<commit_before># Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import numpy as np
import pylab as pb
import sys, pdb
from GPLVM import GPLVM
from sparse_GP import sparse_GP
from GPy.util.linalg import pdinv
from ..likelihoods import Gaussian
from .. import kern

class Bayesian_GPLVM(sparse_GP, GPLVM):
    """
    Bayesian Gaussian Process Latent Variable Model

    :param Y: observed data
    :type Y: np.ndarray
    :param Q: latent dimensionality
    :type Q: int
    :param init: initialisation method for the latent space
    :type init: 'PCA'|'random'

    """
    def __init__(self, Y, Q, init='PCA', M=10, Z=None, **kwargs):
        X = self.initialise_latent(init, Q, Y)

        if Z is None:
            Z = np.random.permutation(X.copy())[:M]
        else:
            assert Z.shape[1]==X.shape[1]

        kernel = kern.rbf(Q) + kern.white(Q)

        S = np.ones_like(X) * 1e-2#
        sparse_GP.__init__(self, X, Gaussian(Y), X_uncertainty=S, Z=Z,**kwargs)

    def _get_param_names(self):
        X_names = sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
        S_names = sum([['S_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
        return (X_names + S_names + sparse_GP._get_param_names(self))

    def _get_params(self):
        """
        Horizontally stacks the parameters in order to present them to the optimizer.<commit_msg>added a default kernel option in BGPLVM<commit_after># Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import numpy as np
import pylab as pb
import sys, pdb
from GPLVM import GPLVM
from sparse_GP import sparse_GP
from GPy.util.linalg import pdinv
from ..likelihoods import Gaussian
from .. import kern

class Bayesian_GPLVM(sparse_GP, GPLVM):
    """
    Bayesian Gaussian Process Latent Variable Model

    :param Y: observed data
    :type Y: np.ndarray
    :param Q: latent dimensionality
    :type Q: int
    :param init: initialisation method for the latent space
    :type init: 'PCA'|'random'

    """
    def __init__(self, Y, Q, init='PCA', M=10, Z=None, kernel=None, **kwargs):
        X = self.initialise_latent(init, Q, Y)

        if Z is None:
            Z = np.random.permutation(X.copy())[:M]
        else:
            assert Z.shape[1]==X.shape[1]

        if kernel is None:
            kernel = kern.rbf(Q) + kern.white(Q)

        S = np.ones_like(X) * 1e-2#
        sparse_GP.__init__(self, X, Gaussian(Y), kernel, Z=Z, X_uncertainty=S, **kwargs)

    def _get_param_names(self):
        X_names = sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
        S_names = sum([['S_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
        return (X_names + S_names + sparse_GP._get_param_names(self))

    def _get_params(self):
        """
        Horizontally stacks the parameters in order to present them to the optimizer.
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