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Merge branch 'master' of github.com:SheffieldML/GPy

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This commit is contained in:
Nicolo Fusi 2014-01-02 01:39:37 -08:00
commit 53cd17f55a
40 changed files with 1198 additions and 773 deletions
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1
.travis.yml
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@ -24,3 +24,4 @@ install:
# command to run tests, e.g. python setup.py test # command to run tests, e.g. python setup.py test
script: script:
- nosetests GPy/testing - nosetests GPy/testing
#- yes | nosetests GPy/testing

12
GPy/__init__.py
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@ -4,17 +4,17 @@ import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=DeprecationWarning)
import os import os
import core
import models
import mappings
import inference
import util import util
import examples import core
import kern
import mappings
import likelihoods import likelihoods
import inference
import models
import examples
import testing import testing
from numpy.testing import Tester from numpy.testing import Tester
from nose.tools import nottest from nose.tools import nottest
import kern
from core import priors from core import priors
@nottest @nottest

6
GPy/core/gp_base.py
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@ -218,8 +218,8 @@ class GPBase(Model):
Y = self.likelihood.data Y = self.likelihood.data
for d in which_data_ycols: for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T m_d = m[:,d].reshape(resolution, resolution).T
ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) contour = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
ax.scatter(self.X[which_data_rows, free_dims[0]], self.X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.) scatter = ax.scatter(self.X[which_data_rows, free_dims[0]], self.X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
#set the limits of the plot to some sensible values #set the limits of the plot to some sensible values
ax.set_xlim(xmin[0], xmax[0]) ax.set_xlim(xmin[0], xmax[0])
@ -227,7 +227,7 @@ class GPBase(Model):
if samples: if samples:
warnings.warn("Samples are rather difficult to plot for 2D inputs...") warnings.warn("Samples are rather difficult to plot for 2D inputs...")
return contour, scatter
else: else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions" raise NotImplementedError, "Cannot define a frame with more than two input dimensions"

5
GPy/core/sparse_gp.py
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@ -417,6 +417,11 @@ class SparseGP(GPBase):
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
""" """
#deal work out which ax to plot on #deal work out which ax to plot on
#Need these because we use which_data_rows in this function not just base
if which_data_rows == 'all':
which_data_rows = slice(None)
if which_data_ycols == 'all':
which_data_ycols = np.arange(self.output_dim)
if ax is None: if ax is None:
fig = pb.figure(num=fignum) fig = pb.figure(num=fignum)
ax = fig.add_subplot(111) ax = fig.add_subplot(111)

21
GPy/core/svigp.py
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@ -37,7 +37,6 @@ class SVIGP(GPBase):
self.Y = self.likelihood.Y.copy() self.Y = self.likelihood.Y.copy()
self.Z = Z self.Z = Z
self.num_inducing = Z.shape[0] self.num_inducing = Z.shape[0]
self.batchcounter = 0 self.batchcounter = 0
self.epochs = 0 self.epochs = 0
self.iterations = 0 self.iterations = 0
@ -78,6 +77,8 @@ class SVIGP(GPBase):
self._param_steplength_trace = [] self._param_steplength_trace = []
self._vb_steplength_trace = [] self._vb_steplength_trace = []
self.ensure_default_constraints()
def getstate(self): def getstate(self):
steplength_params = [self.hbar_t, self.tau_t, self.gbar_t, self.gbar_t1, self.gbar_t2, self.hbar_tp, self.tau_tp, self.gbar_tp, self.adapt_param_steplength, self.adapt_vb_steplength, self.vb_steplength, self.param_steplength] steplength_params = [self.hbar_t, self.tau_t, self.gbar_t, self.gbar_t1, self.gbar_t2, self.hbar_tp, self.tau_tp, self.gbar_tp, self.adapt_param_steplength, self.adapt_vb_steplength, self.vb_steplength, self.param_steplength]
return GPBase.getstate(self) + \ return GPBase.getstate(self) + \
@ -308,7 +309,7 @@ class SVIGP(GPBase):
self._param_trace.append(self._get_params()) self._param_trace.append(self._get_params())
self._ll_trace.append(self.log_likelihood() + self.log_prior()) self._ll_trace.append(self.log_likelihood() + self.log_prior())
#load a batch #load a batch and do the appropriate computations (kernel matrices, etc)
self.load_batch() self.load_batch()
#compute the (stochastic) gradient #compute the (stochastic) gradient
@ -318,7 +319,8 @@ class SVIGP(GPBase):
#compute the steps in all parameters #compute the steps in all parameters
vb_step = self.vb_steplength*natgrads[0] vb_step = self.vb_steplength*natgrads[0]
if (self.epochs>=1):#only move the parameters after the first epoch #only move the parameters after the first epoch and only if the steplength is nonzero
if (self.epochs>=1) and (self.param_steplength > 0):
param_step = self.momentum*param_step + self.param_steplength*grads param_step = self.momentum*param_step + self.param_steplength*grads
else: else:
param_step = 0. param_step = 0.
@ -340,6 +342,8 @@ class SVIGP(GPBase):
if self.epochs > 10: if self.epochs > 10:
self._adapt_steplength() self._adapt_steplength()
self._vb_steplength_trace.append(self.vb_steplength)
self._param_steplength_trace.append(self.param_steplength)
self.iterations += 1 self.iterations += 1
@ -348,17 +352,20 @@ class SVIGP(GPBase):
if self.adapt_vb_steplength: if self.adapt_vb_steplength:
# self._adaptive_vb_steplength() # self._adaptive_vb_steplength()
self._adaptive_vb_steplength_KL() self._adaptive_vb_steplength_KL()
self._vb_steplength_trace.append(self.vb_steplength) #self._vb_steplength_trace.append(self.vb_steplength)
assert self.vb_steplength > 0 assert self.vb_steplength >= 0
if self.adapt_param_steplength: if self.adapt_param_steplength:
self._adaptive_param_steplength() self._adaptive_param_steplength()
# self._adaptive_param_steplength_log() # self._adaptive_param_steplength_log()
# self._adaptive_param_steplength_from_vb() # self._adaptive_param_steplength_from_vb()
self._param_steplength_trace.append(self.param_steplength) #self._param_steplength_trace.append(self.param_steplength)
def _adaptive_param_steplength(self): def _adaptive_param_steplength(self):
decr_factor = 0.02 if hasattr(self, 'adapt_param_steplength_decr'):
decr_factor = self.adapt_param_steplength_decr
else:
decr_factor = 0.02
g_tp = self._transform_gradients(self._log_likelihood_gradients()) g_tp = self._transform_gradients(self._log_likelihood_gradients())
self.gbar_tp = (1-1/self.tau_tp)*self.gbar_tp + 1/self.tau_tp * g_tp self.gbar_tp = (1-1/self.tau_tp)*self.gbar_tp + 1/self.tau_tp * g_tp
self.hbar_tp = (1-1/self.tau_tp)*self.hbar_tp + 1/self.tau_tp * np.dot(g_tp.T, g_tp) self.hbar_tp = (1-1/self.tau_tp)*self.hbar_tp + 1/self.tau_tp * np.dot(g_tp.T, g_tp)

19
GPy/core/variational.py
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@ -1,19 +0,0 @@
'''
Created on 6 Nov 2013
@author: maxz
'''
from parameterized import Parameterized
from parameter import Param
class Normal(Parameterized):
'''
Normal distribution for variational approximations.
holds the means and variances for a factorizing multivariate normal distribution
'''
def __init__(self, name, means, variances):
Parameterized.__init__(self, name=name)
self.means = Param("mean", means)
self.variances = Param('variance', variances)
self.add_parameters(self.means, self.variances)

21
GPy/examples/dimensionality_reduction.py
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@ -37,20 +37,32 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True) # k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing) m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
#===========================================================================
# randomly obstruct data with percentage p
p = .8
Y_obstruct = Y.copy()
Y_obstruct[_np.random.uniform(size=(Y.shape)) < p] = _np.nan
#===========================================================================
m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales m.lengthscales = lengthscales
if plot: if plot:
import matplotlib.pyplot as pb import matplotlib.pyplot as pb
m.plot() m.plot()
pb.title('PCA initialisation') pb.title('PCA initialisation')
m2.plot()
pb.title('PCA initialisation')
if optimize: if optimize:
m.optimize('scg', messages=verbose) m.optimize('scg', messages=verbose)
m2.optimize('scg', messages=verbose)
if plot: if plot:
m.plot() m.plot()
pb.title('After optimisation') pb.title('After optimisation')
m2.plot()
pb.title('After optimisation')
return m return m, m2
def gplvm_oil_100(optimize=True, verbose=1, plot=True): def gplvm_oil_100(optimize=True, verbose=1, plot=True):
import GPy import GPy
@ -217,7 +229,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
ax.legend() ax.legend()
for i, Y in enumerate(Ylist): for i, Y in enumerate(Ylist):
ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i) ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
ax.imshow(Y, aspect='auto', cmap=cm.gray) ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
ax.set_title("Y{}".format(i + 1)) ax.set_title("Y{}".format(i + 1))
pylab.draw() pylab.draw()
pylab.tight_layout() pylab.tight_layout()
@ -451,12 +463,9 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
if in_place: if in_place:
# Make figure move in place. # Make figure move in place.
data['Y'][:, 0:3] = 0.0 data['Y'][:, 0:3] = 0.0
m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True) m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
if optimize: if optimize: m.optimize(messages=verbose, max_f_eval=10000)
m.optimize(messages=verbose, max_f_eval=10000)
if plot: if plot:
ax = m.plot_latent() ax = m.plot_latent()
y = m.likelihood.Y[0, :] y = m.likelihood.Y[0, :]

21
GPy/examples/regression.py
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@ -273,27 +273,6 @@ def toy_rbf_1d_50(optimize=True, plot=True):
return m return m
def toy_poisson_rbf_1d(optimize=True, plot=True):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
x_len = 400
X = np.linspace(0, 10, x_len)[:, None]
f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
Y = np.array([np.random.poisson(np.exp(f)) for f in f_true]).reshape(x_len,1)
noise_model = GPy.likelihoods.poisson()
likelihood = GPy.likelihoods.EP(Y,noise_model)
# create simple GP Model
m = GPy.models.GPRegression(X, Y, likelihood=likelihood)
if optimize:
m.optimize('bfgs')
if plot:
m.plot()
return m
def toy_poisson_rbf_1d_laplace(optimize=True, plot=True): def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.""" """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
optimizer='scg' optimizer='scg'

