mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-11 15:15:15 +02:00
fix: use BGPLVM as base class for GMM
This commit is contained in:
mzwiessele
parent
ea7e75f4a6
commit
913b01322d
2 changed files with 32 additions and 83 deletions
|
|
@ -24,7 +24,7 @@ class BayesianGPLVM(SparseGP_MPI):
|
|||
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
|
||||
Z=None, kernel=None, inference_method=None, likelihood=None,
|
||||
name='bayesian gplvm', mpi_comm=None, normalizer=None,
|
||||
missing_data=False, stochastic=False, batchsize=1, Y_metadata=None):
|
||||
missing_data=False, stochastic=False, batchsize=1, Y_metadata=None, variational_prior=None):
|
||||
|
||||
self.logger = logging.getLogger(self.__class__.__name__)
|
||||
if X is None:
|
||||
|
|
@ -52,7 +52,9 @@ class BayesianGPLVM(SparseGP_MPI):
|
|||
if likelihood is None:
|
||||
likelihood = Gaussian()
|
||||
|
||||
self.variational_prior = NormalPrior()
|
||||
if variational_prior is None:
|
||||
variational_prior = NormalPrior()
|
||||
|
||||
X = NormalPosterior(X, X_variance)
|
||||
|
||||
if inference_method is None:
|
||||
|
|
@ -68,7 +70,7 @@ class BayesianGPLVM(SparseGP_MPI):
|
|||
super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood,
|
||||
name=name, inference_method=inference_method,
|
||||
normalizer=normalizer, mpi_comm=mpi_comm,
|
||||
variational_prior=self.variational_prior,
|
||||
variational_prior=variational_prior,
|
||||
Y_metadata=Y_metadata
|
||||
)
|
||||
self.link_parameter(self.X, index=0)
|
||||
|
|
|
|||
|
|
@ -2,14 +2,11 @@
|
|||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
|
||||
import numpy as np
|
||||
from .. import kern
|
||||
from ..core.sparse_gp_mpi import SparseGP_MPI
|
||||
from ..likelihoods import Gaussian
|
||||
from ..core.parameterization.variational import NormalPosterior, GmmNormalPrior
|
||||
from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
|
||||
import logging
|
||||
from . import BayesianGPLVM
|
||||
|
||||
class GmmBayesianGPLVM(SparseGP_MPI):
|
||||
class GmmBayesianGPLVM(BayesianGPLVM):
|
||||
"""
|
||||
Gaussian mixture model Bayesian Gaussian Process Latent Variable Model
|
||||
|
||||
|
|
@ -26,38 +23,14 @@ class GmmBayesianGPLVM(SparseGP_MPI):
|
|||
name='gmm bayesian gplvm', mpi_comm=None, normalizer=None,
|
||||
missing_data=False, stochastic=False, batchsize=1, Y_metadata=None):
|
||||
|
||||
self.logger = logging.getLogger(self.__class__.__name__)
|
||||
if X is None:
|
||||
from ..util.initialization import initialize_latent
|
||||
self.logger.info("initializing latent space X with method {}".format(init))
|
||||
X, fracs = initialize_latent(init, input_dim, Y)
|
||||
else:
|
||||
fracs = np.ones(input_dim)
|
||||
|
||||
self.init = init
|
||||
|
||||
if X_variance is None:
|
||||
self.logger.info("initializing latent space variance ~ uniform(0,.1)")
|
||||
X_variance = np.random.uniform(0,.1,X.shape)
|
||||
|
||||
if Z is None:
|
||||
self.logger.info("initializing inducing inputs")
|
||||
Z = np.random.permutation(X.copy())[:num_inducing]
|
||||
assert Z.shape[1] == X.shape[1]
|
||||
|
||||
if kernel is None:
|
||||
self.logger.info("initializing kernel RBF")
|
||||
kernel = kern.RBF(input_dim, lengthscale=1./fracs, ARD=True) #+ kern.Bias(input_dim) + kern.White(input_dim)
|
||||
|
||||
if likelihood is None:
|
||||
likelihood = Gaussian()
|
||||
|
||||
N = Y.shape[0]
|
||||
Q = input_dim
|
||||
# Need to define what the model is initialised like
|
||||
# pi = np.ones(n_component) / float(n_component) # p(k)
|
||||
|
||||
# pi = (np.array(range(3),dtype = float)+1) / (np.array(range(3),dtype = float)+1).sum()
|
||||
# wi = (np.array(range(3),dtype = float)+1)
|
||||
wi = np.ones((n_component, X_variance.shape[0]))
|
||||
wi = np.ones((n_component, N))
|
||||
# wi = (np.ones((X_variance.shape[0], n_component)) * (range(1, n_component+1))).T
|
||||
variational_wi = wi.copy()
|
||||
pi = np.exp(wi)/np.exp(wi).sum(axis = 0)
|
||||
|
|
@ -69,33 +42,20 @@ class GmmBayesianGPLVM(SparseGP_MPI):
|
|||
# px_mu = np.zeros((n_component, X_variance.shape[0], X_variance.shape[1]))
|
||||
# px_var = np.ones((n_component, X_variance.shape[0], X_variance.shape[1]))
|
||||
|
||||
px_mu = (np.ones((X_variance.shape[1], n_component )) * (range(n_component))).T + np.random.randn(n_component, X_variance.shape[1]) # initialization can be changed
