mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-10 20:42:39 +02:00
Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Zhenwen Dai
commit
105a6c5377
19 changed files with 138 additions and 464 deletions
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@ -415,7 +415,6 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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from GPy import kern
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from GPy.models import MRD
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from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
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D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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@ -429,12 +428,8 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
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inanlist.append(inan)
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Y[inan] = _np.nan
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imlist = []
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for inan in inanlist:
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imlist.append(VarDTCMissingData(limit=1, inan=inan))
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m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing,
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kernel=k, inference_method=imlist,
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kernel=k, inference_method=None,
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initx="random", initz='permute', **kw)
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if optimize:
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@ -494,7 +489,7 @@ def olivetti_faces(optimize=True, verbose=True, plot=True):
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def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=True):
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import GPy
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import pods
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import pods
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data = pods.datasets.osu_run1()
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# optimize
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@ -167,7 +167,7 @@ class VarDTC(LatentFunctionInference):
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woodbury_vector = Cpsi1Vf # == Cpsi1V
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else:
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print 'foobar'
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stop
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import ipdb; ipdb.set_trace()
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psi1V = np.dot(Y.T*beta, psi1).T
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tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
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tmp, _ = dpotrs(LB, tmp, lower=1)
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@ -1,47 +0,0 @@
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# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import sympy as sym
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from GPy.util.symbolic import gammaln, normcdfln, normcdf, IndMatrix, create_matrix
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import numpy as np
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import link_functions
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from symbolic import Symbolic
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from scipy import stats
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class Ordinal(Symbolic):
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"""
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Ordinal
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.. math::
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p(y_{i}|\pi(f_{i})) = \left(\frac{r}{r+f_i}\right)^r \frac{\Gamma(r+y_i)}{y!\Gamma(r)}\left(\frac{f_i}{r+f_i}\right)^{y_i}
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.. Note::
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Y takes non zero integer values..
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link function should have a positive domain, e.g. log (default).
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.. See also::
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symbolic.py, for the parent class
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"""
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def __init__(self, categories=3, gp_link=None):
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if gp_link is None:
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gp_link = link_functions.Identity()
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dispersion = sym.Symbol('width', positive=True, real=True)
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y_0 = sym.Symbol('y_0', nonnegative=True, integer=True)
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f_0 = sym.Symbol('f_0', positive=True, real=True)
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log_pdf = create_matrix('log_pdf', 1, categories)
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log_pdf[0] = normcdfln(-f_0)
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if categories>2:
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w = create_matrix('w', 1, categories)
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log_pdf[categories-1] = normcdfln(w.sum() + f_0)
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for i in range(1, categories-1):
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log_pdf[i] = sym.log(normcdf(w[0, 0:i-1].sum() + f_0) - normcdf(w[0, 0:i].sum()-f_0) )
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else:
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log_pdf[1] = normcdfln(f_0)
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log_pdf.index_var = y_0
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super(Ordinal, self).__init__(log_pdf=log_pdf, gp_link=gp_link, name='Ordinal')
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# TODO: Check this.
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self.log_concave = True
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@ -13,11 +13,11 @@ from ..inference.latent_function_inference import InferenceMethodList
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from ..likelihoods import Gaussian
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from ..util.initialization import initialize_latent
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from ..core.sparse_gp import SparseGP, GP
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from GPy.models.bayesian_gplvm import BayesianGPLVM
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from GPy.core.parameterization.variational import VariationalPosterior
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from GPy.core.sparse_gp_mpi import SparseGP_MPI
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from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
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from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
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class MRD(BayesianGPLVM):
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class MRD(BayesianGPLVMMiniBatch):
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"""
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!WARNING: This is bleeding edge code and still in development.
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Functionality may change fundamentally during development!
