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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
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@ -108,9 +108,15 @@ class GP(Model):
# The predictive variable to be used to predict using the posterior object's # The predictive variable to be used to predict using the posterior object's
# woodbury_vector and woodbury_inv is defined as predictive_variable # 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). # 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" # 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 self._predictive_variable = self.X
@ -205,7 +211,7 @@ class GP(Model):
if kern is None: if kern is None:
kern = self.kern 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) mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if len(mu.shape)==1: if len(mu.shape)==1:
mu = mu.reshape(-1,1) mu = mu.reshape(-1,1)
@ -213,7 +219,7 @@ class GP(Model):
Kxx = kern.K(Xnew) Kxx = kern.K(Xnew)
if self.posterior.woodbury_inv.ndim == 2: if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx)) 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])) var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
from ..util.linalg import mdot from ..util.linalg import mdot
for i in range(var.shape[2]): for i in range(var.shape[2]):
@ -223,7 +229,7 @@ class GP(Model):
Kxx = kern.Kdiag(Xnew) Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2: if self.posterior.woodbury_inv.ndim == 2:
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None] 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])) var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]): for i in range(var.shape[1]):
var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0))) 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) var_jac = dK2_dXdX - np.einsum('qim,miq->iq', dK_dXnew_full.T.dot(wi), dK_dXnew_full)
return var_jac return var_jac
if self.posterior.woodbury_inv.ndim == 3: if self.posterior.woodbury_inv.ndim == 3: # Missing data:
var_jac = [] if full_cov:
for d in range(self.posterior.woodbury_inv.shape[2]): var_jac = np.empty((Xnew.shape[0],Xnew.shape[0],Xnew.shape[1],self.output_dim))
var_jac.append(compute_cov_inner(self.posterior.woodbury_inv[:, :, d])) for d in range(self.posterior.woodbury_inv.shape[2]):
var_jac = np.concatenate(var_jac) 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: else:
var_jac = compute_cov_inner(self.posterior.woodbury_inv) var_jac = compute_cov_inner(self.posterior.woodbury_inv)
return mean_jac, var_jac return mean_jac, var_jac
@ -391,10 +401,11 @@ class GP(Model):
mu_jac, var_jac = self.predict_jacobian(Xnew, kern, full_cov=False) mu_jac, var_jac = self.predict_jacobian(Xnew, kern, full_cov=False)
mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac) mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac)
Sigma = np.zeros(mumuT.shape)
if var_jac.ndim == 3: 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: 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. G = 0.
if mean: if mean:
G += mumuT G += mumuT
@ -412,8 +423,13 @@ class GP(Model):
""" """
G = self.predict_wishard_embedding(Xnew, kern, mean, covariance) G = self.predict_wishard_embedding(Xnew, kern, mean, covariance)
from ..util.linalg import jitchol 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])]) mag = np.empty(Xnew.shape[0])
#return np.array([np.sqrt(np.linalg.det(G[n, :, :])) for n in range(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): def posterior_samples_f(self,X,size=10, full_cov=True):
""" """

4
GPy/core/parameterization/parameterized.py
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@ -74,7 +74,7 @@ class Parameterized(Parameterizable):
# Metaclass for parameters changed after init. # Metaclass for parameters changed after init.
# This makes sure, that parameters changed will always be called after __init__ # This makes sure, that parameters changed will always be called after __init__
# **Never** call parameters_changed() yourself # **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 #__metaclass__ = ParametersChangedMeta
#The six module is used to support both Python 2 and 3 simultaneously #The six module is used to support both Python 2 and 3 simultaneously
#=========================================================================== #===========================================================================
@ -316,7 +316,7 @@ class Parameterized(Parameterizable):
param[:] = val; return param[:] = val; return
except AttributeError: except AttributeError:
pass pass
object.__setattr__(self, name, val); return object.__setattr__(self, name, val);
#=========================================================================== #===========================================================================
# Pickling # Pickling

2
GPy/core/parameterization/transformations.py
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@ -488,7 +488,7 @@ class Logistic(Transformation):
return instance() return instance()
newfunc = super(Transformation, cls).__new__ newfunc = super(Transformation, cls).__new__
if newfunc is object.__new__: if newfunc is object.__new__:
o = newfunc(cls) o = newfunc(cls)
else: else:
o = newfunc(cls, lower, upper, *args, **kwargs) o = newfunc(cls, lower, upper, *args, **kwargs)
cls._instances.append(weakref.ref(o)) cls._instances.append(weakref.ref(o))

51
GPy/core/sparse_gp.py
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@ -49,7 +49,7 @@ class SparseGP(GP):
else: else:
#inference_method = ?? #inference_method = ??
