def test_nodelikelihood_2tiptreeSymmetricQ2x2difblens_returnslikelihood(self):
charstates = self.charstates_01
tree = self.simpletreedifblens
for i, node in enumerate(tree.leaves()):
node.charstate = charstates[i]
Q = self.Q2x2_sym
t = [ node.length for node in tree.descendants() ]
t = np.array(t, dtype=np.double)
p = cyexpokit.dexpm_tree(Q,t)
for i, node in enumerate(tree.descendants()):
node.pmat = p[i]
node = tree
predictedLikelihood = 0.22559418195297778
calculatedLikelihood = discrete.nodeLikelihood(node)
self.assertTrue(np.isclose(predictedLikelihood, calculatedLikelihood))
def test_nodelikelihood_2tiptreeSymmetricQ2x2_returnslikelihood(self):
charstates = self.charstates_01
tree = self.simpletree
for i, node in enumerate(tree.leaves()):
node.charstate = charstates[i]
Q = self.Q2x2_sym
t = [node.length for node in tree.descendants()]
t = np.array(t, dtype=np.double)
p = cyexpokit.dexpm_tree(Q, t)
for i, node in enumerate(tree.descendants()):
node.pmat = p[i]
node = tree
predictedLikelihood = 0.16483997131
calculatedLikelihood = discrete.nodeLikelihood(node)
self.assertTrue(np.isclose(predictedLikelihood, calculatedLikelihood))
def test_nodelikelihood_2tiptreesinglenodeSymmetricQ3x3_returnslikelihood(self):
charstates = self.charstates_01
tree = self.simpletree
for i, node in enumerate(tree.leaves()):
node.charstate = charstates[i]
Q = self.Q3x3_sym
t = [ node.length for node in tree.descendants() ]
t = np.array(t, dtype=np.double)
p = cyexpokit.dexpm_tree(Q,t)
for i, node in enumerate(tree.descendants()):
node.pmat = p[i]
node = tree
predictedLikelihood = 0.0863939177214389
calculatedLikelihood = discrete.nodeLikelihood(node)
self.assertTrue((predictedLikelihood, calculatedLikelihood))