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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Alan Saul 2015-09-08 15:53:59 +01:00
commit 92b880bf4b
46 changed files with 626 additions and 373 deletions
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42
GPy/core/gp.py
View file

@ -108,9 +108,15 @@ class GP(Model):
# The predictive variable to be used to predict using the posterior object's
# woodbury_vector and woodbury_inv is defined as predictive_variable
# as long as the posterior has the right woodbury entries.
# It is the input variable used for the covariance between
# X_star and the posterior of the GP.
# This is usually just a link to self.X (full GP) or self.Z (sparse GP).
# Make sure to name this variable and the predict functions will "just work"
# as long as the posterior has the right woodbury entries.
# In maths the predictive variable is:
# K_{xx} - K_{xp}W_{pp}^{-1}K_{px}
# W_{pp} := \texttt{Woodbury inv}
# p := _predictive_variable
self._predictive_variable = self.X
@ -205,7 +211,7 @@ class GP(Model):
if kern is None:
kern = self.kern
Kx = kern.K(self.X, Xnew)
Kx = kern.K(self._predictive_variable, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if len(mu.shape)==1:
mu = mu.reshape(-1,1)
@ -213,7 +219,7 @@ class GP(Model):
Kxx = kern.K(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
elif self.posterior.woodbury_inv.ndim == 3: # Missing data
var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
from ..util.linalg import mdot
for i in range(var.shape[2]):
@ -223,7 +229,7 @@ class GP(Model):
Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None]
elif self.posterior.woodbury_inv.ndim == 3:
elif self.posterior.woodbury_inv.ndim == 3: # Missing data
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
@ -364,11 +370,15 @@ class GP(Model):
var_jac = dK2_dXdX - np.einsum('qim,miq->iq', dK_dXnew_full.T.dot(wi), dK_dXnew_full)
return var_jac
if self.posterior.woodbury_inv.ndim == 3:
var_jac = []
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac.append(compute_cov_inner(self.posterior.woodbury_inv[:, :, d]))
var_jac = np.concatenate(var_jac)
if self.posterior.woodbury_inv.ndim == 3: # Missing data:
if full_cov:
var_jac = np.empty((Xnew.shape[0],Xnew.shape[0],Xnew.shape[1],self.output_dim))
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac[:, :, :, d] = compute_cov_inner(self.posterior.woodbury_inv[:, :, d])
else:
var_jac = np.empty((Xnew.shape[0],Xnew.shape[1],self.output_dim))
for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac[:, :, d] = compute_cov_inner(self.posterior.woodbury_inv[:, :, d])
else:
var_jac = compute_cov_inner(self.posterior.woodbury_inv)
return mean_jac, var_jac
@ -391,10 +401,11 @@ class GP(Model):
mu_jac, var_jac = self.predict_jacobian(Xnew, kern, full_cov=False)
mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac)
Sigma = np.zeros(mumuT.shape)
if var_jac.ndim == 3:
Sigma = np.einsum('iqd,ipd->iqp', var_jac, var_jac)
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = var_jac.sum(-1)
else:
Sigma = self.output_dim*np.einsum('iq,ip->iqp', var_jac, var_jac)
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = self.output_dim*var_jac
G = 0.
if mean:
G += mumuT
@ -412,8 +423,13 @@ class GP(Model):
"""
G = self.predict_wishard_embedding(Xnew, kern, mean, covariance)
from ..util.linalg import jitchol
return np.array([np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :])))))) for n in range(Xnew.shape[0])])
#return np.array([np.sqrt(np.linalg.det(G[n, :, :])) for n in range(Xnew.shape[0])])
mag = np.empty(Xnew.shape[0])
for n in range(Xnew.shape[0]):
try:
mag[n] = np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :]))))))
except:
mag[n] = np.sqrt(np.linalg.det(G[n, :, :]))
return mag
def posterior_samples_f(self,X,size=10, full_cov=True):
"""

4
GPy/core/parameterization/parameterized.py
View file

@ -74,7 +74,7 @@ class Parameterized(Parameterizable):
# Metaclass for parameters changed after init.
# This makes sure, that parameters changed will always be called after __init__
# **Never** call parameters_changed() yourself
#This is ignored in Python 3 -- you need to put the meta class in the function definition.
#This is ignored in Python 3 -- you need to put the meta class in the function definition.
#__metaclass__ = ParametersChangedMeta
#The six module is used to support both Python 2 and 3 simultaneously
#===========================================================================
@ -316,7 +316,7 @@ class Parameterized(Parameterizable):
param[:] = val; return
except AttributeError:
pass
object.__setattr__(self, name, val);
return object.__setattr__(self, name, val);
#===========================================================================
# Pickling

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

@ -488,7 +488,7 @@ class Logistic(Transformation):
return instance()
newfunc = super(Transformation, cls).__new__
if newfunc is object.__new__:
o = newfunc(cls)
o = newfunc(cls)
else:
o = newfunc(cls, lower, upper, *args, **kwargs)
cls._instances.append(weakref.ref(o))

51
GPy/core/sparse_gp.py
View file

@ -49,7 +49,7 @@ class SparseGP(GP):
else:
#inference_method = ??
raise NotImplementedError("what to do what to do?")
print("defaulting to ", inference_method, "for latent function inference")
print(("defaulting to ", inference_method, "for latent function inference"))
self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0]
@ -128,29 +128,30 @@ class SparseGP(GP):
if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior):
Kx = kern.K(self._predictive_variable, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = kern.K(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[2]):
var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx))
var = var
else:
Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None]
elif self.posterior.woodbury_inv.ndim == 3:
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
var = var
#add in the mean function
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
# Kx = kern.K(self._predictive_variable, Xnew)
# mu = np.dot(Kx.T, self.posterior.woodbury_vector)
# if full_cov:
# Kxx = kern.K(Xnew)
# if self.posterior.woodbury_inv.ndim == 2:
# var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
# elif self.posterior.woodbury_inv.ndim == 3:
# var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
# for i in range(var.shape[2]):
# var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx))
# var = var
# else:
# Kxx = kern.Kdiag(Xnew)
# if self.posterior.woodbury_inv.ndim == 2:
# var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None]
# elif self.posterior.woodbury_inv.ndim == 3:
# var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
# for i in range(var.shape[1]):
# var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
# var = var
# #add in the mean function
# if self.mean_function is not None:
# mu += self.mean_function.f(Xnew)
mu, var = super(SparseGP, self)._raw_predict(Xnew, full_cov, kern)
else:
psi0_star = kern.psi0(self._predictive_variable, Xnew)
psi1_star = kern.psi1(self._predictive_variable, Xnew)
@ -159,7 +160,7 @@ class SparseGP(GP):
mu = np.dot(psi1_star, la) # TODO: dimensions?
if full_cov:
raise NotImplementedError, "Full covariance for Sparse GP predicted with uncertain inputs not implemented yet."
raise NotImplementedError("Full covariance for Sparse GP predicted with uncertain inputs not implemented yet.")
var = np.empty((Xnew.shape[0], la.shape[1], la.shape[1]))
di = np.diag_indices(la.shape[1])
else:

2
GPy/core/svgp.py
View file

@ -1,5 +1,5 @@
# Copyright (c) 2014, James Hensman, Alex Matthews
# Distributed under the terms of the GNU General public License, see LICENSE.txt
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..util import choleskies

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

@ -171,7 +171,7 @@ class Laplace(LatentFunctionInference):
#define the objective function (to be maximised)
def obj(Ki_f, f):
ll = -0.5*np.sum(np.dot(Ki_f.T, f)) + np.sum(likelihood.logpdf(f, Y, Y_metadata=Y_metadata))
print ll
print(ll)
if np.isnan(ll):
import ipdb; ipdb.set_trace() # XXX BREAKPOINT
return -np.inf

4
GPy/inference/optimization/stochastics.py
View file

@ -40,7 +40,7 @@ class SparseGPMissing(StochasticStorage):
bdict = {}
#For N > 1000 array2string default crops
opt = np.get_printoptions()
np.set_printoptions(threshold='nan')
np.set_printoptions(threshold=np.inf)
for d in range(self.Y.shape[1]):
inan = np.isnan(self.Y)[:, d]
arr_str = np.array2string(inan, np.inf, 0, True, '', formatter={'bool':lambda x: '1' if x else '0'})
@ -74,7 +74,7 @@ class SparseGPStochastics(StochasticStorage):
bdict = {}
if self.missing_data:
opt = np.get_printoptions()
np.set_printoptions(threshold='nan')
np.set_printoptions(threshold=np.inf)
for d in self.d:
inan = np.isnan(self.Y[:, d])
arr_str = np.array2string(inan,np.inf, 0,True, '',formatter={'bool':lambda x: '1' if x else '0'})