raise NotImplementedError("what to do what to do?") 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.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0] self.num_inducing = Z.shape[0]
@ -128,29 +128,30 @@ class SparseGP(GP):
if kern is None: kern = self.kern if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior): if not isinstance(Xnew, VariationalPosterior):
Kx = kern.K(self._predictive_variable, Xnew) # Kx = kern.K(self._predictive_variable, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector) # mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov: # if full_cov:
Kxx = kern.K(Xnew) # Kxx = kern.K(Xnew)
if self.posterior.woodbury_inv.ndim == 2: # if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx)) # 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:
var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2])) # var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[2]): # for i in range(var.shape[2]):
var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx)) # var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx))
var = var # var = var
else: # else:
Kxx = kern.Kdiag(Xnew) # Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2: # if self.posterior.woodbury_inv.ndim == 2:
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None] # 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:
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2])) # var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]): # for i in range(var.shape[1]):
var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0))) # var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
var = var # var = var
#add in the mean function # #add in the mean function
if self.mean_function is not None: # if self.mean_function is not None:
mu += self.mean_function.f(Xnew) # mu += self.mean_function.f(Xnew)
mu, var = super(SparseGP, self)._raw_predict(Xnew, full_cov, kern)
else: else:
psi0_star = kern.psi0(self._predictive_variable, Xnew) psi0_star = kern.psi0(self._predictive_variable, Xnew)
psi1_star = kern.psi1(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? mu = np.dot(psi1_star, la) # TODO: dimensions?
if full_cov: 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])) var = np.empty((Xnew.shape[0], la.shape[1], la.shape[1]))
di = np.diag_indices(la.shape[1]) di = np.diag_indices(la.shape[1])
else: else:

2
GPy/core/svgp.py
View file

@ -1,5 +1,5 @@
# Copyright (c) 2014, James Hensman, Alex Matthews # 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 import numpy as np
from ..util import choleskies 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) #define the objective function (to be maximised)
def obj(Ki_f, f): 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)) 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): if np.isnan(ll):
import ipdb; ipdb.set_trace() # XXX BREAKPOINT import ipdb; ipdb.set_trace() # XXX BREAKPOINT
return -np.inf return -np.inf

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

@ -40,7 +40,7 @@ class SparseGPMissing(StochasticStorage):
bdict = {} bdict = {}
#For N > 1000 array2string default crops #For N > 1000 array2string default crops
opt = np.get_printoptions() opt = np.get_printoptions()
np.set_printoptions(threshold='nan') np.set_printoptions(threshold=np.inf)
for d in range(self.Y.shape[1]): for d in range(self.Y.shape[1]):
inan = np.isnan(self.Y)[:, 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'}) 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 = {} bdict = {}
if self.missing_data: if self.missing_data:
opt = np.get_printoptions() opt = np.get_printoptions()
np.set_printoptions(threshold='nan') np.set_printoptions(threshold=np.inf)
for d in self.d: for d in self.d:
inan = np.isnan(self.Y[:, 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'}) 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. Compute the kernel function.
.. math::
K_{ij} = k(X_i, X_j)
:param X: the first set of inputs to the kernel :param X: the first set of inputs to the kernel
:param X2: (optional) the second set of arguments to the kernel. If X2 :param X2: (optional) the second set of arguments to the kernel. If X2
is None, this is passed throgh to the 'part' object, which is None, this is passed throgh to the 'part' object, which
@ -77,22 +80,64 @@ class Kern(Parameterized):
""" """
raise NotImplementedError raise NotImplementedError
def Kdiag(self, X): def Kdiag(self, X):
"""
The diagonal of the kernel matrix K
.. math::
Kdiag_{i} = k(X_i, X_i)
"""
raise NotImplementedError raise NotImplementedError
def psi0(self, Z, variational_posterior): 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] return self.psicomp.psicomputations(self, Z, variational_posterior)[0]
def psi1(self, Z, variational_posterior): 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] return self.psicomp.psicomputations(self, Z, variational_posterior)[1]
def psi2(self, Z, variational_posterior): 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] return self.psicomp.psicomputations(self, Z, variational_posterior, return_psi2_n=False)[2]
def psi2n(self, Z, variational_posterior): 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] return self.psicomp.psicomputations(self, Z, variational_posterior, return_psi2_n=True)[2]