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@ -92,7 +92,8 @@ class MRD(BayesianGPLVM):
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else:
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fracs = [X.var(0)]*len(Ylist)
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self.Z = Param('inducing inputs', self._init_Z(initz, X))
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Z = self._init_Z(initz, X)
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self.Z = Param('inducing inputs', Z)
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self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
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# sort out the kernels
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@ -104,6 +105,7 @@ class MRD(BayesianGPLVM):
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kernels = []
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for i in range(len(Ylist)):
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k = kernel.copy()
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print k is kernel, k.observers, k.constraints
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kernels.append(k)
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else:
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assert len(kernel) == len(Ylist), "need one kernel per output"
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@ -114,7 +116,7 @@ class MRD(BayesianGPLVM):
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X_variance = np.random.uniform(0.1, 0.2, X.shape)
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self.variational_prior = NormalPrior()
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self.X = NormalPosterior(X, X_variance)
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#self.X = NormalPosterior(X, X_variance)
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if likelihoods is None:
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likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
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@ -123,48 +125,33 @@ class MRD(BayesianGPLVM):
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self.logger.info("adding X and Z")
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super(MRD, self).__init__(Y, input_dim, X=X, X_variance=X_variance, num_inducing=num_inducing,
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Z=self.Z, kernel=None, inference_method=self.inference_method, likelihood=Gaussian(),
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name='bayesian gplvm', mpi_comm=None, normalizer=None,
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name='manifold relevance determination', normalizer=None,
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missing_data=False, stochastic=False, batchsize=1)
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import GPy
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self._log_marginal_likelihood = 0
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print "------------"
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "------------"
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self.unlink_parameter(self.likelihood)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "------------"
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self.unlink_parameter(self.kern)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "------------"
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print
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print '================='
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del self.kern
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del self.likelihood
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self.num_data = Ylist[0].shape[0]
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if isinstance(batchsize, int):
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batchsize = itertools.repeat(batchsize)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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self.bgplvms = []
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for i, n, k, l, Y, im, bs in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
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assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
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md = np.isnan(Y).any()
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spgp = SparseGP(self.X, Y, self.Z, k, l, im, n, None, normalizer, md, stochastic, bs)
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spgp = SparseGPMiniBatch(self.X, Y, Z, k, l, im, n, None, normalizer, md, stochastic, bs)
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spgp.unlink_parameter(spgp.Z)
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del spgp.Z
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del spgp.X
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spgp.Z = self.Z
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spgp.X = self.X
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self.link_parameter(spgp, i+2)
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print self.constraints[GPy.constraints.Logexp()][-10:]
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self.link_parameter(self.Z, 2)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "==========="
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self.bgplvms.append(spgp)
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self.posterior = None
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self.logger.info("init done")
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@ -173,7 +160,9 @@ class MRD(BayesianGPLVM):
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self._log_marginal_likelihood = 0
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self.Z.gradient[:] = 0.
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self.X.gradient[:] = 0.
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for b, i in itertools.izip(self.parameters[3:], self.inference_method):
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for b, i in itertools.izip(self.bgplvms, self.inference_method):
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self._log_marginal_likelihood += b._log_marginal_likelihood
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self.logger.info('working on im <{}>'.format(hex(id(i))))
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self.Z.gradient[:] += b.full_values['Zgrad']
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grad_dict = b.grad_dict
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@ -195,6 +184,7 @@ class MRD(BayesianGPLVM):
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# update for the KL divergence
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self.variational_prior.update_gradients_KL(self.X)
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self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
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pass
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def log_likelihood(self):
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return self._log_marginal_likelihood
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@ -268,7 +258,7 @@ class MRD(BayesianGPLVM):
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Prediction for data set Yindex[default=0].
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This predicts the output mean and variance for the dataset given in Ylist[Yindex]
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"""
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b = self.parameters[Yindex+2]
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b = self.bgplvms[Yindex]
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self.posterior = b.posterior
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self.kern = b.kern
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self.likelihood = b.likelihood
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@ -317,16 +307,20 @@ class MRD(BayesianGPLVM):
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from ..plotting.matplot_dep import dim_reduction_plots
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if "Yindex" not in predict_kwargs:
