diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py index f51c3c13..df1e3f47 100644 --- a/GPy/kern/kern.py +++ b/GPy/kern/kern.py @@ -487,12 +487,11 @@ class kern(Parameterized): p1.psi1(Z, mu, S, psi11) Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) - + p2.psi1(Z, Mu, Sigma, psi12) eK2 = psi12.reshape(N, M, M) crossterms = eK2 * (psi11[:, :, None] + psi11[:, None, :]) target += crossterms - #import ipdb;ipdb.set_trace() else: raise NotImplementedError, "psi2 cannot be computed for this kernel" return target @@ -540,15 +539,15 @@ class kern(Parameterized): # turn around to have rbf in front p1, p2 = self.parts[i2], self.parts[i1] ps1, ps2 = self.param_slices[i2], self.param_slices[i1] - + N, M = mu.shape[0], Z.shape[0]; NM=N*M psi11 = np.zeros((N, M)) p1.psi1(Z, mu, S, psi11) - + Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) - + tmp1 = np.zeros_like(target[ps1]) tmp2 = np.zeros_like(target[ps2]) # for n in range(N): @@ -559,7 +558,7 @@ class kern(Parameterized): # Mu, Sigma= Mu.reshape(N,M,self.input_dim), Sigma.reshape(N,M,self.input_dim) # p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m_prime:m_prime+1]))[0], Z[m:m+1], Mu[n:n+1,m], Sigma[n:n+1,m], target[ps2]) # p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m:m+1]))[0], Z[m_prime:m_prime+1], Mu[n:n+1, m_prime], Sigma[n:n+1, m_prime], target[ps2])#Z[m_prime:m_prime+1], Mu[n+m:(n+m)+1], Sigma[n+m:(n+m)+1], target[ps2]) - + if isinstance(p1, RBF) and isinstance(p2, RBF): psi12 = np.zeros((N, M)) p2.psi1(Z, mu, S, psi12) @@ -571,11 +570,11 @@ class kern(Parameterized): if isinstance(p1, RBF) and isinstance(p2, Linear): #import ipdb;ipdb.set_trace() pass - + p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, tmp2) - + target[ps1] += tmp1 - target[ps2] += tmp2 + target[ps2] += tmp2 else: raise NotImplementedError, "psi2 cannot be computed for this kernel" @@ -615,17 +614,17 @@ class kern(Parameterized): psi11 = np.zeros((N, M)) psi12 = np.zeros((NM, M)) #psi12_t = np.zeros((N,M)) - + p1.psi1(Z, mu, S, psi11) Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) - + p2.psi1(Z, Mu, Sigma, psi12) tmp1 = np.zeros_like(target) p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, tmp1) p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, tmp1) target += tmp1 - + #p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target) p2.dpsi1_dZ((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target) else: @@ -666,21 +665,21 @@ class kern(Parameterized): psi11 = np.zeros((N, M)) psi12 = np.zeros((NM, M)) #psi12_t = np.zeros((N,M)) - + p1.psi1(Z, mu, S, psi11) Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) - + p2.psi1(Z, Mu, Sigma, psi12) p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, target_mu, target_S) p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, target_mu, target_S) - + #p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target) p2.dpsi1_dmuS((dL_dpsi2*(psi11[:,:,None])).sum(1)*2, Z, Mu.reshape(N,M,self.input_dim).sum(1), Sigma.reshape(N,M,self.input_dim).sum(1), target_mu, target_S) else: raise NotImplementedError, "psi2 cannot be computed for this kernel" return target_mu, target_S - + def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs): if which_parts == 'all': which_parts = [True] * self.num_parts @@ -753,7 +752,7 @@ class Kern_check_model(Model): dL_dK = np.ones((X.shape[0], X.shape[0])) else: dL_dK = np.ones((X.shape[0], X2.shape[0])) - + self.kernel=kernel self.X = X self.X2 = X2 @@ -768,7 +767,7 @@ class Kern_check_model(Model): return False else: return True - + def _get_params(self): return self.kernel._get_params() @@ -783,7 +782,7 @@ class Kern_check_model(Model): def _log_likelihood_gradients(self): raise NotImplementedError, "This needs to be implemented to use the kern_check_model class." - + class Kern_check_dK_dtheta(Kern_check_model): """This class allows gradient checks for the gradient of a kernel with respect to parameters. """ def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): @@ -798,7 +797,7 @@ class Kern_check_dKdiag_dtheta(Kern_check_model): Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None) if dL_dK==None: self.dL_dK = np.ones((self.X.shape[0])) - + def log_likelihood(self): return (self.dL_dK*self.kernel.Kdiag(self.X)).sum() @@ -815,7 +814,7 @@ class Kern_check_dK_dX(Kern_check_model): def _get_param_names(self): return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])] - + def _get_params(self): return self.X.flatten() @@ -837,7 +836,7 @@ class Kern_check_dKdiag_dX(Kern_check_model): def _get_param_names(self): return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])] - + def _get_params(self): return self.X.flatten() @@ -863,7 +862,6 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive= if output_ind is not None: assert(output_ind