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svgp working with reordered chols

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James Hensman 2015-05-15 08:59:19 +01:00
parent 2249ec06a5
commit 77093a304c
4 changed files with 51 additions and 59 deletions
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20
GPy/inference/latent_function_inference/svgp.py
View file

@ -24,8 +24,6 @@ class SVGP(LatentFunctionInference):
if np.any(np.isinf(Si)): if np.any(np.isinf(Si)):
raise ValueError("Cholesky representation unstable") 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 #compute mean function stuff
if mean_function is not None: if mean_function is not None:
@ -35,27 +33,21 @@ class SVGP(LatentFunctionInference):
prior_mean_u = np.zeros((num_inducing, num_outputs)) prior_mean_u = np.zeros((num_inducing, num_outputs))
prior_mean_f = np.zeros((num_data, num_outputs)) prior_mean_f = np.zeros((num_data, num_outputs))
#compute kernel related stuff #compute kernel related stuff
Kmm = kern.K(Z) Kmm = kern.K(Z)
Kmn = kern.K(Z, X) Kmn = kern.K(Z, X)
Knn_diag = kern.Kdiag(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) #compute the marginal means and variances of q(f)
A = np.dot(Kmmi, Kmn) A, _ = linalg.dpotrs(Lm, Kmn)
mu = prior_mean_f + np.dot(A.T, q_u_mean - prior_mean_u) mu = prior_mean_f + np.dot(A.T, q_u_mean - prior_mean_u)
LA = L.reshape(-1, num_inducing).dot(A).reshape(num_outputs, num_inducing, num_data) LA = L.reshape(-1, num_inducing).dot(A).reshape(num_outputs, num_inducing, num_data)
#LA = np.empty((num_outputs, num_inducing, num_data)) #TODO? possibly use dtrmm for the above line?
#Af = np.asfortranarray(A)
#for Li, LAi in zip(L, LA):
#LAi[:,:] = dtrmm(1., Li.T, Af, side=0, lower=0, trans_a=1, overwrite_b=0)
#stop
#assert np.allclose(LA, LA_)
v = (Knn_diag - np.sum(A*Kmn,0))[:,None] + np.sum(np.square(LA),1).T v = (Knn_diag - np.sum(A*Kmn,0))[:,None] + np.sum(np.square(LA),1).T
#compute the KL term #compute the KL term
Kmmim = np.dot(Kmmi, q_u_mean) 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,1).sum(1) + 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)
@ -90,7 +82,7 @@ class SVGP(LatentFunctionInference):
#derivatives of expected likelihood, assuming zero mean function #derivatives of expected likelihood, assuming zero mean function
Adv = A[None,:,:]*dF_dv.T[:,None,:] # As if dF_Dv is diagonal, D, M, N Adv = A[None,:,:]*dF_dv.T[:,None,:] # As if dF_Dv is diagonal, D, M, N
Admu = A.dot(dF_dmu) Admu = A.dot(dF_dmu)
Adv = np.ascontiguousarray(Adv) # makes for faster operations later... Adv = np.ascontiguousarray(Adv) # makes for faster operations later...(inc dsymm)
AdvA = np.dot(Adv.reshape(-1, num_data),A.T).reshape(num_outputs, num_inducing, num_inducing ) AdvA = np.dot(Adv.reshape(-1, num_data),A.T).reshape(num_outputs, num_inducing, num_inducing )
tmp = np.sum([np.dot(a,s) for a, s in zip(AdvA, S)],0).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(0) - tmp - tmp.T dF_dKmm = -Admu.dot(Kmmim.T) + AdvA.sum(0) - tmp - tmp.T

10
GPy/util/choleskies.py
View file

@ -15,19 +15,19 @@ def safe_root(N):
return j return j
def _flat_to_triang_pure(flat_mat): def _flat_to_triang_pure(flat_mat):
D, N = flat_mat.shape N, D = flat_mat.shape