|
||||
px_mu = (np.ones((Q, n_component )) * (range(n_component))).T + np.random.randn(n_component, Q) # initialization can be changed
|
||||
# print px_mu
|
||||
# px_mu = np.zeros(( n_component, X_variance.shape[1]))
|
||||
px_lmatrix = np.zeros(( n_component, X_variance.shape[1], X_variance.shape[1] ))+ np.eye(X_variance.shape[1])[np.newaxis, :,:]
|
||||
px_lmatrix = np.zeros(( n_component, Q, Q ))+ np.eye(Q)[np.newaxis, :,:]
|
||||
|
||||
self.variational_prior = GmmNormalPrior(px_mu=px_mu, px_lmatrix=px_lmatrix, pi = pi, wi=wi,
|
||||
n_component=n_component, variational_wi=variational_wi)
|
||||
|
||||
X = NormalPosterior(X, X_variance)
|
||||
|
||||
if inference_method is None:
|
||||
if mpi_comm is not None:
|
||||
inference_method = VarDTC_minibatch(mpi_comm=mpi_comm)
|
||||
else:
|
||||
from ..inference.latent_function_inference.var_dtc import VarDTC
|
||||
self.logger.debug("creating inference_method var_dtc")
|
||||
inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1])
|
||||
if isinstance(inference_method,VarDTC_minibatch):
|
||||
inference_method.mpi_comm = mpi_comm
|
||||
|
||||
super(GmmBayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood,
|
||||
name=name, inference_method=inference_method,
|
||||
normalizer=normalizer, mpi_comm=mpi_comm,
|
||||
variational_prior=self.variational_prior,
|
||||
Y_metadata=Y_metadata
|
||||
)
|
||||
self.link_parameter(self.X, index=0)
|
||||
super(GmmBayesianGPLVM, self).__init__(Y, input_dim, X, X_variance, init, num_inducing,
|
||||
Z=Z, kernel=kernel, inference_method=inference_method, likelihood=likelihood,
|
||||
name=name, mpi_comm=mpi_comm, normalizer=normalizer,
|
||||
missing_data=missing_data, stochastic=stochastic,
|
||||
batchsize=batchsize, Y_metadata=Y_metadata, variational_prior=self.variational_prior)
|
||||
|
||||
|
||||
def set_X_gradients(self, X, X_grad):
|
||||
"""Set the gradients of the posterior distribution of X in its specific form."""
|
||||
|
|
@ -107,22 +67,9 @@ class GmmBayesianGPLVM(SparseGP_MPI):
|
|||
|
||||
def parameters_changed(self):
|
||||
super(GmmBayesianGPLVM,self).parameters_changed()
|
||||
if isinstance(self.inference_method, VarDTC_minibatch):
|
||||
return
|
||||
|
||||
kl_fctr = 1.
|
||||
self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)
|
||||
|
||||
self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_qX_expectations(
|
||||
variational_posterior=self.X,
|
||||
Z=self.Z,
|
||||
dL_dpsi0=self.grad_dict['dL_dpsi0'],
|
||||
dL_dpsi1=self.grad_dict['dL_dpsi1'],
|
||||
dL_dpsi2=self.grad_dict['dL_dpsi2'])
|
||||
|
||||
|
||||
self.variational_prior.update_gradients_KL(self.X)
|
||||
|
||||
|
||||
#super(BayesianGPLVM, self).parameters_changed()
|
||||
#self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
|
||||
|
||||
|
|
@ -147,19 +94,19 @@ class GmmBayesianGPLVM(SparseGP_MPI):
|
|||
# update for the KL divergence
|
||||
#self.variational_prior.update_gradients_KL(self.X)
|
||||
|
||||
def plot_latent(self, labels=None, which_indices=None,
|
||||
resolution=50, ax=None, marker='o', s=40,
|
||||
fignum=None, plot_inducing=True, legend=True,
|
||||
plot_limits=None,
|
||||
aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
|
||||
import sys
|
||||
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
from ..plotting.matplot_dep import dim_reduction_plots
|
||||
|
||||
return dim_reduction_plots.plot_latent(self, labels, which_indices,
|
||||
resolution, ax, marker, s,
|
||||
fignum, plot_inducing, legend,
|
||||
plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
|
||||
# def plot_latent(self, labels=None, which_indices=None,
|
||||
# resolution=50, ax=None, marker='o', s=40,
|
||||
# fignum=None, plot_inducing=True, legend=True,
|
||||
# plot_limits=None,
|
||||
# aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
|
||||
# import sys
|
||||
# assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
|
||||
# from ..plotting.matplot_dep import dim_reduction_plots
|
||||
#
|
||||
# return dim_reduction_plots.plot_latent(self, labels, which_indices,
|
||||
# resolution, ax, marker, s,
|
||||
# fignum, plot_inducing, legend,
|
||||
# plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
|
||||
|
||||
def do_test_latents(self, Y):
|
||||
"""
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue