def test_carteisan_product(self):
"""cartesian_product should return expected results."""
a = 'abc'
b = [1,2,3]
c = [1.0]
d = [0,1]
#cartesian_product of list of single list should be same list
self.assertEqual(cartesian_product([c]), [(1.0,)])
self.assertEqual(cartesian_product([a]), [('a',),('b',),('c',)])
#should combine two lists correctly
self.assertEqual(cartesian_product([a,b]), \
[('a',1),('a',2),('a',3),('b',1),('b',2),\
('b',3),('c',1),('c',2),('c',3)])
#should combine three lists correctly
self.assertEqual(cartesian_product([d,d,d]), \
[(0,0,0),(0,0,1),(0,1,0),(0,1,1),(1,0,0),(1,0,1),(1,1,0),(1,1,1)])
self.assertEqual(cartesian_product([c,d,d]), \
[(1.0,0,0),(1.0,0,1),(1.0,1,0),(1.0,1,1)])
def random_source(a, k, random_f=random):
"""Makes a random Markov source on alphabet a with memory k.
Specifically, for all words k, pr(i|k) = rand().
"""
result = dict.fromkeys(list(map(''.join, cartesian_product([a]*k))))
for k in result:
result[k] = Freqs(dict(list(zip(a, random_f(len(a))))))
return result
def __new__(cls, data=[], Gap=None, MolType=None):
"""Fills in the tuple with tuples from the enumerations in data."""
sub_enums = cls._coerce_enumerations(data)
return Enumeration.__new__(cls, cartesian_product(sub_enums), \
MolType=MolType)
def __new__(cls, data=[], Gap=None, MolType=None):
"""Fills in the tuple with tuples from the enumerations in data."""
sub_enums = cls._coerce_enumerations(data)
return Enumeration.__new__(cls, cartesian_product(sub_enums), \
MolType=MolType)
