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Merge branc( 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
James Hensman
commit
2a69312aa0
2 changed files with 83 additions and 28 deletions
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@ -14,7 +14,7 @@ from _src.ODE_UYC import ODE_UYC
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from _src.ODE_st import ODE_st
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from _src.ODE_t import ODE_t
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from _src.poly import Poly
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from _src.splitKern import SplitKern
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from _src.splitKern import SplitKern,DiffGenomeKern
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# TODO: put this in an init file somewhere
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#I'm commenting this out because the files were not added. JH. Remember to add the files before commiting
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@ -7,18 +7,69 @@ from kern import Kern,CombinationKernel
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from .independent_outputs import index_to_slices
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import itertools
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class DiffGenomeKern(Kern):
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def __init__(self, kernel, idx_p, Xp, index_dim=-1, name='DiffGenomeKern'):
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self.idx_p = idx_p
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self.index_dim=index_dim
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self.kern = SplitKern(kernel,Xp, index_dim=index_dim)
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super(DiffGenomeKern, self).__init__(input_dim=kernel.input_dim+1, active_dims=None, name=name)
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self.add_parameter(self.kern)
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def K(self, X, X2=None):
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assert X2==None
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K = self.kern.K(X,X2)
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slices = index_to_slices(X[:,self.index_dim])
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idx_start = slices[1][0].start
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idx_end = idx_start+self.idx_p
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K_c = K[idx_start:idx_end,idx_start:idx_end].copy()
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K[idx_start:idx_end,:] = K[:self.idx_p,:]
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K[:,idx_start:idx_end] = K[:,:self.idx_p]
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K[idx_start:idx_end,idx_start:idx_end] = K_c
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return K
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def Kdiag(self,X):
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Kdiag = self.kern.Kdiag(X)
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slices = index_to_slices(X[:,self.index_dim])
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idx_start = slices[1][0].start
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idx_end = idx_start+self.idx_p
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Kdiag[idx_start:idx_end] = Kdiag[:self.idx_p]
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return Kdiag
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def update_gradients_full(self,dL_dK,X,X2=None):
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assert X2==None
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slices = index_to_slices(X[:,self.index_dim])
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idx_start = slices[1][0].start
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idx_end = idx_start+self.idx_p
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self.kern.update_gradients_full(dL_dK[idx_start:idx_end,:], X[:self.idx_p],X)
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grad_p1 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dK[:,idx_start:idx_end], X, X[:self.idx_p])
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grad_p2 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dK[idx_start:idx_end,idx_start:idx_end], X[:self.idx_p],X[idx_start:idx_end])
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grad_p3 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dK[idx_start:idx_end,idx_start:idx_end], X[idx_start:idx_end], X[:self.idx_p])
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grad_p4 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dK[idx_start:idx_end,:], X[idx_start:idx_end],X)
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grad_n1 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dK[:,idx_start:idx_end], X, X[idx_start:idx_end])
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grad_n2 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dK[idx_start:idx_end,idx_start:idx_end], X[idx_start:idx_end], X[idx_start:idx_end])
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grad_n3 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dK, X)
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self.kern.gradient += grad_p1+grad_p2-grad_p3-grad_p4-grad_n1-grad_n2+2*grad_n3
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def update_gradients_diag(self, dL_dKdiag, X):
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pass
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class SplitKern(CombinationKernel):
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"""
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A kernel which can represent several independent functions. this kernel
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'switches off' parts of the matrix where the output indexes are different.
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The index of the functions is given by the last column in the input X the
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rest of the columns of X are passed to the underlying kernel for
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computation (in blocks).
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:param kernels: either a kernel, or list of kernels to work with. If it is
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a list of kernels the indices in the index_dim, index the kernels you gave!
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"""
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def __init__(self, kernel, Xp, index_dim=-1, name='SplitKern'):
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assert isinstance(index_dim, int), "The index dimension must be an integer!"