59
GPy/kern/_src/kern.py
View file

@ -70,6 +70,9 @@ class Kern(Parameterized):
"""
Compute the kernel function.
.. math::
K_{ij} = k(X_i, X_j)
:param X: the first set of inputs to the kernel
:param X2: (optional) the second set of arguments to the kernel. If X2
is None, this is passed throgh to the 'part' object, which
@ -77,22 +80,64 @@ class Kern(Parameterized):
"""
raise NotImplementedError
def Kdiag(self, X):
"""
The diagonal of the kernel matrix K
.. math::
Kdiag_{i} = k(X_i, X_i)
"""
raise NotImplementedError
def psi0(self, Z, variational_posterior):
"""
.. math::
\psi_0 = \sum_{i=0}^{n}E_{q(X)}[k(X_i, X_i)]
"""
return self.psicomp.psicomputations(self, Z, variational_posterior)[0]
def psi1(self, Z, variational_posterior):
"""
.. math::
\psi_1^{n,m} = E_{q(X)}[k(X_n, Z_m)]
"""
return self.psicomp.psicomputations(self, Z, variational_posterior)[1]
def psi2(self, Z, variational_posterior):
"""
.. math::
\psi_2^{m,m'} = \sum_{i=0}^{n}E_{q(X)}[ k(Z_m, X_i) k(X_i, Z_{m'})]
"""
return self.psicomp.psicomputations(self, Z, variational_posterior, return_psi2_n=False)[2]
def psi2n(self, Z, variational_posterior):
"""
.. math::
\psi_2^{n,m,m'} = E_{q(X)}[ k(Z_m, X_n) k(X_n, Z_{m'})]
Thus, we do not sum out n, compared to psi2
"""
return self.psicomp.psicomputations(self, Z, variational_posterior, return_psi2_n=True)[2]
def gradients_X(self, dL_dK, X, X2):
"""
.. math::
\\frac{\partial L}{\partial X} = \\frac{\partial L}{\partial K}\\frac{\partial K}{\partial X}
"""
raise NotImplementedError
def gradients_X_X2(self, dL_dK, X, X2):
return self.gradients_X(dL_dK, X, X2), self.gradients_X(dL_dK.T, X2, X)
def gradients_XX(self, dL_dK, X, X2):
"""
.. math::
\\frac{\partial^2 L}{\partial X\partial X_2} = \\frac{\partial L}{\partial K}\\frac{\partial^2 K}{\partial X\partial X_2}
"""
raise(NotImplementedError, "This is the second derivative of K wrt X and X2, and not implemented for this kernel")
def gradients_XX_diag(self, dL_dKdiag, X):
"""
The diagonal of the second derivative w.r.t. X and X2
"""
raise(NotImplementedError, "This is the diagonal of the second derivative of K wrt X and X2, and not implemented for this kernel")
def gradients_X_diag(self, dL_dKdiag, X):
"""
The diagonal of the derivative w.r.t. X
"""
raise NotImplementedError
def update_gradients_diag(self, dL_dKdiag, X):
@ -108,11 +153,17 @@ class Kern(Parameterized):
Set the gradients of all parameters when doing inference with
uncertain inputs, using expectations of the kernel.
The esential maths is
The essential maths is
dL_d{theta_i} = dL_dpsi0 * dpsi0_d{theta_i} +
dL_dpsi1 * dpsi1_d{theta_i} +
dL_dpsi2 * dpsi2_d{theta_i}
.. math::
\\frac{\partial L}{\partial \\theta_i} & = \\frac{\partial L}{\partial \psi_0}\\frac{\partial \psi_0}{\partial \\theta_i}\\
& \quad + \\frac{\partial L}{\partial \psi_1}\\frac{\partial \psi_1}{\partial \\theta_i}\\
& \quad + \\frac{\partial L}{\partial \psi_2}\\frac{\partial \psi_2}{\partial \\theta_i}
Thus, we push the different derivatives through the gradients of the psi
statistics. Be sure to set the gradients for all kernel
parameters here.
"""
dtheta = self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[0]
self.gradient[:] = dtheta

7
GPy/kern/_src/kernel_slice_operations.py
View file

@ -1,7 +1,11 @@
'''
Created on 11 Mar 2014
@author: maxz
@author: @mzwiessele
This module provides a meta class for the kernels. The meta class is for
slicing the inputs (X, X2) for the kernels, before K (or any other method involving X)
gets calls. The `active_dims` of a kernel decide which dimensions the kernel works on.
'''
from ...core.parameterization.parameterized import ParametersChangedMeta
import numpy as np
@ -19,6 +23,7 @@ class KernCallsViaSlicerMeta(ParametersChangedMeta):
put_clean(dct, 'update_gradients_full', _slice_update_gradients_full)
put_clean(dct, 'update_gradients_diag', _slice_update_gradients_diag)
put_clean(dct, 'gradients_X', _slice_gradients_X)
put_clean(dct, 'gradients_X_X2', _slice_gradients_X)
put_clean(dct, 'gradients_XX', _slice_gradients_XX)
put_clean(dct, 'gradients_XX_diag', _slice_gradients_X_diag)
put_clean(dct, 'gradients_X_diag', _slice_gradients_X_diag)

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

@ -17,7 +17,7 @@ class Linear(Kern):
.. math::
k(x,y) = \sum_{i=1}^input_dim \sigma^2_i x_iy_i
k(x,y) = \sum_{i=1}^{\\text{input_dim}} \sigma^2_i x_iy_i
:param input_dim: the number of input dimensions
:type input_dim: int

7
GPy/kern/_src/mlp.py
View file

@ -5,6 +5,7 @@ from .kern import Kern
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
import numpy as np
from ...util.linalg import tdot
from ...util.caching import Cache_this
four_over_tau = 2./np.pi
@ -40,6 +41,7 @@ class MLP(Kern):
self.link_parameters(self.variance, self.weight_variance, self.bias_variance)
@Cache_this(limit=20, ignore_args=())
def K(self, X, X2=None):
if X2 is None:
X_denom = np.sqrt(self._comp_prod(X)+1.)
@ -51,6 +53,7 @@ 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=())
def Kdiag(self, X):
"""Compute the diagonal of the covariance matrix for X."""
X_prod = self._comp_prod(X)
@ -73,6 +76,10 @@ class MLP(Kern):
"""Derivative of the covariance matrix with respect to X"""
return self._comp_grads(dL_dK, X, X2)[3]
def gradients_X_X2(self, dL_dK, X, X2):
"""Derivative of the covariance matrix with respect to X"""
return self._comp_grads(dL_dK, X, X2)[3:]
def gradients_X_diag(self, dL_dKdiag, X):
"""Gradient of diagonal of covariance with respect to X"""
return self._comp_grads_diag(dL_dKdiag, X)[3]

5
GPy/kern/_src/psi_comp/gaussherm.py
View file

@ -80,8 +80,9 @@ class PSICOMP_GH(PSICOMP):
dL_dkfu = (dL_dpsi1+ 2.*Kfu.dot(dL_dpsi2))*self.weights[i]
kern.update_gradients_full(dL_dkfu, X, Z)
dtheta += kern.gradient
dX += kern.gradients_X(dL_dkfu, X, Z)
dZ += kern.gradients_X(dL_dkfu.T, Z, X)
dX_i, dZ_i = kern.gradients_X_X2(dL_dkfu, X, Z)
dX += dX_i
dZ += dZ_i
dmu += dX
dS += dX*self.locs[i]/(2.*S_sq)
kern.gradient[:] = dtheta_old

