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Changing definitions again...

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Alan Saul 2013-03-21 14:00:22 +00:00
parent a9d5555976
commit 474d5484b0
3 changed files with 43 additions and 26 deletions
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44
python/likelihoods/Laplace.py
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@ -1,7 +1,7 @@
import numpy as np
import scipy as sp
import GPy
from scipy.linalg import cholesky, eig, inv
from scipy.linalg import cholesky, eig, inv, det
from functools import partial
from GPy.likelihoods.likelihood import likelihood
from GPy.util.linalg import pdinv,mdot
@ -43,8 +43,10 @@ class Laplace(likelihood):
self.Z = 0
self.YYT = None
def predictive_values(self,mu,var):
return self.likelihood_function.predictive_values(mu,var)
def predictive_values(self, mu, var, full_cov):
if full_cov:
raise NotImplementedError("Cannot make correlated predictions with an EP likelihood")
return self.likelihood_function.predictive_values(mu, var)
def _get_params(self):
return np.zeros(0)
@ -52,10 +54,10 @@ class Laplace(likelihood):
def _get_param_names(self):
return []
def _set_params(self,p):
def _set_params(self, p):
pass # TODO: Laplace likelihood might want to take some parameters...
def _gradients(self,partial):
def _gradients(self, partial):
return np.zeros(0) # TODO: Laplace likelihood might want to take some parameters...
raise NotImplementedError
@ -83,7 +85,13 @@ class Laplace(likelihood):
and $$\ln \tilde{z} = \ln z + \frac{N}{2}\ln 2\pi + \frac{1}{2}\tilde{Y}\tilde{\Sigma}^{-1}\tilde{Y}$$
"""
self.Sigma_tilde_i = self.hess_hat_i #self.W #self.hess_hat_i - self.Ki
self.Sigma_tilde_i = self.W #self.hess_hat_i
#Check it isn't singular!
epsilon = 1e-2
"""
if np.abs(det(self.Sigma_tilde_i)) < epsilon:
raise ValueError("inverse covariance must be non-singular to inverse!")
"""
#Do we really need to inverse Sigma_tilde_i? :(
if self.likelihood_function.log_concave:
(self.Sigma_tilde, _, _, _) = pdinv(self.Sigma_tilde_i)
@ -91,12 +99,17 @@ class Laplace(likelihood):
self.Sigma_tilde = inv(self.Sigma_tilde_i)
#f_hat? should be f but we must have optimized for them I guess?
Y_tilde = mdot(self.Sigma_tilde, self.hess_hat, self.f_hat)
self.Z_tilde = np.exp(self.ln_z_hat - self.NORMAL_CONST
- 0.5*mdot(self.f_hat, self.hess_hat, self.f_hat)
+ 0.5*mdot(Y_tilde.T, (self.Sigma_tilde_i, Y_tilde))
)
#Z_tilde = (self.ln_z_hat - self.NORMAL_CONST
#- 0.5*mdot(self.f_hat, self.hess_hat, self.f_hat)
#+ 0.5*mdot(Y_tilde.T, (self.Sigma_tilde_i, Y_tilde))
#)
Z_tilde = (self.ln_z_hat - self.NORMAL_CONST
+ 0.5*self.log_hess_hat_det
+ 0.5*mdot(self.f_hat, self.Ki , self.f_hat)
+ 0.5*mdot(Y_tilde.T, (self.Sigma_tilde_i, Y_tilde))
)
self.Z = self.Z_tilde
self.Z = Z_tilde
self.Y = Y_tilde
self.covariance_matrix = self.Sigma_tilde
self.precision = 1 / np.diag(self.Sigma_tilde)[:, None]
@ -128,7 +141,7 @@ class Laplace(likelihood):
return np.squeeze(res)
def obj_hess(f):
res = -1 * (-np.diag(self.likelihood_function.link_hess(self.data[:, 0], f)) - self.Ki)
res = -1 * (--np.diag(self.likelihood_function.link_hess(self.data[:, 0], f)) - self.Ki)
return np.squeeze(res)
self.f_hat = sp.optimize.fmin_ncg(obj, f, fprime=obj_grad, fhess=obj_hess)
@ -153,7 +166,10 @@ class Laplace(likelihood):
#the area of p(f)p(y|f) we do this by matching the height of the distributions at the mode
#z_hat = -0.5*ln|H| - 0.5*ln|K| - 0.5*f_hat*K^{-1}*f_hat \sum_{n} ln p(y_n|f_n)
#Unsure whether its log_hess or log_hess_i
self.ln_z_hat = -0.5*np.log(self.log_hess_hat_det) - 0.5*self.log_Kdet + -1*self.likelihood_function.link_function(self.data[:,0], self.f_hat) - mdot(self.f_hat.T, (self.Ki, self.f_hat))
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
self.ln_z_hat = (-0.5*self.log_hess_hat_det
- 0.5*self.log_Kdet
-1*self.likelihood_function.link_function(self.data[:,0], self.f_hat)
- mdot(self.f_hat.T, (self.Ki, self.f_hat))
)
return self._compute_GP_variables()

10
python/likelihoods/likelihood_function.py
View file

@ -81,11 +81,7 @@ class student_t(likelihood_function):
Compute mean, and conficence interval (percentiles 5 and 95) of the prediction
"""
mean = np.exp(mu)
p_025 = stats.t.ppf(025,mean)
p_975 = stats.t.ppf(975,mean)
#p_025 = tmp[:,0]
#p_975 = tmp[:,1]
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
return mean,p_025,p_975
p_025 = stats.t.ppf(.025, mean)
p_975 = stats.t.ppf(.975, mean)
return mean, np.nan*mean, p_025, p_975