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predict_kwargs['Yindex'] = 0
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Yindex = predict_kwargs['Yindex']
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if ax is None:
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fig = plt.figure(num=fignum)
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ax = fig.add_subplot(111)
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else:
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fig = ax.figure
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self.kern = self.bgplvms[Yindex].kern
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self.likelihood = self.bgplvms[Yindex].likelihood
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plot = dim_reduction_plots.plot_latent(self, labels, which_indices,
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resolution, ax, marker, s,
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fignum, plot_inducing, legend,
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plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
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ax.set_title(self.bgplvms[predict_kwargs['Yindex']].name)
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ax.set_title(self.bgplvms[Yindex].name)
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try:
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fig.tight_layout()
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except:
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@ -336,8 +330,10 @@ class MRD(BayesianGPLVM):
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def __getstate__(self):
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state = super(MRD, self).__getstate__()
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del state['kern']
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del state['likelihood']
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if state.has_key('kern'):
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del state['kern']
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if state.has_key('likelihood'):
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del state['likelihood']
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return state
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def __setstate__(self, state):
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|
|
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@ -447,6 +447,7 @@ class GradientTests(np.testing.TestCase):
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m = GPy.models.GPHeteroscedasticRegression(X, Y, kern)
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self.assertTrue(m.checkgrad())
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def test_gp_kronecker_gaussian(self):
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N1, N2 = 30, 20
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X1 = np.random.randn(N1, 1)
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|
|
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|
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@ -130,7 +130,6 @@ class Test(unittest.TestCase):
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self.assertEqual(self._first, self._trigger, 'priority should be second')
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self.assertEqual(self._second, self._trigger_priority, 'priority should be second')
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|
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if __name__ == "__main__":
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#import sys;sys.argv = ['', 'Test.testName']
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unittest.main()
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@ -221,6 +221,31 @@ class ParameterizedTest(unittest.TestCase):
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np.testing.assert_equal(t.x.constraints[Logistic(0,1)], c[Logistic(0,1)])
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np.testing.assert_equal(t.x.constraints['fixed'], c['fixed'])
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def test_parameter_modify_in_init(self):
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class TestLikelihood(Parameterized):
|
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def __init__(self, param1 = 2., param2 = 3.):
|
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super(TestLikelihood, self).__init__("TestLike")
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self.p1 = Param('param1', param1)
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self.p2 = Param('param2', param2)
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|
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self.link_parameter(self.p1)
|
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self.link_parameter(self.p2)
|
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|
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self.p1.fix()
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self.p1.unfix()
|
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self.p2.constrain_negative()
|
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self.p1.fix()
|
||||
self.p2.constrain_positive()
|
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self.p2.fix()
|
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self.p2.constrain_positive()
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|
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m = TestLikelihood()
|
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print m
|
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val = m.p1.values.copy()
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self.assert_(m.p1.is_fixed)
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self.assert_(m.constraints[GPy.constraints.Logexp()].tolist(), [1])
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m.randomize()
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self.assertEqual(m.p1, val)
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|
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def test_printing(self):
|
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print self.test1
|
||||
|
|
|
|||
|
|
@ -141,6 +141,7 @@ class Test(ListDictTestCase):
|
|||
pcopy.optimize('bfgs')
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par.optimize('bfgs')
|
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np.testing.assert_allclose(pcopy.param_array, par.param_array, atol=1e-6)
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par.randomize()
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with tempfile.TemporaryFile('w+b') as f:
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par.pickle(f)
|
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f.seek(0)
|
||||
|
|
|
|||
|
|
@ -4,11 +4,10 @@ The module of tools for parallelization (MPI)
|
|||
|
||||
try:
|
||||
from mpi4py import MPI
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||||
def get_id_within_node(comm=MPI.COMM_WORLD):
|
||||
rank = comm.rank
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||||
nodename = MPI.Get_processor_name()
|
||||
nodelist = comm.allgather(nodename)
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||||
return len([i for i in nodelist[:rank] if i==nodename])
|
||||
except:
|
||||
pass
|
||||
|
||||
def get_id_within_node(comm=MPI.COMM_WORLD):
|
||||
rank = comm.rank
|
||||
nodename = MPI.Get_processor_name()
|
||||
nodelist = comm.allgather(nodename)
|
||||
return len([i for i in nodelist[:rank] if i==nodename])
|
||||
|
|
|
|||
|
|
@ -3,11 +3,11 @@ GPy
|
|||
|
||||
A Gaussian processes framework in Python.
|
||||
|
||||
* [GPy homepage](http://sheffieldml.github.io/GPy/)
|
||||
* [User mailing list](https://lists.shef.ac.uk/sympa/subscribe/gpy-users)
|
||||
* [Online documentation](https://gpy.readthedocs.org/en/latest/)
|
||||
* [Unit tests (Travis-CI)](https://travis-ci.org/SheffieldML/GPy)
|
||||
|
||||
|
||||
Continuous integration status: 
|
||||
|
||||
Citation
|
||||
|
|
@ -20,6 +20,10 @@ Citation
|
|||
year = {2012--2014}
|
||||
}
|
||||
|
||||
Pronounciation
|
||||
==============
|
||||
We like to pronounce it 'Gee-pie'.