94
GPy/kern/parts/ODE_UY.py
View file

@ -114,17 +114,24 @@ class ODE_UY(Kernpart):
Vu=self.varianceU Vu=self.varianceU
Vy=self.varianceY Vy=self.varianceY
# kernel for kuu matern3/2
kuu = lambda dist:Vu * (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist)) kuu = lambda dist:Vu * (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
# kernel for kyy
k1 = lambda dist:np.exp(-ly*np.abs(dist))*(2*lu+ly)/(lu+ly)**2 k1 = lambda dist:np.exp(-ly*np.abs(dist))*(2*lu+ly)/(lu+ly)**2
k2 = lambda dist:(np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2 k2 = lambda dist:(np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
k3 = lambda dist:np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 ) k3 = lambda dist:np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
kyy = lambda dist:Vu*Vy*(k1(dist) + k2(dist) + k3(dist)) kyy = lambda dist:Vu*Vy*(k1(dist) + k2(dist) + k3(dist))
# cross covariance function
kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly))) kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
# cross covariance kyu
kyup = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t>0 kyu kyup = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t>0 kyu
kyun = lambda dist:Vu*Vy*(kyu3(dist)) #t<0 kyu kyun = lambda dist:Vu*Vy*(kyu3(dist)) #t<0 kyu
# cross covariance kuy
kuyp = lambda dist:Vu*Vy*(kyu3(dist)) #t>0 kuy kuyp = lambda dist:Vu*Vy*(kyu3(dist)) #t>0 kuy
kuyn = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t<0 kuy kuyn = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t<0 kuy
@ -135,12 +142,13 @@ class ODE_UY(Kernpart):
if i==0 and j==0: if i==0 and j==0:
target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2])) target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
elif i==0 and j==1: elif i==0 and j==1:
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) ) #target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[ss1,ss2]) ) )
elif i==1 and j==1: elif i==1 and j==1:
target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2])) target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
else: else:
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) ) #target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[ss1,ss2]) ) )
#KUU = kuu(np.abs(rdist[:iu,:iu])) #KUU = kuu(np.abs(rdist[:iu,:iu]))
@ -186,20 +194,30 @@ class ODE_UY(Kernpart):
def dK_dtheta(self, dL_dK, X, X2, target): def dK_dtheta(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to the parameters.""" """derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.abs(X - X2.T)
X,slices = X[:,:-1],index_to_slices(X[:,-1]) X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None: if X2 is None:
X2,slices2 = X,slices X2,slices2 = X,slices
else: else:
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1]) X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
#rdist = X[:,0][:,None] - X2[:,0][:,None].T
rdist = X - X2.T
ly=1/self.lengthscaleY ly=1/self.lengthscaleY
lu=np.sqrt(3)/self.lengthscaleU lu=np.sqrt(3)/self.lengthscaleU
#ly=self.lengthscaleY
#lu=self.lengthscaleU rd=rdist.shape[0]
dktheta1 = np.zeros([rd,rd])
dktheta2 = np.zeros([rd,rd])
dkUdvar = np.zeros([rd,rd])
dkYdvar = np.zeros([rd,rd])
# dk dtheta for UU
UUdtheta1 = lambda dist: np.exp(-lu* dist)*dist + (-dist)*np.exp(-lu* dist)*(1+lu*dist)
UUdtheta2 = lambda dist: 0
#UUdvar = lambda dist: (1 + lu*dist)*np.exp(-lu*dist)
UUdvar = lambda dist: (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
# dk dtheta for YY
dk1theta1 = lambda dist: np.exp(-ly*dist)*2*(-lu)/(lu+ly)**3 dk1theta1 = lambda dist: np.exp(-ly*dist)*2*(-lu)/(lu+ly)**3
#c=np.sqrt(3) #c=np.sqrt(3)
@ -216,7 +234,7 @@ class ODE_UY(Kernpart):
dk3theta1 = lambda dist: np.exp(-dist*lu)*(lu+ly)**(-2)*((2*lu+ly+dist*lu**2+lu*ly*dist)*(-dist-2/(lu+ly))+2+2*lu*dist+ly*dist) dk3theta1 = lambda dist: np.exp(-dist*lu)*(lu+ly)**(-2)*((2*lu+ly+dist*lu**2+lu*ly*dist)*(-dist-2/(lu+ly))+2+2*lu*dist+ly*dist)
dktheta1 = lambda dist: self.varianceU*self.varianceY*(dk1theta1+dk2theta1+dk3theta1) #dktheta1 = lambda dist: self.varianceU*self.varianceY*(dk1theta1+dk2theta1+dk3theta1)
@ -230,14 +248,35 @@ class ODE_UY(Kernpart):
dk3theta2 = lambda dist: np.exp(-dist*lu) * (-3*lu-ly-dist*lu**2-lu*ly*dist)/(lu+ly)**3 dk3theta2 = lambda dist: np.exp(-dist*lu) * (-3*lu-ly-dist*lu**2-lu*ly*dist)/(lu+ly)**3
dktheta2 = lambda dist: self.varianceU*self.varianceY*(dk1theta2 + dk2theta2 +dk3theta2) #dktheta2 = lambda dist: self.varianceU*self.varianceY*(dk1theta2 + dk2theta2 +dk3theta2)
# kyy kernel
#k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
#k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
#k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2 k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2 k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 ) k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
dkdvar = k1+k2+k3 #dkdvar = k1+k2+k3
#cross covariance kernel
kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
# dk dtheta for UY
dkcrtheta2 = lambda dist: np.exp(-lu*dist) * ( (-1)*(lu+ly)**(-2)*(1+lu*dist+lu*(lu+ly)**(-1)) + (lu+ly)**(-1)*(-lu)*(lu+ly)**(-2) )
dkcrtheta1 = lambda dist: np.exp(-lu*dist)*(lu+ly)**(-1)* ( (-dist)*(1+dist*lu+lu*(lu+ly)**(-1)) - (lu+ly)**(-1)*(1+dist*lu+lu*(lu+ly)**(-1)) +dist+(lu+ly)**(-1)-lu*(lu+ly)**(-2) )
#dkuyp dtheta
#dkuyp dtheta1 = self.varianceU*self.varianceY* (dk1theta1() + dk2theta1())
#dkuyp dtheta2 = self.varianceU*self.varianceY* (dk1theta2() + dk2theta2())
#dkuyp dVar = k1() + k2()
#dkyup dtheta
#dkyun dtheta1 = self.varianceU*self.varianceY* (dk1theta1() + dk2theta1())
#dkyun dtheta2 = self.varianceU*self.varianceY* (dk1theta2() + dk2theta2())
#dkyup dVar = k1() + k2() #
for i, s1 in enumerate(slices): for i, s1 in enumerate(slices):
@ -246,20 +285,35 @@ class ODE_UY(Kernpart):
for ss2 in s2: for ss2 in s2:
if i==0 and j==0: if i==0 and j==0:
#target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2])) #target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*UUdtheta1(np.abs(rdist[ss1,ss2]))
dktheta2[ss1,ss2] = 0
dkUdvar[ss1,ss2] = UUdvar(np.abs(rdist[ss1,ss2]))
dkYdvar[ss1,ss2] = 0
elif i==0 and j==1: elif i==0 and j==1:
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) ) #target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
#dktheta1[ss1,ss2] =
#dktheta2[ss1,ss2] =
#dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta1(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) )
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta2(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) )
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyu3(np.abs(rdist[ss1,ss2])) ,k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])) )
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
elif i==1 and j==1: elif i==1 and j==1:
#target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2])) #target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))+dk3theta1(np.abs(rdist[ss1,ss2])))
dktheta2[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
dkUdvar[ss1,ss2] = (k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
else: else:
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) ) #target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) , dkcrtheta1(np.abs(rdist[ss1,ss2])) )
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) , dkcrtheta2(np.abs(rdist[ss1,ss2])) )
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])), kyu3(np.abs(rdist[ss1,ss2])) )
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
target[0] += np.sum(self.varianceY*dkUdvar * dL_dK)
target[1] += np.sum(self.varianceU*dkYdvar * dL_dK)
target[0] += np.sum(self.varianceY*dkdvar * dL_dK)
target[1] += np.sum(self.varianceU*dkdvar * dL_dK)
target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK) target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK)
target[3] += np.sum(dktheta2*(-self.lengthscaleY**(-2)) * dL_dK) target[3] += np.sum(dktheta2*(-self.lengthscaleY**(-2)) * dL_dK)

6
GPy/kern/parts/sympykern.py
View file

@ -6,7 +6,7 @@ from scipy import weave
import re import re
import os import os
import sys import sys
current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__))) current_dir = os.path.dirname(os.path.abspath(__file__))
import tempfile import tempfile
import pdb import pdb
import ast import ast
@ -107,9 +107,9 @@ class spkern(Kernpart):
self.weave_kwargs = { self.weave_kwargs = {
'support_code':self._function_code, 'support_code':self._function_code,
'include_dirs':[tempfile.gettempdir(), os.path.join(current_dir,'parts/')], 'include_dirs':[tempfile.gettempdir(), current_dir],
'headers':['"sympy_helpers.h"'], 'headers':['"sympy_helpers.h"'],
'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp")], 'sources':[os.path.join(current_dir,"sympy_helpers.cpp")],
'extra_compile_args':extra_compile_args, 'extra_compile_args':extra_compile_args,
'extra_link_args':[], 'extra_link_args':[],
'verbose':True} 'verbose':True}

17
GPy/likelihoods/laplace.py
View file

@ -250,8 +250,11 @@ class Laplace(likelihood):
self.W = -self.noise_model.d2logpdf_df2(self.f_hat, self.data, extra_data=self.extra_data) self.W = -self.noise_model.d2logpdf_df2(self.f_hat, self.data, extra_data=self.extra_data)
if not self.noise_model.log_concave: if not self.noise_model.log_concave:
#print "Under 1e-10: {}".format(np.sum(self.W < 1e-6)) i = self.W < 1e-6
self.W[self.W < 1e-6] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur if np.any(i):
warnings.warn('truncating non log-concave likelihood curvature')
# FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
self.W[i] = 1e-6
self.W12BiW12, self.ln_B_det = self._compute_B_statistics(self.K, self.W, np.eye(self.N)) self.W12BiW12, self.ln_B_det = self._compute_B_statistics(self.K, self.W, np.eye(self.N))
@ -270,14 +273,14 @@ class Laplace(likelihood):
:type W: Vector of diagonal values of hessian (1xN) :type W: Vector of diagonal values of hessian (1xN)
:param a: Matrix to calculate W12BiW12a :param a: Matrix to calculate W12BiW12a
:type a: Matrix NxN :type a: Matrix NxN
:returns: (W12BiW12, ln_B_det) :returns: (W12BiW12a, ln_B_det)
""" """
if not self.noise_model.log_concave: if not self.noise_model.log_concave:
#print "Under 1e-10: {}".format(np.sum(W < 1e-6)) #print "Under 1e-10: {}".format(np.sum(W < 1e-6))
W[W < 1e-6] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur W[W < 1e-10] = 1e-10 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
# If the likelihood is non-log-concave. We wan't to say that there is a negative variance # If the likelihood is non-log-concave. We wan't to say that there is a negative variance
# To cause the posterior to become less certain than the prior and likelihood, # To cause the posterior to become less certain than the prior and likelihood,
# This is a property only held by non-log-concave likelihoods # This is a property only held by non-log-concave likelihoods
#W is diagonal so its sqrt is just the sqrt of the diagonal elements #W is diagonal so its sqrt is just the sqrt of the diagonal elements