def gradients_X(self, dL_dK, X, X2): 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 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): 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") 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): 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") 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): def gradients_X_diag(self, dL_dKdiag, X):
"""
The diagonal of the derivative w.r.t. X
"""
raise NotImplementedError raise NotImplementedError
def update_gradients_diag(self, dL_dKdiag, X): 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 Set the gradients of all parameters when doing inference with
uncertain inputs, using expectations of the kernel. 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} + .. math::
dL_dpsi1 * dpsi1_d{theta_i} +
dL_dpsi2 * dpsi2_d{theta_i} \\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] dtheta = self.psicomp.psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)[0]
self.gradient[:] = dtheta self.gradient[:] = dtheta

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

@ -1,7 +1,11 @@
''' '''
Created on 11 Mar 2014 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 from ...core.parameterization.parameterized import ParametersChangedMeta
import numpy as np 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_full', _slice_update_gradients_full)
put_clean(dct, 'update_gradients_diag', _slice_update_gradients_diag) put_clean(dct, 'update_gradients_diag', _slice_update_gradients_diag)
put_clean(dct, 'gradients_X', _slice_gradients_X) 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', _slice_gradients_XX)
put_clean(dct, 'gradients_XX_diag', _slice_gradients_X_diag) put_clean(dct, 'gradients_XX_diag', _slice_gradients_X_diag)
put_clean(dct, 'gradients_X_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:: .. 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 :param input_dim: the number of input dimensions
:type input_dim: int :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 import Param
from ...core.parameterization.transformations import Logexp from ...core.parameterization.transformations import Logexp
import numpy as np import numpy as np
from ...util.linalg import tdot
from ...util.caching import Cache_this from ...util.caching import Cache_this
four_over_tau = 2./np.pi four_over_tau = 2./np.pi
@ -40,6 +41,7 @@ class MLP(Kern):
self.link_parameters(self.variance, self.weight_variance, self.bias_variance) self.link_parameters(self.variance, self.weight_variance, self.bias_variance)
@Cache_this(limit=20, ignore_args=())
def K(self, X, X2=None): def K(self, X, X2=None):
if X2 is None: if X2 is None:
X_denom = np.sqrt(self._comp_prod(X)+1.) 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,:] XTX = self._comp_prod(X,X2)/X_denom[:,None]/X2_denom[None,:]
return self.variance*four_over_tau*np.arcsin(XTX) return self.variance*four_over_tau*np.arcsin(XTX)
@Cache_this(limit=20, ignore_args=())
def Kdiag(self, X): def Kdiag(self, X):
"""Compute the diagonal of the covariance matrix for X.""" """Compute the diagonal of the covariance matrix for X."""
X_prod = self._comp_prod(X) X_prod = self._comp_prod(X)
@ -73,6 +76,10 @@ class MLP(Kern):
"""Derivative of the covariance matrix with respect to X""" """Derivative of the covariance matrix with respect to X"""
return self._comp_grads(dL_dK, X, X2)[3] 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): def gradients_X_diag(self, dL_dKdiag, X):
"""Gradient of diagonal of covariance with respect to X""" """Gradient of diagonal of covariance with respect to X"""
return self._comp_grads_diag(dL_dKdiag, X)[3] 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] dL_dkfu = (dL_dpsi1+ 2.*Kfu.dot(dL_dpsi2))*self.weights[i]
kern.update_gradients_full(dL_dkfu, X, Z) kern.update_gradients_full(dL_dkfu, X, Z)
dtheta += kern.gradient dtheta += kern.gradient
dX += kern.gradients_X(dL_dkfu, X, Z) dX_i, dZ_i = kern.gradients_X_X2(dL_dkfu, X, Z)
dZ += kern.gradients_X(dL_dkfu.T, Z, X) dX += dX_i
dZ += dZ_i
dmu += dX dmu += dX
dS += dX*self.locs[i]/(2.*S_sq) dS += dX*self.locs[i]/(2.*S_sq)
kern.gradient[:] = dtheta_old 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 from GPy.util.caching import Cacher
def psicomputations(variance, lengthscale, Z, variational_posterior, return_psi2_n=False): 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 # here are the "statistics" for psi0, psi1 and psi2
# Produced intermediate results: # Produced intermediate results:
# _psi1 NxM # _psi1 NxM
@ -26,12 +20,6 @@ def psicomputations(variance, lengthscale, Z, variational_posterior, return_psi2
return psi0, psi1, psi2 return psi0, psi1, psi2
def __psi1computations(variance, lengthscale, Z, mu, S): def __psi1computations(variance, lengthscale, Z, mu, S):
"""
Z - MxQ
mu - NxQ
S - NxQ
gamma - NxQ
"""
# here are the "statistics" for psi1 # here are the "statistics" for psi1
# Produced intermediate results: # Produced intermediate results:
# _psi1 NxM # _psi1 NxM
@ -46,12 +34,6 @@ def __psi1computations(variance, lengthscale, Z, mu, S):
return _psi1 return _psi1
def __psi2computations(variance, lengthscale, Z, mu, S): def __psi2computations(variance, lengthscale, Z, mu, S):
"""
Z - MxQ
mu - NxQ
S - NxQ
gamma - NxQ
"""
# here are the "statistics" for psi2 # here are the "statistics" for psi2
# Produced intermediate results: # Produced intermediate results:
# _psi2 MxM # _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 return dL_dvar, dL_dlengscale, dL_dZ, dL_dmu, dL_dS
def _psi1compDer(dL_dpsi1, variance, lengthscale, Z, mu, S): 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 # here are the "statistics" for psi1
# Produced intermediate results: dL_dparams w.r.t. psi1 # Produced intermediate results: dL_dparams w.r.t. psi1
# _dL_dvariance 1 # _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 return _dL_dvar, _dL_dl, _dL_dZ, _dL_dmu, _dL_dS
def _psi2compDer(dL_dpsi2, variance, lengthscale, Z, mu, S): 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 # here are the "statistics" for psi2
# Produced the derivatives w.r.t. psi2: # Produced the derivatives w.r.t. psi2:
# _dL_dvariance 1 # _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 . import PSICOMP_RBF
from ....util import gpu_init 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 = """ gpu_code = """
// define THREADNUM // define THREADNUM
@ -242,6 +235,10 @@ gpu_code = """
class PSICOMP_RBF_GPU(PSICOMP_RBF): class PSICOMP_RBF_GPU(PSICOMP_RBF):
def __init__(self, threadnum=256, blocknum=30, GPU_direct=False): 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.GPU_direct = GPU_direct
self.gpuCache = None self.gpuCache = None
@ -264,7 +261,8 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
memo[id(self)] = s memo[id(self)] = s
return s return s
def _initGPUCache(self, N, M, Q): def _initGPUCache(self, N, M, Q):
import pycuda.gpuarray as gpuarray
if self.gpuCache == None: if self.gpuCache == None:
self.gpuCache = { self.gpuCache = {
'l_gpu' :gpuarray.empty((Q,),np.float64,order='F'), '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): def get_dimensions(self, Z, variational_posterior):
return variational_posterior.mean.shape[0], Z.shape[0], Z.shape[1] return variational_posterior.mean.shape[0], Z.shape[0], Z.shape[1]
@Cache_this(limit=1, ignore_args=(0,)) @Cache_this(limit=5, ignore_args=(0,))
def psicomputations(self, variance, lengthscale, Z, variational_posterior): def psicomputations(self, kern, Z, variational_posterior, return_psi2_n=False):
""" """
Z - MxQ Z - MxQ
mu - NxQ mu - NxQ
S - NxQ S - NxQ
""" """
variance, lengthscale = kern.variance, kern.lengthscale
N,M,Q = self.get_dimensions(Z, variational_posterior) N,M,Q = self.get_dimensions(Z, variational_posterior)
self._initGPUCache(N,M,Q) self._initGPUCache(N,M,Q)
self.sync_params(lengthscale, Z, variational_posterior.mean, variational_posterior.variance) self.sync_params(lengthscale, Z, variational_posterior.mean, variational_posterior.variance)
@ -355,8 +354,10 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
else: else:
return psi0, psi1_gpu.get(), psi2_gpu.get() return psi0, psi1_gpu.get(), psi2_gpu.get()
@Cache_this(limit=1, ignore_args=(0,1,2,3)) @Cache_this(limit=5, ignore_args=(0,2,3,4))
def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior): 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) ARD = (len(lengthscale)!=1)
N,M,Q = self.get_dimensions(Z, variational_posterior) 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 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 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 import numpy as np
from ....util.caching import Cache_this from ....util.caching import Cache_this
from . import PSICOMP_RBF 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 = """ gpu_code = """
// define THREADNUM // define THREADNUM
@ -292,6 +285,11 @@ gpu_code = """
class PSICOMP_SSRBF_GPU(PSICOMP_RBF): class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
def __init__(self, threadnum=128, blocknum=15, GPU_direct=False): 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.GPU_direct = GPU_direct
self.gpuCache = None self.gpuCache = None
@ -314,7 +312,8 @@ class PSICOMP_SSRBF_GPU(PSICOMP_RBF):
memo[id(self)] = s memo[id(self)] = s
return s return s
def _initGPUCache(self, N, M, Q): def _initGPUCache(self, N, M, Q):
import pycuda.gpuarray as gpuarray
if self.gpuCache == None: if self.gpuCache == None:
self.gpuCache = { self.gpuCache = {
'l_gpu' :gpuarray.empty((Q,),np.float64,order='F'), '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] return variational_posterior.mean.shape[0], Z.shape[0], Z.shape[1]
@Cache_this(limit=1, ignore_args=(0,)) @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 Z - MxQ
mu - NxQ mu - NxQ
S - NxQ S - NxQ
""" """
variance, lengthscale = kern.variance, kern.lengthscale
N,M,Q = self.get_dimensions(Z, variational_posterior) N,M,Q = self.get_dimensions(Z, variational_posterior)
self._initGPUCache(N,M,Q) self._initGPUCache(N,M,Q)
self.sync_params(lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob) 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: else:
return psi0, psi1_gpu.get(), psi2_gpu.get() return psi0, psi1_gpu.get(), psi2_gpu.get()
@Cache_this(limit=1, ignore_args=(0,1,2,3)) @Cache_this(limit=1, ignore_args=(0,2,3,4))
def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior): 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) ARD = (len(lengthscale)!=1)
N,M,Q = self.get_dimensions(Z, variational_posterior) 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 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). The covariance function k(x, x' can then be written k(r).