32
GPy/kern/_src/psi_comp/rbf_psi_comp.py
View file

@ -6,12 +6,6 @@ import numpy as np
from GPy.util.caching import Cacher
def psicomputations(variance, lengthscale, Z, variational_posterior, return_psi2_n=False):
"""
Z - MxQ
mu - NxQ
S - NxQ
gamma - NxQ
"""
# here are the "statistics" for psi0, psi1 and psi2
# Produced intermediate results:
# _psi1 NxM
@ -26,12 +20,6 @@ def psicomputations(variance, lengthscale, Z, variational_posterior, return_psi2
return psi0, psi1, psi2
def __psi1computations(variance, lengthscale, Z, mu, S):
"""
Z - MxQ
mu - NxQ
S - NxQ
gamma - NxQ
"""
# here are the "statistics" for psi1
# Produced intermediate results:
# _psi1 NxM
@ -46,12 +34,6 @@ def __psi1computations(variance, lengthscale, Z, mu, S):
return _psi1
def __psi2computations(variance, lengthscale, Z, mu, S):
"""
Z - MxQ
mu - NxQ
S - NxQ
gamma - NxQ
"""
# here are the "statistics" for psi2
# Produced intermediate results:
# _psi2 MxM
@ -86,13 +68,6 @@ def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscal
return dL_dvar, dL_dlengscale, dL_dZ, dL_dmu, dL_dS
def _psi1compDer(dL_dpsi1, variance, lengthscale, Z, mu, S):
"""
dL_dpsi1 - NxM
Z - MxQ
mu - NxQ
S - NxQ
gamma - NxQ
"""
# here are the "statistics" for psi1
# Produced intermediate results: dL_dparams w.r.t. psi1
# _dL_dvariance 1
@ -118,13 +93,6 @@ def _psi1compDer(dL_dpsi1, variance, lengthscale, Z, mu, S):
return _dL_dvar, _dL_dl, _dL_dZ, _dL_dmu, _dL_dS
def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S):
"""
Z - MxQ
mu - NxQ
S - NxQ
gamma - NxQ
dL_dpsi2 - MxM
"""
# here are the "statistics" for psi2
# Produced the derivatives w.r.t. psi2:
# _dL_dvariance 1

25
GPy/kern/_src/psi_comp/rbf_psi_gpucomp.py
View file

@ -7,13 +7,6 @@ from ....util.caching import Cache_this
from . import PSICOMP_RBF
from ....util import gpu_init
try:
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
from ....util.linalg_gpu import sum_axis
except:
pass
gpu_code = """
// define THREADNUM
@ -242,6 +235,10 @@ gpu_code = """
class PSICOMP_RBF_GPU(PSICOMP_RBF):
def __init__(self, threadnum=256, blocknum=30, GPU_direct=False):
from pycuda.compiler import SourceModule
from ....util.gpu_init import initGPU
initGPU()
self.GPU_direct = GPU_direct
self.gpuCache = None
@ -264,7 +261,8 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
memo[id(self)] = s
return s
def _initGPUCache(self, N, M, Q):
def _initGPUCache(self, N, M, Q):
import pycuda.gpuarray as gpuarray
if self.gpuCache == None:
self.gpuCache = {
'l_gpu' :gpuarray.empty((Q,),np.float64,order='F'),
@ -320,13 +318,14 @@ class PSICOMP_RBF_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,))
def psicomputations(self, variance, lengthscale, Z, variational_posterior):
@Cache_this(limit=5, ignore_args=(0,))
def psicomputations(self, kern, Z, variational_posterior, return_psi2_n=False):
"""
Z - MxQ
mu - NxQ
S - NxQ
"""
variance, lengthscale = kern.variance, kern.lengthscale
N,M,Q = self.get_dimensions(Z, variational_posterior)
self._initGPUCache(N,M,Q)
self.sync_params(lengthscale, Z, variational_posterior.mean, variational_posterior.variance)
@ -355,8 +354,10 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
else:
return psi0, psi1_gpu.get(), psi2_gpu.get()
@Cache_this(limit=1, ignore_args=(0,1,2,3))
def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
@Cache_this(limit=5, 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
ARD = (len(lengthscale)!=1)
N,M,Q = self.get_dimensions(Z, variational_posterior)

2
GPy/kern/_src/psi_comp/sslinear_psi_comp.py
View file

@ -9,7 +9,7 @@ from ....util.linalg import tdot
import numpy as np
def psicomputations(variance, Z, variational_posterior):
def psicomputations(variance, Z, variational_posterior, return_psi2_n=False):
"""
Compute psi-statistics for ss-linear kernel
"""

24
GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
View file

@ -6,14 +6,7 @@ The module for psi-statistics for RBF kernel for Spike-and-Slab GPLVM
import numpy as np
from ....util.caching import Cache_this
from . import PSICOMP_RBF
from ....util import gpu_init
try:
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
from ....util.linalg_gpu import sum_axis
except:
pass
gpu_code = """
// define THREADNUM
@ -292,6 +285,11 @@ gpu_code = """
class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
def __init__(self, threadnum=128, blocknum=15, GPU_direct=False):
from pycuda.compiler import SourceModule
from ....util.gpu_init import initGPU
initGPU()
self.GPU_direct = GPU_direct
self.gpuCache = None
@ -314,7 +312,8 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
memo[id(self)] = s
return s
def _initGPUCache(self, N, M, Q):
def _initGPUCache(self, N, M, Q):
import pycuda.gpuarray as gpuarray
if self.gpuCache == None:
self.gpuCache = {
'l_gpu' :gpuarray.empty((Q,),np.float64,order='F'),
@ -377,12 +376,13 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
return variational_posterior.mean.shape[0], Z.shape[0], Z.shape[1]
@Cache_this(limit=1, ignore_args=(0,))
def psicomputations(self, variance, lengthscale, Z, variational_posterior):
def psicomputations(self, kern, Z, variational_posterior, return_psi2_n=False):
"""
Z - MxQ
mu - NxQ
S - NxQ
"""
variance, lengthscale = kern.variance, kern.lengthscale
N,M,Q = self.get_dimensions(Z, variational_posterior)
self._initGPUCache(N,M,Q)
self.sync_params(lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -409,8 +409,10 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
else:
return psi0, psi1_gpu.get(), psi2_gpu.get()
@Cache_this(limit=1, ignore_args=(0,1,2,3))
def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
@Cache_this(limit=1, 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
ARD = (len(lengthscale)!=1)
N,M,Q = self.get_dimensions(Z, variational_posterior)

27
GPy/kern/_src/stationary.py
View file

@ -25,13 +25,16 @@ class Stationary(Kern):
Stationary covariance fucntion depend only on r, where r is defined as
r = \sqrt{ \sum_{q=1}^Q (x_q - x'_q)^2 }
.. math::
r(x, x') = \\sqrt{ \\sum_{q=1}^Q (x_q - x'_q)^2 }
The covariance function k(x, x' can then be written k(r).
In this implementation, r is scaled by the lengthscales parameter(s):
r = \sqrt{ \sum_{q=1}^Q \frac{(x_q - x'_q)^2}{\ell_q^2} }.
.. math::
r(x, x') = \\sqrt{ \\sum_{q=1}^Q \\frac{(x_q - x'_q)^2}{\ell_q^2} }.
By default, there's only one lengthscale: seaprate lengthscales for each
dimension can be enables by setting ARD=True.
@ -39,11 +42,12 @@ class Stationary(Kern):
To implement a stationary covariance function using this class, one need
only define the covariance function k(r), and it derivative.
...
def K_of_r(self, r):
return foo
def dK_dr(self, r):
return bar
```
def K_of_r(self, r):
return foo
def dK_dr(self, r):
return bar
```
The lengthscale(s) and variance parameters are added to the structure automatically.
@ -128,7 +132,8 @@ class Stationary(Kern):
"""
Efficiently compute the scaled distance, r.
r = \sqrt( \sum_{q=1}^Q (x_q - x'q)^2/l_q^2 )
..math::
r = \sqrt( \sum_{q=1}^Q (x_q - x'q)^2/l_q^2 )
Note that if thre is only one lengthscale, l comes outside the sum. In
this case we compute the unscaled distance first (in a separate
@ -321,7 +326,7 @@ class OU(Stationary):
.. math::
k(r) = \\sigma^2 \exp(- r) \\ \\ \\ \\ \\text{ where } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
k(r) = \\sigma^2 \exp(- r) \\ \\ \\ \\ \\text{ where } r = \sqrt{\sum_{i=1}^{\text{input_dim}} \\frac{(x_i-y_i)^2}{\ell_i^2} }
"""
@ -341,7 +346,7 @@ class Matern32(Stationary):
.. math::
k(r) = \\sigma^2 (1 + \\sqrt{3} r) \exp(- \sqrt{3} r) \\ \\ \\ \\ \\text{ where } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
k(r) = \\sigma^2 (1 + \\sqrt{3} r) \exp(- \sqrt{3} r) \\ \\ \\ \\ \\text{ where } r = \sqrt{\sum_{i=1}^{\\text{input_dim}} \\frac{(x_i-y_i)^2}{\ell_i^2} }
"""
@ -388,7 +393,7 @@ class Matern52(Stationary):
.. math::
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r)
"""
"""
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Mat52'):
super(Matern52, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)