19
GPy/likelihoods/noise_models/noise_distributions.py
View file

@ -153,9 +153,11 @@ class NoiseDistribution(object):
:param sigma: standard deviation of posterior :param sigma: standard deviation of posterior
""" """
#import ipdb; ipdb.set_trace()
def int_mean(f,m,v): def int_mean(f,m,v):
return self._mean(f)*np.exp(-(0.5/v)*np.square(f - m)) return self._mean(f)*np.exp(-(0.5/v)*np.square(f - m))
scaled_mean = [quad(int_mean, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)] #scaled_mean = [quad(int_mean, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
scaled_mean = [quad(int_mean, mj-6*np.sqrt(s2j), mj+6*np.sqrt(s2j), args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance)) mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance))
return mean return mean
@ -172,16 +174,16 @@ class NoiseDistribution(object):
:predictive_mean: output's predictive mean, if None _predictive_mean function will be called. :predictive_mean: output's predictive mean, if None _predictive_mean function will be called.
""" """
#sigma2 = sigma**2
normalizer = np.sqrt(2*np.pi*variance) normalizer = np.sqrt(2*np.pi*variance)
# E( V(Y_star|f_star) ) # E( V(Y_star|f_star) )
def int_var(f,m,v): def int_var(f,m,v):
return self._variance(f)*np.exp(-(0.5/v)*np.square(f - m)) return self._variance(f)*np.exp(-(0.5/v)*np.square(f - m))
scaled_exp_variance = [quad(int_var, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)] #Most of the weight is within 6 stds and this avoids some negative infinity and infinity problems of taking f^2
scaled_exp_variance = [quad(int_var, mj-6*np.sqrt(s2j), mj+6*np.sqrt(s2j), args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
exp_var = np.array(scaled_exp_variance)[:,None] / normalizer exp_var = np.array(scaled_exp_variance)[:,None] / normalizer
#V( E(Y_star|f_star) ) = E( E(Y_star|f_star)**2 ) - E( E(Y_star|f_star) )**2 #V( E(Y_star|f_star) ) = E( E(Y_star|f_star)**2 ) - E( E(Y_star|f_star) )**2
#E( E(Y_star|f_star) )**2 #E( E(Y_star|f_star) )**2
if predictive_mean is None: if predictive_mean is None:
@ -189,9 +191,9 @@ class NoiseDistribution(object):
predictive_mean_sq = predictive_mean**2 predictive_mean_sq = predictive_mean**2
#E( E(Y_star|f_star)**2 ) #E( E(Y_star|f_star)**2 )
def int_pred_mean_sq(f,m,v,predictive_mean_sq): def int_pred_mean_sq(f,m,v):
return self._mean(f)**2*np.exp(-(0.5/v)*np.square(f - m)) return self._mean(f)**2*np.exp(-(0.5/v)*np.square(f - m))
scaled_exp_exp2 = [quad(int_pred_mean_sq, -np.inf, np.inf,args=(mj,s2j,pm2j))[0] for mj,s2j,pm2j in zip(mu,variance,predictive_mean_sq)] scaled_exp_exp2 = [quad(int_pred_mean_sq, mj-6*np.sqrt(s2j), mj+6*np.sqrt(s2j), args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
exp_exp2 = np.array(scaled_exp_exp2)[:,None] / normalizer exp_exp2 = np.array(scaled_exp_exp2)[:,None] / normalizer
var_exp = exp_exp2 - predictive_mean_sq var_exp = exp_exp2 - predictive_mean_sq
@ -408,17 +410,16 @@ class NoiseDistribution(object):
axis=-1 axis=-1
#Calculate mean, variance and precentiles from samples #Calculate mean, variance and precentiles from samples
print "WARNING: Using sampling to calculate mean, variance and predictive quantiles." warnings.warn("Using sampling to calculate mean, variance and predictive quantiles.")
pred_mean = np.mean(samples, axis=axis)[:,None] pred_mean = np.mean(samples, axis=axis)[:,None]
pred_var = np.var(samples, axis=axis)[:,None] pred_var = np.var(samples, axis=axis)[:,None]
q1 = np.percentile(samples, 2.5, axis=axis)[:,None] q1 = np.percentile(samples, 2.5, axis=axis)[:,None]
q3 = np.percentile(samples, 97.5, axis=axis)[:,None] q3 = np.percentile(samples, 97.5, axis=axis)[:,None]
else: else:
pred_mean = self.predictive_mean(mu, var) pred_mean = self.predictive_mean(mu, var)
pred_var = self.predictive_variance(mu, var, pred_mean) pred_var = self.predictive_variance(mu, var, pred_mean)
print "WARNING: Predictive quantiles are only computed when sampling." warnings.warn("Predictive quantiles are only computed when sampling.")
q1 = np.repeat(np.nan,pred_mean.size)[:,None] q1 = np.repeat(np.nan,pred_mean.size)[:,None]
q3 = q1.copy() q3 = q1.copy()

12
GPy/models.py
View file

@ -1,18 +1,20 @@
''' '''
GPy Models .. module:: GPy.models
==========
Implementations for common models used in GP regression and classification. Implementations for common models used in GP regression and classification.
The different models can be viewed in :mod:`GPy.models_modules`, which holds The different models can be viewed in :mod:`GPy.models_modules`, which holds
detailed explanations for the different models. detailed explanations for the different models.
:warning: This module is a convienince module for endusers to use. For developers .. note::
see :mod:`GPy.models_modules`, which holds the implementions for each model. This module is a convienince module for endusers to use. For developers
see :mod:`GPy.models_modules`, which holds the implementions for each model.:
.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
''' '''
__updated__ = '2013-11-28' __updated__ = '2013-11-28'
from models_modules.bayesian_gplvm import BayesianGPLVM from models_modules.bayesian_gplvm import BayesianGPLVM, BayesianGPLVMWithMissingData
from models_modules.gp_regression import GPRegression from models_modules.gp_regression import GPRegression
from models_modules.gp_classification import GPClassification#; _gp_classification = gp_classification ; del gp_classification from models_modules.gp_classification import GPClassification#; _gp_classification = gp_classification ; del gp_classification
from models_modules.sparse_gp_regression import SparseGPRegression#; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression from models_modules.sparse_gp_regression import SparseGPRegression#; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression

92
GPy/models_modules/bayesian_gplvm.py
View file

@ -2,17 +2,18 @@
# 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 itertools
from matplotlib import pyplot
from ..core.sparse_gp import SparseGP from ..core.sparse_gp import SparseGP
from ..likelihoods import Gaussian from ..likelihoods import Gaussian
from .. import kern from .. import kern
import itertools from ..inference.optimization import SCG
from matplotlib.colors import colorConverter from ..util import plot_latent, linalg
from GPy.inference.optimization import SCG from .gplvm import GPLVM, initialise_latent
from GPy.util import plot_latent, linalg from ..util.plot_latent import most_significant_input_dimensions
from .gplvm import GPLVM from ..core.model import Model
from GPy.util.plot_latent import most_significant_input_dimensions from ..util.subarray_and_sorting import common_subarrays
from matplotlib import pyplot
from GPy.core.model import Model
class BayesianGPLVM(SparseGP, GPLVM): class BayesianGPLVM(SparseGP, GPLVM):
""" """
@ -34,7 +35,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
likelihood = likelihood_or_Y likelihood = likelihood_or_Y
if X == None: if X == None:
X = self.initialise_latent(init, input_dim, likelihood.Y) X = initialise_latent(init, input_dim, likelihood.Y)
self.init = init self.init = init
if X_variance is None: if X_variance is None:
@ -308,14 +309,36 @@ class BayesianGPLVMWithMissingData(Model):
:type init: 'PCA' | 'random' :type init: 'PCA' | 'random'
""" """
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10, def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, missing=np.nan, **kwargs): Z=None, kernel=None, **kwargs):
#=======================================================================
# Filter Y, such that same missing data is at same positions.
# If full rows are missing, delete them entirely!
if type(likelihood_or_Y) is np.ndarray: if type(likelihood_or_Y) is np.ndarray:
likelihood = Gaussian(likelihood_or_Y) Y = likelihood_or_Y
likelihood = Gaussian
params = 1.
normalize=None
else: else:
likelihood = likelihood_or_Y Y = likelihood_or_Y.Y
likelihood = likelihood_or_Y.__class__
params = likelihood_or_Y._get_params()
if isinstance(likelihood_or_Y, Gaussian):
normalize = True
scale = likelihood_or_Y._scale
offset = likelihood_or_Y._offset
# Get common subrows
filter_ = np.isnan(Y)
self.subarray_indices = common_subarrays(filter_,axis=1)
likelihoods = [likelihood(Y[~np.array(v,dtype=bool),:][:,ind]) for v,ind in self.subarray_indices.iteritems()]
for l in likelihoods:
l._set_params(params)
if normalize: # get normalization in common
l._scale = scale
l._offset = offset
#=======================================================================
if X == None: if X == None:
X = self.initialise_latent(init, input_dim, likelihood.Y) X = initialise_latent(init, input_dim, Y[:,np.any(np.isnan(Y),1)])
self.init = init self.init = init
if X_variance is None: if X_variance is None:
@ -328,13 +351,52 @@ class BayesianGPLVMWithMissingData(Model):
if kernel is None: if kernel is None:
kernel = kern.rbf(input_dim) # + kern.white(input_dim) kernel = kern.rbf(input_dim) # + kern.white(input_dim)
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs) self.submodels = [BayesianGPLVM(l, input_dim, X, X_variance, init, num_inducing, Z, kernel) for l in likelihoods]
self.gref = self.submodels[0]
#:type self.gref: BayesianGPLVM
self.ensure_default_constraints() self.ensure_default_constraints()
def log_likelihood(self):
ll = -self.gref.KL_divergence()
for g in self.submodels:
ll += SparseGP.log_likelihood(g)
return ll
def _log_likelihood_gradients(self):
dLdmu, dLdS = reduce(lambda a, b: [a[0] + b[0], a[1] + b[1]], (g.dL_dmuS() for g in self.bgplvms))
dKLmu, dKLdS = self.gref.dKL_dmuS()
dLdmu -= dKLmu
dLdS -= dKLdS
dLdmuS = np.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.gref.num_inducing*self.gref.input_dim] for g in self.submodels))
return np.hstack((dLdmuS,
dldzt1,
np.hstack([np.hstack([g.dL_dtheta(),
g.likelihood._gradients(\
partial=g.partial_for_likelihood)]) \
for g in self.submodels])))
def getstate(self):
return Model.getstate(self)+[self.submodels,self.subarray_indices]
def setstate(self, state):
self.subarray_indices = state.pop()
self.submodels = state.pop()
self.gref = self.submodels[0]
Model.setstate(self, state)
self._set_params(self._get_params())
def _get_param_names(self): def _get_param_names(self):
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
return (X_names + S_names + SparseGP._get_param_names(self)) return (X_names + S_names + SparseGP._get_param_names(self.gref))
def _get_params(self):
return self.gref._get_params()
def _set_params(self, x):
[g._set_params(x) for g in self.submodels]
pass pass

19
GPy/models_modules/gplvm.py
View file

@ -9,7 +9,14 @@ from ..core import priors
from ..core import GP from ..core import GP
from ..likelihoods import Gaussian from ..likelihoods import Gaussian
from .. import util from .. import util
from ..util.linalg import pca
def initialise_latent(init, input_dim, Y):
Xr = np.random.randn(Y.shape[0], input_dim)
if init.lower() == 'pca':
PC = pca(Y, input_dim)[0]
Xr[:PC.shape[0], :PC.shape[1]] = PC
return Xr
class GPLVM(GP): class GPLVM(GP):
""" """
@ -20,12 +27,12 @@ class GPLVM(GP):
:param input_dim: latent dimensionality :param input_dim: latent dimensionality
:type input_dim: int :type input_dim: int
:param init: initialisation method for the latent space :param init: initialisation method for the latent space
:type init: 'PCA'|'random' :type init: 'pca'|'random'
""" """
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False): def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False):
if X is None: if X is None:
X = self.initialise_latent(init, input_dim, Y) X = initialise_latent(init, input_dim, Y)
if kernel is None: if kernel is None:
kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2)) kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.)) likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.))
@ -33,14 +40,6 @@ class GPLVM(GP):
self.set_prior('.*X', priors.Gaussian(0, 1)) self.set_prior('.*X', priors.Gaussian(0, 1))
self.ensure_default_constraints() self.ensure_default_constraints()
def initialise_latent(self, init, input_dim, Y):
Xr = np.random.randn(Y.shape[0], input_dim)
if init == 'PCA':
from ..util.linalg import PCA
PC = PCA(Y, input_dim)[0]
Xr[:PC.shape[0], :PC.shape[1]] = PC
return Xr
def _get_param_names(self): def _get_param_names(self):
return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self) return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)

