apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-15 06:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Robustified binomial likelihood

Browse source
This commit is contained in:
Siivola Eero 2017-08-07 18:07:18 +03:00
parent a97cdc3a07
commit 60d0e2f79d
1 changed files with 30 additions and 5 deletions
Download patch file Download diff file Expand all files Collapse all files

35
GPy/likelihoods/binomial.py
View file

@ -66,7 +66,14 @@ class Binomial(Likelihood):
np.testing.assert_array_equal(N.shape, y.shape)
nchoosey = special.gammaln(N+1) - special.gammaln(y+1) - special.gammaln(N-y+1)
return nchoosey + y*np.log(inv_link_f) + (N-y)*np.log(1.-inv_link_f)
Ny = N-y
t1 = np.zeros(y.shape)
t2 = np.zeros(y.shape)
t1[y>0] = y[y>0]*np.log(inv_link_f[y>0])
t2[Ny>0] = Ny[Ny>0]*np.log(1.-inv_link_f[Ny>0])
return nchoosey + t1 + t2
def dlogpdf_dlink(self, inv_link_f, y, Y_metadata=None):
"""
@ -86,7 +93,13 @@ class Binomial(Likelihood):
N = Y_metadata['trials']
np.testing.assert_array_equal(N.shape, y.shape)
return y/inv_link_f - (N-y)/(1.-inv_link_f)
Ny = N-y
t1 = np.zeros(y.shape)
t2 = np.zeros(y.shape)
t1[y>0] = y[y>0]/inv_link_f[y>0]
t2[Ny>0] = (Ny[Ny>0])/(1.-inv_link_f[Ny>0])
return t1 - t2
def d2logpdf_dlink2(self, inv_link_f, y, Y_metadata=None):
"""
@ -111,7 +124,13 @@ class Binomial(Likelihood):
"""
N = Y_metadata['trials']
np.testing.assert_array_equal(N.shape, y.shape)
return -y/np.square(inv_link_f) - (N-y)/np.square(1.-inv_link_f)
Ny = N-y
t1 = np.zeros(y.shape)
t2 = np.zeros(y.shape)
t1[y>0] = -y[y>0]/np.square(inv_link_f[y>0])
t2[Ny>0] = -(Ny[Ny>0])/np.square(1.-inv_link_f[Ny>0])
return t1+t2
def d3logpdf_dlink3(self, inv_link_f, y, Y_metadata=None):
"""
@ -135,8 +154,14 @@ class Binomial(Likelihood):
N = Y_metadata['trials']
np.testing.assert_array_equal(N.shape, y.shape)
inv_link_f2 = np.square(inv_link_f)
return 2*y/inv_link_f**3 - 2*(N-y)/(1.-inv_link_f)**3
#inv_link_f2 = np.square(inv_link_f) #TODO Remove. Why is this here?
Ny = N-y
t1 = np.zeros(y.shape)
t2 = np.zeros(y.shape)
t1[y>0] = 2*y[y>0]/inv_link_f[y>0]**3
t2[Ny>0] = - 2*(Ny[Ny>0])/(1.-inv_link_f[Ny>0])**3
return t1 + t2
def samples(self, gp, Y_metadata=None, **kw):
"""