10
GPy/kern/_src/trunclinear.py
View file

@ -15,7 +15,7 @@ class TruncLinear(Kern):
.. math::
k(x,y) = \sum_{i=1}^input_dim \sigma^2_i \max(0, x_iy_i - \simga_q)
k(x,y) = \sum_{i=1}^input_dim \sigma^2_i \max(0, x_iy_i - \sigma_q)
:param input_dim: the number of input dimensions
:type input_dim: int
@ -54,7 +54,7 @@ class TruncLinear(Kern):
self.delta = Param('delta', delta)
self.add_parameter(self.variances)
self.add_parameter(self.delta)
@Cache_this(limit=2)
def K(self, X, X2=None):
XX = self.variances*self._product(X, X2)
@ -114,7 +114,7 @@ class TruncLinear_inf(Kern):
.. math::
k(x,y) = \sum_{i=1}^input_dim \sigma^2_i \max(0, x_iy_i - \simga_q)
k(x,y) = \sum_{i=1}^input_dim \sigma^2_i \max(0, x_iy_i - \sigma_q)
:param input_dim: the number of input dimensions
:type input_dim: int
@ -148,8 +148,8 @@ class TruncLinear_inf(Kern):
self.variances = Param('variances', variances, Logexp())
self.add_parameter(self.variances)
# @Cache_this(limit=2)
def K(self, X, X2=None):
tmp = self._product(X, X2)

2
GPy/likelihoods/gaussian.py
View file

@ -48,7 +48,7 @@ class Gaussian(Likelihood):
def betaY(self,Y,Y_metadata=None):
#TODO: ~Ricardo this does not live here
raise RuntimeError, "Please notify the GPy developers, this should not happen"
raise RuntimeError("Please notify the GPy developers, this should not happen")
return Y/self.gaussian_variance(Y_metadata)
def gaussian_variance(self, Y_metadata=None):

5
GPy/likelihoods/link_functions.py
View file

@ -2,6 +2,7 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import scipy
from ..util.univariate_Gaussian import std_norm_cdf, std_norm_pdf
import scipy as sp
from ..util.misc import safe_exp, safe_square, safe_cube, safe_quad, safe_three_times
@ -67,7 +68,7 @@ class Probit(GPTransformation):
.. math::
g(f) = \\Phi^{-1} (mu)
"""
def transf(self,f):
return std_norm_cdf(f)
@ -140,7 +141,7 @@ class Log_ex_1(GPTransformation):
"""
def transf(self,f):
return np.log1p(safe_exp(f))
return scipy.special.log1p(safe_exp(f))
def dtransf_df(self,f):
ef = safe_exp(f)

2
GPy/models/gp_kronecker_gaussian_regression.py
View file

@ -1,5 +1,5 @@
# Copyright (c) 2014, James Hensman, Alan Saul
# Distributed under the terms of the GNU General public License, see LICENSE.txt
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core.model import Model

6
GPy/models/gp_regression.py
View file

@ -26,12 +26,12 @@ class GPRegression(GP):
"""
def __init__(self, X, Y, kernel=None, Y_metadata=None, normalizer=None, noise_var=1.):
def __init__(self, X, Y, kernel=None, Y_metadata=None, normalizer=None, noise_var=1., mean_function=None):
if kernel is None:
kernel = kern.RBF(X.shape[1])
likelihood = likelihoods.Gaussian(variance=noise_var)
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer)
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer, mean_function=mean_function)

2
GPy/models/gp_var_gauss.py
View file

@ -1,5 +1,5 @@
# Copyright (c) 2014, James Hensman, Alan Saul
# Distributed under the terms of the GNU General public License, see LICENSE.txt
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import GP

2
GPy/models/gplvm.py
View file

@ -36,8 +36,10 @@ class GPLVM(GP):
likelihood = Gaussian()
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
self.X = Param('latent_mean', X)
self.link_parameter(self.X, index=0)
self._predictive_variable = self.X
def parameters_changed(self):
super(GPLVM, self).parameters_changed()

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

@ -304,7 +304,7 @@ def plot_magnification(model, labels=None, which_indices=None,
view = ImshowController(ax, plot_function,
(xmin, ymin, xmax, ymax),
resolution, aspect=aspect, interpolation='bilinear',
cmap=cm.gray)
cmap=cm.get_cmap('Greys'))
# make sure labels are in order of input:
ulabels = []

6
GPy/plotting/matplot_dep/models_plots.py
View file

@ -3,7 +3,7 @@
import numpy as np
from . import Tango
from base_plots import gpplot, x_frame1D, x_frame2D,gperrors
from .base_plots import gpplot, x_frame1D, x_frame2D,gperrors
from ...models.gp_coregionalized_regression import GPCoregionalizedRegression
from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
from scipy import sparse
@ -186,8 +186,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata)
print Ysim.shape
print Xnew.shape
print(Ysim.shape)
print(Xnew.shape)
for yi in Ysim.T:
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], '#3300FF', linewidth=0.25)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.

37
GPy/testing/cacher_tests.py Normal file
View file

@ -0,0 +1,37 @@
'''
Created on 4 Sep 2015
@author: maxz
'''
import unittest
from GPy.util.caching import Cacher
from pickle import PickleError
class Test(unittest.TestCase):
def setUp(self):
def op(x):
return x
self.cache = Cacher(op, 1)
def test_pickling(self):
self.assertRaises(PickleError, self.cache.__getstate__)
self.assertRaises(PickleError, self.cache.__setstate__)
def test_copy(self):
tmp = self.cache.__deepcopy__()
assert(tmp.operation is self.cache.operation)
self.assertEqual(tmp.limit, self.cache.limit)
def test_reset(self):
self.cache.reset()
self.assertDictEqual(self.cache.cached_input_ids, {}, )
self.assertDictEqual(self.cache.cached_outputs, {}, )
self.assertDictEqual(self.cache.inputs_changed, {}, )
def test_name(self):
assert(self.cache.__name__ == self.cache.operation.__name__)
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()

11
GPy/testing/cython_tests.py
View file

@ -6,13 +6,14 @@ from ..util.config import config
import unittest
try:
from . import linalg_cython
from ..util import linalg_cython
from ..util import choleskies_cython
config.set('cython', 'working', 'True')
except ImportError:
config.set('cython', 'working', 'False')
"""
These tests make sure that the opure python and cython codes work the same
These tests make sure that the pure python and cython codes work the same
"""
@unittest.skipIf(not config.getboolean('cython', 'working'),"Cython modules have not been built on this machine")
@ -67,8 +68,8 @@ class test_choleskies_backprop(np.testing.TestCase):
self.L = GPy.util.linalg.jitchol(A)
self.dL = np.random.randn(10,10)
def test(self):
r1 = GPy.util.choleskies._backprop_gradient_pure(self.dL, self.L)
r2 = GPy.util.choleskies.choleskies_cython.backprop_gradient(self.dL, self.L)
r3 = GPy.util.choleskies.choleskies_cython.backprop_gradient_par_c(self.dL, self.L)
r1 = choleskies._backprop_gradient_pure(self.dL, self.L)
r2 = choleskies_cython.backprop_gradient(self.dL, self.L)
r3 = choleskies_cython.backprop_gradient_par_c(self.dL, self.L)
np.testing.assert_allclose(r1, r2)
np.testing.assert_allclose(r1, r3)

