def testOneSampleMatrix():
EndPointSampler.cached=True
nStates=2
stationaryWeights = np.array((0.619, 0.592))
bivariateWeights = 1.499
bivariateFeatIndexDictionary = getHardCodedDictChainGraph(nStates=nStates)
rateMtxObj = ReversibleRateMtxPiAndBinaryWeightsWithGraphicalStructure(nStates, stationaryWeights,
bivariateWeights,
bivariateFeatIndexDictionary)
stationaryDist= rateMtxObj.getStationaryDist()
rateMatrix = rateMtxObj.getRateMtx()
T = 5.0
pathStat2 = PathStatistics(nStates)
postSampler = EndPointSampler(rateMatrix, T)
fwdSampler = ForwardSimulation(T, rateMatrix)
transition = np.zeros((nStates, nStates))
sojournTime = np.zeros(nStates)
nIters = 1000000
for i in range(nIters):
current = Path()
prng = np.random.RandomState(i)
startState = prng.choice(nStates, 1, replace=True, p=stationaryDist)
curResult = fwdSampler.sampleStateTimeSeq(prng, startState)
print(curResult['transitCount'])
transition = transition + curResult["transitCount"]
sojournTime = sojournTime + curResult["sojourn"]
current.states = curResult["states"]
current.times = curResult["time"]
p2 = Path()
postSampler.sample(np.random.RandomState(i + nIters), current.firstState(), current.lastState(), T, pathStat2, p2)
print(i)
m2 = pathStat2.getCountsAsSimpleMatrix() / nIters
m1 = transition
np.fill_diagonal(m1, sojournTime)
m1 = m1 / nIters
print(np.round(m1, 3))
print(np.round(m2, 3))
def testNonNormalizedGTRRateMtx():
EndPointSampler.cached = True
## define a known rate matrix K80
nStates = 4
## set a seed for the random generator
np.random.seed(1)
## randomly generate a GTR rate matrix from the weights
weights = np.random.normal(0, 1, 10)
rateMtxObj = ReversibleRateMtx(nStates, weights)
stationaryDist = rateMtxObj.getStationaryDist()
k80 = ReversibleRateMtx(nStates, weights).getRateMtx()
T = 3.0
pathStat2 = PathStatistics(nStates)
postSampler = EndPointSampler(k80, T)
fwdSampler = ForwardSimulation(T, k80)
transition = np.zeros((nStates, nStates))
sojournTime = np.zeros(nStates)
nIters = 1000000
for i in range(nIters):
current = Path()
startState = np.argmax(np.random.multinomial(1, stationaryDist, 1))
curResult = fwdSampler.sampleStateTimeSeq(startState)
transition = transition + curResult["transitCount"]
sojournTime = sojournTime + curResult["sojourn"]
current.states = curResult["states"]
current.times = curResult["time"]
p2 = Path()
postSampler.sample(np.random.RandomState(i + nIters), current.firstState(), current.lastState(), T, pathStat2, p2)
print(i)
m2 = pathStat2.getCountsAsSimpleMatrix() / nIters
m1 = transition
np.fill_diagonal(m1, sojournTime)
m1 = m1 / nIters
print(np.round(m1, 3))
print(np.round(m2, 3))
