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Still tidying up, laplace now working again, gaussian and student_t

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likelihoods now done
This commit is contained in:
Alan Saul 2013-10-15 18:58:41 +01:00
parent 96f189113a
commit 0344324571
6 changed files with 167 additions and 81 deletions
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47
GPy/likelihoods/noise_models/student_t_noise.py
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@ -40,27 +40,9 @@ class StudentT(NoiseDistribution):
def variance(self, extra_data=None):
return (self.v / float(self.v - 2)) * self.sigma2
def _nlog_mass(self, link_f, y, extra_data=None):
NotImplementedError("Deprecated, now doing chain in likelihood.py for link function evaluation\
Please negate your function and use logpdf in noise_model.py, if implementing a likelihood\
rederivate the derivative without doing the chain and put in logpdf, dlogpdf_dlink or\
its derivatives")
def _dnlog_mass_dgp(self, link_f, y, extra_data=None):
NotImplementedError("Deprecated, now doing chain in likelihood.py for link function evaluation\
Please negate your function and use logpdf in noise_model.py, if implementing a likelihood\
rederivate the derivative without doing the chain and put in logpdf, dlogpdf_dlink or\
its derivatives")
def _d2nlog_mass_dgp2(self, link_f, y, extra_data=None):
NotImplementedError("Deprecated, now doing chain in likelihood.py for link function evaluation\
Please negate your function and use logpdf in noise_model.py, if implementing a likelihood\
rederivate the derivative without doing the chain and put in logpdf, dlogpdf_dlink or\
its derivatives")
def logpdf(self, link_f, y, extra_data=None):
def logpdf_link(self, link_f, y, extra_data=None):
"""
Log Likelihood Function
Log Likelihood Function given link(f)
.. math::
\\ln p(y_{i}|f_{i}) = \\ln \\Gamma(\\frac{v+1}{2}) - \\ln \\Gamma(\\frac{v}{2})\\sqrt{v \\pi}\sigma - \\frac{v+1}{2}\\ln (1 + \\frac{1}{v}\\left(\\frac{y_{i} - f_{i}}{\\sigma}\\right)^2
@ -151,7 +133,7 @@ class StudentT(NoiseDistribution):
)
return d3lik_dlink3
def dlogpdf_dvar(self, link_f, y, extra_data=None):
def dlogpdf_link_dvar(self, link_f, y, extra_data=None):
"""
Gradient of the log-likelihood function at y given f, w.r.t variance parameter (t_noise)
@ -169,7 +151,6 @@ class StudentT(NoiseDistribution):
assert y.shape == link_f.shape
e = y - link_f
dlogpdf_dvar = self.v*(e**2 - self.sigma2)/(2*self.sigma2*(self.sigma2*self.v + e**2))
#FIXME: Careful as this hasn't been chained with dlink_var, not sure if we want link functions on our parameters?! Shouldn't need them with constraints
return np.sum(dlogpdf_dvar)
def dlogpdf_dlink_dvar(self, link_f, y, extra_data=None):
@ -214,17 +195,17 @@ class StudentT(NoiseDistribution):
)
return d2logpdf_dlink2_dvar
def _laplace_gradients(self, y, f, extra_data=None):
#must be listed in same order as 'get_param_names'
derivs = ([self.dlogpdf_dvar(f, y, extra_data=extra_data)],
[self.dlogpdf_dlink_dvar(f, y, extra_data=extra_data)],
[self.d2logpdf_dlink2_dvar(f, y, extra_data=extra_data)]
) # lists as we might learn many parameters
# ensure we have gradients for every parameter we want to optimize
assert len(derivs[0]) == len(self._get_param_names())
assert len(derivs[1]) == len(self._get_param_names())
assert len(derivs[2]) == len(self._get_param_names())
return derivs
def dlogpdf_link_dtheta(self, f, y, extra_data=None):
dlogpdf_dvar = self.dlogpdf_link_dvar(f, y, extra_data=extra_data)
return np.asarray([[dlogpdf_dvar]])
def dlogpdf_dlink_dtheta(self, f, y, extra_data=None):
dlogpdf_dlink_dvar = self.dlogpdf_dlink_dvar(f, y, extra_data=extra_data)
return dlogpdf_dlink_dvar
def d2logpdf_dlink2_dtheta(self, f, y, extra_data=None):
d2logpdf_dlink2_dvar = self.d2logpdf_dlink2_dvar(f, y, extra_data=extra_data)
return d2logpdf_dlink2_dvar
def _predictive_variance_analytical(self, mu, sigma, predictive_mean=None):
"""