Merge branch 'reorder_choleskies' into devel

GPy/core/svgp.py

@@ -46,7 +46,7 @@ class SVGP(SparseGP):
             num_latent_functions = Y.shape[1]
 
         self.m = Param('q_u_mean', np.zeros((self.num_inducing, num_latent_functions)))
-        chol = choleskies.triang_to_flat(np.tile(np.eye(self.num_inducing)[:,:,None], (1,1,num_latent_functions)))
+        chol = choleskies.triang_to_flat(np.tile(np.eye(self.num_inducing)[None,:,:], (num_latent_functions, 1,1)))
         self.chol = Param('q_u_chol', chol)
         self.link_parameter(self.chol)
         self.link_parameter(self.m)

GPy/inference/latent_function_inference/svgp.py

@@ -3,6 +3,7 @@ from ...util import linalg
 from ...util import choleskies
 import numpy as np
 from .posterior import Posterior
+from scipy.linalg.blas import dgemm, dsymm, dtrmm
 
 class SVGP(LatentFunctionInference):
 
@@ -16,16 +17,13 @@ class SVGP(LatentFunctionInference):
 
 
         S = np.empty((num_outputs, num_inducing, num_inducing))
-        [np.dot(L[:,:,i], L[:,:,i].T, S[i,:,:]) for i in range(num_outputs)]
+        [np.dot(L[i,:,:], L[i,:,:].T, S[i,:,:]) for i in range(num_outputs)]
-        S = S.swapaxes(0,2)
         #Si,_ = linalg.dpotri(np.asfortranarray(L), lower=1)
         Si = choleskies.multiple_dpotri(L)
-        logdetS = np.array([2.*np.sum(np.log(np.abs(np.diag(L[:,:,i])))) for i in range(L.shape[-1])])
+        logdetS = np.array([2.*np.sum(np.log(np.abs(np.diag(L[i,:,:])))) for i in range(L.shape[0])])
 
         if np.any(np.isinf(Si)):
             raise ValueError("Cholesky representation unstable")
-            #S = S + np.eye(S.shape[0])*1e-5*np.max(np.max(S))
-            #Si, Lnew, _,_ = linalg.pdinv(S)
 
         #compute mean function stuff
         if mean_function is not None:
@@ -35,27 +33,29 @@ class SVGP(LatentFunctionInference):
             prior_mean_u = np.zeros((num_inducing, num_outputs))
             prior_mean_f = np.zeros((num_data, num_outputs))
 
-
         #compute kernel related stuff
         Kmm = kern.K(Z)
-        Knm = kern.K(X, Z)
+        Kmn = kern.K(Z, X)
         Knn_diag = kern.Kdiag(X)
-        Kmmi, Lm, Lmi, logdetKmm = linalg.pdinv(Kmm)
+        Lm = linalg.jitchol(Kmm)
+        logdetKmm = 2.*np.sum(np.log(np.diag(Lm)))
+        Kmmi, _ = linalg.dpotri(Lm)
 
         #compute the marginal means and variances of q(f)
-        A = np.dot(Knm, Kmmi)
+        A, _ = linalg.dpotrs(Lm, Kmn)
-        mu = prior_mean_f + np.dot(A, q_u_mean - prior_mean_u)
+        mu = prior_mean_f + np.dot(A.T, q_u_mean - prior_mean_u)
-        #v = Knn_diag[:,None] - np.sum(A*Knm,1)[:,None] + np.sum(A[:,:,None] * np.einsum('ij,jlk->ilk', A, S),1)
+        LA = L.reshape(-1, num_inducing).dot(A).reshape(num_outputs, num_inducing, num_data)
-        v = Knn_diag[:,None] - np.sum(A*Knm,1)[:,None] + np.sum(A[:,:,None] * linalg.ij_jlk_to_ilk(A, S),1)
+        #TODO? possibly use dtrmm for the above line?
+        v = (Knn_diag - np.sum(A*Kmn,0))[:,None] + np.sum(np.square(LA),1).T
 
         #compute the KL term
         Kmmim = np.dot(Kmmi, q_u_mean)
-        KLs = -0.5*logdetS -0.5*num_inducing + 0.5*logdetKmm + 0.5*np.sum(Kmmi[:,:,None]*S,0).sum(0) + 0.5*np.sum(q_u_mean*Kmmim,0)
+        KLs = -0.5*logdetS -0.5*num_inducing + 0.5*logdetKmm + 0.5*np.sum(Kmmi[None,:,:]*S,1).sum(1) + 0.5*np.sum(q_u_mean*Kmmim,0)
         KL = KLs.sum()
         #gradient of the KL term (assuming zero mean function)
         dKL_dm = Kmmim.copy()
-        dKL_dS = 0.5*(Kmmi[:,:,None] - Si)
+        dKL_dS = 0.5*(Kmmi[None,:,:] - Si)
-        dKL_dKmm = 0.5*num_outputs*Kmmi - 0.5*Kmmi.dot(S.sum(-1)).dot(Kmmi) - 0.5*Kmmim.dot(Kmmim.T)
+        dKL_dKmm = 0.5*num_outputs*Kmmi - 0.5*Kmmi.dot(S.sum(0)).dot(Kmmi) - 0.5*Kmmim.dot(Kmmim.T)
 
         if mean_function is not None:
             #adjust KL term for mean function
@@ -80,17 +80,22 @@ class SVGP(LatentFunctionInference):
             dF_dthetaL =  dF_dthetaL.sum(1).sum(1)*batch_scale
 
