def __init__(self,
Y,
input_dim,
X=None,
kernel=None,
init='PCA',
num_inducing=10):
if X is None:
from ..util.initialization import initialize_latent
X, fracs = initialize_latent(init, input_dim, Y)
SparseGPRegression.__init__(self,
X,
Y,
kernel=kernel,
num_inducing=num_inducing)
def log_likelihood(self):
return SparseGPRegression.log_likelihood(self)
def _set_params(self, x):
self.X = x[:self.X.size].reshape(self.num_data, self.input_dim).copy()
SparseGPRegression._set_params(self, x[self.X.size:])
def _get_params(self):
return np.hstack((self.X.flatten(), SparseGPRegression._get_params(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)], [])
+ SparseGPRegression._get_param_names(self))
def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
X = self.initialise_latent(init, input_dim, Y)
SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
def __init__(self, Y, input_dim, X=None, kernel=None, init='PCA', num_inducing=10):
if X is None:
from ..util.initialization import initialize_latent
X, fracs = initialize_latent(init, input_dim, Y)
SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dX().flatten(), SparseGPRegression._log_likelihood_gradients(self)))
def _set_params(self, x):
self.X = x[:self.X.size].reshape(self.num_data, self.input_dim).copy()
SparseGPRegression._set_params(self, x[self.X.size:])
def log_likelihood(self):
return SparseGPRegression.log_likelihood(self)
def _get_params(self):
return np.hstack((self.X.flatten(), SparseGPRegression._get_params(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)], [])
+ SparseGPRegression._get_param_names(self))
def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
X = self.initialise_latent(init, input_dim, Y)
SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
self.ensure_default_constraints()
def _log_likelihood_gradients(self):
return np.hstack((self.dL_dX().flatten(), SparseGPRegression._log_likelihood_gradients(self)))
def plot(self):
GPLVM.plot(self)
# passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
mu, var, upper, lower = SparseGPRegression.predict(self, self.Z + np.random.randn(*self.Z.shape) * 0.0001)
pb.plot(mu[:, 0] , mu[:, 1], 'ko')
def plot(self):
GPLVM.plot(self)
# passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
mu, var, upper, lower = SparseGPRegression.predict(self, self.Z + np.random.randn(*self.Z.shape) * 0.0001)
pb.plot(mu[:, 0] , mu[:, 1], 'ko')
def model_builder(X, Y, kernel):
m = SparseGPRegression(X, Y, kernel=kernel)
m.Z.fix()
return m
