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17 changes: 11 additions & 6 deletions ot/lp/emd_wrap.pyx
Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand All @@ -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':
Expand All @@ -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:
Expand All @@ -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