10
GPy/models_modules/mrd.py
View file

@ -5,7 +5,7 @@ Created on 10 Apr 2013
''' '''
from GPy.core import Model from GPy.core import Model
from GPy.core import SparseGP from GPy.core import SparseGP
from GPy.util.linalg import PCA from GPy.util.linalg import pca
import numpy import numpy
import itertools import itertools
import pylab import pylab
@ -26,8 +26,8 @@ class MRD(Model):
:type input_dim: int :type input_dim: int
:param initx: initialisation method for the latent space : :param initx: initialisation method for the latent space :
* 'concat' - PCA on concatenation of all datasets * 'concat' - pca on concatenation of all datasets
* 'single' - Concatenation of PCA on datasets, respectively * 'single' - Concatenation of pca on datasets, respectively
* 'random' - Random draw from a normal * 'random' - Random draw from a normal
:type initx: ['concat'|'single'|'random'] :type initx: ['concat'|'single'|'random']
@ -248,11 +248,11 @@ class MRD(Model):
Ylist.append(likelihood_or_Y.Y) Ylist.append(likelihood_or_Y.Y)
del likelihood_list del likelihood_list
if init in "PCA_concat": if init in "PCA_concat":
X = PCA(numpy.hstack(Ylist), self.input_dim)[0] X = pca(numpy.hstack(Ylist), self.input_dim)[0]
elif init in "PCA_single": elif init in "PCA_single":
X = numpy.zeros((Ylist[0].shape[0], self.input_dim)) X = numpy.zeros((Ylist[0].shape[0], self.input_dim))
for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.input_dim), len(Ylist)), Ylist): for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.input_dim), len(Ylist)), Ylist):
X[:, qs] = PCA(Y, len(qs))[0] X[:, qs] = pca(Y, len(qs))[0]
else: # init == 'random': else: # init == 'random':
X = numpy.random.randn(Ylist[0].shape[0], self.input_dim) X = numpy.random.randn(Ylist[0].shape[0], self.input_dim)
self.X = X self.X = X

7
GPy/models_modules/sparse_gplvm.py
View file

@ -6,10 +6,7 @@ import numpy as np
import pylab as pb import pylab as pb
import sys, pdb import sys, pdb
from sparse_gp_regression import SparseGPRegression from sparse_gp_regression import SparseGPRegression
from gplvm import GPLVM from gplvm import GPLVM, initialise_latent
# from .. import kern
# from ..core import model
# from ..util.linalg import pdinv, PCA
class SparseGPLVM(SparseGPRegression, GPLVM): class SparseGPLVM(SparseGPRegression, GPLVM):
""" """
@ -24,7 +21,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
""" """
def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10): def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
X = self.initialise_latent(init, input_dim, Y) X = initialise_latent(init, input_dim, Y)
SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing) SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
self.ensure_default_constraints() self.ensure_default_constraints()

27
GPy/testing/examples_tests.py
View file

@ -19,25 +19,12 @@ class ExamplesTests(unittest.TestCase):
def _model_instance(self, Model): def _model_instance(self, Model):
self.assertTrue(isinstance(Model, GPy.models)) self.assertTrue(isinstance(Model, GPy.models))
"""
def model_instance_generator(model):
def check_model_returned(self):
self._model_instance(model)
return check_model_returned
def checkgrads_generator(model):
def model_checkgrads(self):
self._checkgrad(model)
return model_checkgrads
"""
def model_checkgrads(model): def model_checkgrads(model):
model.randomize() model.randomize()
#assert model.checkgrad() #NOTE: Step as 1e-4, this should be acceptable for more peaky models
return model.checkgrad() return model.checkgrad(step=1e-4)
def model_instance(model): def model_instance(model):
#assert isinstance(model, GPy.core.model)
return isinstance(model, GPy.core.model.Model) return isinstance(model, GPy.core.model.Model)
def flatten_nested(lst): def flatten_nested(lst):
@ -49,7 +36,7 @@ def flatten_nested(lst):
result.append(element) result.append(element)
return result return result
#@nottest @nottest
def test_models(): def test_models():
optimize=False optimize=False
plot=True plot=True
@ -66,9 +53,11 @@ def test_models():
print "After" print "After"
print functions print functions
for example in functions: for example in functions:
#if example[0] in ['oil', 'silhouette', 'GPLVM_oil_100', 'brendan_faces']: if example[0] in ['epomeo_gpx']:
#print "SKIPPING" #These are the edge cases that we might want to handle specially
#continue if example[0] == 'epomeo_gpx' and not GPy.util.datasets.gpxpy_available:
print "Skipping as gpxpy is not available to parse GPS"
continue
print "Testing example: ", example[0] print "Testing example: ", example[0]
# Generate model # Generate model

1
GPy/testing/likelihoods_tests.py → GPy/testing/likelihood_tests.py
View file

@ -593,7 +593,6 @@ class LaplaceTests(unittest.TestCase):
grad.checkgrad(verbose=1) grad.checkgrad(verbose=1)
self.assertTrue(grad.checkgrad()) self.assertTrue(grad.checkgrad())
#@unittest.skip('Not working yet, needs to be checked')
def test_laplace_log_likelihood(self): def test_laplace_log_likelihood(self):
debug = False debug = False
real_std = 0.1 real_std = 0.1

3
GPy/util/data_resources.json
View file

@ -29,7 +29,8 @@
"urls":[ "urls":[
"http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/ankur_pose_data/" "http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/ankur_pose_data/"
], ],
"details":"Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing." "details":"Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing.",
"size":1
}, },
"osu_accad":{ "osu_accad":{
"files":[ "files":[

13
GPy/util/datasets.py
View file

@ -26,13 +26,16 @@ def reporthook(a,b,c):
# Global variables # Global variables
data_path = os.path.join(os.path.dirname(__file__), 'datasets') data_path = os.path.join(os.path.dirname(__file__), 'datasets')
default_seed = 10000 default_seed = 10000
overide_manual_authorize=False overide_manual_authorize=True
neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/' neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/'
# Read data resources from json file. # Read data resources from json file.
path = os.path.join(os.path.dirname(__file__), 'data_resources.json') # Don't do this when ReadTheDocs is scanning as it breaks things
json_data=open(path).read() on_rtd = os.environ.get('READTHEDOCS', None) == 'True' #Checks if RTD is scanning
data_resources = json.loads(json_data) if not (on_rtd):
path = os.path.join(os.path.dirname(__file__), 'data_resources.json')
json_data=open(path).read()
data_resources = json.loads(json_data)
def prompt_user(prompt): def prompt_user(prompt):
@ -232,7 +235,7 @@ if gpxpy_available:
gpx_file.close() gpx_file.close()
return data_details_return({'X' : X, 'info' : 'Data is an array containing time in seconds, latitude, longitude and elevation in that order.'}, data_set) return data_details_return({'X' : X, 'info' : 'Data is an array containing time in seconds, latitude, longitude and elevation in that order.'}, data_set)
del gpxpy_available #del gpxpy_available

114
GPy/util/diag.py Normal file
View file

@ -0,0 +1,114 @@
'''
.. module:: GPy.util.diag
.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
'''
__updated__ = '2013-12-03'
import numpy as np
def view(A, offset=0):
"""
Get a view on the diagonal elements of a 2D array.
This is actually a view (!) on the diagonal of the array, so you can
in-place adjust the view.
:param :class:`ndarray` A: 2 dimensional numpy array
:param int offset: view offset to give back (negative entries allowed)
:rtype: :class:`ndarray` view of diag(A)
>>> import numpy as np
>>> X = np.arange(9).reshape(3,3)
>>> view(X)
array([0, 4, 8])
>>> d = view(X)
>>> d += 2
>>> view(X)
array([ 2, 6, 10])
>>> view(X, offset=-1)
array([3, 7])
>>> subtract(X, 3, offset=-1)
array([[ 2, 1, 2],
[ 0, 6, 5],
[ 6, 4, 10]])
"""
from numpy.lib.stride_tricks import as_strided
assert A.ndim == 2, "only implemented for 2 dimensions"
assert A.shape[0] == A.shape[1], "attempting to get the view of non-square matrix?!"
if offset > 0:
return as_strided(A[0, offset:], shape=(A.shape[0] - offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
elif offset < 0:
return as_strided(A[-offset:, 0], shape=(A.shape[0] + offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
else:
return as_strided(A, shape=(A.shape[0], ), strides=((A.shape[0]+1)*A.itemsize, ))
def _diag_ufunc(A,b,offset,func):
dA = view(A, offset); func(dA,b,dA)
return A
def times(A, b, offset=0):
"""
Times the view of A with b in place (!).
Returns modified A
Broadcasting is allowed, thus b can be scalar.
if offset is not zero, make sure b is of right shape!
:param ndarray A: 2 dimensional array
:param ndarray-like b: either one dimensional or scalar
:param int offset: same as in view.
:rtype: view of A, which is adjusted inplace
"""
return _diag_ufunc(A, b, offset, np.multiply)
multiply = times
def divide(A, b, offset=0):
"""
Divide the view of A by b in place (!).
Returns modified A
Broadcasting is allowed, thus b can be scalar.
if offset is not zero, make sure b is of right shape!
:param ndarray A: 2 dimensional array
:param ndarray-like b: either one dimensional or scalar
:param int offset: same as in view.
:rtype: view of A, which is adjusted inplace
"""
return _diag_ufunc(A, b, offset, np.divide)
def add(A, b, offset=0):
"""
Add b to the view of A in place (!).
Returns modified A.
Broadcasting is allowed, thus b can be scalar.
if offset is not zero, make sure b is of right shape!
:param ndarray A: 2 dimensional array
:param ndarray-like b: either one dimensional or scalar
:param int offset: same as in view.
:rtype: view of A, which is adjusted inplace
"""
return _diag_ufunc(A, b, offset, np.add)
def subtract(A, b, offset=0):
"""
Subtract b from the view of A in place (!).
Returns modified A.
Broadcasting is allowed, thus b can be scalar.
if offset is not zero, make sure b is of right shape!
:param ndarray A: 2 dimensional array
:param ndarray-like b: either one dimensional or scalar
:param int offset: same as in view.
:rtype: view of A, which is adjusted inplace
"""
return _diag_ufunc(A, b, offset, np.subtract)
if __name__ == '__main__':
import doctest
doctest.testmod()

