tree = join_neighbors(tree, i, j, di, dj)
d = m[0][1]
return join_neighbors(tree, 0, 1, d / 2, d / 2)[0]
def _random_joining(ids):
"""Generates a tree by repeatedly joining elements randomly."""
r = Random()
tree = [Leaf(id_) for id_ in ids]
for _ in xrange(len(ids) - 1):
i, j = sorted(r.sample(xrange(len(tree)), 2))
tree = join_neighbors(tree, i, j, r.random(), r.random())
return tree[0]
if __name__ == '__main__':
from tree import test_tree
expected_tree, m, ids = test_tree()
tree = neighbor_joining(m, ids)
print tree, expected_tree
# Random test
from tree import distance_matrix
expected_tree = _random_joining(map(str, xrange(10)))
m, ids = distance_matrix(expected_tree)
tree = neighbor_joining(m, ids)
print tree, expected_tree
i, j = sorted(matrix_argmin(q))
m, di, dj = update_distance_matrix(m, s, i, j)
tree = join_neighbors(tree, i, j, di, dj)
d = m[0][1]
return join_neighbors(tree, 0, 1, d/2, d/2)[0]
def _random_joining(ids):
"""Generates a tree by repeatedly joining elements randomly."""
r = Random()
tree = [Leaf(id_) for id_ in ids]
for _ in xrange(len(ids) - 1):
i, j = sorted(r.sample(xrange(len(tree)), 2))
tree = join_neighbors(tree, i, j, r.random(), r.random())
return tree[0]
if __name__ == '__main__':
from tree import test_tree
expected_tree, m, ids = test_tree()
tree = neighbor_joining(m, ids)
print tree, expected_tree
# Random test
from tree import distance_matrix
expected_tree = _random_joining(map(str, xrange(10)))
m, ids = distance_matrix(expected_tree)
tree = neighbor_joining(m, ids)
print tree, expected_tree
from tree import test_tree from treecollection import test_tree_collection from taxonomyreference import test_taxonomy_reference from taxa import test_taxa __doc__ = "" __doc__ += test_tree() __doc__ += test_tree_collection() __doc__ += test_taxonomy_reference() __doc__ += test_taxa()
