def split_nj(D):
"""
Split a distance matrix according to the neighbor joining criterion.
@param D: a distance matrix
@return: a set of two index sets defining a split of the indices
"""
n = len(D)
# there is no reason to split a 3x3 distance matrix
assert n > 3, n
Q = Euclid.edm_to_q(D)
pairs = [(i, j) for i in range(n) for j in range(n) if i < j]
best_value, best_pair = min((Q[pair], pair) for pair in pairs)
return full_split(best_pair, range(n))
def split_nj(D):
"""
Split a distance matrix according to the neighbor joining criterion.
@param D: a distance matrix
@return: a set of two index sets defining a split of the indices
"""
n = len(D)
# there is no reason to split a 3x3 distance matrix
assert n > 3, n
Q = Euclid.edm_to_q(D)
pairs = [(i, j) for i in range(n) for j in range(n) if i < j]
best_value, best_pair = min((Q[pair], pair) for pair in pairs)
return full_split(best_pair, range(n))