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self.kern = kernel
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@ -43,11 +94,11 @@ class SplitKern(CombinationKernel):
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assert len(slices2)<=2, 'The Split kernel only support two different indices'
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target = np.zeros((X.shape[0], X2.shape[0]))
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# diagonal blocks
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[[target.__setitem__((s,s2), self.kern.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices[i], slices2[i])] for i in xrange(min(len(slices),len(slices)))]
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[[target.__setitem__((s,s2), self.kern.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices[i], slices2[i])] for i in xrange(min(len(slices),len(slices2)))]
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if len(slices)>1:
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[target.__setitem__((s,s2), self.kern.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices[1], slices2[0])]
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[target.__setitem__((s,s2), self.kern_cross.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices[1], slices2[0])]
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if len(slices2)>1:
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[target.__setitem__((s,s2), self.kern.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices[0], slices2[1])]
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[target.__setitem__((s,s2), self.kern_cross.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices[0], slices2[1])]
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return target
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def Kdiag(self,X):
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@ -60,23 +111,25 @@ class SplitKern(CombinationKernel):
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def collate_grads(dL, X, X2, cross=False):
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if cross:
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self.kern_cross.update_gradients_full(dL,X,X2)
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target[:] += self.kern_cross.gradient
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target[:] += self.kern_cross.kern.gradient
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else:
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self.kern.update_gradients_full(dL,X,X2)
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target[:] += self.kern.gradient
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if X2 is None:
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assert dL_dK.shape==(X.shape[0],X.shape[0])
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[[collate_grads(dL_dK[s,ss], X[s], X[ss]) for s,ss in itertools.product(slices_i, slices_i)] for slices_i in slices]
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if len(slices)>1:
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[collate_grads(dL_dK[s,ss], X[s], X[ss], True) for s,ss in itertools.product(slices[0], slices[1])]
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[collate_grads(dL_dK[s,ss], X[s], X[ss], True) for s,ss in itertools.product(slices[1], slices[0])]
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else:
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assert dL_dK.shape==(X.shape[0],X2.shape[0])
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slices2 = index_to_slices(X2[:,self.index_dim])
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[[collate_grads(dL_dK[s,s2],X[s],X2[s2]) for s,s2 in itertools.product(slices[i], slices2[i])] for i in xrange(min(len(slices),len(slices)))]
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[[collate_grads(dL_dK[s,s2],X[s],X2[s2]) for s,s2 in itertools.product(slices[i], slices2[i])] for i in xrange(min(len(slices),len(slices2)))]
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if len(slices)>1:
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[collate_grads(dL_dK[s,ss], X[s], X2[s2], True) for s,s2 in itertools.product(slices[1], slices2[0])]
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[collate_grads(dL_dK[s,s2], X[s], X2[s2], True) for s,s2 in itertools.product(slices[1], slices2[0])]
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if len(slices2)>1:
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[collate_grads(dL_dK[s,ss], X[s], X2[s2], True) for s,s2 in itertools.product(slices[0], slices2[1])]
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[collate_grads(dL_dK[s,s2], X[s], X2[s2], True) for s,s2 in itertools.product(slices[0], slices2[1])]
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self.kern.gradient = target
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def update_gradients_diag(self, dL_dKdiag, X):
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@ -87,6 +140,8 @@ class SplitKern_cross(Kern):
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def __init__(self, kernel, Xp, name='SplitKern_cross'):
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assert isinstance(kernel, Kern)
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self.kern = kernel
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if not isinstance(Xp,np.ndarray):
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Xp = np.array([[Xp]])
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self.Xp = Xp
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super(SplitKern_cross, self).__init__(input_dim=kernel.input_dim, active_dims=None, name=name)
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@ -102,22 +157,22 @@ class SplitKern_cross(Kern):
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def update_gradients_full(self, dL_dK, X, X2=None):
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if X2 is None:
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X2 = X
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k1 = self.kern.K(X,self.Xp)
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k2 = self.kern.K(self.Xp,X2)
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k3 = self.kern.K(self.Xp,self.Xp)
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dL_dk1 = np.einsum('ij,j->i',dL_dK,k2.flat)/k3.flat
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dL_dk2 = np.einsum('ij,i->j',dL_dK,k1.flat)/k3.flat
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dL_dk3 = np.einsum('ij,ij->',dL_dK,-np.dot(k1,k2)/(k3.flat*k3.flat))
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dL_dk1 = np.einsum('ij,j->i',dL_dK,k2[0])/k3[0,0]
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dL_dk2 = np.einsum('ij,i->j',dL_dK,k1[:,0])/k3[0,0]
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dL_dk3 = np.einsum('ij,ij->',dL_dK,-np.dot(k1,k2)/(k3[0,0]*k3[0,0]))
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self.kern.update_gradients_full(dL_dk1[:,None],X,self.Xp)
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grad1 = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dk2[None,:],self.Xp,X)
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grad2 = self.kern.gradient.copy()
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grad = self.kern.gradient.copy()
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self.kern.update_gradients_full(dL_dk2[None,:],self.Xp,X2)
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grad += self.kern.gradient.copy()
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self.kern.update_gradients_full(np.array([[dL_dk3]]),self.Xp,self.Xp)
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grad3 = self.kern.gradient.copy()
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grad += self.kern.gradient.copy()
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self.kern.gradient = grad1+grad2+grad3
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self.kern.gradient = grad
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def update_gradients_diag(self, dL_dKdiag, X):
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k1 = self.kern.K(X,self.Xp)
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