122
GPy/util/linalg.py
View file

@ -217,7 +217,7 @@ def multiple_pdinv(A):
return np.dstack(invs), np.array(halflogdets) return np.dstack(invs), np.array(halflogdets)
def PCA(Y, input_dim): def pca(Y, input_dim):
""" """
Principal component analysis: maximum likelihood solution by SVD Principal component analysis: maximum likelihood solution by SVD
@ -230,7 +230,7 @@ def PCA(Y, input_dim):
""" """
if not np.allclose(Y.mean(axis=0), 0.0): if not np.allclose(Y.mean(axis=0), 0.0):
print "Y is not zero mean, centering it locally (GPy.util.linalg.PCA)" print "Y is not zero mean, centering it locally (GPy.util.linalg.pca)"
# Y -= Y.mean(axis=0) # Y -= Y.mean(axis=0)
@ -241,6 +241,124 @@ def PCA(Y, input_dim):
W *= v; W *= v;
return X, W.T return X, W.T
def ppca(Y, Q, iterations=100):
"""
EM implementation for probabilistic pca.
:param array-like Y: Observed Data
:param int Q: Dimensionality for reduced array
:param int iterations: number of iterations for EM
"""
from numpy.ma import dot as madot
N, D = Y.shape
# Initialise W randomly
W = np.random.randn(D, Q) * 1e-3
Y = np.ma.masked_invalid(Y, copy=0)
mu = Y.mean(0)
Ycentered = Y - mu
try:
for _ in range(iterations):
exp_x = np.asarray_chkfinite(np.linalg.solve(W.T.dot(W), madot(W.T, Ycentered.T))).T
W = np.asarray_chkfinite(np.linalg.solve(exp_x.T.dot(exp_x), madot(exp_x.T, Ycentered))).T
except np.linalg.linalg.LinAlgError:
#"converged"
pass
return np.asarray_chkfinite(exp_x), np.asarray_chkfinite(W)
def ppca_missing_data_at_random(Y, Q, iters=100):
"""
EM implementation of Probabilistic pca for when there is missing data.
Taken from <SheffieldML, https://github.com/SheffieldML>
.. math:
\\mathbf{Y} = \mathbf{XW} + \\epsilon \\text{, where}
\\epsilon = \\mathcal{N}(0, \\sigma^2 \mathbf{I})
:returns: X, W, sigma^2
"""
from numpy.ma import dot as madot
import diag
from GPy.util.subarray_and_sorting import common_subarrays
import time
debug = 1
# Initialise W randomly
N, D = Y.shape
W = np.random.randn(Q, D) * 1e-3
Y = np.ma.masked_invalid(Y, copy=1)
nu = 1.
#num_obs_i = 1./Y.count()
Ycentered = Y - Y.mean(0)
X = np.zeros((N,Q))
cs = common_subarrays(Y.mask)
cr = common_subarrays(Y.mask, 1)
Sigma = np.zeros((N, Q, Q))
Sigma2 = np.zeros((N, Q, Q))
mu = np.zeros(D)
if debug:
import matplotlib.pyplot as pylab
fig = pylab.figure("FIT MISSING DATA");
ax = fig.gca()
ax.cla()
lines = pylab.plot(np.zeros((N,Q)).dot(W))
W2 = np.zeros((Q,D))
for i in range(iters):
# Sigma = np.linalg.solve(diag.add(madot(W,W.T), nu), diag.times(np.eye(Q),nu))
# exp_x = madot(madot(Ycentered, W.T),Sigma)/nu
# Ycentered = (Y - exp_x.dot(W).mean(0))
# #import ipdb;ipdb.set_trace()
# #Ycentered = mu
# W = np.linalg.solve(madot(exp_x.T,exp_x) + Sigma, madot(exp_x.T, Ycentered))
# nu = (((Ycentered - madot(exp_x, W))**2).sum(0) + madot(W.T,madot(Sigma,W)).sum(0)).sum()/N
for csi, (mask, index) in enumerate(cs.iteritems()):
mask = ~np.array(mask)
Sigma2[index, :, :] = nu * np.linalg.inv(diag.add(W2[:,mask].dot(W2[:,mask].T), nu))
#X[index,:] = madot((Sigma[csi]/nu),madot(W,Ycentered[index].T))[:,0]
X2 = ((Sigma2/nu) * (madot(Ycentered,W2.T).base)[:,:,None]).sum(-1)
mu2 = (Y - X.dot(W)).mean(0)
for n in range(N):
Sigma[n] = nu * np.linalg.inv(diag.add(W[:,~Y.mask[n]].dot(W[:,~Y.mask[n]].T), nu))
X[n, :] = (Sigma[n]/nu).dot(W[:,~Y.mask[n]].dot(Ycentered[n,~Y.mask[n]].T))
for d in range(D):
mu[d] = (Y[~Y.mask[:,d], d] - X[~Y.mask[:,d]].dot(W[:, d])).mean()
Ycentered = (Y - mu)
nu3 = 0.
for cri, (mask, index) in enumerate(cr.iteritems()):
mask = ~np.array(mask)
W2[:,index] = np.linalg.solve(X[mask].T.dot(X[mask]) + Sigma[mask].sum(0), madot(X[mask].T, Ycentered[mask,index]))[:,None]
W2[:,index] = np.linalg.solve(X.T.dot(X) + Sigma.sum(0), madot(X.T, Ycentered[:,index]))
#nu += (((Ycentered[mask,index] - X[mask].dot(W[:,index]))**2).sum(0) + W[:,index].T.dot(Sigma[mask].sum(0).dot(W[:,index])).sum(0)).sum()
nu3 += (((Ycentered[index] - X.dot(W[:,index]))**2).sum(0) + W[:,index].T.dot(Sigma.sum(0).dot(W[:,index])).sum(0)).sum()
nu3 /= N
nu = 0.
nu2 = 0.
W = np.zeros((Q,D))
for j in range(D):
W[:,j] = np.linalg.solve(X[~Y.mask[:,j]].T.dot(X[~Y.mask[:,j]]) + Sigma[~Y.mask[:,j]].sum(0), madot(X[~Y.mask[:,j]].T, Ycentered[~Y.mask[:,j],j]))
nu2f = np.tensordot(W[:,j].T, Sigma[~Y.mask[:,j],:,:], [0,1]).dot(W[:,j])
nu2s = W[:,j].T.dot(Sigma[~Y.mask[:,j],:,:].sum(0).dot(W[:,j]))
nu2 += (((Ycentered[~Y.mask[:,j],j] - X[~Y.mask[:,j],:].dot(W[:,j]))**2) + nu2f).sum()
for i in range(N):
if not Y.mask[i,j]:
nu += ((Ycentered[i,j] - X[i,:].dot(W[:,j]))**2) + W[:,j].T.dot(Sigma[i,:,:].dot(W[:,j]))
nu /= N
nu2 /= N
nu4 = (((Ycentered - X.dot(W))**2).sum(0) + W.T.dot(Sigma.sum(0).dot(W)).sum(0)).sum()/N
import ipdb;ipdb.set_trace()
if debug:
#print Sigma[0]
print "nu:", nu, "sum(X):", X.sum()
pred_y = X.dot(W)
for x, l in zip(pred_y.T, lines):
l.set_ydata(x)
ax.autoscale_view()
ax.set_ylim(pred_y.min(), pred_y.max())
fig.canvas.draw()
time.sleep(.3)
return np.asarray_chkfinite(X), np.asarray_chkfinite(W), nu
def tdot_numpy(mat, out=None): def tdot_numpy(mat, out=None):
return np.dot(mat, mat.T, out) return np.dot(mat, mat.T, out)

2
GPy/util/plot_latent.py
View file

@ -48,7 +48,7 @@ def plot_latent(model, labels=None, which_indices=None,
var = var[:, :1] var = var[:, :1]
return np.log(var) return np.log(var)
view = ImshowController(ax, plot_function, view = ImshowController(ax, plot_function,
tuple(model.X.min(0)[:, [input_1, input_2]]) + tuple(model.X.max(0)[:, [input_1, input_2]]), tuple(model.X[:, [input_1, input_2]].min(0)) + tuple(model.X[:, [input_1, input_2]].max(0)),
resolution, aspect=aspect, interpolation='bilinear', resolution, aspect=aspect, interpolation='bilinear',
cmap=pb.cm.binary) cmap=pb.cm.binary)

56
GPy/util/subarray_and_sorting.py Normal file
View file

@ -0,0 +1,56 @@
'''
.. module:: GPy.util.subarray_and_sorting
.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
'''
__updated__ = '2013-12-02'
import numpy as np
def common_subarrays(X, axis=0):
"""
Find common subarrays of 2 dimensional X, where axis is the axis to apply the search over.
Common subarrays are returned as a dictionary of <subarray, [index]> pairs, where
the subarray is a tuple representing the subarray and the index is the index
for the subarray in X, where index is the index to the remaining axis.
:param :class:`np.ndarray` X: 2d array to check for common subarrays in
:param int axis: axis to apply subarray detection over.
When the index is 0, compare rows, columns, otherwise.
Examples:
=========
In a 2d array:
>>> import numpy as np
>>> X = np.zeros((3,6), dtype=bool)
>>> X[[1,1,1],[0,4,5]] = 1; X[1:,[2,3]] = 1
>>> X
array([[False, False, False, False, False, False],
[ True, False, True, True, True, True],
[False, False, True, True, False, False]], dtype=bool)
>>> d = common_subarrays(X,axis=1)
>>> len(d)
3
>>> X[:, d[tuple(X[:,0])]]
array([[False, False, False],
[ True, True, True],
[False, False, False]], dtype=bool)
>>> d[tuple(X[:,4])] == d[tuple(X[:,0])] == [0, 4, 5]
True
>>> d[tuple(X[:,1])]
[1]
"""
from collections import defaultdict
from itertools import count
from operator import iadd
assert X.ndim == 2 and axis in (0,1), "Only implemented for 2D arrays"
subarrays = defaultdict(list)
cnt = count()
np.apply_along_axis(lambda x: iadd(subarrays[tuple(x)], [cnt.next()]), 1-axis, X)
return subarrays
if __name__ == '__main__':
import doctest
doctest.testmod()

65
doc/GPy.core.rst
View file

@ -1,107 +1,102 @@
core Package GPy.core package
============ ================
:mod:`core` Package Submodules
------------------- ----------
.. automodule:: GPy.core GPy.core.domains module
:members: -----------------------
:undoc-members:
:show-inheritance:
:mod:`domains` Module
---------------------
.. automodule:: GPy.core.domains .. automodule:: GPy.core.domains
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`fitc` Module GPy.core.fitc module
------------------ --------------------
.. automodule:: GPy.core.fitc .. automodule:: GPy.core.fitc
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gp` Module GPy.core.gp module
---------------- ------------------
.. automodule:: GPy.core.gp .. automodule:: GPy.core.gp
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gp_base` Module GPy.core.gp_base module
--------------------- -----------------------
.. automodule:: GPy.core.gp_base .. automodule:: GPy.core.gp_base
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`mapping` Module GPy.core.mapping module
--------------------- -----------------------
.. automodule:: GPy.core.mapping .. automodule:: GPy.core.mapping
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`model` Module GPy.core.model module
------------------- ---------------------
.. automodule:: GPy.core.model .. automodule:: GPy.core.model
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`parameterized` Module GPy.core.parameterized module
--------------------------- -----------------------------
.. automodule:: GPy.core.parameterized .. automodule:: GPy.core.parameterized
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`priors` Module GPy.core.priors module
-------------------- ----------------------
.. automodule:: GPy.core.priors .. automodule:: GPy.core.priors
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sparse_gp` Module GPy.core.sparse_gp module
----------------------- -------------------------
.. automodule:: GPy.core.sparse_gp .. automodule:: GPy.core.sparse_gp
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`svigp` Module GPy.core.svigp module
------------------- ---------------------
.. automodule:: GPy.core.svigp .. automodule:: GPy.core.svigp
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`transformations` Module GPy.core.transformations module
----------------------------- -------------------------------
.. automodule:: GPy.core.transformations .. automodule:: GPy.core.transformations
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`variational` Module
-------------------------
.. automodule:: GPy.core.variational Module contents
---------------
.. automodule:: GPy.core
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:

