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R paramter renamed as W_columns, and Nout renamed as num_outputs
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Ricardo
parent
c941da9d3c
commit
81eb22dffd
1 changed files with 34 additions and 29 deletions
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@ -9,24 +9,32 @@ from scipy import weave
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class Coregionalise(Kernpart):
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"""
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Kernel for Intrinsic Corregionalization Models
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Kernel for Intrinsic Coregionalization Models
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This kernel has the form: K = np.dot(W,W.T) + np.diag(kappa)
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An intrinsec coregionalization kernel is obtained as the tensor product between a different kernel and the coregionalize kernel.
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:param num_outputs: number of outputs to coregionalize
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:param W_columns: number of columns of the W matrix (this parameter is ignored if parameter W is not None)
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:param W: array of shape (num_outputs, W_columns)
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:param kappa: array of dimensions (num_outputs,)
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"""
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def __init__(self,Nout,R=1, W=None, kappa=None):
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def __init__(self,num_outputs,W_columns=1, W=None, kappa=None):
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self.input_dim = 1
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self.name = 'coregion'
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self.Nout = Nout
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self.R = R
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self.num_outputs = num_outputs
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self.W_columns = W_columns
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if W is None:
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self.W = np.ones((self.Nout,self.R))
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self.W = np.ones((self.num_outputs,self.W_columns))
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else:
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assert W.shape==(self.Nout,self.R)
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assert W.shape==(self.num_outputs,self.W_columns)
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self.W = W
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if kappa is None:
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kappa = np.ones(self.Nout)
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kappa = np.ones(self.num_outputs)
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else:
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assert kappa.shape==(self.Nout,)
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assert kappa.shape==(self.num_outputs,)
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self.kappa = kappa
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self.num_params = self.Nout*(self.R + 1)
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self.num_params = self.num_outputs*(self.W_columns + 1)
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self._set_params(np.hstack([self.W.flatten(),self.kappa]))
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def _get_params(self):
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@ -34,12 +42,12 @@ class Coregionalise(Kernpart):
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def _set_params(self,x):
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assert x.size == self.num_params
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self.kappa = x[-self.Nout:]
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self.W = x[:-self.Nout].reshape(self.Nout,self.R)
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self.kappa = x[-self.num_outputs:]
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self.W = x[:-self.num_outputs].reshape(self.num_outputs,self.W_columns)
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self.B = np.dot(self.W,self.W.T) + np.diag(self.kappa)
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def _get_param_names(self):
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return sum([['W%i_%i'%(i,j) for j in range(self.R)] for i in range(self.Nout)],[]) + ['kappa_%i'%i for i in range(self.Nout)]
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return sum([['W%i_%i'%(i,j) for j in range(self.W_columns)] for i in range(self.num_outputs)],[]) + ['kappa_%i'%i for i in range(self.num_outputs)]
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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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@ -57,26 +65,26 @@ class Coregionalise(Kernpart):
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if index2 is None:
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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]+Nout*index[i]];
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target[i+i*N] += B[index[i]+num_outputs*index[i]];
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for(int j=0; j<i; j++){
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target[j+i*N] += B[index[i]+Nout*index[j]];
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target[j+i*N] += B[index[i]+num_outputs*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,Nout = index.size, self.B, self.Nout
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weave.inline(code,['target','index','N','B','Nout'])
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N,B,num_outputs = index.size, self.B, self.num_outputs
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weave.inline(code,['target','index','N','B','num_outputs'])
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else:
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index2 = np.asarray(index2,dtype=np.int)
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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[Nout*index[j]+index2[i]];
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target[i+j*num_inducing] += B[num_outputs*index[j]+index2[i]];
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}
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}
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"""
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N,num_inducing,B,Nout = index.size,index2.size, self.B, self.Nout
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weave.inline(code,['target','index','index2','N','num_inducing','B','Nout'])
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N,num_inducing,B,num_outputs = index.size,index2.size, self.B, self.num_outputs
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weave.inline(code,['target','index','index2','N','num_inducing','B','num_outputs'])
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def Kdiag(self,index,target):
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@ -93,12 +101,12 @@ class Coregionalise(Kernpart):
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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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dL_dK_small[index[j] + Nout*index2[i]] += dL_dK[i+j*num_inducing];
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dL_dK_small[index[j] + num_outputs*index2[i]] += dL_dK[i+j*num_inducing];
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}
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}
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"""
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N, num_inducing, Nout = index.size, index2.size, self.Nout
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weave.inline(code, ['N','num_inducing','Nout','dL_dK','dL_dK_small','index','index2'])
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N, num_inducing, num_outputs = index.size, index2.size, self.num_outputs
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weave.inline(code, ['N','num_inducing','num_outputs','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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@ -115,8 +123,8 @@ class Coregionalise(Kernpart):
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ii,jj = ii.T, jj.T
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dL_dK_small = np.zeros_like(self.B)
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for i in range(self.Nout):
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for j in range(self.Nout):
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for i in range(self.num_outputs):
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for j in range(self.num_outputs):
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tmp = np.sum(dL_dK[(ii==i)*(jj==j)])
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dL_dK_small[i,j] = tmp
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@ -128,8 +136,8 @@ class Coregionalise(Kernpart):
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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.Nout)
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for i in range(self.Nout):
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dL_dKdiag_small = np.zeros(self.num_outputs)
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for i in range(self.num_outputs):
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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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@ -137,6 +145,3 @@ class Coregionalise(Kernpart):
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def dK_dX(self,dL_dK,X,X2,target):
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pass
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