diff options
Diffstat (limited to 'ot/lp/emd_wrap.pyx')
| -rw-r--r-- | ot/lp/emd_wrap.pyx | 32 |
1 files changed, 19 insertions, 13 deletions
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index c167964..42e08f4 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -20,6 +20,7 @@ import warnings cdef extern from "EMD.h": int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) nogil + int EMD_wrap_omp(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter, int numThreads) nogil cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -38,7 +39,7 @@ def check_result(result_code): @cython.boundscheck(False) @cython.wraparound(False) -def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter): +def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter, int numThreads): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -97,8 +98,6 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([0, 0]) cdef np.ndarray[double, ndim=1, mode="c"] Gv=np.zeros(0) - cdef np.ndarray[long, ndim=1, mode="c"] iG=np.zeros(0,dtype=np.int) - cdef np.ndarray[long, ndim=1, mode="c"] jG=np.zeros(0,dtype=np.int) if not len(a): a=np.ones((n1,))/n1 @@ -111,8 +110,10 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod # calling the function with nogil: - result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter) - + if numThreads == 1: + result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter) + else: + result_code = EMD_wrap_omp(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter, numThreads) return G, cost, alpha, beta, result_code @@ -157,22 +158,22 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, cost associated to the optimal transportation """ cdef double cost = 0. - cdef int n = u_weights.shape[0] - cdef int m = v_weights.shape[0] + cdef Py_ssize_t n = u_weights.shape[0] + cdef Py_ssize_t m = v_weights.shape[0] - cdef int i = 0 + cdef Py_ssize_t i = 0 cdef double w_i = u_weights[0] - cdef int j = 0 + cdef Py_ssize_t j = 0 cdef double w_j = v_weights[0] cdef double m_ij = 0. cdef np.ndarray[double, ndim=1, mode="c"] G = np.zeros((n + m - 1, ), dtype=np.float64) - cdef np.ndarray[long, ndim=2, mode="c"] indices = np.zeros((n + m - 1, 2), - dtype=np.int) - cdef int cur_idx = 0 - while i < n and j < m: + cdef np.ndarray[long long, ndim=2, mode="c"] indices = np.zeros((n + m - 1, 2), + dtype=np.int64) + cdef Py_ssize_t cur_idx = 0 + while True: if metric == 'sqeuclidean': m_ij = (u[i] - v[j]) * (u[i] - v[j]) elif metric == 'cityblock' or metric == 'euclidean': @@ -188,6 +189,8 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, indices[cur_idx, 0] = i indices[cur_idx, 1] = j i += 1 + if i == n: + break w_j -= w_i w_i = u_weights[i] else: @@ -196,7 +199,10 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, indices[cur_idx, 0] = i indices[cur_idx, 1] = j j += 1 + if j == m: + break w_i -= w_j w_j = v_weights[j] cur_idx += 1 + cur_idx += 1 return G[:cur_idx], indices[:cur_idx], cost |