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working on coregionalize
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James Hensman
parent
78ceef01c3
commit
fddc663f28
1 changed files with 44 additions and 47 deletions
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@ -5,6 +5,7 @@ from kern import Kern
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import numpy as np
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from scipy import weave
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from ...core.parameterization import Param
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from ...core.parameterization.transformations import Logexp
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class Coregionalize(Kern):
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"""
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@ -20,7 +21,7 @@ class Coregionalize(Kern):
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k_2(x, y)=\mathbf{B} k(x, y)
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it is obtained as the tensor product between a covariance function
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k(x,y) and B.
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k(x, y) and B.
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:param output_dim: number of outputs to coregionalize
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:type output_dim: int
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@ -29,7 +30,7 @@ class Coregionalize(Kern):
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:param W: a low rank matrix that determines the correlations between the different outputs, together with kappa it forms the coregionalization matrix B
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:type W: numpy array of dimensionality (num_outpus, W_columns)
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:param kappa: a vector which allows the outputs to behave independently
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:type kappa: numpy array of dimensionality (output_dim,)
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:type kappa: numpy array of dimensionality (output_dim, )
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.. note: see coregionalization examples in GPy.examples.regression for some usage.
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"""
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@ -37,18 +38,18 @@ class Coregionalize(Kern):
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super(Coregionalize, self).__init__(input_dim=1, name=name)
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self.output_dim = output_dim
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self.rank = rank
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if self.rank>output_dim-1:
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if self.rank>output_dim:
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print("Warning: Unusual choice of rank, it should normally be less than the output_dim.")
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if W is None:
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W = 0.5*np.random.randn(self.output_dim,self.rank)/np.sqrt(self.rank)
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W = 0.5*np.random.randn(self.output_dim, self.rank)/np.sqrt(self.rank)
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else:
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assert W.shape==(self.output_dim,self.rank)
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self.W = Param('W',W)
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assert W.shape==(self.output_dim, self.rank)
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self.W = Param('W', W)
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if kappa is None:
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kappa = 0.5*np.ones(self.output_dim)
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else:
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assert kappa.shape==(self.output_dim,)
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self.kappa = Param('kappa', kappa)
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assert kappa.shape==(self.output_dim, )
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self.kappa = Param('kappa', kappa, Logexp())
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self.add_parameters(self.W, self.kappa)
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self.parameters_changed()
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@ -56,54 +57,58 @@ class Coregionalize(Kern):
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def parameters_changed(self):
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self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)
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def K(self,index,index2,target):
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index = np.asarray(index,dtype=np.int)
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def K(self, X, X2=None):
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index = np.asarray(X, dtype=np.int)
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#here's the old code (numpy)
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#if index2 is None:
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#index2 = index
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#else:
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#index2 = np.asarray(index2,dtype=np.int)
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#index2 = np.asarray(index2, dtype=np.int)
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#false_target = target.copy()
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#ii,jj = np.meshgrid(index,index2)
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#ii,jj = ii.T, jj.T
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#false_target += self.B[ii,jj]
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#ii, jj = np.meshgrid(index, index2)
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#ii, jj = ii.T, jj.T
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#false_target += self.B[ii, jj]
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if index2 is None:
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if X2 is None:
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target = np.empty((X.shape[0], X.shape[0]), dtype=np.float64)
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code="""
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for(int i=0;i<N; i++){
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target[i+i*N] += B[index[i]+output_dim*index[i]];
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target[i+i*N] = B[index[i]+output_dim*index[i]];
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for(int j=0; j<i; j++){
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target[j+i*N] += B[index[i]+output_dim*index[j]];
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target[i+j*N] += target[j+i*N];