M = (-1 + safe_root(8*N+1))//2 M = (-1 + safe_root(8*N+1))//2
ret = np.zeros((D, M, M)) ret = np.zeros((D, M, M))
for d in range(D): for d in range(D):
count = 0 count = 0
for m in range(M): for m in range(M):
for mm in range(m+1): for mm in range(m+1):
ret[d,m, mm] = flat_mat[d, count]; ret[d,m, mm] = flat_mat[count, d];
count = count+1 count = count+1
return ret return ret
def _flat_to_triang_cython(flat_mat): def _flat_to_triang_cython(flat_mat):
D, N = flat_mat.shape N, D = flat_mat.shape
M = (-1 + safe_root(8*N+1))//2 M = (-1 + safe_root(8*N+1))//2
return choleskies_cython.flat_to_triang(flat_mat, M) return choleskies_cython.flat_to_triang(flat_mat, M)
@ -36,12 +36,12 @@ def _triang_to_flat_pure(L):
D, _, M = L.shape D, _, M = L.shape
N = M*(M+1)//2 N = M*(M+1)//2
flat = np.empty((D, N)) flat = np.empty((N, D))
for d in range(D): for d in range(D):
count = 0; count = 0;
for m in range(M): for m in range(M):
for mm in range(m+1): for mm in range(m+1):
flat[d,count] = L[d, m, mm] flat[count,d] = L[d, m, mm]
count = count +1 count = count +1
return flat return flat

68
GPy/util/choleskies_cython.c
View file

@ -1167,13 +1167,13 @@ static PyObject *__pyx_codeobj__12;
* cimport numpy as np * cimport numpy as np
* *
* def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<< * def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<<
* """take a matrix D x N and return a D X M x M array where * """take a matrix N x D and return a D X M x M array where
* *
*/ */
/* Python wrapper */ /* Python wrapper */
static PyObject *__pyx_pw_3GPy_4util_17choleskies_cython_1flat_to_triang(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_3GPy_4util_17choleskies_cython_1flat_to_triang(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static char __pyx_doc_3GPy_4util_17choleskies_cython_flat_to_triang[] = "take a matrix D x N and return a D X M x M array where\n\n N = M(M+1)/2\n\n the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat.\n "; static char __pyx_doc_3GPy_4util_17choleskies_cython_flat_to_triang[] = "take a matrix N x D and return a D X M x M array where\n\n N = M(M+1)/2\n\n the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat.\n ";
static PyMethodDef __pyx_mdef_3GPy_4util_17choleskies_cython_1flat_to_triang = {"flat_to_triang", (PyCFunction)__pyx_pw_3GPy_4util_17choleskies_cython_1flat_to_triang, METH_VARARGS|METH_KEYWORDS, __pyx_doc_3GPy_4util_17choleskies_cython_flat_to_triang}; static PyMethodDef __pyx_mdef_3GPy_4util_17choleskies_cython_1flat_to_triang = {"flat_to_triang", (PyCFunction)__pyx_pw_3GPy_4util_17choleskies_cython_1flat_to_triang, METH_VARARGS|METH_KEYWORDS, __pyx_doc_3GPy_4util_17choleskies_cython_flat_to_triang};
static PyObject *__pyx_pw_3GPy_4util_17choleskies_cython_1flat_to_triang(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { static PyObject *__pyx_pw_3GPy_4util_17choleskies_cython_1flat_to_triang(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyArrayObject *__pyx_v_flat = 0; PyArrayObject *__pyx_v_flat = 0;
@ -1292,24 +1292,24 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
/* "GPy/util/choleskies_cython.pyx":17 /* "GPy/util/choleskies_cython.pyx":17
* the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat. * the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat.
