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93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
import numpy as np
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from scipy import stats
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from ..util.linalg import pdinv,mdot,jitchol,chol_inv,DSYR,tdot,dtrtrs
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from likelihood import likelihood
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class EP(object):
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def __init__(self, epsilon=1e-6, eta=1., delta=1.):
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"""
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The expectation-propagation algorithm.
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For nomenclature see Rasmussen & Williams 2006.
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:param epsilon: Convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
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:type epsilon: float
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:param eta: Power EP thing TODO: Ricardo: what, exactly?
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:type eta: float64
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:param delta: Power EP thing TODO: Ricardo: what, exactly?
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:type delta: float64
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"""
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self.epsilon, self.eta, self.delta = epsilon, eta, delta
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self.reset()
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def reset(self):
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self.old_mutilde, self.old_vtilde = None, None
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def inference(self, kern, X, likelihood, Y, Y_metadata=None):
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K = kern.K(X)
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mu_tilde, tau_tilde = self.expectation_propagation()
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def expectation_propagation(self, K, Y, Y_metadata, likelihood)
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num_data, data_dim = Y.shape
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assert data_dim == 1, "This EP methods only works for 1D outputs"
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#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
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mu = np.zeros(self.num_data)
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Sigma = K.copy()
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#Initial values - Marginal moments
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Z_hat = np.empty(num_data,dtype=np.float64)
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mu_hat = np.empty(num_data,dtype=np.float64)
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sigma2_hat = np.empty(num_data,dtype=np.float64)
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#initial values - Gaussian factors
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if self.old_mutilde is None:
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tau_tilde, mu_tilde, v_tilde = np.zeros((3, num_data, num_data))
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else:
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assert old_mutilde.size == num_data, "data size mis-match: did you change the data? try resetting!"
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mu_tilde, v_tilde = self.old_mutilde, self.old_vtilde
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tau_tilde = v_tilde/mu_tilde
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#Approximation
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epsilon_np1 = self.epsilon + 1.
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epsilon_np2 = self.epsilon + 1.
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iterations = 0
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while (epsilon_np1 > self.epsilon) or (epsilon_np2 > self.epsilon):
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update_order = np.random.permutation(num_data)
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for i in update_order:
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#Cavity distribution parameters
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tau_cav = 1./Sigma[i,i] - self.eta*tau_tilde[i]
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v_cav = mu[i]/Sigma[i,i] - self.eta*v_tilde[i]
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#Marginal moments
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Z_hat[i], mu_hat[i], sigma2_hat[i] = likelihood.moments_match(Y[i], tau_cav, v_cav, Y_metadata=(None if Y_metadata is None else Y_metadata[i]))
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#Site parameters update
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delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
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delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
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tau_tilde[i] += delta_tau
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v_tilde[i] += delta_v
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#Posterior distribution parameters update
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DSYR(Sigma, Sigma[:,i].copy(), -Delta_tau/(1.+ Delta_tau*Sigma[i,i]))
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mu = np.dot(Sigma, v_tilde)
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iterations += 1
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#(re) compute Sigma and mu using full Cholesky decompy
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tau_tilde_root = np.sqrt(tau_tilde)
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Sroot_tilde_K = tau_tilde_root[:,None] * K
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B = np.eye(num_data) + Sroot_tilde_K * tau_tilde_root[None,:]
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L = jitchol(B)
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V, _ = dtrtrs(L, Sroot_tilde_K, lower=1)
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Sigma = K - np.dot(V.T,V)
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mu = np.dot(Sigma,v_tilde)
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#monitor convergence
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epsilon_np1 = np.mean(np.square(tau_tilde-tau_tilde_old))
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epsilon_np2 = np.mean(np.square(v_tilde-v_tilde_old))
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tau_tilde_old = tau_tilde.copy()
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v_tilde_old = v_tilde.copy()
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return mu, Sigma, mu_tilde, tau_tilde
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