def PLexMax(triple, K, threshold, numSteps, tempScale):
for outG in triple.taxaSet:
stepScale = tempScale
branchData = triple.branches(outG)
if len(branchData) < 2:
continue
cArray = list(np.zeros(K))
pArray = [1. / K] * K
lmbd = np.mean(branchData)
for x in range(1, K):
cArray[x] = x
steps = 0
while steps < numSteps:
XQList = cyq.XQUpdate(branchData, cArray, pArray, lmbd)
pArray = cyq.pUpdate(XQList)
for indy in range(1, K):
cArray[indy] = cyq.cSearch(branchData, XQList, indy, lmbd,
cArray)
storedlmbd = lmbd
lmbd, stepScale = cyq.gradAscent(branchData, XQList, cArray,
pArray, lmbd, stepScale, numSteps,
threshold)
cArray = [x * (storedlmbd / lmbd) for x in cArray]
steps += 1
triple.setModel(outG, list(cArray), list(pArray), lmbd)
triple.setBIC(
outG,
np.log(len(branchData)) * (2 * K - 1) - 2 *
(cyq.modelLogLik(branchData, list(cArray), list(pArray), lmbd)))
triple.setNull(outG, oneDistNull(branchData))
return triple
def oneDistNull(branchData):
lmbd = np.mean(branchData)
bic = np.log(len(branchData)) - 2 * (cyq.modelLogLik(
branchData, [0.0], [1.0], lmbd))
return (lmbd, bic)