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Moved transf_data to make data -1 or 1 from 0 or 1 for bernoulli

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with probit into the analytical moment match (but it 10% slower),
needs removing from epmixednoise
This commit is contained in:
Alan Saul 2013-10-22 15:30:56 +01:00
parent 5f9d7eb709
commit 7c9eda482c
2 changed files with 18 additions and 13 deletions
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7
GPy/likelihoods/ep.py
View file

@ -19,7 +19,6 @@ class EP(likelihood):
self.num_data, self.output_dim = self.data.shape
self.is_heteroscedastic = True
self.num_params = 0
self._transf_data = self.noise_model._preprocess_values(data)
#Initial values - Likelihood approximation parameters:
#p(y|f) = t(f|tau_tilde,v_tilde)
@ -134,7 +133,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma[i,i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma[i,i] - self.eta*self.v_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
@ -233,7 +232,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])
@ -336,7 +335,7 @@ class EP(likelihood):
self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]
self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]
#Marginal moments
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self._transf_data[i],self.tau_[i],self.v_[i])
self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])
#Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])

24
GPy/likelihoods/noise_models/bernoulli_noise.py
View file

@ -45,18 +45,24 @@ class Bernoulli(NoiseDistribution):
:param tau_i: precision of the cavity distribution (float)
:param v_i: mean/variance of the cavity distribution (float)
"""
if data_i == 1:
sign = 1.
elif data_i == 0:
sign = -1
else:
raise ValueError("bad value for Bernouilli observation (0,1)")
if isinstance(self.gp_link,gp_transformations.Probit):
z = data_i*v_i/np.sqrt(tau_i**2 + tau_i)
z = sign*v_i/np.sqrt(tau_i**2 + tau_i)
Z_hat = std_norm_cdf(z)
phi = std_norm_pdf(z)
mu_hat = v_i/tau_i + data_i*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i))
mu_hat = v_i/tau_i + sign*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i))
sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat)
elif isinstance(self.gp_link,gp_transformations.Heaviside):
a = data_i*v_i/np.sqrt(tau_i)
a = sign*v_i/np.sqrt(tau_i)
Z_hat = std_norm_cdf(a)
N = std_norm_pdf(a)
mu_hat = v_i/tau_i + data_i*N/Z_hat/np.sqrt(tau_i)
mu_hat = v_i/tau_i + sign*N/Z_hat/np.sqrt(tau_i)
sigma2_hat = (1. - a*N/Z_hat - np.square(N/Z_hat))/tau_i
if np.any(np.isnan([Z_hat, mu_hat, sigma2_hat])):
stop
@ -97,7 +103,7 @@ class Bernoulli(NoiseDistribution):
.. Note:
Each y_i must be in {0,1}
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
objective = (link_f**y) * ((1.-link_f)**(1.-y))
return np.exp(np.sum(np.log(objective)))
@ -116,7 +122,7 @@ class Bernoulli(NoiseDistribution):
:returns: log likelihood evaluated at points link(f)
:rtype: float
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
#objective = y*np.log(link_f) + (1.-y)*np.log(link_f)
objective = np.where(y==1, np.log(link_f), np.log(1-link_f))
return np.sum(objective)
@ -136,7 +142,7 @@ class Bernoulli(NoiseDistribution):
:returns: gradient of log likelihood evaluated at points link(f)
:rtype: Nx1 array
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
grad = (y/link_f) - (1.-y)/(1-link_f)
return grad
@ -161,7 +167,7 @@ class Bernoulli(NoiseDistribution):
Will return diagonal of hessian, since every where else it is 0, as the likelihood factorizes over cases
(the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
d2logpdf_dlink2 = -y/(link_f**2) - (1-y)/((1-link_f)**2)
return d2logpdf_dlink2
@ -180,7 +186,7 @@ class Bernoulli(NoiseDistribution):
:returns: third derivative of log likelihood evaluated at points link(f)
:rtype: Nx1 array
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
d3logpdf_dlink3 = 2*(y/(link_f**3) - (1-y)/((1-link_f)**3))
return d3logpdf_dlink3