* """ * """
* cdef int D = flat.shape[0] # <<<<<<<<<<<<<< * cdef int D = flat.shape[1] # <<<<<<<<<<<<<<
* cdef int N = flat.shape[1] * cdef int N = flat.shape[0]
* cdef int count = 0 * cdef int count = 0
*/ */
__pyx_v_D = (__pyx_v_flat->dimensions[0]); __pyx_v_D = (__pyx_v_flat->dimensions[1]);
/* "GPy/util/choleskies_cython.pyx":18 /* "GPy/util/choleskies_cython.pyx":18
* """ * """
* cdef int D = flat.shape[0] * cdef int D = flat.shape[1]
* cdef int N = flat.shape[1] # <<<<<<<<<<<<<< * cdef int N = flat.shape[0] # <<<<<<<<<<<<<<
* cdef int count = 0 * cdef int count = 0
* cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M)) * cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M))
*/ */
__pyx_v_N = (__pyx_v_flat->dimensions[1]); __pyx_v_N = (__pyx_v_flat->dimensions[0]);
/* "GPy/util/choleskies_cython.pyx":19 /* "GPy/util/choleskies_cython.pyx":19
* cdef int D = flat.shape[0] * cdef int D = flat.shape[1]
* cdef int N = flat.shape[1] * cdef int N = flat.shape[0]
* cdef int count = 0 # <<<<<<<<<<<<<< * cdef int count = 0 # <<<<<<<<<<<<<<
* cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M)) * cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M))
* cdef int d, m, mm * cdef int d, m, mm
@ -1317,7 +1317,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
__pyx_v_count = 0; __pyx_v_count = 0;
/* "GPy/util/choleskies_cython.pyx":20 /* "GPy/util/choleskies_cython.pyx":20
* cdef int N = flat.shape[1] * cdef int N = flat.shape[0]
* cdef int count = 0 * cdef int count = 0
* cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M)) # <<<<<<<<<<<<<< * cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M)) # <<<<<<<<<<<<<<
* cdef int d, m, mm * cdef int d, m, mm
@ -1410,7 +1410,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
* count = 0 * count = 0
* for m in range(M): # <<<<<<<<<<<<<< * for m in range(M): # <<<<<<<<<<<<<<
* for mm in range(m+1): * for mm in range(m+1):
* ret[d, m, mm] = flat[d,count] * ret[d, m, mm] = flat[count,d]
*/ */
__pyx_t_10 = __pyx_v_M; __pyx_t_10 = __pyx_v_M;
for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) { for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
@ -1420,7 +1420,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
* count = 0 * count = 0
* for m in range(M): * for m in range(M):
* for mm in range(m+1): # <<<<<<<<<<<<<< * for mm in range(m+1): # <<<<<<<<<<<<<<
* ret[d, m, mm] = flat[d,count] * ret[d, m, mm] = flat[count,d]
* count += 1 * count += 1
*/ */
__pyx_t_12 = (__pyx_v_m + 1); __pyx_t_12 = (__pyx_v_m + 1);
@ -1430,12 +1430,12 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
/* "GPy/util/choleskies_cython.pyx":26 /* "GPy/util/choleskies_cython.pyx":26
* for m in range(M): * for m in range(M):
* for mm in range(m+1): * for mm in range(m+1):
* ret[d, m, mm] = flat[d,count] # <<<<<<<<<<<<<< * ret[d, m, mm] = flat[count,d] # <<<<<<<<<<<<<<
* count += 1 * count += 1
* return ret * return ret
*/ */
__pyx_t_14 = __pyx_v_d; __pyx_t_14 = __pyx_v_count;
__pyx_t_15 = __pyx_v_count; __pyx_t_15 = __pyx_v_d;
if (__pyx_t_14 < 0) __pyx_t_14 += __pyx_pybuffernd_flat.diminfo[0].shape; if (__pyx_t_14 < 0) __pyx_t_14 += __pyx_pybuffernd_flat.diminfo[0].shape;
if (__pyx_t_15 < 0) __pyx_t_15 += __pyx_pybuffernd_flat.diminfo[1].shape; if (__pyx_t_15 < 0) __pyx_t_15 += __pyx_pybuffernd_flat.diminfo[1].shape;
__pyx_t_16 = __pyx_v_d; __pyx_t_16 = __pyx_v_d;
@ -1448,7 +1448,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
