apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

some changes for coregionalize

Browse source
This commit is contained in:
James Hensman 2014-01-28 15:04:12 +00:00
parent 18a5c437e8
commit a99537d3cc
1 changed files with 24 additions and 51 deletions
Download patch file Download diff file Expand all files Collapse all files

75
GPy/kern/parts/coregionalize.py
View file

@ -3,9 +3,8 @@
from kernpart import Kernpart from kernpart import Kernpart
import numpy as np import numpy as np
from GPy.util.linalg import mdot, pdinv
import pdb
from scipy import weave from scipy import weave
from ...core.parameterization import Param
class Coregionalize(Kernpart): class Coregionalize(Kernpart):
""" """
@ -34,37 +33,28 @@ class Coregionalize(Kernpart):
.. note: see coregionalization examples in GPy.examples.regression for some usage. .. note: see coregionalization examples in GPy.examples.regression for some usage.
""" """
def __init__(self, output_dim, rank=1, W=None, kappa=None): def __init__(self, output_dim, rank=1, W=None, kappa=None, name='coregion'):
self.input_dim = 1 super(Coregionalize, self).__init__(input_dim=1, name=name)
self.name = 'coregion'
self.output_dim = output_dim self.output_dim = output_dim
self.rank = rank self.rank = rank
if self.rank>output_dim-1: if self.rank>output_dim-1:
print("Warning: Unusual choice of rank, it should normally be less than the output_dim.") print("Warning: Unusual choice of rank, it should normally be less than the output_dim.")
if W is None: if W is None:
self.W = 0.5*np.random.randn(self.output_dim,self.rank)/np.sqrt(self.rank) W = 0.5*np.random.randn(self.output_dim,self.rank)/np.sqrt(self.rank)
else: else:
assert W.shape==(self.output_dim,self.rank) assert W.shape==(self.output_dim,self.rank)
self.W = W self.W = Param('W',W)
if kappa is None: if kappa is None:
kappa = 0.5*np.ones(self.output_dim) kappa = 0.5*np.ones(self.output_dim)
else: else:
assert kappa.shape==(self.output_dim,) assert kappa.shape==(self.output_dim,)
self.kappa = kappa self.kappa = Param('kappa', kappa)
self.num_params = self.output_dim*(self.rank + 1) self.add_parameters(self.W, self.kappa)
self._set_params(np.hstack([self.W.flatten(),self.kappa])) self.parameters_changed()
def _get_params(self):
return np.hstack([self.W.flatten(),self.kappa])
def _set_params(self,x): def parameters_changed(self):
assert x.size == self.num_params self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)
self.kappa = x[-self.output_dim:]
self.W = x[:-self.output_dim].reshape(self.output_dim,self.rank)
self.B = np.dot(self.W,self.W.T) + np.diag(self.kappa)
def _get_param_names(self):
return sum([['W%i_%i'%(i,j) for j in range(self.rank)] for i in range(self.output_dim)],[]) + ['kappa_%i'%i for i in range(self.output_dim)]
def K(self,index,index2,target): def K(self,index,index2,target):
index = np.asarray(index,dtype=np.int) index = np.asarray(index,dtype=np.int)
@ -107,7 +97,7 @@ class Coregionalize(Kernpart):
def Kdiag(self,index,target): def Kdiag(self,index,target):
target += np.diag(self.B)[np.asarray(index,dtype=np.int).flatten()] target += np.diag(self.B)[np.asarray(index,dtype=np.int).flatten()]
def dK_dtheta(self,dL_dK,index,index2,target): def update_gradients_full(self,dL_dK, index, index2=None):
index = np.asarray(index,dtype=np.int) index = np.asarray(index,dtype=np.int)
dL_dK_small = np.zeros_like(self.B) dL_dK_small = np.zeros_like(self.B)
if index2 is None: if index2 is None:
@ -129,37 +119,20 @@ class Coregionalize(Kernpart):
dL_dK_small += dL_dK_small.T dL_dK_small += dL_dK_small.T
dW = (self.W[:,None,:]*dL_dK_small[:,:,None]).sum(0) dW = (self.W[:,None,:]*dL_dK_small[:,:,None]).sum(0)
target += np.hstack([dW.flatten(),dkappa]) self.W.gradient = dW
self.kappa.gradient = dkappa
def dK_dtheta_old(self,dL_dK,index,index2,target): def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
if index2 is None: raise NotImplementedError, "some code below"
index2 = index #def dKdiag_dtheta(self,dL_dKdiag,index,target):
else: #index = np.asarray(index,dtype=np.int).flatten()
index2 = np.asarray(index2,dtype=np.int) #dL_dKdiag_small = np.zeros(self.output_dim)
ii,jj = np.meshgrid(index,index2) #for i in range(self.output_dim):
ii,jj = ii.T, jj.T #dL_dKdiag_small[i] += np.sum(dL_dKdiag[index==i])
#dW = 2.*self.W*dL_dKdiag_small[:,None]
#dkappa = dL_dKdiag_small
#target += np.hstack([dW.flatten(),dkappa])
dL_dK_small = np.zeros_like(self.B) def gradients_X(self,dL_dK,X,X2,target):
for i in range(self.output_dim):
for j in range(self.output_dim):
tmp = np.sum(dL_dK[(ii==i)*(jj==j)])
dL_dK_small[i,j] = tmp
dkappa = np.diag(dL_dK_small)
dL_dK_small += dL_dK_small.T
dW = (self.W[:,None,:]*dL_dK_small[:,:,None]).sum(0)
target += np.hstack([dW.flatten(),dkappa])
def dKdiag_dtheta(self,dL_dKdiag,index,target):
index = np.asarray(index,dtype=np.int).flatten()
dL_dKdiag_small = np.zeros(self.output_dim)
for i in range(self.output_dim):
dL_dKdiag_small[i] += np.sum(dL_dKdiag[index==i])
dW = 2.*self.W*dL_dKdiag_small[:,None]
dkappa = dL_dKdiag_small
target += np.hstack([dW.flatten(),dkappa])
def dK_dX(self,dL_dK,X,X2,target):
#NOTE In this case, pass is equivalent to returning zero. #NOTE In this case, pass is equivalent to returning zero.
pass pass