99
GPy/testing/gp_tests.py Normal file
View file

@ -0,0 +1,99 @@
'''
Created on 4 Sep 2015
@author: maxz
'''
import unittest
import numpy as np, GPy
from GPy.core.parameterization.variational import NormalPosterior
class Test(unittest.TestCase):
def setUp(self):
np.random.seed(12345)
self.N = 20
self.N_new = 50
self.D = 1
self.X = np.random.uniform(-3., 3., (self.N, 1))
self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
self.X_new = np.random.uniform(-3., 3., (self.N_new, 1))
def test_setxy_bgplvm(self):
k = GPy.kern.RBF(1)
m = GPy.models.BayesianGPLVM(self.Y, 2, kernel=k)
mu, var = m.predict(m.X)
X = m.X.copy()
Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy())
m.set_XY(Xnew, m.Y[:10])
assert(m.checkgrad())
m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X)
np.testing.assert_allclose(mu, mu2)
np.testing.assert_allclose(var, var2)
def test_setxy_gplvm(self):
k = GPy.kern.RBF(1)
m = GPy.models.GPLVM(self.Y, 2, kernel=k)
mu, var = m.predict(m.X)
X = m.X.copy()
Xnew = X[:10].copy()
m.set_XY(Xnew, m.Y[:10])
assert(m.checkgrad())
m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X)
np.testing.assert_allclose(mu, mu2)
np.testing.assert_allclose(var, var2)
def test_setxy_gp(self):
k = GPy.kern.RBF(1)
m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
mu, var = m.predict(m.X)
X = m.X.copy()
m.set_XY(m.X[:10], m.Y[:10])
assert(m.checkgrad())
m.set_XY(X, self.Y)
mu2, var2 = m.predict(m.X)
np.testing.assert_allclose(mu, mu2)
np.testing.assert_allclose(var, var2)
def test_mean_function(self):
from GPy.core.parameterization.param import Param
from GPy.core.mapping import Mapping
class Parabola(Mapping):
def __init__(self, variance, degree=2, name='parabola'):
super(Parabola, self).__init__(1, 1, name)
self.variance = Param('variance', np.ones(degree+1) * variance)
self.degree = degree
self.link_parameter(self.variance)
def f(self, X):
p = self.variance[0] * np.ones(X.shape)
for i in range(1, self.degree+1):
p += self.variance[i] * X**(i)
return p
def gradients_X(self, dL_dF, X):
grad = np.zeros(X.shape)
for i in range(1, self.degree+1):
grad += (i) * self.variance[i] * X**(i-1)
return grad
def update_gradients(self, dL_dF, X):
for i in range(self.degree+1):
self.variance.gradient[i] = (dL_dF * X**(i)).sum(0)
X = np.linspace(-2, 2, 100)[:, None]
k = GPy.kern.RBF(1)
k.randomize()
p = Parabola(.3)
p.randomize()
Y = p.f(X) + np.random.multivariate_normal(np.zeros(X.shape[0]), k.K(X)+np.eye(X.shape[0])*1e-8)[:,None] + np.random.normal(0, .1, (X.shape[0], 1))
m = GPy.models.GPRegression(X, Y, mean_function=p)
m.randomize()
assert(m.checkgrad())
_ = m.predict(m.X)
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()

2
GPy/testing/kernel_tests.py
View file

@ -11,7 +11,7 @@ from ..util.config import config
verbose = 0
try:
from . import linalg_cython
from ..util import linalg_cython
config.set('cython', 'working', 'True')
except ImportError:
config.set('cython', 'working', 'False')

10
GPy/testing/link_function_tests.py
View file

@ -1,5 +1,5 @@
import numpy as np
import scipy as sp
import scipy
from scipy.special import cbrt
from GPy.models import GradientChecker
_lim_val = np.finfo(np.float64).max
@ -92,18 +92,18 @@ class LinkFunctionTests(np.testing.TestCase):
link = Log_ex_1()
lim_of_inf = _lim_val_exp
np.testing.assert_almost_equal(np.log1p(np.exp(self.mid_f)), link.transf(self.mid_f))
assert np.isinf(np.log1p(np.exp(np.log(self.f_upper_lim))))
np.testing.assert_almost_equal(scipy.special.log1p(np.exp(self.mid_f)), link.transf(self.mid_f))
assert np.isinf(scipy.special.log1p(np.exp(np.log(self.f_upper_lim))))
#Check the clipping works
np.testing.assert_almost_equal(link.transf(self.f_lower_lim), 0, decimal=5)
#Need to look at most significant figures here rather than the decimals
np.testing.assert_approx_equal(link.transf(self.f_upper_lim), np.log1p(_lim_val), significant=5)
np.testing.assert_approx_equal(link.transf(self.f_upper_lim), scipy.special.log1p(_lim_val), significant=5)
self.check_overflow(link, lim_of_inf)
#Check that it would otherwise fail
beyond_lim_of_inf = lim_of_inf + 10.0
old_err_state = np.seterr(over='ignore')
self.assertTrue(np.isinf(np.log1p(np.exp(beyond_lim_of_inf))))
self.assertTrue(np.isinf(scipy.special.log1p(np.exp(beyond_lim_of_inf))))
np.seterr(**old_err_state)

5
GPy/testing/misc_tests.py
View file

@ -1,3 +1,4 @@
from __future__ import print_function
import numpy as np
import scipy as sp
import GPy
@ -18,8 +19,8 @@ class MiscTests(np.testing.TestCase):
assert np.isinf(np.exp(self._lim_val_exp + 1))
assert np.isfinite(GPy.util.misc.safe_exp(self._lim_val_exp + 1))
print w
print len(w)
print(w)
print(len(w))
assert len(w)==1 # should have one overflow warning
def test_safe_exp_lower(self):

63
GPy/testing/model_tests.py
View file

@ -55,13 +55,59 @@ class MiscTests(unittest.TestCase):
np.testing.assert_allclose(mu1, (mu2*std)+mu)
np.testing.assert_allclose(var1, var2)
q50n = m.predict_quantiles(m.X, (50,))
q50 = m2.predict_quantiles(m2.X, (50,))
np.testing.assert_allclose(q50n[0], (q50[0]*std)+mu)
def check_jacobian(self):
try:
import autograd.numpy as np, autograd as ag, GPy, matplotlib.pyplot as plt
from GPy.models import GradientChecker, GPRegression
except:
raise self.skipTest("autograd not available to check gradients")
def k(X, X2, alpha=1., lengthscale=None):
if lengthscale is None:
lengthscale = np.ones(X.shape[1])
exp = 0.
for q in range(X.shape[1]):
exp += ((X[:, [q]] - X2[:, [q]].T)/lengthscale[q])**2
#exp = np.sqrt(exp)
return alpha * np.exp(-.5*exp)
dk = ag.elementwise_grad(lambda x, x2: k(x, x2, alpha=ke.variance.values, lengthscale=ke.lengthscale.values))
dkdk = ag.elementwise_grad(dk, argnum=1)
ke = GPy.kern.RBF(1, ARD=True)
#ke.randomize()
ke.variance = .2#.randomize()
ke.lengthscale[:] = .5
ke.randomize()
X = np.linspace(-1, 1, 1000)[:,None]
X2 = np.array([[0.]]).T
np.testing.assert_allclose(ke.gradients_X([[1.]], X, X), dk(X, X))
np.testing.assert_allclose(ke.gradients_XX([[1.]], X, X).sum(0), dkdk(X, X))
np.testing.assert_allclose(ke.gradients_X([[1.]], X, X2), dk(X, X2))
np.testing.assert_allclose(ke.gradients_XX([[1.]], X, X2).sum(0), dkdk(X, X2))
m = GPRegression(self.X, self.Y)
def f(x):
m.X[:] = x
return m.log_likelihood()
def df(x):
m.X[:] = x
return m.kern.gradients_X(m.grad_dict['dL_dK'], X)
def ddf(x):
m.X[:] = x
return m.kern.gradients_XX(m.grad_dict['dL_dK'], X).sum(0)
gc = GradientChecker(f, df, self.X)
gc2 = GradientChecker(df, ddf, self.X)
assert(gc.checkgrad())
assert(gc2.checkgrad())
def test_sparse_raw_predict(self):
k = GPy.kern.RBF(1)
m = GPy.models.SparseGPRegression(self.X, self.Y, kernel=k)
m.randomize()
Z = m.Z[:]
X = self.X[:]
# Not easy to check if woodbury_inv is correct in itself as it requires a large derivation and expression
Kinv = m.posterior.woodbury_inv
@ -147,11 +193,24 @@ class MiscTests(unittest.TestCase):
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
mul, varl = m.predict(m.X)
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
m2 = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
m2.kern.rbf.lengthscale[:] = 1e6
m2.X[:] = m.X.param_array
m2.likelihood[:] = m.likelihood[:]
m2.kern.white[:] = m.kern.white[:]
mu, var = m.predict(m.X)
np.testing.assert_allclose(mul, mu)
np.testing.assert_allclose(varl, var)
q50 = m.predict_quantiles(m.X, (50,))
np.testing.assert_allclose(mul, q50[0])
def test_likelihood_replicate_kern(self):
m = GPy.models.GPRegression(self.X, self.Y)

