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epsilon and power_ep now are parameters of update_likelihood.

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Ricardo 2013-09-20 13:22:38 +01:00
parent c8fec98071
commit a51af5b8c4
5 changed files with 62 additions and 30 deletions
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43
GPy/likelihoods/ep.py
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@ -4,18 +4,17 @@ from ..util.linalg import pdinv,mdot,jitchol,chol_inv,DSYR,tdot,dtrtrs
from likelihood import likelihood
class EP(likelihood):
def __init__(self,data,noise_model,epsilon=1e-3,power_ep=[1.,1.]):
def __init__(self,data,noise_model):
"""
Expectation Propagation
Arguments
---------
epsilon : Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
noise_model : a likelihood function (see likelihood_functions.py)
:param data: data to model
:type data: numpy array
:param noise_model: noise distribution
:type noise_model: A GPy noise model
"""
self.noise_model = noise_model
self.epsilon = epsilon
self.eta, self.delta = power_ep
self.data = data
self.N, self.output_dim = self.data.shape
self.is_heteroscedastic = True
@ -87,11 +86,19 @@ class EP(likelihood):
self.VVT_factor = self.V
self.trYYT = np.trace(self.YYT)
def fit_full(self,K):
def fit_full(self, K, epsilon=1e-3,power_ep=[1.,1.]):
"""
The expectation-propagation algorithm.
For nomenclature see Rasmussen & Williams 2006.
:param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
:type epsilon: float
:param power_ep: Power EP parameters
:type power_ep: list of floats
"""
self.epsilon = epsilon
self.eta, self.delta = power_ep
#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
mu = np.zeros(self.N)
Sigma = K.copy()
@ -149,11 +156,19 @@ class EP(likelihood):
return self._compute_GP_variables()
def fit_DTC(self, Kmm, Kmn):
def fit_DTC(self, Kmm, Kmn, epsilon=1e-3,power_ep=[1.,1.]):
"""
The expectation-propagation algorithm with sparse pseudo-input.
For nomenclature see ... 2013.
:param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
:type epsilon: float
:param power_ep: Power EP parameters
:type power_ep: list of floats
"""
self.epsilon = epsilon
self.eta, self.delta = power_ep
num_inducing = Kmm.shape[0]
#TODO: this doesn't work with uncertain inputs!
@ -245,11 +260,19 @@ class EP(likelihood):
self._compute_GP_variables()
def fit_FITC(self, Kmm, Kmn, Knn_diag):
def fit_FITC(self, Kmm, Kmn, Knn_diag, epsilon=1e-3,power_ep=[1.,1.]):
"""
The expectation-propagation algorithm with sparse pseudo-input.
For nomenclature see Naish-Guzman and Holden, 2008.
:param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
:type epsilon: float
:param power_ep: Power EP parameters
:type power_ep: list of floats
"""
self.epsilon = epsilon
self.eta, self.delta = power_ep
num_inducing = Kmm.shape[0]
"""