In this implementation, r is scaled by the lengthscales parameter(s): 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 By default, there's only one lengthscale: seaprate lengthscales for each
dimension can be enables by setting ARD=True. 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 To implement a stationary covariance function using this class, one need
only define the covariance function k(r), and it derivative. only define the covariance function k(r), and it derivative.
... ```
def K_of_r(self, r): def K_of_r(self, r):
return foo return foo
def dK_dr(self, r): def dK_dr(self, r):
return bar return bar
```
The lengthscale(s) and variance parameters are added to the structure automatically. 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. 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 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 this case we compute the unscaled distance first (in a separate
@ -321,7 +326,7 @@ class OU(Stationary):
.. math:: .. 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:: .. 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:: .. math::
k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) 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'): 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) 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:: .. 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 :param input_dim: the number of input dimensions
:type input_dim: int :type input_dim: int
@ -54,7 +54,7 @@ class TruncLinear(Kern):
self.delta = Param('delta', delta) self.delta = Param('delta', delta)
self.add_parameter(self.variances) self.add_parameter(self.variances)
self.add_parameter(self.delta) self.add_parameter(self.delta)
@Cache_this(limit=2) @Cache_this(limit=2)
def K(self, X, X2=None): def K(self, X, X2=None):
XX = self.variances*self._product(X, X2) XX = self.variances*self._product(X, X2)
@ -114,7 +114,7 @@ class TruncLinear_inf(Kern):
.. math:: .. 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 :param input_dim: the number of input dimensions
:type input_dim: int :type input_dim: int
@ -148,8 +148,8 @@ class TruncLinear_inf(Kern):
self.variances = Param('variances', variances, Logexp()) self.variances = Param('variances', variances, Logexp())
self.add_parameter(self.variances) self.add_parameter(self.variances)
# @Cache_this(limit=2) # @Cache_this(limit=2)
def K(self, X, X2=None): def K(self, X, X2=None):
tmp = self._product(X, X2) 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): def betaY(self,Y,Y_metadata=None):
#TODO: ~Ricardo this does not live here #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) return Y/self.gaussian_variance(Y_metadata)
def gaussian_variance(self, Y_metadata=None): 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) # Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np import numpy as np
import scipy
from ..util.univariate_Gaussian import std_norm_cdf, std_norm_pdf from ..util.univariate_Gaussian import std_norm_cdf, std_norm_pdf
import scipy as sp import scipy as sp
from ..util.misc import safe_exp, safe_square, safe_cube, safe_quad, safe_three_times from ..util.misc import safe_exp, safe_square, safe_cube, safe_quad, safe_three_times
@ -67,7 +68,7 @@ class Probit(GPTransformation):
.. math:: .. math::
g(f) = \\Phi^{-1} (mu) g(f) = \\Phi^{-1} (mu)
""" """
def transf(self,f): def transf(self,f):
return std_norm_cdf(f) return std_norm_cdf(f)
@ -140,7 +141,7 @@ class Log_ex_1(GPTransformation):
""" """
def transf(self,f): def transf(self,f):
return np.log1p(safe_exp(f)) return scipy.special.log1p(safe_exp(f))
def dtransf_df(self,f): def dtransf_df(self,f):
ef = safe_exp(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 # 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 import numpy as np
from ..core.model import Model 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: if kernel is None:
kernel = kern.RBF(X.shape[1]) kernel = kern.RBF(X.shape[1])
likelihood = likelihoods.Gaussian(variance=noise_var) likelihood = likelihoods.Gaussian(variance=noise_var)
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer) 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 # 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 import numpy as np
from ..core import GP from ..core import GP

2
GPy/models/gplvm.py
View file

@ -36,8 +36,10 @@ class GPLVM(GP):
likelihood = Gaussian() likelihood = Gaussian()
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM') super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
self.X = Param('latent_mean', X) self.X = Param('latent_mean', X)
self.link_parameter(self.X, index=0) self.link_parameter(self.X, index=0)
self._predictive_variable = self.X
def parameters_changed(self): def parameters_changed(self):
super(GPLVM, self).parameters_changed() 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, view = ImshowController(ax, plot_function,
(xmin, ymin, xmax, ymax), (xmin, ymin, xmax, ymax),
resolution, aspect=aspect, interpolation='bilinear', resolution, aspect=aspect, interpolation='bilinear',