6
GPy/testing/parameterized_tests.py
View file

@ -248,10 +248,16 @@ class ParameterizedTest(unittest.TestCase):
m.randomize()
self.assertEqual(m.p1, val)
def test_checkgrad(self):
assert(self.testmodel.kern.checkgrad())
assert(self.testmodel.kern.lengthscale.checkgrad())
assert(self.testmodel.likelihood.lengthscale.checkgrad())
def test_printing(self):
print(self.test1)
print(self.param)
print(self.test1[''])
print(self.testmodel.hierarchy_name(False))
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.test_add_parameter']

2
GPy/testing/run_coverage.sh
View file

@ -1 +1 @@
nosetests . --with-coverage --cover-html --cover-html-dir=coverage --cover-package=GPy --cover-erase
nosetests . --with-coverage --logging-level=INFO --cover-html --cover-html-dir=coverage --cover-package=GPy --cover-erase

1
GPy/util/__init__.py
View file

@ -15,6 +15,5 @@ from . import caching
from . import diag
from . import initialization
from . import multioutput
from . import linalg_gpu
from . import parallel

2
GPy/util/choleskies.py
View file

@ -1,5 +1,5 @@
# Copyright James Hensman and Max Zwiessele 2014, 2015
# Licensed under the GNU GPL version 3.0
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from . import linalg

63
GPy/util/erfcx.py
View file

@ -1,63 +0,0 @@
## Copyright (C) 2010 Soren Hauberg
##
## Copyright James Hensman 2011
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or (at
## your option) any later version.
##
## This program is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program; see the file COPYING. If not, see
## <http://www.gnu.org/licenses/>.
import numpy as np
def erfcx (arg):
arg = np.atleast_1d(arg)
assert(np.all(np.isreal(arg)),"erfcx: input must be real")
## Get precision dependent thresholds -- or not :p
xneg = -26.628;
xmax = 2.53e+307;
## Allocate output
result = np.zeros (arg.shape)
## Find values where erfcx can be evaluated
idx_neg = (arg < xneg);
idx_max = (arg > xmax);
idx = ~(idx_neg | idx_max);
arg = arg [idx];
## Perform the actual computation
t = 3.97886080735226 / (np.abs (arg) + 3.97886080735226);
u = t - 0.5;
y = (((((((((u * 0.00127109764952614092 + 1.19314022838340944e-4) * u \
- 0.003963850973605135) * u - 8.70779635317295828e-4) * u + \
0.00773672528313526668) * u + 0.00383335126264887303) * u - \
0.0127223813782122755) * u - 0.0133823644533460069) * u + \
0.0161315329733252248) * u + 0.0390976845588484035) * u + \
0.00249367200053503304;
y = ((((((((((((y * u - 0.0838864557023001992) * u - \
0.119463959964325415) * u + 0.0166207924969367356) * u + \
0.357524274449531043) * u + 0.805276408752910567) * u + \
1.18902982909273333) * u + 1.37040217682338167) * u + \
1.31314653831023098) * u + 1.07925515155856677) * u + \
0.774368199119538609) * u + 0.490165080585318424) * u + \
0.275374741597376782) * t;
y [arg < 0] = 2 * np.exp (arg [arg < 0]**2) - y [arg < 0];
## Put the results back into something with the same size is the original input
result [idx] = y;
result [idx_neg] = np.inf;
## result (idx_max) = 0; # not needed as we initialise with zeros
return(result)

44
GPy/util/gpu_init.py
View file

@ -16,33 +16,27 @@ try:
except:
pass
try:
if MPI_enabled and MPI.COMM_WORLD.size>1:
from .parallel import get_id_within_node
gpuid = get_id_within_node()
import pycuda.driver
pycuda.driver.init()
if gpuid>=pycuda.driver.Device.count():
print('['+MPI.Get_processor_name()+'] more processes than the GPU numbers!')
#MPI.COMM_WORLD.Abort()
raise
gpu_device = pycuda.driver.Device(gpuid)
gpu_context = gpu_device.make_context()
gpu_initialized = True
else:
import pycuda.autoinit
gpu_initialized = True
except:
pass
try:
from scikits.cuda import cublas
import scikits.cuda.linalg as culinalg
culinalg.init()
cublas_handle = cublas.cublasCreate()
except:
pass
def initGPU():
try:
if MPI_enabled and MPI.COMM_WORLD.size>1:
from .parallel import get_id_within_node
gpuid = get_id_within_node()
import pycuda.driver
pycuda.driver.init()
if gpuid>=pycuda.driver.Device.count():
print('['+MPI.Get_processor_name()+'] more processes than the GPU numbers!')
raise
gpu_device = pycuda.driver.Device(gpuid)
gpu_context = gpu_device.make_context()
gpu_initialized = True
else:
import pycuda.autoinit
gpu_initialized = True
except:
pass
def closeGPU():
if gpu_context is not None:
gpu_context.detach()

7
GPy/util/linalg_gpu.py
View file

@ -61,12 +61,5 @@ try:
except:
pass
try:
import scikits.cuda.linalg as culinalg
from scikits.cuda import cublas
from scikits.cuda.cula import culaExceptions
except:
pass

15
GPy/util/ln_diff_erfs.py
View file

@ -2,12 +2,7 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
#Only works for scipy 0.12+
try:
from scipy.special import erfcx, erf
except ImportError:
from scipy.special import erf
from .erfcx import erfcx
from scipy.special import erfcx, erf
import numpy as np
def ln_diff_erfs(x1, x2, return_sign=False):
@ -17,7 +12,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
:param x2 : argument of the negative erf
:type x2: ndarray
:return: tuple containing (log(abs(erf(x1) - erf(x2))), sign(erf(x1) - erf(x2)))
Based on MATLAB code that was written by Antti Honkela and modified by David Luengo and originally derived from code by Neil Lawrence.
"""
x1 = np.require(x1).real
@ -26,7 +21,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
x1 = np.reshape(x1, (1, 1))
if x2.size==1:
x2 = np.reshape(x2, (1, 1))
if x1.shape==x2.shape:
v = np.zeros_like(x1)
else:
@ -36,7 +31,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
v = np.zeros(x1.shape)
else:
raise ValueError("This function does not broadcast unless provided with a scalar.")
if x1.size == 1:
x1 = np.tile(x1, x2.shape)
@ -92,7 +87,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
v[flags] = np.log(erfcx(-_x1)
-erfcx(-_x2)*np.exp(_x1**2
-_x2**2))-_x1**2
# TODO: switch back on log of zero warnings.
if return_sign:

19
GPy/util/parallel.py
View file

@ -39,3 +39,22 @@ def divide_data(datanum, rank, size):
size = datanum/size
offset = size*rank+residue
return offset, offset+size, datanum_list
def optimize_parallel(model, optimizer=None, messages=True, max_iters=1000, outpath='.', interval=100, name=None):
from math import ceil
from datetime import datetime
import os
if name is None: name = model.name
stop = 0
for iter in range(int(ceil(float(max_iters)/interval))):
model.optimize(optimizer=optimizer, messages= True if messages and model.mpi_comm.rank==model.mpi_root else False, max_iters=interval)
if model.mpi_comm.rank==model.mpi_root:
timenow = datetime.now()
timestr = timenow.strftime('%Y:%m:%d_%H:%M:%S')
model.save(os.path.join(outpath, name+'_'+timestr+'.h5'))
opt = model.optimization_runs[-1]
if opt.funct_eval<opt.max_f_eval:
stop = 1
stop = model.mpi_comm.bcast(stop, root=model.mpi_root)
if stop:
break