/* "GPy/util/choleskies_cython.pyx":27 /* "GPy/util/choleskies_cython.pyx":27
* for mm in range(m+1): * for mm in range(m+1):
* ret[d, m, mm] = flat[d,count] * ret[d, m, mm] = flat[count,d]
* count += 1 # <<<<<<<<<<<<<< * count += 1 # <<<<<<<<<<<<<<
* return ret * return ret
* *
@ -1459,7 +1459,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
} }
/* "GPy/util/choleskies_cython.pyx":28 /* "GPy/util/choleskies_cython.pyx":28
* ret[d, m, mm] = flat[d,count] * ret[d, m, mm] = flat[count,d]
* count += 1 * count += 1
* return ret # <<<<<<<<<<<<<< * return ret # <<<<<<<<<<<<<<
* *
@ -1474,7 +1474,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_flat_to_triang(CYTHON_U
* cimport numpy as np * cimport numpy as np
* *
* def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<< * def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<<
* """take a matrix D x N and return a D X M x M array where * """take a matrix N x D and return a D X M x M array where
* *
*/ */
@ -1607,7 +1607,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
* cdef int M = L.shape[1] * cdef int M = L.shape[1]
* cdef int N = M*(M+1)/2 # <<<<<<<<<<<<<< * cdef int N = M*(M+1)/2 # <<<<<<<<<<<<<<
* cdef int count = 0 * cdef int count = 0
* cdef np.ndarray[double, ndim=2] flat = np.empty((D, N)) * cdef np.ndarray[double, ndim=2] flat = np.empty((N, D))
*/ */
__pyx_v_N = __Pyx_div_long((__pyx_v_M * (__pyx_v_M + 1)), 2); __pyx_v_N = __Pyx_div_long((__pyx_v_M * (__pyx_v_M + 1)), 2);
@ -1615,7 +1615,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
* cdef int M = L.shape[1] * cdef int M = L.shape[1]
* cdef int N = M*(M+1)/2 * cdef int N = M*(M+1)/2
* cdef int count = 0 # <<<<<<<<<<<<<< * cdef int count = 0 # <<<<<<<<<<<<<<
* cdef np.ndarray[double, ndim=2] flat = np.empty((D, N)) * cdef np.ndarray[double, ndim=2] flat = np.empty((N, D))
* cdef int d, m, mm * cdef int d, m, mm
*/ */
__pyx_v_count = 0; __pyx_v_count = 0;
@ -1623,7 +1623,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
/* "GPy/util/choleskies_cython.pyx":35 /* "GPy/util/choleskies_cython.pyx":35
* cdef int N = M*(M+1)/2 * cdef int N = M*(M+1)/2
* cdef int count = 0 * cdef int count = 0
* cdef np.ndarray[double, ndim=2] flat = np.empty((D, N)) # <<<<<<<<<<<<<< * cdef np.ndarray[double, ndim=2] flat = np.empty((N, D)) # <<<<<<<<<<<<<<
* cdef int d, m, mm * cdef int d, m, mm
* for d in range(D): * for d in range(D):
*/ */
@ -1632,9 +1632,9 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
__pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_empty); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_empty); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_3); __Pyx_GOTREF(__pyx_t_3);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
__pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_D); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_N); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_N); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_D); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4); __Pyx_GOTREF(__pyx_t_4);
__pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_5);
@ -1685,7 +1685,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
__pyx_t_1 = 0; __pyx_t_1 = 0;
/* "GPy/util/choleskies_cython.pyx":37 /* "GPy/util/choleskies_cython.pyx":37
* cdef np.ndarray[double, ndim=2] flat = np.empty((D, N)) * cdef np.ndarray[double, ndim=2] flat = np.empty((N, D))
* cdef int d, m, mm * cdef int d, m, mm