cmap=cm.gray) cmap=cm.get_cmap('Greys'))
# make sure labels are in order of input: # make sure labels are in order of input:
ulabels = [] ulabels = []

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

@ -3,7 +3,7 @@
import numpy as np import numpy as np
from . import Tango 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.gp_coregionalized_regression import GPCoregionalizedRegression
from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
from scipy import sparse from scipy import sparse
@ -186,8 +186,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#optionally plot some samples #optionally plot some samples
if samples: #NOTE not tested with fixed_inputs if samples: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata) Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata)
print Ysim.shape print(Ysim.shape)
print Xnew.shape print(Xnew.shape)
for yi in Ysim.T: for yi in Ysim.T:
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], '#3300FF', linewidth=0.25) 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. #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 import unittest
try: try:
from . import linalg_cython from ..util import linalg_cython
from ..util import choleskies_cython
config.set('cython', 'working', 'True') config.set('cython', 'working', 'True')
except ImportError: except ImportError:
config.set('cython', 'working', 'False') 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") @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.L = GPy.util.linalg.jitchol(A)
self.dL = np.random.randn(10,10) self.dL = np.random.randn(10,10)
def test(self): def test(self):
r1 = GPy.util.choleskies._backprop_gradient_pure(self.dL, self.L) r1 = choleskies._backprop_gradient_pure(self.dL, self.L)
r2 = GPy.util.choleskies.choleskies_cython.backprop_gradient(self.dL, self.L) r2 = choleskies_cython.backprop_gradient(self.dL, self.L)
r3 = GPy.util.choleskies.choleskies_cython.backprop_gradient_par_c(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, r2)
np.testing.assert_allclose(r1, r3) 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 verbose = 0
try: try:
from . import linalg_cython from ..util import linalg_cython
config.set('cython', 'working', 'True') config.set('cython', 'working', 'True')
except ImportError: except ImportError:
config.set('cython', 'working', 'False') config.set('cython', 'working', 'False')

10
GPy/testing/link_function_tests.py
View file

@ -1,5 +1,5 @@
import numpy as np import numpy as np
import scipy as sp import scipy
from scipy.special import cbrt from scipy.special import cbrt
from GPy.models import GradientChecker from GPy.models import GradientChecker
_lim_val = np.finfo(np.float64).max _lim_val = np.finfo(np.float64).max
@ -92,18 +92,18 @@ class LinkFunctionTests(np.testing.TestCase):
link = Log_ex_1() link = Log_ex_1()
lim_of_inf = _lim_val_exp lim_of_inf = _lim_val_exp
np.testing.assert_almost_equal(np.log1p(np.exp(self.mid_f)), link.transf(self.mid_f)) np.testing.assert_almost_equal(scipy.special.log1p(np.exp(self.mid_f)), link.transf(self.mid_f))
assert np.isinf(np.log1p(np.exp(np.log(self.f_upper_lim)))) assert np.isinf(scipy.special.log1p(np.exp(np.log(self.f_upper_lim))))
#Check the clipping works #Check the clipping works
np.testing.assert_almost_equal(link.transf(self.f_lower_lim), 0, decimal=5) 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 #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) self.check_overflow(link, lim_of_inf)
#Check that it would otherwise fail #Check that it would otherwise fail
beyond_lim_of_inf = lim_of_inf + 10.0 beyond_lim_of_inf = lim_of_inf + 10.0
old_err_state = np.seterr(over='ignore') 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) 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 numpy as np
import scipy as sp import scipy as sp
import GPy import GPy
@ -18,8 +19,8 @@ class MiscTests(np.testing.TestCase):
assert np.isinf(np.exp(self._lim_val_exp + 1)) assert np.isinf(np.exp(self._lim_val_exp + 1))
assert np.isfinite(GPy.util.misc.safe_exp(self._lim_val_exp + 1)) assert np.isfinite(GPy.util.misc.safe_exp(self._lim_val_exp + 1))
print w print(w)
print len(w) print(len(w))
assert len(w)==1 # should have one overflow warning assert len(w)==1 # should have one overflow warning
def test_safe_exp_lower(self): 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(mu1, (mu2*std)+mu)
np.testing.assert_allclose(var1, var2) 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): def test_sparse_raw_predict(self):
k = GPy.kern.RBF(1) k = GPy.kern.RBF(1)
m = GPy.models.SparseGPRegression(self.X, self.Y, kernel=k) m = GPy.models.SparseGPRegression(self.X, self.Y, kernel=k)
m.randomize() m.randomize()
Z = m.Z[:] 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 # Not easy to check if woodbury_inv is correct in itself as it requires a large derivation and expression
Kinv = m.posterior.woodbury_inv Kinv = m.posterior.woodbury_inv
@ -147,11 +193,24 @@ class MiscTests(unittest.TestCase):