         #derivatives of expected likelihood, assuming zero mean function
-        Adv = A.T[:,:,None]*dF_dv[None,:,:] # As if dF_Dv is diagonal
+        Adv = A[None,:,:]*dF_dv.T[:,None,:] # As if dF_Dv is diagonal, D, M, N
-        Admu = A.T.dot(dF_dmu)
+        Admu = A.dot(dF_dmu)
-        AdvA = np.dstack([np.dot(A.T, Adv[:,:,i].T) for i in range(num_outputs)])
+        Adv = np.ascontiguousarray(Adv) # makes for faster operations later...(inc dsymm)
-        #tmp = np.einsum('ijk,jlk->il', AdvA, S).dot(Kmmi)
+        AdvA = np.dot(Adv.reshape(-1, num_data),A.T).reshape(num_outputs, num_inducing, num_inducing )
-        tmp = linalg.ijk_jlk_to_il(AdvA, S).dot(Kmmi)
+        tmp = np.sum([np.dot(a,s) for a, s in zip(AdvA, S)],0).dot(Kmmi)
-        dF_dKmm = -Admu.dot(Kmmim.T) + AdvA.sum(-1) - tmp - tmp.T
+        dF_dKmm = -Admu.dot(Kmmim.T) + AdvA.sum(0) - tmp - tmp.T
         dF_dKmm = 0.5*(dF_dKmm + dF_dKmm.T) # necessary? GPy bug?
-        #tmp = 2.*(np.einsum('ij,jlk->ilk', Kmmi,S) - np.eye(num_inducing)[:,:,None])
+        tmp = S.reshape(-1, num_inducing).dot(Kmmi).reshape(num_outputs, num_inducing , num_inducing )
-        tmp = 2.*(linalg.ij_jlk_to_ilk(Kmmi, S) - np.eye(num_inducing)[:,:,None])
+        tmp = 2.*(tmp - np.eye(num_inducing)[None, :,:])
-        #dF_dKmn = np.einsum('ijk,jlk->il', tmp, Adv) + Kmmim.dot(dF_dmu.T)
+
-        dF_dKmn = linalg.ijk_jlk_to_il(tmp, Adv) + Kmmim.dot(dF_dmu.T)
+        dF_dKnm = Kmmim.dot(dF_dmu.T).T
+        assert dF_dKnm.flags['F_CONTIGUOUS'] # needed for dsymm in place call:
+        for a,b in zip(tmp, Adv):
+            dsymm(1.0, a.T, b.T, beta=1., side=1, c=dF_dKnm, overwrite_c=True)
+        dF_dKmn = dF_dKnm.T
+
         dF_dm = Admu
         dF_dS = AdvA
 
@@ -106,11 +111,11 @@ class SVGP(LatentFunctionInference):
         log_marginal = F.sum() - KL
         dL_dm, dL_dS, dL_dKmm, dL_dKmn = dF_dm - dKL_dm, dF_dS- dKL_dS, dF_dKmm- dKL_dKmm, dF_dKmn
 
-        dL_dchol = np.dstack([2.*np.dot(dL_dS[:,:,i], L[:,:,i]) for i in range(num_outputs)])
+        dL_dchol = 2.*np.array([np.dot(a,b) for a, b in zip(dL_dS, L) ])
         dL_dchol = choleskies.triang_to_flat(dL_dchol)
 
         grad_dict = {'dL_dKmm':dL_dKmm, 'dL_dKmn':dL_dKmn, 'dL_dKdiag': dF_dv.sum(1), 'dL_dm':dL_dm, 'dL_dchol':dL_dchol, 'dL_dthetaL':dF_dthetaL}
         if mean_function is not None:
             grad_dict['dL_dmfZ'] = dF_dmfZ - dKL_dmfZ
             grad_dict['dL_dmfX'] = dF_dmfX
-        return Posterior(mean=q_u_mean, cov=S, K=Kmm, prior_mean=prior_mean_u), log_marginal, grad_dict
+        return Posterior(mean=q_u_mean, cov=S.T, K=Kmm, prior_mean=prior_mean_u), log_marginal, grad_dict