* for d in range(D): # <<<<<<<<<<<<<< * for d in range(D): # <<<<<<<<<<<<<<
* count = 0 * count = 0
@ -1709,7 +1709,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
* count = 0 * count = 0
* for m in range(M): # <<<<<<<<<<<<<< * for m in range(M): # <<<<<<<<<<<<<<
* for mm in range(m+1): * for mm in range(m+1):
* flat[d,count] = L[d, m, mm] * flat[count,d] = L[d, m, mm]
*/ */
__pyx_t_9 = __pyx_v_M; __pyx_t_9 = __pyx_v_M;
for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_9; __pyx_t_10+=1) { for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_9; __pyx_t_10+=1) {
@ -1719,7 +1719,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
* count = 0 * count = 0
* for m in range(M): * for m in range(M):
* for mm in range(m+1): # <<<<<<<<<<<<<< * for mm in range(m+1): # <<<<<<<<<<<<<<
* flat[d,count] = L[d, m, mm] * flat[count,d] = L[d, m, mm]
* count += 1 * count += 1
*/ */
__pyx_t_11 = (__pyx_v_m + 1); __pyx_t_11 = (__pyx_v_m + 1);
@ -1729,7 +1729,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
/* "GPy/util/choleskies_cython.pyx":41 /* "GPy/util/choleskies_cython.pyx":41
* for m in range(M): * for m in range(M):
* for mm in range(m+1): * for mm in range(m+1):
* flat[d,count] = L[d, m, mm] # <<<<<<<<<<<<<< * flat[count,d] = L[d, m, mm] # <<<<<<<<<<<<<<
* count += 1 * count += 1
* return flat * return flat
*/ */
@ -1739,15 +1739,15 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
if (__pyx_t_13 < 0) __pyx_t_13 += __pyx_pybuffernd_L.diminfo[0].shape; if (__pyx_t_13 < 0) __pyx_t_13 += __pyx_pybuffernd_L.diminfo[0].shape;
if (__pyx_t_14 < 0) __pyx_t_14 += __pyx_pybuffernd_L.diminfo[1].shape; if (__pyx_t_14 < 0) __pyx_t_14 += __pyx_pybuffernd_L.diminfo[1].shape;
if (__pyx_t_15 < 0) __pyx_t_15 += __pyx_pybuffernd_L.diminfo[2].shape; if (__pyx_t_15 < 0) __pyx_t_15 += __pyx_pybuffernd_L.diminfo[2].shape;
__pyx_t_16 = __pyx_v_d; __pyx_t_16 = __pyx_v_count;
__pyx_t_17 = __pyx_v_count; __pyx_t_17 = __pyx_v_d;
if (__pyx_t_16 < 0) __pyx_t_16 += __pyx_pybuffernd_flat.diminfo[0].shape; if (__pyx_t_16 < 0) __pyx_t_16 += __pyx_pybuffernd_flat.diminfo[0].shape;
if (__pyx_t_17 < 0) __pyx_t_17 += __pyx_pybuffernd_flat.diminfo[1].shape; if (__pyx_t_17 < 0) __pyx_t_17 += __pyx_pybuffernd_flat.diminfo[1].shape;
*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_flat.rcbuffer->pybuffer.buf, __pyx_t_16, __pyx_pybuffernd_flat.diminfo[0].strides, __pyx_t_17, __pyx_pybuffernd_flat.diminfo[1].strides) = (*__Pyx_BufPtrStrided3d(double *, __pyx_pybuffernd_L.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_L.diminfo[0].strides, __pyx_t_14, __pyx_pybuffernd_L.diminfo[1].strides, __pyx_t_15, __pyx_pybuffernd_L.diminfo[2].strides)); *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_flat.rcbuffer->pybuffer.buf, __pyx_t_16, __pyx_pybuffernd_flat.diminfo[0].strides, __pyx_t_17, __pyx_pybuffernd_flat.diminfo[1].strides) = (*__Pyx_BufPtrStrided3d(double *, __pyx_pybuffernd_L.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_L.diminfo[0].strides, __pyx_t_14, __pyx_pybuffernd_L.diminfo[1].strides, __pyx_t_15, __pyx_pybuffernd_L.diminfo[2].strides));
/* "GPy/util/choleskies_cython.pyx":42 /* "GPy/util/choleskies_cython.pyx":42
* for mm in range(m+1): * for mm in range(m+1):
* flat[d,count] = L[d, m, mm] * flat[count,d] = L[d, m, mm]
* count += 1 # <<<<<<<<<<<<<< * count += 1 # <<<<<<<<<<<<<<
* return flat * return flat
* *
@ -1758,7 +1758,7 @@ static PyObject *__pyx_pf_3GPy_4util_17choleskies_cython_2triang_to_flat(CYTHON_
} }
