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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Alan Saul 2013-06-06 14:04:21 +01:00
commit f15df22f43
4 changed files with 19 additions and 42 deletions
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3
GPy/kern/linear.py
View file

@ -100,6 +100,9 @@ class linear(Kernpart):
def dK_dX(self, dL_dK, X, X2, target):
target += (((X2[:, None, :] * self.variances)) * dL_dK[:, :, None]).sum(0)
def dKdiag_dX(self,dL_dKdiag,X,target):
target += 2.*self.variances*dL_dKdiag[:,None]*X
#---------------------------------------#
# PSI statistics #
#---------------------------------------#

16
GPy/kern/rbf.py
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@ -96,13 +96,13 @@ class rbf(Kernpart):
var_len3 = self.variance / np.power(self.lengthscale, 3)
if X2 is None:
# save computation for the symmetrical case
dvardLdK += dvardLdK.T
dvardLdK = dvardLdK + dvardLdK.T
code = """
int q,i,j;
double tmp;
for(q=0; q<input_dim; q++){
tmp = 0;
for(i=0; i<N; i++){
for(i=0; i<num_data; i++){
for(j=0; j<i; j++){
tmp += (X(i,q)-X(j,q))*(X(i,q)-X(j,q))*dvardLdK(i,j);
}
@ -110,14 +110,15 @@ class rbf(Kernpart):
target(q+1) += var_len3(q)*tmp;
}
"""
N, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
num_data, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
weave.inline(code, arg_names=['num_data','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
else:
code = """
int q,i,j;
double tmp;
for(q=0; q<input_dim; q++){
tmp = 0;
for(i=0; i<N; i++){
for(i=0; i<num_data; i++){
for(j=0; j<num_inducing; j++){
tmp += (X(i,q)-X2(j,q))*(X(i,q)-X2(j,q))*dvardLdK(i,j);
}
@ -125,10 +126,9 @@ class rbf(Kernpart):
target(q+1) += var_len3(q)*tmp;
}
"""
N, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
# [np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
weave.inline(code, arg_names=['N','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3'],
type_converters=weave.converters.blitz, **self.weave_options)
num_data, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
#[np.add(target[1+q:2+q],var_len3[q]*np.sum(dvardLdK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.input_dim)]
weave.inline(code, arg_names=['num_data','num_inducing','input_dim','X','X2','target','dvardLdK','var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
else:
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)

40
GPy/likelihoods/link_functions.py
View file

@ -19,41 +19,15 @@ class LinkFunction(object):
def __init__(self):
pass
class Identity(LinkFunction):
def transf(self,mu):
return mu
def inv_transf(self,f):
return f
def log_inv_transf(self,f):
return np.log(f)
class Log(LinkFunction):
def transf(self,mu):
return np.log(mu)
def inv_transf(self,f):
return np.exp(f)
def log_inv_transf(self,f):
return f
class Log_ex_1(LinkFunction):
def transf(self,mu):
return np.log(np.exp(mu) - 1)
def inv_transf(self,f):
return np.log(np.exp(f)+1)
def log_inv_tranf(self,f):
return np.log(np.log(np.exp(f)+1))
class Probit(LinkFunction):
"""
Probit link function: Squashes a likelihood between 0 and 1
"""
def transf(self,mu):
pass
def inv_transf(self,f):
return std_norm_cdf(f)
pass
def log_inv_transf(self,f):
return np.log(std_norm_cdf(f))
pass

2
GPy/util/__init__.py
View file

@ -11,6 +11,6 @@ import misc
import warping_functions
import datasets
import mocap
import visualize
#import visualize
import decorators
import classification