37
README.md
View file

@ -10,6 +10,26 @@ A Gaussian processes framework in Python.
Continuous integration status: ![CI status](https://travis-ci.org/SheffieldML/GPy.png)
### Getting started: installing with pip
We are now requiring the newest version of ![scipy](http://www.scipy.org/) and thus, we strongly recommend using
the ![anaconda python distribution](http://continuum.io/downloads).
With anaconda you can install GPy by the following:
conda update scipy
pip install gpy
. We've also had luck with ![enthought](http://www.enthought.com), although enthought currently (as of 8th Sep. 2015) does not support scipy 0.16.
On a fresh install of windows 8.1, we downloaded the Anaconda python distribution, started the anaconda command prompt and typed
conda update scipy
pip install GPy
Everything seems to work: from here you can type `ipython` and then `import GPy; GPy.tests()`. Working as of 21/11/14
If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on.
### Python 3 Compatibility
Work is underway to make GPy run on Python 3.
@ -48,23 +68,6 @@ The command `python3 setup.py build_ext --inplace` builds the Cython extensions.
We like to pronounce it 'Gee-pie'.
### Getting started: installing with pip
The simplest way to install GPy is using pip. Ubuntu users can do:
sudo apt-get install python-pip
pip install gpy
On windows, we recommend the ![anaconda python distribution](http://continuum.io/downloads). We've also had luck with ![enthought](http://www.enthought.com).
On a fresh install of windows 8.1, we downloaded the Anaconda python distribution, started the anaconda command prompt and typed
pip install GPy
Everything seems to work: from here you can type `ipython` and then `import GPy; GPy.tests()`. Working as of 21/11/14
If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on.
### Ubuntu hackers
For the most part, the developers are using ubuntu. To install the required packages:

14
doc/conf.py
View file

@ -92,13 +92,17 @@ print "Importing extensions"
extensions = ['sphinx.ext.autodoc',
#'sphinx.ext.doctest'
'sphinx.ext.viewcode',
'sphinx.ext.pngmath',
#'sphinx.ext.pngmath',
'sphinx.ext.mathjax',
'ipython_directive',
'ipython_console_highlighting'
#'matplotlib.sphinxext.plot_directive'
]
plot_formats = [('png', 80), ('pdf', 50)]
#pngmath_latex_preamble=r'\usepackage[active]{preview}\usepackage{MnSymbol}' # + other custom stuff for inline math, such as non-default math fonts etc.
#pngmath_use_preview=True
print "finished importing"
##############################################################################
@ -218,7 +222,7 @@ exclude_patterns = ['_build']
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
pygments_style = 'default'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
@ -228,10 +232,10 @@ pygments_style = 'sphinx'
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
html_theme = 'alabaster'
# This is to revert to the default theme on readthedocs
html_style = '/default.css'
html_style = '/alabaster.css'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
@ -317,7 +321,7 @@ latex_elements = {
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
'preamble': '\\usepackage{MnSymbol}',
'preamble': '\\usepackage{MnSymbol,amsmath}',
}
# Grouping the document tree into LaTeX files. List of tuples

2
doc/index.rst
View file

@ -25,7 +25,7 @@ The code can be found on our `Github project page <https://github.com/SheffieldM
Contents:
.. toctree::
:maxdepth: 2
:maxdepth: 3
GPy

