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some changes for coregionalize

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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
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75
GPy/kern/parts/coregionalize.py
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@ -3,9 +3,8 @@
from kernpart import Kernpart
import numpy as np
from GPy.util.linalg import mdot, pdinv
import pdb
from scipy import weave
from ...core.parameterization import Param
class Coregionalize(Kernpart):
"""
@ -34,37 +33,28 @@ class Coregionalize(Kernpart):
.. note: see coregionalization examples in GPy.examples.regression for some usage.
"""
def __init__(self, output_dim, rank=1, W=None, kappa=None):
self.input_dim = 1
self.name = 'coregion'
def __init__(self, output_dim, rank=1, W=None, kappa=None, name='coregion'):
super(Coregionalize, self).__init__(input_dim=1, name=name)
self.output_dim = output_dim
self.rank = rank
if self.rank>output_dim-1:
print("Warning: Unusual choice of rank, it should normally be less than the output_dim.")
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:
assert W.shape==(self.output_dim,self.rank)
self.W = W
self.W = Param('W',W)
if kappa is None:
kappa = 0.5*np.ones(self.output_dim)
else:
assert kappa.shape==(self.output_dim,)
self.kappa = kappa
self.num_params = self.output_dim*(self.rank + 1)
self._set_params(np.hstack([self.W.flatten(),self.kappa]))
self.kappa = Param('kappa', kappa)
self.add_parameters(self.W, self.kappa)
self.parameters_changed()
def _get_params(self):
return np.hstack([self.W.flatten(),self.kappa])
def _set_params(self,x):
assert x.size == self.num_params
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 parameters_changed(self):
self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)
def K(self,index,index2,target):
index = np.asarray(index,dtype=np.int)
@ -107,7 +97,7 @@ class Coregionalize(Kernpart):
def Kdiag(self,index,target):
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)
dL_dK_small = np.zeros_like(self.B)
if index2 is None:
@ -129,37 +119,20 @@ class Coregionalize(Kernpart):
dL_dK_small += dL_dK_small.T
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):
if index2 is None:
index2 = index
else:
index2 = np.asarray(index2,dtype=np.int)
ii,jj = np.meshgrid(index,index2)
ii,jj = ii.T, jj.T
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
raise NotImplementedError, "some code below"
#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])
dL_dK_small = np.zeros_like(self.B)
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):
def gradients_X(self,dL_dK,X,X2,target):
#NOTE In this case, pass is equivalent to returning zero.
pass