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Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

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mzwiessele 2015-07-29 10:48:27 +02:00
commit d6defa6645
9 changed files with 92 additions and 92 deletions
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12
GPy/core/gp.py
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@ -244,7 +244,7 @@ class GP(Model):
mu, var = self.normalizer.inverse_mean(mu), self.normalizer.inverse_variance(var)
# now push through likelihood
mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata)
mean, var = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata=Y_metadata)
return mean, var
def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None):
@ -261,7 +261,7 @@ class GP(Model):
m, v = self._raw_predict(X, full_cov=False)
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata)
return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)
def predictive_gradients(self, Xnew):
"""
@ -331,7 +331,7 @@ class GP(Model):
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
fsim = self.posterior_samples_f(X, size, full_cov=full_cov)
Ysim = self.likelihood.samples(fsim, Y_metadata)
Ysim = self.likelihood.samples(fsim, Y_metadata=Y_metadata)
return Ysim
def plot_f(self, plot_limits=None, which_data_rows='all',
@ -473,16 +473,16 @@ class GP(Model):
self.inference_method.on_optimization_end()
raise
def infer_newX(self, Y_new, optimize=True, ):
def infer_newX(self, Y_new, optimize=True):
"""
Infer the distribution of X for the new observed data *Y_new*.
Infer X for the new observed data *Y_new*.
:param Y_new: the new observed data for inference
:type Y_new: numpy.ndarray
:param optimize: whether to optimize the location of new X (True by default)
:type optimize: boolean
:return: a tuple containing the posterior estimation of X and the model that optimize X
:rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` or numpy.ndarray, :class:`~GPy.core.model.Model`)
:rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` and numpy.ndarray, :class:`~GPy.core.model.Model`)
"""
from ..inference.latent_function_inference.inferenceX import infer_newX
return infer_newX(self, Y_new, optimize=optimize)

23
GPy/core/parameterization/priors.py
View file

@ -522,16 +522,9 @@ class DGPLVM(Prior):
"""
domain = _REAL
# _instances = []
# def __new__(cls, mu, sigma): # Singleton:
# if cls._instances:
# cls._instances[:] = [instance for instance in cls._instances if instance()]
# for instance in cls._instances:
# if instance().mu == mu and instance().sigma == sigma:
# return instance()
# o = super(Prior, cls).__new__(cls, mu, sigma)
# cls._instances.append(weakref.ref(o))
# return cls._instances[-1]()
def __new__(cls, sigma2, lbl, x_shape):
return super(Prior, cls).__new__(cls, sigma2, lbl, x_shape)
def __init__(self, sigma2, lbl, x_shape):
self.sigma2 = sigma2
@ -843,7 +836,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Calculating beta and Bi for Sb
def compute_sig_beta_Bi(self, data_idx, M_i, M_0, lst_idx_all):
import pdb
# import pdb
# pdb.set_trace()
B_i = np.zeros((self.classnum, self.dim))
Sig_beta_B_i_all = np.zeros((self.datanum, self.dim))
@ -909,8 +902,8 @@ class DGPLVM_Lamda(Prior, Parameterized):
Sw = self.compute_Sw(cls, M_i)
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.5))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
return (-1 / self.sigma2) * np.trace(Sb_inv_N.dot(Sw))
# This function calculates derivative of the log of prior function
@ -933,8 +926,8 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Calculating inverse of Sb and its transpose and minus
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.5))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
Sb_inv_N_trans = np.transpose(Sb_inv_N)
Sb_inv_N_trans_minus = -1 * Sb_inv_N_trans
Sw_trans = np.transpose(Sw)