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    <p>The usual technique of hashing any sequence is combining the values (or hashes thereof) of its elements in some mathematical way. I don't think a tree would be any different in this respect. </p> <p>For example, here is the hash function for tuples in Python (taken from Objects/tupleobject.c in the source of Python 2.6):</p> <pre><code>static long tuplehash(PyTupleObject *v) { register long x, y; register Py_ssize_t len = Py_SIZE(v); register PyObject **p; long mult = 1000003L; x = 0x345678L; p = v-&gt;ob_item; while (--len &gt;= 0) { y = PyObject_Hash(*p++); if (y == -1) return -1; x = (x ^ y) * mult; /* the cast might truncate len; that doesn't change hash stability */ mult += (long)(82520L + len + len); } x += 97531L; if (x == -1) x = -2; return x; } </code></pre> <p>It's a relatively complex combination with constants experimentally chosen for best results for tuples of typical lengths. What I'm trying to show with this code snippet is that the issue is very complex and very heuristic, and the quality of the results probably depend on the more specific aspects of your data - i.e. domain knowledge may help you reach better results. However, for good-enough results you shouldn't look too far. I would guess that taking this algorithm and combining all the nodes of the tree instead of all the tuple elements, plus adding their position into play will give you a pretty good algorithm.</p> <p>One option of taking the position into account is the node's position in an inorder walk of the tree.</p>
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