def test_Integate_Mean(self):
a = smctc.sampler(10, smctc.HistoryType.SMC_HISTORY_NONE)
a.SetResampleParams(smctc.ResampleType.SMC_RESAMPLE_RESIDUAL, 0.5)
fmove = smctc.moveset(fInitialise, fMove)
a.SetMoveSet(fmove)
a.Initialise()
a.Integrate_Mean(0)
return smctc.particle(value,logLikelihood(0,value))
def fMove(lTime, pFrom, pRng):
"""
The proposal function.
param: lTime[int] The sampler iteration.
param: pFrom[smc.particle] The particle to move.
param: pRng[smc.rng] A random number generator.
"""
cv_to = pFrom.GetValue() # cv_state, list
cv_to[0] += cv_to[2] * Delta + pRng.Normal(0,sqrt(var_s))
cv_to[2] += pRng.Normal(0,sqrt(var_u))
cv_to[1] += cv_to[3] * Delta + pRng.Normal(0,sqrt(var_s))
cv_to[3] += pRng.Normal(0,sqrt(var_u))
pFrom.AddToLogWeight(logLikelihood(lTime, cv_to))
if __name__ == '__main__':
load_observation()
fmove = smctc.moveset(fInitialise, fMove)
a=smctc.sampler(1000,smctc.HistoryType.SMC_HISTORY_NONE)
a.SetResampleParams(smctc.ResampleType.SMC_RESAMPLE_RESIDUAL,0.5)
a.SetMoveSet(fmove)
a.Initialise()
for i in range(len(y)-1):
a.Iterate()
xm = a.Integrate_Mean(0)
ym = a.Integrate_Mean(1)
print(xm, ym)
def test_DoInit(self):
fmove = smctc.moveset(fInitialise, fMove)
mrng = smctc.rng()
pp = fmove.DoInit(mrng)
def test_DoMove(self):
a = smctc.particle([1, 2.0], 2.3)
mrng = smctc.rng()
fmove = smctc.moveset(fInitialise, fMove)
fmove.DoMove(0, a, mrng)
self.assertAlmostEqual(a.GetLogWeight(), 2.4)
def test_Constructor(self):
smctc.moveset()
smctc.moveset(fInitialise, fMove)