def test_logm_taylor(self):
"""logm_taylor should return same result as logm"""
q_eig = logm([[ 0.86758487, 0.05575623, 0.0196798 , 0.0569791 ],
[ 0.01827347, 0.93312148, 0.02109664, 0.02750842],
[ 0.04782582, 0.1375742 , 0.80046869, 0.01413129],
[ 0.23022035, 0.22306947, 0.06995306, 0.47675713]])
q_taylor = logm_taylor([[0.86758487, 0.05575623, 0.0196798, 0.0569791],
[ 0.01827347, 0.93312148, 0.02109664, 0.02750842],
[ 0.04782582, 0.1375742 , 0.80046869, 0.01413129],
[ 0.23022035, 0.22306947, 0.06995306, 0.47675713]])
self.assertFloatEqual(q_taylor, q_eig)
def test_toRates(self):
"""Probs toRates should return log of probs, optionally normalized"""
a = Alphabet('abc')**2
p = Probs([0.9,0.05,0.05,0.1,0.85,0.05,0.02,0.02,0.96], a)
assert p.isValid()
r = p.toRates()
assert isinstance(r, Rates)
assert r.isValid()
assert not r.isComplex()
self.assertEqual(r._data, logm(p._data))
r_norm = p.toRates(normalize=True)
self.assertFloatEqual(trace(r_norm._data), -1.0)
def test_toRates(self):
"""Probs toRates should return log of probs, optionally normalized"""
a = Alphabet('abc')**2
p = Probs([0.9, 0.05, 0.05, 0.1, 0.85, 0.05, 0.02, 0.02, 0.96], a)
assert p.isValid()
r = p.toRates()
assert isinstance(r, Rates)
assert r.isValid()
assert not r.isComplex()
self.assertEqual(r._data, logm(p._data))
r_norm = p.toRates(normalize=True)
self.assertFloatEqual(trace(r_norm._data), -1.0)
def test_logm(self):
"""logm results should match scipy's"""
p = array([[ 0.86758487, 0.05575623, 0.0196798 , 0.0569791 ],
[ 0.01827347, 0.93312148, 0.02109664, 0.02750842],
[ 0.04782582, 0.1375742 , 0.80046869, 0.01413129],
[ 0.23022035, 0.22306947, 0.06995306, 0.47675713]])
q = logm(p)
self.assertFloatEqual(q, \
array([[-0.15572053, 0.04947485, 0.01918653, 0.08705915],
[ 0.01405019, -0.07652296, 0.02252941, 0.03994336],
[ 0.05365208, 0.15569116, -0.22588966, 0.01654642],
[ 0.35144866, 0.31279003, 0.10478999, -0.76902868]]))
def test_toProbs(self):
"""Rates toProbs should return correct probability matrix"""
a = self.abc_pairs
p = Probs([0.75, 0.1, 0.15, 0.2, 0.7, 0.1, 0.05, 0.1, 0.85], a)
q = p.toRates()
self.assertEqual(q._data, logm(p._data))
p2 = q.toProbs()
self.assertFloatEqual(p2._data, p._data)
#test a case that didn't work for DNA
q = Rates(array(
[[-0.64098451, 0.0217681 , 0.35576469, 0.26345171],
[ 0.31144238, -0.90915091, 0.25825858, 0.33944995],
[ 0.01578521, 0.43162879, -0.99257581, 0.54516182],
[ 0.13229986, 0.04027147, 0.05817791, -0.23074925]]),
DnaPairs)
self.assertFloatEqual(q.toProbs(0.5)._data, expm(q._data)(t=0.5))
def test_toProbs(self):
"""Rates toProbs should return correct probability matrix"""
a = self.abc_pairs
p = Probs([0.75, 0.1, 0.15, 0.2, 0.7, 0.1, 0.05, 0.1, 0.85], a)
q = p.toRates()
self.assertEqual(q._data, logm(p._data))
p2 = q.toProbs()
self.assertFloatEqual(p2._data, p._data)
#test a case that didn't work for DNA
q = Rates(
array([[-0.64098451, 0.0217681, 0.35576469, 0.26345171],
[0.31144238, -0.90915091, 0.25825858, 0.33944995],
[0.01578521, 0.43162879, -0.99257581, 0.54516182],
[0.13229986, 0.04027147, 0.05817791, -0.23074925]]),
DnaPairs)
self.assertFloatEqual(q.toProbs(0.5)._data, expm(q._data)(t=0.5))
def toRates(self, normalize=False):
"""Returns rate matrix. Does not normalize by default."""
return Rates(logm(self._data), self.Alphabet, self.Name, normalize)
def toRates(self, normalize=False):
"""Returns rate matrix. Does not normalize by default."""
return Rates(logm(self._data), self.Alphabet, self.Name, normalize)