47
doc/GPy.examples.rst
View file

@ -1,59 +1,62 @@
examples Package GPy.examples package
================ ====================
:mod:`examples` Package Submodules
----------------------- ----------
.. automodule:: GPy.examples GPy.examples.classification module
:members: ----------------------------------
:undoc-members:
:show-inheritance:
:mod:`classification` Module
----------------------------
.. automodule:: GPy.examples.classification .. automodule:: GPy.examples.classification
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`dimensionality_reduction` Module GPy.examples.dimensionality_reduction module
-------------------------------------- --------------------------------------------
.. automodule:: GPy.examples.dimensionality_reduction .. automodule:: GPy.examples.dimensionality_reduction
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`laplace_approximations` Module GPy.examples.non_gaussian module
------------------------------------ --------------------------------
.. automodule:: GPy.examples.laplace_approximations .. automodule:: GPy.examples.non_gaussian
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`regression` Module GPy.examples.regression module
------------------------ ------------------------------
.. automodule:: GPy.examples.regression .. automodule:: GPy.examples.regression
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`stochastic` Module GPy.examples.stochastic module
------------------------ ------------------------------
.. automodule:: GPy.examples.stochastic .. automodule:: GPy.examples.stochastic
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`tutorials` Module GPy.examples.tutorials module
----------------------- -----------------------------
.. automodule:: GPy.examples.tutorials .. automodule:: GPy.examples.tutorials
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.examples
:members:
:undoc-members:
:show-inheritance:

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@ -1,51 +1,62 @@
inference Package GPy.inference package
================= =====================
:mod:`conjugate_gradient_descent` Module Submodules
---------------------------------------- ----------
GPy.inference.conjugate_gradient_descent module
-----------------------------------------------
.. automodule:: GPy.inference.conjugate_gradient_descent .. automodule:: GPy.inference.conjugate_gradient_descent
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gradient_descent_update_rules` Module GPy.inference.gradient_descent_update_rules module
------------------------------------------- --------------------------------------------------
.. automodule:: GPy.inference.gradient_descent_update_rules .. automodule:: GPy.inference.gradient_descent_update_rules
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`optimization` Module GPy.inference.optimization module
-------------------------- ---------------------------------
.. automodule:: GPy.inference.optimization .. automodule:: GPy.inference.optimization
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`samplers` Module GPy.inference.samplers module
---------------------- -----------------------------
.. automodule:: GPy.inference.samplers .. automodule:: GPy.inference.samplers
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`scg` Module GPy.inference.scg module
----------------- ------------------------
.. automodule:: GPy.inference.scg .. automodule:: GPy.inference.scg
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sgd` Module GPy.inference.sgd module
----------------- ------------------------
.. automodule:: GPy.inference.sgd .. automodule:: GPy.inference.sgd
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.inference
:members:
:undoc-members:
:show-inheritance:

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parts Package GPy.kern.parts package
============= ======================
:mod:`parts` Package Submodules
-------------------- ----------
.. automodule:: GPy.kern.parts GPy.kern.parts.Brownian module
:members: ------------------------------
:undoc-members:
:show-inheritance:
:mod:`Brownian` Module
----------------------
.. automodule:: GPy.kern.parts.Brownian .. automodule:: GPy.kern.parts.Brownian
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`Matern32` Module GPy.kern.parts.Matern32 module
---------------------- ------------------------------
.. automodule:: GPy.kern.parts.Matern32 .. automodule:: GPy.kern.parts.Matern32
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`Matern52` Module GPy.kern.parts.Matern52 module
---------------------- ------------------------------
.. automodule:: GPy.kern.parts.Matern52 .. automodule:: GPy.kern.parts.Matern52
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`ODE_1` Module GPy.kern.parts.ODE_1 module
------------------- ---------------------------
.. automodule:: GPy.kern.parts.ODE_1 .. automodule:: GPy.kern.parts.ODE_1
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`ODE_UY` Module GPy.kern.parts.ODE_UY module
-------------------- ----------------------------
.. automodule:: GPy.kern.parts.ODE_UY .. automodule:: GPy.kern.parts.ODE_UY
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`bias` Module GPy.kern.parts.bias module
------------------ --------------------------
.. automodule:: GPy.kern.parts.bias .. automodule:: GPy.kern.parts.bias
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`coregionalize` Module GPy.kern.parts.coregionalize module
--------------------------- -----------------------------------
.. automodule:: GPy.kern.parts.coregionalize .. automodule:: GPy.kern.parts.coregionalize
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`eq_ode1` Module GPy.kern.parts.eq_ode1 module
--------------------- -----------------------------
.. automodule:: GPy.kern.parts.eq_ode1 .. automodule:: GPy.kern.parts.eq_ode1
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`exponential` Module GPy.kern.parts.exponential module
------------------------- ---------------------------------
.. automodule:: GPy.kern.parts.exponential .. automodule:: GPy.kern.parts.exponential
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`finite_dimensional` Module GPy.kern.parts.finite_dimensional module
-------------------------------- ----------------------------------------
.. automodule:: GPy.kern.parts.finite_dimensional .. automodule:: GPy.kern.parts.finite_dimensional
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`fixed` Module GPy.kern.parts.fixed module
------------------- ---------------------------
.. automodule:: GPy.kern.parts.fixed .. automodule:: GPy.kern.parts.fixed
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gibbs` Module GPy.kern.parts.gibbs module
------------------- ---------------------------
.. automodule:: GPy.kern.parts.gibbs .. automodule:: GPy.kern.parts.gibbs
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`hetero` Module GPy.kern.parts.hetero module
-------------------- ----------------------------
.. automodule:: GPy.kern.parts.hetero .. automodule:: GPy.kern.parts.hetero
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`hierarchical` Module GPy.kern.parts.hierarchical module
-------------------------- ----------------------------------
.. automodule:: GPy.kern.parts.hierarchical .. automodule:: GPy.kern.parts.hierarchical
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`independent_outputs` Module GPy.kern.parts.independent_outputs module
--------------------------------- -----------------------------------------
.. automodule:: GPy.kern.parts.independent_outputs .. automodule:: GPy.kern.parts.independent_outputs
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`kernpart` Module GPy.kern.parts.kernpart module
---------------------- ------------------------------
.. automodule:: GPy.kern.parts.kernpart .. automodule:: GPy.kern.parts.kernpart
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`linear` Module GPy.kern.parts.linear module
-------------------- ----------------------------
.. automodule:: GPy.kern.parts.linear .. automodule:: GPy.kern.parts.linear
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`mlp` Module GPy.kern.parts.mlp module
----------------- -------------------------
.. automodule:: GPy.kern.parts.mlp .. automodule:: GPy.kern.parts.mlp
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`periodic_Matern32` Module GPy.kern.parts.periodic_Matern32 module
------------------------------- ---------------------------------------
.. automodule:: GPy.kern.parts.periodic_Matern32 .. automodule:: GPy.kern.parts.periodic_Matern32
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`periodic_Matern52` Module GPy.kern.parts.periodic_Matern52 module
------------------------------- ---------------------------------------
.. automodule:: GPy.kern.parts.periodic_Matern52 .. automodule:: GPy.kern.parts.periodic_Matern52
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`periodic_exponential` Module GPy.kern.parts.periodic_exponential module
---------------------------------- ------------------------------------------
.. automodule:: GPy.kern.parts.periodic_exponential .. automodule:: GPy.kern.parts.periodic_exponential
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`poly` Module GPy.kern.parts.poly module
------------------ --------------------------
.. automodule:: GPy.kern.parts.poly .. automodule:: GPy.kern.parts.poly
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`prod` Module GPy.kern.parts.prod module
------------------ --------------------------
.. automodule:: GPy.kern.parts.prod .. automodule:: GPy.kern.parts.prod
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`prod_orthogonal` Module GPy.kern.parts.prod_orthogonal module
----------------------------- -------------------------------------
.. automodule:: GPy.kern.parts.prod_orthogonal .. automodule:: GPy.kern.parts.prod_orthogonal
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`rational_quadratic` Module GPy.kern.parts.rational_quadratic module
-------------------------------- ----------------------------------------
.. automodule:: GPy.kern.parts.rational_quadratic .. automodule:: GPy.kern.parts.rational_quadratic
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`rbf` Module GPy.kern.parts.rbf module
----------------- -------------------------
.. automodule:: GPy.kern.parts.rbf .. automodule:: GPy.kern.parts.rbf
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`rbf_inv` Module GPy.kern.parts.rbf_inv module
--------------------- -----------------------------
.. automodule:: GPy.kern.parts.rbf_inv .. automodule:: GPy.kern.parts.rbf_inv
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`rbfcos` Module GPy.kern.parts.rbfcos module
-------------------- ----------------------------
.. automodule:: GPy.kern.parts.rbfcos .. automodule:: GPy.kern.parts.rbfcos
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`spline` Module GPy.kern.parts.spline module
-------------------- ----------------------------
.. automodule:: GPy.kern.parts.spline .. automodule:: GPy.kern.parts.spline
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`symmetric` Module GPy.kern.parts.symmetric module
----------------------- -------------------------------
.. automodule:: GPy.kern.parts.symmetric .. automodule:: GPy.kern.parts.symmetric
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sympy_helpers` Module GPy.kern.parts.sympy_helpers module
--------------------------- -----------------------------------
.. automodule:: GPy.kern.parts.sympy_helpers .. automodule:: GPy.kern.parts.sympy_helpers
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sympykern` Module GPy.kern.parts.sympykern module
----------------------- -------------------------------
.. automodule:: GPy.kern.parts.sympykern .. automodule:: GPy.kern.parts.sympykern
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`white` Module GPy.kern.parts.white module
------------------- ---------------------------
.. automodule:: GPy.kern.parts.white .. automodule:: GPy.kern.parts.white
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.kern.parts
:members:
:undoc-members:
:show-inheritance:

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@ -1,29 +1,5 @@
kern Package GPy.kern package
============ ================
:mod:`kern` Package
-------------------
.. automodule:: GPy.kern
:members:
:undoc-members:
:show-inheritance:
:mod:`constructors` Module
--------------------------
.. automodule:: GPy.kern.constructors
:members:
:undoc-members:
:show-inheritance:
:mod:`kern` Module
------------------
.. automodule:: GPy.kern.kern
:members:
:undoc-members:
:show-inheritance:
Subpackages Subpackages
----------- -----------
@ -32,3 +8,30 @@ Subpackages
GPy.kern.parts GPy.kern.parts
Submodules
----------
GPy.kern.constructors module
----------------------------
.. automodule:: GPy.kern.constructors
:members:
:undoc-members:
:show-inheritance:
GPy.kern.kern module
--------------------
.. automodule:: GPy.kern.kern
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: GPy.kern
:members:
:undoc-members:
:show-inheritance:

