diff options
-rw-r--r-- | ot/lp/emd_wrap.pyx | 17 |
1 files changed, 11 insertions, 6 deletions
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index c167964..d79d0ca 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -157,12 +157,12 @@ 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. @@ -171,8 +171,8 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, 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 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 +188,8 @@ 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 +198,10 @@ 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 |