226
doc/log.txt
View file

@ -1,176 +1,222 @@
/home/alans/Work/GPy/GPy/__init__.py:docstring of GPy.load:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.optimize:8: ERROR: Unknown interpreted text role "module".
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/core/model.py:docstring of GPy.core.model.Model.optimize_restarts:29: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/doc/GPy.core.rst:65: WARNING: autodoc: failed to import module u'GPy.core.symbolic'; the following exception was raised:
/home/maxz/Documents/gpy/GPy/__init__.py:docstring of GPy.load:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.optimize:8: ERROR: Unknown interpreted text role "module".
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predict_wishard_embedding:6: WARNING: Field list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:10: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/core/model.py:docstring of GPy.core.model.Model.optimize_restarts:29: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/doc/GPy.core.rst:57: WARNING: autodoc: failed to import module u'GPy.core.svigp'; the following exception was raised:
Traceback (most recent call last):
File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
File "/home/alans/Work/GPy/GPy/core/symbolic.py", line 10, in <module>
ImportError: No module named svigp
/home/maxz/Documents/gpy/doc/GPy.core.rst:65: WARNING: autodoc: failed to import module u'GPy.core.symbolic'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
File "/home/maxz/Documents/gpy/GPy/core/symbolic.py", line 10, in <module>
from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
ImportError: No module named lambdify
/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Indexable.unset_priors:1: WARNING: Inline emphasis start-string without end-string.
/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Nameable.hierarchy_name:4: WARNING: Field list ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Observable.notify_observers:5: SEVERE: Unexpected section title or transition.
^^^^^^^^^^^^^^^^
/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Observable.notify_observers:6: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline emphasis start-string without end-string.
/home/alans/Work/GPy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline strong start-string without end-string.
/home/alans/Work/GPy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:18: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:20: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/core/parameterization/ties_and_remappings.py:docstring of GPy.core.parameterization.ties_and_remappings.Tie:18: SEVERE: Unexpected section title or transition.
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Indexable.unset_priors:1: WARNING: Inline emphasis start-string without end-string.
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Nameable.hierarchy_name:4: WARNING: Field list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline emphasis start-string without end-string.
/home/maxz/Documents/gpy/GPy/core/parameterization/parameter_core.py:docstring of GPy.core.parameterization.parameter_core.Parameterizable.traverse:1: WARNING: Inline strong start-string without end-string.
/home/maxz/Documents/gpy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:18: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/GPy/core/parameterization/parameterized.py:docstring of GPy.core.parameterization.parameterized.Parameterized:20: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/core/parameterization/ties_and_remappings.py:docstring of GPy.core.parameterization.ties_and_remappings.Tie:18: SEVERE: Unexpected section title or transition.
================================
/home/alans/Work/GPy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation.
/home/alans/Work/GPy/doc/GPy.kern._src.rst:73: WARNING: autodoc: failed to import module u'GPy.kern._src.hierarchical'; the following exception was raised:
/home/maxz/Documents/gpy/doc/GPy.examples.rst:50: WARNING: autodoc: failed to import module u'GPy.examples.stochastic'; the following exception was raised:
Traceback (most recent call last):
File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
File "/home/alans/Work/GPy/GPy/kern/_src/hierarchical.py", line 4, in <module>
from kernpart import Kernpart
ImportError: No module named kernpart
/home/alans/Work/GPy/GPy/kern/_src/independent_outputs.py:docstring of GPy.kern._src.independent_outputs.IndependentOutputs:9: WARNING: Field list ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:22: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:23: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/doc/GPy.kern._src.rst:177: WARNING: autodoc: failed to import module u'GPy.kern._src.symbolic'; the following exception was raised:
ImportError: No module named stochastic
/home/maxz/Documents/gpy/doc/GPy.examples.rst:58: WARNING: autodoc: failed to import module u'GPy.examples.tutorials'; the following exception was raised:
Traceback (most recent call last):
File "/home/alans/anaconda/envs/GPy/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 335, in import_object
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
File "/home/alans/Work/GPy/GPy/kern/_src/symbolic.py", line 5, in <module>
ImportError: No module named tutorials
/home/maxz/Documents/gpy/doc/GPy.inference.latent_function_inference.rst:82: WARNING: autodoc: failed to import module u'GPy.inference.latent_function_inference.var_dtc_gpu'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named var_dtc_gpu
/home/maxz/Documents/gpy/doc/GPy.inference.optimization.rst:42: WARNING: autodoc: failed to import module u'GPy.inference.optimization.sgd'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named sgd
/home/maxz/Documents/gpy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/doc/GPy.kern._src.rst:73: WARNING: autodoc: failed to import module u'GPy.kern._src.hierarchical'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named hierarchical
/home/maxz/Documents/gpy/GPy/kern/_src/independent_outputs.py:docstring of GPy.kern._src.independent_outputs.IndependentOutputs:9: WARNING: Field list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:24: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:22: WARNING: Inline literal start-string without end-string.
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:25: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Inline literal start-string without end-string.
/home/maxz/Documents/gpy/GPy/kern/_src/stationary.py:docstring of GPy.kern._src.stationary.Stationary:27: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/maxz/Documents/gpy/doc/GPy.kern._src.rst:177: WARNING: autodoc: failed to import module u'GPy.kern._src.symbolic'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
File "/home/maxz/Documents/gpy/GPy/kern/_src/symbolic.py", line 5, in <module>
from ...core.symbolic import Symbolic_core
File "/home/alans/Work/GPy/GPy/core/symbolic.py", line 10, in <module>
File "/home/maxz/Documents/gpy/GPy/core/symbolic.py", line 10, in <module>
from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
ImportError: No module named lambdify
/home/alans/Work/GPy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:13: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:18: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/models/gp_var_gauss.py:docstring of GPy.models.gp_var_gauss.GPVariationalGaussianApproximation:9: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Field list ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/alans/Work/GPy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:34: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/models/sparse_gp_minibatch.py:docstring of GPy.models.sparse_gp_minibatch.SparseGPMiniBatch:2: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imread:6: SEVERE: Unexpected section title.
/home/maxz/Documents/gpy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:13: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/GPy/models/gp_kronecker_gaussian_regression.py:docstring of GPy.models.gp_kronecker_gaussian_regression.GPKroneckerGaussianRegression:18: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/doc/GPy.models.rst:66: WARNING: autodoc: failed to import module u'GPy.models.gp_multioutput_regression'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named gp_multioutput_regression
/home/maxz/Documents/gpy/GPy/models/gp_var_gauss.py:docstring of GPy.models.gp_var_gauss.GPVariationalGaussianApproximation:9: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Field list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:32: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/maxz/Documents/gpy/GPy/models/mrd.py:docstring of GPy.models.mrd.MRD:34: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/doc/GPy.models.rst:138: WARNING: autodoc: failed to import module u'GPy.models.sparse_gp_multioutput_regression'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named sparse_gp_multioutput_regression
/home/maxz/Documents/gpy/doc/GPy.models.rst:178: WARNING: autodoc: failed to import module u'GPy.models.svigp_regression'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named svigp_regression
/home/maxz/Documents/gpy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imread:6: SEVERE: Unexpected section title.
Examples
--------
/home/alans/Work/GPy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imsave:4: SEVERE: Unexpected section title.
/home/maxz/Documents/gpy/GPy/plotting/matplot_dep/netpbmfile.py:docstring of GPy.plotting.matplot_dep.netpbmfile.imsave:4: SEVERE: Unexpected section title.
Examples
--------
/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:6: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:7: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:7: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:9: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/util/datasets.py:docstring of GPy.util.datasets.hapmap3:7: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.diag".
/home/alans/Work/GPy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imread:6: SEVERE: Unexpected section title.
/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:6: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_checkgrad:7: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:7: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:9: ERROR: Unexpected indentation.
docstring of GPy.util.datasets.hapmap3:7: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/doc/GPy.util.rst:74: WARNING: autodoc: failed to import module u'GPy.util.erfcx'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named erfcx
/home/maxz/Documents/gpy/doc/GPy.util.rst:146: WARNING: autodoc: failed to import module u'GPy.util.mpi'; the following exception was raised:
Traceback (most recent call last):
File "/home/maxz/anaconda/lib/python2.7/site-packages/Sphinx-1.3.1-py2.7.egg/sphinx/ext/autodoc.py", line 385, in import_object
__import__(self.modname)
ImportError: No module named mpi
/home/maxz/Documents/gpy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imread:6: SEVERE: Unexpected section title.
Examples
--------
/home/alans/Work/GPy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imsave:4: SEVERE: Unexpected section title.
/home/maxz/Documents/gpy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imsave:4: SEVERE: Unexpected section title.
Examples
--------
/home/alans/Work/GPy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.subarray_and_sorting".
/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:8: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:11: SEVERE: Unexpected section title.
/home/maxz/Documents/gpy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.subarray_and_sorting".
/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:8: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:11: SEVERE: Unexpected section title.
Examples:
=========
/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:19: ERROR: Unexpected indentation.
/home/alans/Work/GPy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:21: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/alans/Work/GPy/doc/installation.rst:22: ERROR: Unexpected indentation.
/home/alans/Work/GPy/doc/installation.rst:27: ERROR: Unexpected indentation.
/home/alans/Work/GPy/doc/tuto_creating_new_kernels.rst:58: WARNING: Inline literal start-string without end-string.
/home/alans/Work/GPy/doc/tuto_creating_new_models.rst:24: ERROR: Unknown target name: "parameterized".
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:19: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:21: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/maxz/Documents/gpy/GPy/util/block_matrices.py:docstring of GPy.util.block_matrices.block_dot:3: ERROR: Undefined substitution referenced: "A11.B11|B12.B12".
/home/maxz/Documents/gpy/GPy/util/block_matrices.py:docstring of GPy.util.block_matrices.block_dot:3: ERROR: Undefined substitution referenced: "A21.B21|A22.B22".
/home/maxz/Documents/gpy/doc/installation.rst:22: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/doc/installation.rst:27: ERROR: Unexpected indentation.
/home/maxz/Documents/gpy/doc/tuto_creating_new_kernels.rst:58: WARNING: Inline literal start-string without end-string.
/home/maxz/Documents/gpy/doc/tuto_creating_new_models.rst:24: ERROR: Unknown target name: "parameterized".
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
Interacting with Parameters:
=======================
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short.
Interacting with Parameters:
=======================
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:109: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:109: WARNING: Title underline too short.
Regular expressions
----------------
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short.
Setting and fetching parameters `parameter_array`
------------------------------------------
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:164: WARNING: Title underline too short.
Setting and fetching parameters `parameter_array`
------------------------------------------
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short.
Getting the model parameter's gradients
============================
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:220: WARNING: Title underline too short.
Getting the model parameter's gradients
============================
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short.
Adjusting the model's constraints
================================
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:236: WARNING: Title underline too short.
Adjusting the model's constraints
================================
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short.
Available Constraints
==============
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:287: WARNING: Title underline too short.
Available Constraints
==============
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short.
Tying Parameters
============
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:299: WARNING: Title underline too short.
Tying Parameters
============
/home/alans/Work/GPy/doc/tuto_parameterized.rst:3: WARNING: Title overline too short.
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:3: WARNING: Title overline too short.
*******************
Parameterization handling
*******************
/home/alans/Work/GPy/doc/tuto_parameterized.rst:10: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:10: WARNING: Title underline too short.
Parameter handles
==============
/home/alans/Work/GPy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short.
:py:class:`~GPy.core.parameterization.parameterized.Parameterized`
==========
/home/alans/Work/GPy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:16: WARNING: Title underline too short.
:py:class:`~GPy.core.parameterization.parameterized.Parameterized`
==========
/home/alans/Work/GPy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short.
:py:class:`~GPy.core.parameterization.param.Param`
===========
/home/alans/Work/GPy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short.
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:21: WARNING: Title underline too short.
:py:class:`~GPy.core.parameterization.param.Param`
===========
/home/alans/Work/GPy/doc/installation.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/kernel_implementation.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/modules.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/tuto_GP_regression.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/tuto_creating_new_kernels.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/tuto_creating_new_models.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/tuto_kernel_overview.rst:: WARNING: document isn't included in any toctree
/home/alans/Work/GPy/doc/tuto_parameterized.rst:: WARNING: document isn't included in any toctree
WARNING: dvipng command 'dvipng' cannot be run (needed for math display), check the pngmath_dvipng setting
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:336: WARNING: undefined label: creating_new_kernels (if the link has no caption the label must precede a section header)
WARNING: html_static_path entry u'/home/alans/Work/GPy/doc/_static' does not exist
/home/maxz/Documents/gpy/doc/installation.rst:: WARNING: document isn't included in any toctree
/home/maxz/Documents/gpy/doc/kernel_implementation.rst:: WARNING: document isn't included in any toctree
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/home/maxz/Documents/gpy/doc/tuto_GP_regression.rst:: WARNING: document isn't included in any toctree
/home/maxz/Documents/gpy/doc/tuto_creating_new_kernels.rst:: WARNING: document isn't included in any toctree
/home/maxz/Documents/gpy/doc/tuto_creating_new_models.rst:: WARNING: document isn't included in any toctree
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:: WARNING: document isn't included in any toctree
/home/maxz/Documents/gpy/doc/tuto_kernel_overview.rst:: WARNING: document isn't included in any toctree
/home/maxz/Documents/gpy/doc/tuto_parameterized.rst:: WARNING: document isn't included in any toctree
/home/maxz/Documents/gpy/doc/tuto_interacting_with_models.rst:336: WARNING: undefined label: creating_new_kernels (if the link has no caption the label must precede a section header)