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing, m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True) kernel=k, missing_data=True)
assert(m.checkgrad()) assert(m.checkgrad())
mul, varl = m.predict(m.X)
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q) 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) kernel=k, missing_data=True)
assert(m.checkgrad()) 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): def test_likelihood_replicate_kern(self):
m = GPy.models.GPRegression(self.X, self.Y) 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() m.randomize()
self.assertEqual(m.p1, val) 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): def test_printing(self):
print(self.test1) print(self.test1)
print(self.param) print(self.param)
print(self.test1['']) print(self.test1[''])
print(self.testmodel.hierarchy_name(False))
if __name__ == "__main__": if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.test_add_parameter'] #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 diag
from . import initialization from . import initialization
from . import multioutput from . import multioutput
from . import linalg_gpu
from . import parallel from . import parallel

2
GPy/util/choleskies.py
View file

@ -1,5 +1,5 @@
# Copyright James Hensman and Max Zwiessele 2014, 2015 # 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 import numpy as np
from . import linalg 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: except:
pass 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(): def closeGPU():
if gpu_context is not None: if gpu_context is not None:
gpu_context.detach() gpu_context.detach()

7
GPy/util/linalg_gpu.py
View file

@ -61,12 +61,5 @@ try:
except: except:
pass 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) # Licensed under the BSD 3-clause license (see LICENSE.txt)
#Only works for scipy 0.12+ #Only works for scipy 0.12+
try: from scipy.special import erfcx, erf
from scipy.special import erfcx, erf
except ImportError:
from scipy.special import erf
from .erfcx import erfcx
import numpy as np import numpy as np
def ln_diff_erfs(x1, x2, return_sign=False): 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 :param x2 : argument of the negative erf
:type x2: ndarray :type x2: ndarray
:return: tuple containing (log(abs(erf(x1) - erf(x2))), sign(erf(x1) - erf(x2))) :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. 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 x1 = np.require(x1).real
@ -26,7 +21,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
x1 = np.reshape(x1, (1, 1)) x1 = np.reshape(x1, (1, 1))
if x2.size==1: if x2.size==1:
x2 = np.reshape(x2, (1, 1)) x2 = np.reshape(x2, (1, 1))
if x1.shape==x2.shape: if x1.shape==x2.shape:
v = np.zeros_like(x1) v = np.zeros_like(x1)
else: else:
@ -36,7 +31,7 @@ def ln_diff_erfs(x1, x2, return_sign=False):
v = np.zeros(x1.shape) v = np.zeros(x1.shape)
else: else:
raise ValueError("This function does not broadcast unless provided with a scalar.") raise ValueError("This function does not broadcast unless provided with a scalar.")
if x1.size == 1: if x1.size == 1:
x1 = np.tile(x1, x2.shape) 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) v[flags] = np.log(erfcx(-_x1)
-erfcx(-_x2)*np.exp(_x1**2 -erfcx(-_x2)*np.exp(_x1**2
-_x2**2))-_x1**2 -_x2**2))-_x1**2
# TODO: switch back on log of zero warnings. # TODO: switch back on log of zero warnings.
if return_sign: if return_sign:

19
GPy/util/parallel.py
View file

@ -39,3 +39,22 @@ def divide_data(datanum, rank, size):
size = datanum/size size = datanum/size
offset = size*rank+residue offset = size*rank+residue
return offset, offset+size, datanum_list 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) 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 ### Python 3 Compatibility
Work is underway to make GPy run on Python 3. 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'. 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 ### Ubuntu hackers
For the most part, the developers are using ubuntu. To install the required packages: 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', extensions = ['sphinx.ext.autodoc',
#'sphinx.ext.doctest' #'sphinx.ext.doctest'
'sphinx.ext.viewcode', 'sphinx.ext.viewcode',
'sphinx.ext.pngmath', #'sphinx.ext.pngmath',
'sphinx.ext.mathjax',
'ipython_directive', 'ipython_directive',
'ipython_console_highlighting' 'ipython_console_highlighting'
#'matplotlib.sphinxext.plot_directive' #'matplotlib.sphinxext.plot_directive'
] ]
plot_formats = [('png', 80), ('pdf', 50)] 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" print "finished importing"
############################################################################## ##############################################################################
@ -218,7 +222,7 @@ exclude_patterns = ['_build']
#show_authors = False #show_authors = False
# The name of the Pygments (syntax highlighting) style to use. # 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. # A list of ignored prefixes for module index sorting.