/* "GPy/util/choleskies_cython.pyx":43 /* "GPy/util/choleskies_cython.pyx":43
* flat[d,count] = L[d, m, mm] * flat[count,d] = L[d, m, mm]
* count += 1 * count += 1
* return flat # <<<<<<<<<<<<<< * return flat # <<<<<<<<<<<<<<
* *
@ -4381,7 +4381,7 @@ static int __Pyx_InitCachedConstants(void) {
* cimport numpy as np * cimport numpy as np
* *
* def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<< * def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<<
* """take a matrix D x N and return a D X M x M array where * """take a matrix N x D and return a D X M x M array where
* *
*/ */
__pyx_tuple__7 = PyTuple_Pack(9, __pyx_n_s_flat, __pyx_n_s_M, __pyx_n_s_D, __pyx_n_s_N, __pyx_n_s_count, __pyx_n_s_ret, __pyx_n_s_d, __pyx_n_s_m, __pyx_n_s_mm); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_tuple__7 = PyTuple_Pack(9, __pyx_n_s_flat, __pyx_n_s_M, __pyx_n_s_D, __pyx_n_s_N, __pyx_n_s_count, __pyx_n_s_ret, __pyx_n_s_d, __pyx_n_s_m, __pyx_n_s_mm); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@ -4539,7 +4539,7 @@ PyMODINIT_FUNC PyInit_choleskies_cython(void)
* cimport numpy as np * cimport numpy as np
* *
* def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<< * def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): # <<<<<<<<<<<<<<
* """take a matrix D x N and return a D X M x M array where * """take a matrix N x D and return a D X M x M array where
* *
*/ */
__pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_3GPy_4util_17choleskies_cython_1flat_to_triang, NULL, __pyx_n_s_GPy_util_choleskies_cython); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_3GPy_4util_17choleskies_cython_1flat_to_triang, NULL, __pyx_n_s_GPy_util_choleskies_cython); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;}

12
GPy/util/choleskies_cython.pyx
View file

@ -8,14 +8,14 @@ import numpy as np
cimport numpy as np cimport numpy as np
def flat_to_triang(np.ndarray[double, ndim=2] flat, int M): def flat_to_triang(np.ndarray[double, ndim=2] flat, int M):
"""take a matrix D x N and return a D X M x M array where """take a matrix N x D and return a D X M x M array where
N = M(M+1)/2 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. the lower triangluar portion of the d'th slice of the result is filled by the d'th column of flat.
""" """
cdef int D = flat.shape[0] cdef int D = flat.shape[1]
cdef int N = flat.shape[1] cdef int N = flat.shape[0]
cdef int count = 0 cdef int count = 0
cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M)) cdef np.ndarray[double, ndim=3] ret = np.zeros((D, M, M))
cdef int d, m, mm cdef int d, m, mm
@ -23,7 +23,7 @@ def flat_to_triang(np.ndarray[double, ndim=2] flat, int M):
count = 0 count = 0
for m in range(M): for m in range(M):
for mm in range(m+1): for mm in range(m+1):
ret[d, m, mm] = flat[d,count] ret[d, m, mm] = flat[count,d]
count += 1 count += 1
return ret return ret
@ -32,13 +32,13 @@ def triang_to_flat(np.ndarray[double, ndim=3] L):
cdef int M = L.shape[1] cdef int M = L.shape[1]
cdef int N = M*(M+1)/2 cdef int N = M*(M+1)/2
cdef int count = 0 cdef int count = 0
cdef np.ndarray[double, ndim=2] flat = np.empty((D, N)) cdef np.ndarray[double, ndim=2] flat = np.empty((N, D))
cdef int d, m, mm cdef int d, m, mm
for d in range(D): for d in range(D):
count = 0 count = 0
for m in range(M): for m in range(M):
for mm in range(m+1): for mm in range(m+1):
flat[d,count] = L[d, m, mm] flat[count,d] = L[d, m, mm]
count += 1 count += 1
return flat return flat