GPy/kern/_src/stationary_utils.c

@@ -14,6 +14,22 @@ for(nd=0;nd<(D*N);nd++){
 } //grad_X
 
 
+void _lengthscale_grads_unsafe(int N, int M, int Q, double* tmp, double* X, double* X2, double* grad){
+int n,m,nm,q,nQ,mQ;
+double dist;
+#pragma omp parallel for private(n,m,nm,q,nQ,mQ,dist)
+for(nm=0; nm<(N*M); nm++){
+  n = nm/M;
+  m = nm%M; 
+  nQ = n*Q;
+  mQ = m*Q;
+  for(q=0; q<Q; q++){
+    dist = X[nQ+q]-X2[mQ+q];
+    grad[q] += tmp[nm]*dist*dist;
+  }
+}
+} //lengthscale_grads
+
 
 void _lengthscale_grads(int N, int M, int Q, double* tmp, double* X, double* X2, double* grad){
 int n,m,q;
@@ -34,3 +50,5 @@ for(q=0; q<Q; q++){
 
 
 
+
+

GPy/testing/cython_tests.py

@@ -9,8 +9,8 @@ These tests make sure that the opure python and cython codes work the same
 
 class CythonTestChols(np.testing.TestCase):
     def setUp(self):
-        self.flat = np.random.randn(45, 5)
+        self.flat = np.random.randn(5,45)
-        self.triang = np.dstack([np.eye(20)[:,:,None] for i in range(3)])
+        self.triang = np.array([np.eye(20) for i in range(3)])
     def test_flat_to_triang(self):
         L1 = choleskies._flat_to_triang_pure(self.flat)
         L2 = choleskies._flat_to_triang_cython(self.flat)

GPy/util/choleskies.py

@@ -17,12 +17,12 @@ def safe_root(N):
 def _flat_to_triang_pure(flat_mat):
     N, D = flat_mat.shape
     M = (-1 + safe_root(8*N+1))//2
-    ret = np.zeros((M, M, D))
+    ret = np.zeros((D, M, M))
-    count = 0
+    for d in range(D):
-    for m in range(M):
+        count = 0
-        for mm in range(m+1):
+        for m in range(M):
-            for d in range(D):
+            for mm in range(m+1):
-              ret.flat[d + m*D*M + mm*D] = flat_mat.flat[count];
+              ret[d,m, mm] = flat_mat[count, d];
               count = count+1
     return ret
 
@@ -33,15 +33,15 @@ def _flat_to_triang_cython(flat_mat):
 
 
 def _triang_to_flat_pure(L):
-    M, _, D = L.shape
+    D, _, M = L.shape
 
     N = M*(M+1)//2
     flat = np.empty((N, D))
-    count = 0;
+    for d in range(D):
-    for m in range(M):
+        count = 0;
-        for mm in range(m+1):
+        for m in range(M):
-            for d in range(D):
+            for mm in range(m+1):
-                flat.flat[count] = L.flat[d + m*D*M + mm*D];
+                flat[count,d] = L[d, m, mm]
                 count = count +1
     return flat
 
@@ -74,7 +74,7 @@ def triang_to_cov(L):
     return np.dstack([np.dot(L[:,:,i], L[:,:,i].T) for i in range(L.shape[-1])])
 
 def multiple_dpotri(Ls):
-    return np.dstack([linalg.dpotri(np.asfortranarray(Ls[:,:,i]), lower=1)[0] for i in range(Ls.shape[-1])])
+    return np.array([linalg.dpotri(np.asfortranarray(Ls[i]), lower=1)[0] for i in range(Ls.shape[0])])
 
 def indexes_to_fix_for_low_rank(rank, size):
     """

GPy/util/choleskies_cython.pyx

@@ -8,28 +8,28 @@ import numpy as np
 cimport numpy as np
 
 def flat_to_triang(np.ndarray[double, ndim=2] flat, int M):
-    """take a matrix N x D and return a M X M x D array where
+    """take a matrix N x D and return a D X M x M array where
 
     N = M(M+1)/2
 
     the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat.
     """
-    cdef int N = flat.shape[0]
     cdef int D = flat.shape[1]
+    cdef int N = flat.shape[0]
     cdef int count = 0
-    cdef np.ndarray[double, ndim=3] ret = np.zeros((M, M, D))
+    cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M))
     cdef int d, m, mm
     for d in range(D):
         count = 0
         for m in range(M):
             for mm in range(m+1):
-                ret[m, mm, d] = flat[count,d]
+                ret[d, m, mm] = flat[count,d]
                 count += 1
     return ret
 
 def triang_to_flat(np.ndarray[double, ndim=3] L):
-    cdef int M = L.shape[0]
+    cdef int D = L.shape[0]
-    cdef int D = L.shape[2]
+    cdef int M = L.shape[1]
     cdef int N = M*(M+1)/2
     cdef int count = 0
     cdef np.ndarray[double, ndim=2] flat = np.empty((N, D))
@@ -38,7 +38,7 @@ def triang_to_flat(np.ndarray[double, ndim=3] L):
         count = 0
         for m in range(M):
             for mm in range(m+1):
-                flat[count,d] = L[m, mm, d]
+                flat[count,d] = L[d, m, mm]
                 count += 1
     return flat