#modindex_common_prefix = [] #modindex_common_prefix = []
@ -228,10 +232,10 @@ pygments_style = 'sphinx'
# The theme to use for HTML and HTML Help pages. See the documentation for # The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes. # a list of builtin themes.
html_theme = 'default' html_theme = 'alabaster'
# This is to revert to the default theme on readthedocs # 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 # 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 # further. For a list of options available for each theme, see the
@ -317,7 +321,7 @@ latex_elements = {
#'pointsize': '10pt', #'pointsize': '10pt',
# Additional stuff for the LaTeX preamble. # Additional stuff for the LaTeX preamble.
'preamble': '\\usepackage{MnSymbol}', 'preamble': '\\usepackage{MnSymbol,amsmath}',
} }
# Grouping the document tree into LaTeX files. List of tuples # 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: Contents:
.. toctree:: .. toctree::
:maxdepth: 2 :maxdepth: 3
GPy 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/maxz/Documents/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/maxz/Documents/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/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/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/maxz/Documents/gpy/GPy/core/gp.py:docstring of GPy.core.gp.GP.predictive_gradients:5: ERROR: Unexpected indentation.
/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/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/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/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): 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) __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 from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
ImportError: No module named lambdify 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/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/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/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/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/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/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/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/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/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/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/alans/Work/GPy/GPy/kern/_src/coregionalize.py:docstring of GPy.kern._src.coregionalize.Coregionalize:5: ERROR: Unexpected indentation. /home/maxz/Documents/gpy/doc/GPy.examples.rst:50: WARNING: autodoc: failed to import module u'GPy.examples.stochastic'; the following exception was raised:
/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:
Traceback (most recent call last): 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) __import__(self.modname)
File "/home/alans/Work/GPy/GPy/kern/_src/hierarchical.py", line 4, in <module> ImportError: No module named stochastic
from kernpart import Kernpart /home/maxz/Documents/gpy/doc/GPy.examples.rst:58: WARNING: autodoc: failed to import module u'GPy.examples.tutorials'; the following exception was raised:
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:
Traceback (most recent call last): 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) __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 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 from sympy.utilities.lambdify import lambdastr, _imp_namespace, _get_namespace
ImportError: No module named lambdify 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/maxz/Documents/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/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/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/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:
/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. Traceback (most recent call last):
/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. 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
/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. __import__(self.modname)
/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. ImportError: No module named gp_multioutput_regression
/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_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 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 Examples
-------- --------
/home/alans/Work/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: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/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/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/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/alans/Work/GPy/GPy/testing/likelihood_tests.py:docstring of GPy.testing.likelihood_tests.dparam_partial:9: ERROR: Unexpected indentation. /home/maxz/Documents/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. 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/maxz/Documents/gpy/doc/GPy.util.rst:74: WARNING: autodoc: failed to import module u'GPy.util.erfcx'; the following exception was raised:
/home/alans/Work/GPy/GPy/util/netpbmfile.py:docstring of GPy.util.netpbmfile.imread:6: SEVERE: Unexpected section title. 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 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 Examples
-------- --------
/home/alans/Work/GPy/doc/GPy.util.rst:2: SEVERE: Duplicate ID: "module-GPy.util.subarray_and_sorting". /home/maxz/Documents/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/maxz/Documents/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/GPy/util/subarray_and_sorting.py:docstring of GPy.util.subarray_and_sorting.common_subarrays:11: SEVERE: Unexpected section title.
Examples: 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/maxz/Documents/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/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/alans/Work/GPy/doc/installation.rst:22: ERROR: Unexpected indentation. /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/alans/Work/GPy/doc/installation.rst:27: ERROR: Unexpected indentation. /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/alans/Work/GPy/doc/tuto_creating_new_kernels.rst:58: WARNING: Inline literal start-string without end-string. /home/maxz/Documents/gpy/doc/installation.rst:22: ERROR: Unexpected indentation.
/home/alans/Work/GPy/doc/tuto_creating_new_models.rst:24: ERROR: Unknown target name: "parameterized". /home/maxz/Documents/gpy/doc/installation.rst:27: ERROR: Unexpected indentation.
/home/alans/Work/GPy/doc/tuto_interacting_with_models.rst:83: WARNING: Title underline too short. /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: Interacting with Parameters:
======================= =======================
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Interacting with Parameters: Interacting with Parameters:
======================= =======================
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Regular expressions 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` 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` 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 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 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 Adjusting the model's constraints
================================ ================================
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Adjusting the model's constraints 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 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 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 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 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 Parameterization handling
******************* *******************
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Parameter handles 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` :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` :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` :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` :py:class:`~GPy.core.parameterization.param.Param`
=========== ===========
/home/alans/Work/GPy/doc/installation.rst:: WARNING: document isn't included in any toctree /home/maxz/Documents/gpy/doc/installation.rst:: WARNING: document isn't included in any toctree
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