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@ -1,75 +1,78 @@
noise_models Package GPy.likelihoods.noise_models package
==================== ====================================
:mod:`noise_models` Package Submodules
--------------------------- ----------
.. automodule:: GPy.likelihoods.noise_models GPy.likelihoods.noise_models.bernoulli_noise module
:members: ---------------------------------------------------
:undoc-members:
:show-inheritance:
:mod:`bernoulli_noise` Module
-----------------------------
.. automodule:: GPy.likelihoods.noise_models.bernoulli_noise .. automodule:: GPy.likelihoods.noise_models.bernoulli_noise
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`exponential_noise` Module GPy.likelihoods.noise_models.exponential_noise module
------------------------------- -----------------------------------------------------
.. automodule:: GPy.likelihoods.noise_models.exponential_noise .. automodule:: GPy.likelihoods.noise_models.exponential_noise
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gamma_noise` Module GPy.likelihoods.noise_models.gamma_noise module
------------------------- -----------------------------------------------
.. automodule:: GPy.likelihoods.noise_models.gamma_noise .. automodule:: GPy.likelihoods.noise_models.gamma_noise
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gaussian_noise` Module GPy.likelihoods.noise_models.gaussian_noise module
---------------------------- --------------------------------------------------
.. automodule:: GPy.likelihoods.noise_models.gaussian_noise .. automodule:: GPy.likelihoods.noise_models.gaussian_noise
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gp_transformations` Module GPy.likelihoods.noise_models.gp_transformations module
-------------------------------- ------------------------------------------------------
.. automodule:: GPy.likelihoods.noise_models.gp_transformations .. automodule:: GPy.likelihoods.noise_models.gp_transformations
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`noise_distributions` Module GPy.likelihoods.noise_models.noise_distributions module
--------------------------------- -------------------------------------------------------
.. automodule:: GPy.likelihoods.noise_models.noise_distributions .. automodule:: GPy.likelihoods.noise_models.noise_distributions
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`poisson_noise` Module GPy.likelihoods.noise_models.poisson_noise module
--------------------------- -------------------------------------------------
.. automodule:: GPy.likelihoods.noise_models.poisson_noise .. automodule:: GPy.likelihoods.noise_models.poisson_noise
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`student_t_noise` Module GPy.likelihoods.noise_models.student_t_noise module
----------------------------- ---------------------------------------------------
.. automodule:: GPy.likelihoods.noise_models.student_t_noise .. automodule:: GPy.likelihoods.noise_models.student_t_noise
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.likelihoods.noise_models
:members:
:undoc-members:
:show-inheritance:

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@ -1,69 +1,5 @@
likelihoods Package GPy.likelihoods package
=================== =======================
:mod:`likelihoods` Package
--------------------------
.. automodule:: GPy.likelihoods
:members:
:undoc-members:
:show-inheritance:
:mod:`ep` Module
----------------
.. automodule:: GPy.likelihoods.ep
:members:
:undoc-members:
:show-inheritance:
:mod:`ep_mixed_noise` Module
----------------------------
.. automodule:: GPy.likelihoods.ep_mixed_noise
:members:
:undoc-members:
:show-inheritance:
:mod:`gaussian` Module
----------------------
.. automodule:: GPy.likelihoods.gaussian
:members:
:undoc-members:
:show-inheritance:
:mod:`gaussian_mixed_noise` Module
----------------------------------
.. automodule:: GPy.likelihoods.gaussian_mixed_noise
:members:
:undoc-members:
:show-inheritance:
:mod:`laplace` Module
---------------------
.. automodule:: GPy.likelihoods.laplace
:members:
:undoc-members:
:show-inheritance:
:mod:`likelihood` Module
------------------------
.. automodule:: GPy.likelihoods.likelihood
:members:
:undoc-members:
:show-inheritance:
:mod:`noise_model_constructors` Module
--------------------------------------
.. automodule:: GPy.likelihoods.noise_model_constructors
:members:
:undoc-members:
:show-inheritance:
Subpackages Subpackages
----------- -----------
@ -72,3 +8,70 @@ Subpackages
GPy.likelihoods.noise_models GPy.likelihoods.noise_models
Submodules
----------
GPy.likelihoods.ep module
-------------------------
.. automodule:: GPy.likelihoods.ep
:members:
:undoc-members:
:show-inheritance:
GPy.likelihoods.ep_mixed_noise module
-------------------------------------
.. automodule:: GPy.likelihoods.ep_mixed_noise
:members:
:undoc-members:
:show-inheritance:
GPy.likelihoods.gaussian module
-------------------------------
.. automodule:: GPy.likelihoods.gaussian
:members:
:undoc-members:
:show-inheritance:
GPy.likelihoods.gaussian_mixed_noise module
-------------------------------------------
.. automodule:: GPy.likelihoods.gaussian_mixed_noise
:members:
:undoc-members:
:show-inheritance:
GPy.likelihoods.laplace module
------------------------------
.. automodule:: GPy.likelihoods.laplace
:members:
:undoc-members:
:show-inheritance:
GPy.likelihoods.likelihood module
---------------------------------
.. automodule:: GPy.likelihoods.likelihood
:members:
:undoc-members:
:show-inheritance:
GPy.likelihoods.noise_model_constructors module
-----------------------------------------------
.. automodule:: GPy.likelihoods.noise_model_constructors
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: GPy.likelihoods
:members:
:undoc-members:
:show-inheritance:

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@ -1,35 +1,38 @@
mappings Package GPy.mappings package
================ ====================
:mod:`mappings` Package Submodules
----------------------- ----------
.. automodule:: GPy.mappings GPy.mappings.kernel module
:members: --------------------------
:undoc-members:
:show-inheritance:
:mod:`kernel` Module
--------------------
.. automodule:: GPy.mappings.kernel .. automodule:: GPy.mappings.kernel
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`linear` Module GPy.mappings.linear module
-------------------- --------------------------
.. automodule:: GPy.mappings.linear .. automodule:: GPy.mappings.linear
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`mlp` Module GPy.mappings.mlp module
----------------- -----------------------
.. automodule:: GPy.mappings.mlp .. automodule:: GPy.mappings.mlp
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.mappings
:members:
:undoc-members:
:show-inheritance:

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models_modules Package GPy.models_modules package
====================== ==========================
:mod:`models_modules` Package Submodules
----------------------------- ----------
.. automodule:: GPy.models_modules GPy.models_modules.bayesian_gplvm module
:members: ----------------------------------------
:undoc-members:
:show-inheritance:
:mod:`bayesian_gplvm` Module
----------------------------
.. automodule:: GPy.models_modules.bayesian_gplvm .. automodule:: GPy.models_modules.bayesian_gplvm
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`bcgplvm` Module GPy.models_modules.bcgplvm module
--------------------- ---------------------------------
.. automodule:: GPy.models_modules.bcgplvm .. automodule:: GPy.models_modules.bcgplvm
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`fitc_classification` Module GPy.models_modules.fitc_classification module
--------------------------------- ---------------------------------------------
.. automodule:: GPy.models_modules.fitc_classification .. automodule:: GPy.models_modules.fitc_classification
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gp_classification` Module GPy.models_modules.gp_classification module
------------------------------- -------------------------------------------
.. automodule:: GPy.models_modules.gp_classification .. automodule:: GPy.models_modules.gp_classification
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gp_multioutput_regression` Module GPy.models_modules.gp_multioutput_regression module
--------------------------------------- ---------------------------------------------------
.. automodule:: GPy.models_modules.gp_multioutput_regression .. automodule:: GPy.models_modules.gp_multioutput_regression
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gp_regression` Module GPy.models_modules.gp_regression module
--------------------------- ---------------------------------------
.. automodule:: GPy.models_modules.gp_regression .. automodule:: GPy.models_modules.gp_regression
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gplvm` Module GPy.models_modules.gplvm module
------------------- -------------------------------
.. automodule:: GPy.models_modules.gplvm .. automodule:: GPy.models_modules.gplvm
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gradient_checker` Module GPy.models_modules.gradient_checker module
------------------------------ ------------------------------------------
.. automodule:: GPy.models_modules.gradient_checker .. automodule:: GPy.models_modules.gradient_checker
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`mrd` Module GPy.models_modules.mrd module
----------------- -----------------------------
.. automodule:: GPy.models_modules.mrd .. automodule:: GPy.models_modules.mrd
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sparse_gp_classification` Module GPy.models_modules.sparse_gp_classification module
-------------------------------------- --------------------------------------------------
.. automodule:: GPy.models_modules.sparse_gp_classification .. automodule:: GPy.models_modules.sparse_gp_classification
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sparse_gp_multioutput_regression` Module GPy.models_modules.sparse_gp_multioutput_regression module
---------------------------------------------- ----------------------------------------------------------
.. automodule:: GPy.models_modules.sparse_gp_multioutput_regression .. automodule:: GPy.models_modules.sparse_gp_multioutput_regression
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sparse_gp_regression` Module GPy.models_modules.sparse_gp_regression module
---------------------------------- ----------------------------------------------
.. automodule:: GPy.models_modules.sparse_gp_regression .. automodule:: GPy.models_modules.sparse_gp_regression
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sparse_gplvm` Module GPy.models_modules.sparse_gplvm module
-------------------------- --------------------------------------
.. automodule:: GPy.models_modules.sparse_gplvm .. automodule:: GPy.models_modules.sparse_gplvm
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`svigp_regression` Module GPy.models_modules.svigp_regression module
------------------------------ ------------------------------------------
.. automodule:: GPy.models_modules.svigp_regression .. automodule:: GPy.models_modules.svigp_regression
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`warped_gp` Module GPy.models_modules.warped_gp module
----------------------- -----------------------------------
.. automodule:: GPy.models_modules.warped_gp .. automodule:: GPy.models_modules.warped_gp
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.models_modules
:members:
:undoc-members:
:show-inheritance:

37
doc/GPy.rst
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@ -1,22 +1,6 @@
GPy Package GPy package
=========== ===========
:mod:`GPy` Package
------------------
.. automodule:: GPy.__init__
:members:
:undoc-members:
:show-inheritance:
:mod:`models` Module
--------------------
.. automodule:: GPy.models
:members:
:undoc-members:
:show-inheritance:
Subpackages Subpackages
----------- -----------
@ -32,3 +16,22 @@ Subpackages
GPy.testing GPy.testing
GPy.util GPy.util
Submodules
----------
GPy.models module
-----------------
.. automodule:: GPy.models
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: GPy
:members:
:undoc-members:
:show-inheritance:

83
doc/GPy.testing.rst
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@ -1,131 +1,134 @@
testing Package GPy.testing package
=============== ===================
:mod:`testing` Package Submodules
---------------------- ----------
.. automodule:: GPy.testing GPy.testing.bcgplvm_tests module
:members: --------------------------------
:undoc-members:
:show-inheritance:
:mod:`bcgplvm_tests` Module
---------------------------
.. automodule:: GPy.testing.bcgplvm_tests .. automodule:: GPy.testing.bcgplvm_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`bgplvm_tests` Module GPy.testing.bgplvm_tests module
-------------------------- -------------------------------
.. automodule:: GPy.testing.bgplvm_tests .. automodule:: GPy.testing.bgplvm_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`cgd_tests` Module GPy.testing.cgd_tests module
----------------------- ----------------------------
.. automodule:: GPy.testing.cgd_tests .. automodule:: GPy.testing.cgd_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`examples_tests` Module GPy.testing.examples_tests module
---------------------------- ---------------------------------
.. automodule:: GPy.testing.examples_tests .. automodule:: GPy.testing.examples_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gp_transformation_tests` Module GPy.testing.gp_transformation_tests module
------------------------------------- ------------------------------------------
.. automodule:: GPy.testing.gp_transformation_tests .. automodule:: GPy.testing.gp_transformation_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`gplvm_tests` Module GPy.testing.gplvm_tests module
------------------------- ------------------------------
.. automodule:: GPy.testing.gplvm_tests .. automodule:: GPy.testing.gplvm_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`kernel_tests` Module GPy.testing.kernel_tests module
-------------------------- -------------------------------
.. automodule:: GPy.testing.kernel_tests .. automodule:: GPy.testing.kernel_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`likelihoods_tests` Module GPy.testing.likelihood_tests module
------------------------------- -----------------------------------
.. automodule:: GPy.testing.likelihoods_tests .. automodule:: GPy.testing.likelihood_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`mapping_tests` Module GPy.testing.mapping_tests module
--------------------------- --------------------------------
.. automodule:: GPy.testing.mapping_tests .. automodule:: GPy.testing.mapping_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`mrd_tests` Module GPy.testing.mrd_tests module
----------------------- ----------------------------
.. automodule:: GPy.testing.mrd_tests .. automodule:: GPy.testing.mrd_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`prior_tests` Module GPy.testing.prior_tests module
------------------------- ------------------------------
.. automodule:: GPy.testing.prior_tests .. automodule:: GPy.testing.prior_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`psi_stat_expectation_tests` Module GPy.testing.psi_stat_expectation_tests module
---------------------------------------- ---------------------------------------------
.. automodule:: GPy.testing.psi_stat_expectation_tests .. automodule:: GPy.testing.psi_stat_expectation_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`psi_stat_gradient_tests` Module GPy.testing.psi_stat_gradient_tests module
------------------------------------- ------------------------------------------
.. automodule:: GPy.testing.psi_stat_gradient_tests .. automodule:: GPy.testing.psi_stat_gradient_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`sparse_gplvm_tests` Module GPy.testing.sparse_gplvm_tests module
-------------------------------- -------------------------------------
.. automodule:: GPy.testing.sparse_gplvm_tests .. automodule:: GPy.testing.sparse_gplvm_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`unit_tests` Module GPy.testing.unit_tests module
------------------------ -----------------------------
.. automodule:: GPy.testing.unit_tests .. automodule:: GPy.testing.unit_tests
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.testing
:members:
:undoc-members:
:show-inheritance:

