diff options
Diffstat (limited to 'ot/lp/emd_wrap.pyx')
| -rw-r--r-- | ot/lp/emd_wrap.pyx | 25 |
1 files changed, 20 insertions, 5 deletions
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 46c96c1..ed8c416 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -15,13 +15,14 @@ cimport cython cdef extern from "EMD.h": - void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) + cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED @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): +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 numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -48,6 +49,8 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod target histogram M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns @@ -69,13 +72,18 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost) + cdef int resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, numItermax) + if resultSolver != OPTIMAL: + if resultSolver == INFEASIBLE: + print("Problem infeasible. Try to inscrease numItermax.") + elif resultSolver == UNBOUNDED: + print("Problem unbounded") return G @cython.boundscheck(False) @cython.wraparound(False) -def emd2_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): +def emd2_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 numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport loss @@ -102,6 +110,8 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo target histogram M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns @@ -123,7 +133,12 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost) + cdef int resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, numItermax) + if resultSolver != OPTIMAL: + if resultSolver == INFEASIBLE: + print("Problem infeasible. Try to inscrease numItermax.") + elif resultSolver == UNBOUNDED: + print("Problem unbounded") cost=0 for i in range(n1): |