minor edits

GPy/inference/latent_function_inference/dtc.py

@@ -32,7 +32,7 @@ class DTC(object):
         #make sure the noise is not hetero
         beta = 1./np.squeeze(likelihood.variance)
         if beta.size <1:
-            raise NotImplementedError, "no hetero noise with this implementatino of DTC"
+            raise NotImplementedError, "no hetero noise with this implementation of DTC"
 
         Kmm = kern.K(Z)
         Knn = kern.Kdiag(X)
@@ -89,4 +89,85 @@ class DTC(object):
 
         return post, log_marginal, grad_dict
 
+class vDTC(object):
+    def __init__(self):
+        self.const_jitter = 1e-6
+
+    def inference(self, kern, X, X_variance, Z, likelihood, Y):
+        assert X_variance is None, "cannot use X_variance with DTC. Try varDTC."
+
+        #TODO: MAX! fix this!
+        from ...util.misc import param_to_array
+        Y = param_to_array(Y)
+
+        num_inducing, _ = Z.shape
+        num_data, output_dim = Y.shape
+
+        #make sure the noise is not hetero
+        beta = 1./np.squeeze(likelihood.variance)
+        if beta.size <1:
+            raise NotImplementedError, "no hetero noise with this implementation of DTC"
+
+        Kmm = kern.K(Z)
+        Knn = kern.Kdiag(X)
+        Knm = kern.K(X, Z)
+        U = Knm
+        Uy = np.dot(U.T,Y)
+
+        #factor Kmm 
+        Kmmi, L, Li, _ = pdinv(Kmm)
+
+        # Compute A
+        LiUTbeta = np.dot(Li, U.T)*np.sqrt(beta)
+        A_ = tdot(LiUTbeta)
+        trace_term = -0.5*(np.sum(Knn)*beta - np.trace(A_))
+        A = A_ + np.eye(num_inducing)
+
+        # factor A
+        LA = jitchol(A)
+
+        # back substutue to get b, P, v
+        tmp, _ = dtrtrs(L, Uy, lower=1)
+        b, _ = dtrtrs(LA, tmp*beta, lower=1)
+        tmp, _ = dtrtrs(LA, b, lower=1, trans=1)
+        v, _ = dtrtrs(L, tmp, lower=1, trans=1)
+        tmp, _ = dtrtrs(LA, Li, lower=1, trans=0)
+        P = tdot(tmp.T)
+
+        #compute log marginal
+        log_marginal = -0.5*num_data*output_dim*np.log(2*np.pi) + \
+                       -np.sum(np.log(np.diag(LA)))*output_dim + \
+                       0.5*num_data*output_dim*np.log(beta) + \
+                       -0.5*beta*np.sum(np.square(Y)) + \
+                       0.5*np.sum(np.square(b)) + \
+                       trace_term
+
+        # Compute dL_dKmm
+        vvT_P = tdot(v.reshape(-1,1)) + P
+        LAL = Li.T.dot(A).dot(Li)
+        dL_dK = Kmmi - 0.5*(vvT_P + LAL)
+
+        # Compute dL_dU
+        vY = np.dot(v.reshape(-1,1),Y.T)
+        #dL_dU = vY - np.dot(vvT_P, U.T)
+        dL_dU = vY - np.dot(vvT_P - Kmmi, U.T)
+        dL_dU *= beta
+
+        #compute dL_dR
+        Uv = np.dot(U, v)
+        dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - 1./beta + np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1) )*beta**2
+        dL_dR -=beta*trace_term/num_data
+
+        grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn) + -0.5*beta, 'dL_dKnm':dL_dU.T}
+
+        #update gradients
+        kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
+        likelihood.update_gradients(dL_dR)
+
+        #construct a posterior object
+        post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)
+
+
+        return post, log_marginal, grad_dict
+