import theano.tensor as tt
from kusanagi import utils
def SEard(hyp, X1, X2=None, all_pairs=True):
''' Squard exponential kernel with diagonal scaling matrix
(one lengthscale per dimension)'''
n = 1
idims = 1
if X1.ndim == 2:
n, idims = X1.shape
elif X2.ndim == 2:
n, idims = X2.shape
else:
idims = X1.shape[0]
sf2 = hyp[idims]**2
if (not all_pairs) and (X1 is X2 or X2 is None):
# all the distances are going to be zero
K = tt.tile(sf2, (n,))
return K
ls2 = hyp[:idims]**2
D = utils.maha(X1, X2, tt.diag(1.0/ls2),
all_pairs=all_pairs)
K = sf2*tt.exp(-0.5*D)
return K
def Noise(hyp, X1, X2=None, all_pairs=True):
''' Noise kernel. Takes as an input a distance matrix D
and creates a new matrix as Kij = sn2 if Dij == 0 else 0'''
if X2 is None:
X2 = X1
sn2 = hyp**2
if all_pairs and X1 is X2:
# D = (X1[:,None,:] - X2[None,:,:]).sum(2)
K = tt.eye(X1.shape[0])*sn2
return K
else:
# D = (X1 - X2).sum(1)
if X1 is X2:
K = tt.ones((X1.shape[0],))*sn2
else:
K = 0
return K
# K = tt.eq(D,0)*sn2
# return K
def Sum(hyp_l, cov_l, X1, X2=None, all_pairs=True):
''' Returns the sum of multiple covariance functions'''
K = sum([cov_l[i](hyp_l[i], X1, X2, all_pairs=all_pairs)
for i in range(len(cov_l))])
return K
