):
ax.set_xticks([])
else:
removeUpperTicks(ax)
removeUpperTicks(ax)

2
GPy/plotting/plotly_dep/defaults.py
View file

@ -73,4 +73,4 @@ latent = dict(colorscale='Greys', reversescale=True, zsmooth='best')
gradient = dict(colorscale='RdBu', opacity=.7)
magnification = dict(colorscale='Greys', zsmooth='best', reversescale=True)
latent_scatter = dict(marker_kwargs=dict(size='5', opacity=.7))
# annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)
# annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

2
GPy/testing/gp_tests.py
View file

@ -97,4 +97,4 @@ class Test(unittest.TestCase):
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()
unittest.main()

5
GPy/testing/kernel_tests.py
View file

@ -329,6 +329,11 @@ class KernelGradientTestsContinuous(unittest.TestCase):
k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
def test_WhiteHeteroscedastic(self):
k = GPy.kern.WhiteHeteroscedastic(self.D, self.X.shape[0])
k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
def test_standard_periodic(self):
k = GPy.kern.StdPeriodic(self.D, self.D-1)
k.randomize()

2
GPy/testing/minibatch_tests.py
View file

@ -197,4 +197,4 @@ class SparseGPMinibatchTest(unittest.TestCase):
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()
unittest.main()

2
GPy/util/linalg.py
View file

@ -78,7 +78,7 @@ def jitchol(A, maxtries=5):
try: raise
except:
logging.warning('\n'.join(['Added jitter of {:.10e}'.format(jitter),
' in '+traceback.format_list(traceback.extract_stack(limit=2)[-2:-1])[0][2:]]))
' in '+traceback.format_list(traceback.extract_stack(limit=3)[-2:-1])[0][2:]]))
return L
# def dtrtri(L, lower=1):

2
benchmarks/regression/evaluation.py
View file

@ -18,4 +18,4 @@ class RMSE(Evaluation):
def evaluate(self, gt, pred):
return np.sqrt(np.square(gt-pred).astype(np.float).mean())