diff options
Diffstat (limited to 'ot/lp/emd_wrap.pyx')
| -rw-r--r-- | ot/lp/emd_wrap.pyx | 42 |
1 files changed, 36 insertions, 6 deletions
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 2b6c495..c0d7128 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -20,6 +20,9 @@ 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) + int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -39,7 +42,7 @@ 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, bint dense): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -72,7 +75,8 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod max_iter : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. - + dense : bool + Return a sparse transport matrix if set to False Returns ------- @@ -82,12 +86,19 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod """ cdef int n1= M.shape[0] cdef int n2= M.shape[1] + cdef int nmax=n1+n2-1 + cdef int result_code = 0 + cdef int nG=0 cdef double cost=0 - cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) cdef np.ndarray[double, ndim=1, mode="c"] alpha=np.zeros(n1) cdef np.ndarray[double, ndim=1, mode="c"] beta=np.zeros(n2) + 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 @@ -95,10 +106,29 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod if not len(b): b=np.ones((n2,))/n2 - # calling the function - cdef int 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 dense: + # init OT matrix + G=np.zeros([n1, n2]) + + # calling the function + 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) + + return G, cost, alpha, beta, result_code + + + else: + + # init sparse OT matrix + Gv=np.zeros(nmax) + iG=np.zeros(nmax,dtype=np.int) + jG=np.zeros(nmax,dtype=np.int) + + + result_code = EMD_wrap_return_sparse(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <long*> iG.data, <long*> jG.data, <double*> Gv.data, <long*> &nG, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter) + + + return Gv[:nG], iG[:nG], jG[:nG], cost, alpha, beta, result_code - return G, cost, alpha, beta, result_code @cython.boundscheck(False) |