29
doc/GPy.util.latent_space_visualizations.controllers.rst
View file

@ -1,27 +1,30 @@
controllers Package GPy.util.latent_space_visualizations.controllers package
=================== ========================================================
:mod:`controllers` Package Submodules
-------------------------- ----------
.. automodule:: GPy.util.latent_space_visualizations.controllers GPy.util.latent_space_visualizations.controllers.axis_event_controller module
:members: -----------------------------------------------------------------------------
:undoc-members:
:show-inheritance:
:mod:`axis_event_controller` Module
-----------------------------------
.. automodule:: GPy.util.latent_space_visualizations.controllers.axis_event_controller .. automodule:: GPy.util.latent_space_visualizations.controllers.axis_event_controller
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
:mod:`imshow_controller` Module GPy.util.latent_space_visualizations.controllers.imshow_controller module
------------------------------- -------------------------------------------------------------------------
.. automodule:: GPy.util.latent_space_visualizations.controllers.imshow_controller .. automodule:: GPy.util.latent_space_visualizations.controllers.imshow_controller
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: GPy.util.latent_space_visualizations.controllers
:members:
:undoc-members:
:show-inheritance:

20
doc/GPy.util.latent_space_visualizations.rst
View file

@ -1,13 +1,5 @@
latent_space_visualizations Package GPy.util.latent_space_visualizations package
=================================== ============================================
:mod:`latent_space_visualizations` Package
------------------------------------------
.. automodule:: GPy.util.latent_space_visualizations
:members:
:undoc-members:
:show-inheritance:
Subpackages Subpackages
----------- -----------
@ -15,5 +7,11 @@ Subpackages
.. toctree:: .. toctree::
GPy.util.latent_space_visualizations.controllers GPy.util.latent_space_visualizations.controllers
GPy.util.latent_space_visualizations.views
Module contents
---------------
.. automodule:: GPy.util.latent_space_visualizations
:members:
:undoc-members:
:show-inheritance:

367
doc/GPy.util.rst
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@ -1,181 +1,5 @@
util Package GPy.util package
============ ================
:mod:`util` Package
-------------------
.. automodule:: GPy.util
:members:
:undoc-members:
:show-inheritance:
:mod:`Tango` Module
-------------------
.. automodule:: GPy.util.Tango
:members:
:undoc-members:
:show-inheritance:
:mod:`block_matrices` Module
----------------------------
.. automodule:: GPy.util.block_matrices
:members:
:undoc-members:
:show-inheritance:
:mod:`classification` Module
----------------------------
.. automodule:: GPy.util.classification
:members:
:undoc-members:
:show-inheritance:
:mod:`config` Module
--------------------
.. automodule:: GPy.util.config
:members:
:undoc-members:
:show-inheritance:
:mod:`datasets` Module
----------------------
.. automodule:: GPy.util.datasets
:members:
:undoc-members:
:show-inheritance:
:mod:`decorators` Module
------------------------
.. automodule:: GPy.util.decorators
:members:
:undoc-members:
:show-inheritance:
:mod:`erfcx` Module
-------------------
.. automodule:: GPy.util.erfcx
:members:
:undoc-members:
:show-inheritance:
:mod:`linalg` Module
--------------------
.. automodule:: GPy.util.linalg
:members:
:undoc-members:
:show-inheritance:
:mod:`ln_diff_erfs` Module
--------------------------
.. automodule:: GPy.util.ln_diff_erfs
:members:
:undoc-members:
:show-inheritance:
:mod:`misc` Module
------------------
.. automodule:: GPy.util.misc
:members:
:undoc-members:
:show-inheritance:
:mod:`mocap` Module
-------------------
.. automodule:: GPy.util.mocap
:members:
:undoc-members:
:show-inheritance:
:mod:`multioutput` Module
-------------------------
.. automodule:: GPy.util.multioutput
:members:
:undoc-members:
:show-inheritance:
:mod:`netpbmfile` Module
------------------------
.. automodule:: GPy.util.netpbmfile
:members:
:undoc-members:
:show-inheritance:
:mod:`pca` Module
-----------------
.. automodule:: GPy.util.pca
:members:
:undoc-members:
:show-inheritance:
:mod:`plot` Module
------------------
.. automodule:: GPy.util.plot
:members:
:undoc-members:
:show-inheritance:
:mod:`plot_latent` Module
-------------------------
.. automodule:: GPy.util.plot_latent
:members:
:undoc-members:
:show-inheritance:
:mod:`squashers` Module
-----------------------
.. automodule:: GPy.util.squashers
:members:
:undoc-members:
:show-inheritance:
:mod:`symbolic` Module
----------------------
.. automodule:: GPy.util.symbolic
:members:
:undoc-members:
:show-inheritance:
:mod:`univariate_Gaussian` Module
---------------------------------
.. automodule:: GPy.util.univariate_Gaussian
:members:
:undoc-members:
:show-inheritance:
:mod:`visualize` Module
-----------------------
.. automodule:: GPy.util.visualize
:members:
:undoc-members:
:show-inheritance:
:mod:`warping_functions` Module
-------------------------------
.. automodule:: GPy.util.warping_functions
:members:
:undoc-members:
:show-inheritance:
Subpackages Subpackages
----------- -----------
@ -184,3 +8,190 @@ Subpackages
GPy.util.latent_space_visualizations GPy.util.latent_space_visualizations
Submodules
----------
GPy.util.Tango module
---------------------
.. automodule:: GPy.util.Tango
:members:
:undoc-members:
:show-inheritance:
GPy.util.block_matrices module
------------------------------
.. automodule:: GPy.util.block_matrices
:members:
:undoc-members:
:show-inheritance:
GPy.util.classification module
------------------------------
.. automodule:: GPy.util.classification
:members:
:undoc-members:
:show-inheritance:
GPy.util.config module
----------------------
.. automodule:: GPy.util.config
:members:
:undoc-members:
:show-inheritance:
GPy.util.datasets module
------------------------
.. automodule:: GPy.util.datasets
:members:
:undoc-members:
:show-inheritance:
GPy.util.decorators module
--------------------------
.. automodule:: GPy.util.decorators
:members:
:undoc-members:
:show-inheritance:
GPy.util.diag module
--------------------
.. automodule:: GPy.util.diag
:members:
:undoc-members:
:show-inheritance:
GPy.util.erfcx module
---------------------
.. automodule:: GPy.util.erfcx
:members:
:undoc-members:
:show-inheritance:
GPy.util.linalg module
----------------------
.. automodule:: GPy.util.linalg
:members:
:undoc-members:
:show-inheritance:
GPy.util.ln_diff_erfs module
----------------------------
.. automodule:: GPy.util.ln_diff_erfs
:members:
:undoc-members:
:show-inheritance:
GPy.util.misc module
--------------------
.. automodule:: GPy.util.misc
:members:
:undoc-members:
:show-inheritance:
GPy.util.mocap module
---------------------
.. automodule:: GPy.util.mocap
:members:
:undoc-members:
:show-inheritance:
GPy.util.multioutput module
---------------------------
.. automodule:: GPy.util.multioutput
:members:
:undoc-members:
:show-inheritance:
GPy.util.netpbmfile module
--------------------------
.. automodule:: GPy.util.netpbmfile
:members:
:undoc-members:
:show-inheritance:
GPy.util.plot module
--------------------
.. automodule:: GPy.util.plot
:members:
:undoc-members:
:show-inheritance:
GPy.util.plot_latent module
---------------------------
.. automodule:: GPy.util.plot_latent
:members:
:undoc-members:
:show-inheritance:
GPy.util.squashers module
-------------------------
.. automodule:: GPy.util.squashers
:members:
:undoc-members:
:show-inheritance:
GPy.util.subarray_and_sorting module
------------------------------------
.. automodule:: GPy.util.subarray_and_sorting
:members:
:undoc-members:
:show-inheritance:
GPy.util.symbolic module
------------------------
.. automodule:: GPy.util.symbolic
:members:
:undoc-members:
:show-inheritance:
GPy.util.univariate_Gaussian module
-----------------------------------
.. automodule:: GPy.util.univariate_Gaussian
:members:
:undoc-members:
:show-inheritance:
GPy.util.visualize module
-------------------------
.. automodule:: GPy.util.visualize
:members:
:undoc-members:
:show-inheritance:
GPy.util.warping_functions module
---------------------------------
.. automodule:: GPy.util.warping_functions
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: GPy.util
:members:
:undoc-members:
:show-inheritance:

2
setup.py
View file

@ -20,7 +20,7 @@ setup(name = 'GPy',
url = "http://sheffieldml.github.com/GPy/", url = "http://sheffieldml.github.com/GPy/",
packages = ['GPy', 'GPy.core', 'GPy.kern', 'GPy.util', 'GPy.models_modules', 'GPy.inference', 'GPy.examples', 'GPy.likelihoods', 'GPy.testing', 'GPy.util.latent_space_visualizations', 'GPy.util.latent_space_visualizations.controllers', 'GPy.likelihoods.noise_models', 'GPy.kern.parts', 'GPy.mappings'], packages = ['GPy', 'GPy.core', 'GPy.kern', 'GPy.util', 'GPy.models_modules', 'GPy.inference', 'GPy.examples', 'GPy.likelihoods', 'GPy.testing', 'GPy.util.latent_space_visualizations', 'GPy.util.latent_space_visualizations.controllers', 'GPy.likelihoods.noise_models', 'GPy.kern.parts', 'GPy.mappings'],
package_dir={'GPy': 'GPy'}, package_dir={'GPy': 'GPy'},
package_data = {'GPy': ['GPy/examples', 'gpy_config.cfg']}, package_data = {'GPy': ['GPy/examples', 'gpy_config.cfg', 'util/data_resources.json']},
py_modules = ['GPy.__init__'], py_modules = ['GPy.__init__'],
long_description=read('README.md'), long_description=read('README.md'),
install_requires=['numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1', 'nose'], install_requires=['numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1', 'nose'],