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target[j+i*N] = B[index[i]+output_dim*index[j]];
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target[i+j*N] = target[j+i*N];
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}
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}
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"""
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N,B,output_dim = index.size, self.B, self.output_dim
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weave.inline(code,['target','index','N','B','output_dim'])
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N, B, output_dim = index.size, self.B, self.output_dim
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weave.inline(code, ['target', 'index', 'N', 'B', 'output_dim'])
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else:
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index2 = np.asarray(index2,dtype=np.int)
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index2 = np.asarray(X2, dtype=np.int)
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target = np.empty((X.shape[0], X2.shape[0]), dtype=np.float64)
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code="""
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for(int i=0;i<num_inducing; i++){
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for(int j=0; j<N; j++){
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target[i+j*num_inducing] += B[output_dim*index[j]+index2[i]];
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target[i+j*num_inducing] = B[output_dim*index[j]+index2[i]];
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}
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}
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"""
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N,num_inducing,B,output_dim = index.size,index2.size, self.B, self.output_dim
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weave.inline(code,['target','index','index2','N','num_inducing','B','output_dim'])
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N, num_inducing, B, output_dim = index.size, index2.size, self.B, self.output_dim
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weave.inline(code, ['target', 'index', 'index2', 'N', 'num_inducing', 'B', 'output_dim'])
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return target
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def Kdiag(self,index,target):
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target += np.diag(self.B)[np.asarray(index,dtype=np.int).flatten()]
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def Kdiag(self, X):
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return np.diag(self.B)[np.asarray(X, dtype=np.int).flatten()]
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def update_gradients_full(self,dL_dK, index, index2=None):
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index = np.asarray(index,dtype=np.int)
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def update_gradients_full(self, dL_dK, X, X2=None):
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index = np.asarray(X, dtype=np.int)
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dL_dK_small = np.zeros_like(self.B)
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if index2 is None:
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if X2 is None:
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index2 = index
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else:
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index2 = np.asarray(index2,dtype=np.int)
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index2 = np.asarray(X2, dtype=np.int)
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code="""
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for(int i=0; i<num_inducing; i++){
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@ -113,28 +118,20 @@ class Coregionalize(Kern):
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}
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"""
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N, num_inducing, output_dim = index.size, index2.size, self.output_dim
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weave.inline(code, ['N','num_inducing','output_dim','dL_dK','dL_dK_small','index','index2'])
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weave.inline(code, ['N', 'num_inducing', 'output_dim', 'dL_dK', 'dL_dK_small', 'index', 'index2'])
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dkappa = np.diag(dL_dK_small)
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dL_dK_small += dL_dK_small.T
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dW = (self.W[:,None,:]*dL_dK_small[:,:,None]).sum(0)
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dW = (self.W[:, None, :]*dL_dK_small[:, :, None]).sum(0)
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self.W.gradient = dW
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self.kappa.gradient = dkappa
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def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
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raise NotImplementedError, "some code below"
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#def dKdiag_dtheta(self,dL_dKdiag,index,target):
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#index = np.asarray(index,dtype=np.int).flatten()
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#dL_dKdiag_small = np.zeros(self.output_dim)
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#for i in range(self.output_dim):
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#dL_dKdiag_small[i] += np.sum(dL_dKdiag[index==i])
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#dW = 2.*self.W*dL_dKdiag_small[:,None]
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#dkappa = dL_dKdiag_small
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#target += np.hstack([dW.flatten(),dkappa])
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def update_gradients_diag(self, dL_dKdiag, X):
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index = np.asarray(X, dtype=np.int).flatten()
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dL_dKdiag_small = np.array([dL_dKdiag[index==i] for i in xrange(output_dim)])
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self.W.gradient = 2.*self.W*dL_dKdiag_small[:, None]
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self.kappa.gradient = dL_dKdiag_small
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def gradients_X(self,dL_dK,X,X2):
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if X2 is None:
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return np.zeros((X.shape[0], X.shape[0]))
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else:
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return np.zeros((X.shape[0], X2.shape[0]))
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def gradients_X(self, dL_dK, X, X2=None):
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return np.zeros(X.shape)
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