|
||||
|
||||
Getting started
|
||||
===============
|
||||
Installing with pip
|
||||
|
|
|
|||
|
|
@ -44,6 +44,14 @@ GPy.inference.latent_function_inference.fitc module
|
|||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.inference.latent_function_inference.inferenceX module
|
||||
---------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.inference.latent_function_inference.inferenceX
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.inference.latent_function_inference.laplace module
|
||||
------------------------------------------------------
|
||||
|
||||
|
|
|
|||
30
doc/GPy.inference.mcmc.rst
Normal file
30
doc/GPy.inference.mcmc.rst
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
GPy.inference.mcmc package
|
||||
==========================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.inference.mcmc.hmc module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.inference.mcmc.hmc
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.inference.mcmc.samplers module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: GPy.inference.mcmc.samplers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.inference.mcmc
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,246 +0,0 @@
|
|||
GPy.kern.parts package
|
||||
======================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.kern.parts.Brownian module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.Brownian
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.Matern32 module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.Matern32
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.Matern52 module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.Matern52
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.bias module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.bias
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.coregionalize module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.coregionalize
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.exponential module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.exponential
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.finite_dimensional module
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.finite_dimensional
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.fixed module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.fixed
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.gibbs module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.gibbs
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.hetero module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.hetero
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.hierarchical module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.hierarchical
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.independent_outputs module
|
||||
-----------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.independent_outputs
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.kernpart module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.kernpart
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.linear module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.linear
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.mlp module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.mlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.periodic_Matern32 module
|
||||
---------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.periodic_Matern32
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.periodic_Matern52 module
|
||||
---------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.periodic_Matern52
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.periodic_exponential module
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.periodic_exponential
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.poly module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.poly
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.prod module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.prod
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.prod_orthogonal module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.prod_orthogonal
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.rational_quadratic module
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rational_quadratic
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.rbf module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rbf
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.rbf_inv module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rbf_inv
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.rbfcos module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rbfcos
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.spline module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.spline
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.symmetric module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.symmetric
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.sympykern module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.sympykern
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.parts.white module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.white
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.kern.parts
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,70 +0,0 @@
|
|||
GPy.likelihoods.noise_models package
|
||||
====================================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.likelihoods.noise_models.binomial_noise module
|
||||
--------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.binomial_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.noise_models.exponential_noise module
|
||||
-----------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.exponential_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.noise_models.gamma_noise module
|
||||
-----------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.gamma_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.noise_models.gaussian_noise module
|
||||
--------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.gaussian_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.noise_models.gp_transformations module
|
||||
------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.gp_transformations
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.noise_models.noise_distributions module
|
||||
-------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.noise_distributions
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.noise_models.poisson_noise module
|
||||
-------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.poisson_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -12,6 +12,14 @@ GPy.models.bayesian_gplvm module
|
|||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.models.bayesian_gplvm_minibatch module
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models.bayesian_gplvm_minibatch
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.models.bcgplvm module
|
||||
-------------------------
|
||||
|
||||
|
|
@ -116,6 +124,14 @@ GPy.models.sparse_gp_coregionalized_regression module
|
|||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.models.sparse_gp_minibatch module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: GPy.models.sparse_gp_minibatch
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.models.sparse_gp_multioutput_regression module
|
||||
--------------------------------------------------
|
||||
|
||||
|
|
|
|||
|
|
@ -28,6 +28,14 @@ GPy.testing.index_operations_tests module
|
|||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.testing.inference_tests module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.inference_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.testing.kernel_tests module
|
||||
-------------------------------
|
||||
|
||||
|
|
|
|||
|
|
@ -1,30 +0,0 @@
|
|||
GPy.util.latent_space_visualizations.controllers package
|
||||
========================================================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.util.latent_space_visualizations.controllers.axis_event_controller module
|
||||
-----------------------------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations.controllers.axis_event_controller
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.latent_space_visualizations.controllers.imshow_controller module
|
||||
-------------------------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations.controllers.imshow_controller
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations.controllers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
GPy.util.latent_space_visualizations package
|
||||
============================================
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
|
||||
GPy.util.latent_space_visualizations.controllers
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
10
doc/conf.py
10
doc/conf.py
|
|
@ -11,6 +11,9 @@
|
|||
# All configuration values have a default; values that are commented out
|
||||
# serve to show the default.
|
||||
|
||||
autodoc_default_flags = ['members', 'show-inheritance', 'private-members', 'special-members']
|
||||
autodoc_member_order = "source"
|
||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
|
|
@ -114,7 +117,7 @@ for mod_name in MOCK_MODULES:
|
|||
# ----------------------- READTHEDOCS ------------------
|
||||
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
|
||||
|
||||
on_rtd = True
|
||||
#on_rtd = True
|
||||
if on_rtd:
|
||||
sys.path.append(os.path.abspath('../GPy'))
|
||||
|
||||
|
|
@ -126,7 +129,8 @@ if on_rtd:
|
|||
proc = subprocess.Popen("ls ../", stdout=subprocess.PIPE, shell=True)
|
||||
(out, err) = proc.communicate()
|
||||
print "program output:", out
|
||||
proc = subprocess.Popen("sphinx-apidoc -f -o . ../GPy", stdout=subprocess.PIPE, shell=True)
|
||||
#proc = subprocess.Popen("sphinx-apidoc -f -o . ../GPy", stdout=subprocess.PIPE, shell=True)
|
||||
proc = subprocess.Popen("make html", stdout=subprocess.PIPE, shell=True)
|
||||
(out, err) = proc.communicate()
|
||||
print "program output:", out
|
||||
#proc = subprocess.Popen("whereis numpy", stdout=subprocess.PIPE, shell=True)
|
||||
|
|
@ -397,5 +401,3 @@ epub_copyright = u'2013, Author'
|
|||
|
||||
# Allow duplicate toc entries.
|
||||
#epub_tocdup = True
|
||||
|
||||
autodoc_member_order = "source"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue