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Still working on rasmussen, link function needs vectorizing I think

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This commit is contained in:
Alan Saul 2013-04-02 20:00:31 +01:00
parent afa5b1f956
commit 0312f319ad
3 changed files with 154 additions and 54 deletions
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13
python/likelihoods/likelihood_function.py
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@ -36,7 +36,10 @@ class student_t(likelihood_function):
:returns: float(likelihood evaluated for this point)
"""
y = np.squeeze(y)
f = np.squeeze(f)
assert y.shape == f.shape
e = y - f
objective = (gammaln((self.v + 1) * 0.5)
- gammaln(self.v * 0.5)
@ -44,6 +47,7 @@ class student_t(likelihood_function):
- (self.v + 1) * 0.5
* np.log(1 + ((e**2 / self.sigma**2) / self.v))
)
print (e**2).shape
return np.sum(objective)
def link_grad(self, y, f):
@ -57,10 +61,12 @@ class student_t(likelihood_function):
:returns: gradient of likelihood evaluated at points
"""
y = np.squeeze(y)
f = np.squeeze(f)
assert y.shape == f.shape
e = y - f
grad = ((self.v + 1) * e) / (self.v * (self.sigma**2) + (e**2))
return grad
return np.squeeze(grad)
def link_hess(self, y, f):
"""
@ -75,11 +81,12 @@ class student_t(likelihood_function):
:f: latent variables f
:returns: array which is diagonal of covariance matrix (second derivative of likelihood evaluated at points)
"""
y = np.squeeze(y)
f = np.squeeze(f)
assert y.shape == f.shape
e = y - f
#hess = ((self.v + 1) * e) / ((((self.sigma**2) * self.v) + e**2)**2)
hess = ((self.v + 1)*(e**2 - self.v*(self.sigma**2))) / ((((self.sigma**2)*self.v) + e**2)**2)
return hess
return np.squeeze(hess)
def predictive_values(self, mu, var):
"""