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beginnings of gplvm

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James Hensman 2014-01-24 15:56:27 +00:00
parent 7bb6f4ba4e
commit a8c760932e
1 changed files with 22 additions and 29 deletions
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51
GPy/models/gplvm.py
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@ -15,61 +15,54 @@ class GPLVM(GP):
"""
Gaussian Process Latent Variable Model
:param Y: observed data
:type Y: np.ndarray
:param input_dim: latent dimensionality
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
"""
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False, name="gplvm"):
"""
:param Y: observed data
:type Y: np.ndarray
:param input_dim: latent dimensionality
:type input_dim: int
:param init: initialisation method for the latent space
:type init: 'PCA'|'random'
"""
if X is None:
X = self.initialise_latent(init, input_dim, Y)
if kernel is None:
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.))
GP.__init__(self, X, likelihood, kernel, normalize_X=False, name=name)
self.X = Param('q_mean', self.X)
self.add_parameter(self.X, gradient=self.dK_dX, index=0)
self.ensure_default_constraints()
likelihood = Gaussian()
super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')
self.X = Param('X', X)
self.add_parameter(self.X, ndex=0)
def initialise_latent(self, init, input_dim, Y):
Xr = np.random.randn(Y.shape[0], input_dim)
if init == 'PCA':
PC = PCA(Y, input_dim)[0]
Xr[:PC.shape[0], :PC.shape[1]] = PC
else:
raise NotImplementedError
return Xr
def parameters_changed(self):
GP.parameters_changed(self)
self.X.gradient = self.kern.gradients_X(self.posterior.dL_dK, self.X)
def _getstate(self):
return GP._getstate(self)
def _setstate(self, state):
GP._setstate(self, state)
# 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)
#
# def _get_params(self):
# return np.hstack((self.X.flatten(), GP._get_params(self)))
#
# def _set_params(self, x):
# self.X = x[:self.num_data * self.input_dim].reshape(self.num_data, self.input_dim).copy()
# GP._set_params(self, x[self.X.size:])
def dK_dX(self):
return self.kern.dK_dX(self.dL_dK, self.X)
# def _log_likelihood_gradients(self):
# dL_dX = self.kern.dK_dX(self.dL_dK, self.X)
#
# return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self)))
def jacobian(self,X):
target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
for i in range(self.output_dim):
target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
return target
def magnification(self,X):
target=np.zeros(X.shape[0])
#J = np.zeros((X.shape[0],X.shape[1],self.output_dim))