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  1. PO
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    <p>I know this is an old thread, but I recently ran into this same problem and was able to solve it and I figure the solution should be here in case anyone in the future runs into it. Like said above, Jython cannot deal with numpy's pre-compiled c files, but within nltk, the use of numpy is very limited and it's fairly straightforward to rewrite the affected bits of code. That's what I did, and I'm sure it's not the most computationally effective solution, but it works. This code is found in nltk.metrics.Segmentation, and I will only paste relevant code, but it will still be a little much.</p> <pre><code>def _init_mat(nrows, ncols, ins_cost, del_cost): mat = [[4.97232652e-299 for x in xrange(ncols)] for x in xrange(nrows)] for x in range(0,ncols): mat[0][x] = x * ins_cost for x in range(0, nrows): mat[x][0] = x * del_cost return mat def _ghd_aux(mat, rowv, colv, ins_cost, del_cost, shift_cost_coeff): for i, rowi in enumerate(rowv): for j, colj in enumerate(colv): shift_cost = shift_cost_coeff * abs(rowi - colj) + mat[i][j] if rowi == colj: # boundaries are at the same location, no transformation required tcost = mat[i][j] elif rowi &gt; colj: # boundary match through a deletion tcost = del_cost + mat[i][j + 1] else: # boundary match through an insertion tcost = ins_cost + mat[i + 1][j] mat[i + 1][j + 1] = min(tcost, shift_cost) </code></pre> <p>Also at the end of ghd, change the return statement to </p> <pre><code>return mat[-1][-1] </code></pre> <p>I hope this helps someone! I don't know if there are other places where this is any issue, but this is the only one that I have encountered. If there are any other issues of this sort they can be solved in the same way(using a list of lists instead of a numpy array), again, you probably lose some efficiency, but it works.</p>
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