apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-04-27 13:56:23 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Trying to 'debug'

Browse source
This commit is contained in:
Alan Saul 2013-03-14 15:30:22 +00:00
parent 3f114aa020
commit f9535c858a
3 changed files with 52 additions and 32 deletions
Download patch file Download diff file Expand all files Collapse all files

37
python/likelihoods/likelihood_function.py
View file

@ -15,27 +15,27 @@ class student_t(likelihood_function):
dln p(yi|fi)_dfi
d2ln p(yi|fi)_d2fifj
"""
def __init__(self, deg_free, sigma=1):
def __init__(self, deg_free, sigma=2):
self.v = deg_free
self.sigma = 1
self.sigma = sigma
def link_function(self, y, f):
"""link_function $\ln p(y|f)$
$$\ln p(y_{i}|f_{i}) = \ln \Gamma(\frac{v+1}{2}) - \ln \Gamma(\frac{v}{2})\sqrt{v \pi}\sigma - \frac{v+1}{2}\ln (1 + \frac{1}{v}\left(\frac{y_{i} - f_{i}}{\sigma}\right)^2$$
:y: datum number i
:f: latent variable f
:y: data
:f: latent variables f
:returns: float(likelihood evaluated for this point)
"""
assert y.shape[0] == f.shape[0]
e = y - f
#print "Link ", y.shape, f.shape, e.shape
objective = (gammaln((self.v + 1) * 0.5)
- gammaln(self.v * 0.5)
+ np.log(self.sigma * np.sqrt(self.v * np.pi))
- (self.v + 1) * 0.5
* np.log(1 + ((e**2 / self.sigma**2) / self.v))
)
- gammaln(self.v * 0.5)
+ np.log(self.sigma * np.sqrt(self.v * np.pi))
- (self.v + 1) * 0.5
* np.log(1 + ((e**2 / self.sigma**2) / self.v))
)
return np.sum(objective)
def link_grad(self, y, f):
@ -44,13 +44,13 @@ class student_t(likelihood_function):
$$\frac{d}{df}p(y_{i}|f_{i}) = \frac{(v + 1)(y - f)}{v \sigma^{2} + (y_{i} - f_{i})^{2}}$$
:y: datum number i
:f: latent variable f
:returns: float(gradient of likelihood evaluated at this point)
:y: data
:f: latent variables f
:returns: gradient of likelihood evaluated at points
"""
assert y.shape[0] == f.shape[0]
e = y - f
#print "Grad ", y.shape, f.shape, e.shape
grad = ((self.v + 1) * e) / (self.v * (self.sigma**2) + (e**2))
return grad
@ -63,10 +63,11 @@ class student_t(likelihood_function):
$$\frac{d^{2}p(y_{i}|f_{i})}{df^{2}} = \frac{(v + 1)(y - f)}{v \sigma^{2} + (y_{i} - f_{i})^{2}}$$
:y: datum number i
:f: latent variable f
:returns: float(second derivative of likelihood evaluated at this point)
:y: data
:f: latent variables f
:returns: array which is diagonal of covariance matrix (second derivative of likelihood evaluated at points)
"""
assert y.shape[0] == f.shape[0]
e = y - f
hess = ((self.v + 1) * e) / ((((self.sigma**2)*self.v) + e**2)**2)
hess = ((self.v + 1) * e) / ((((self.sigma**2) * self.v) + e**2)**2)
return hess