Dparam = parameters.shape[0] #dimension of the postulated model/parameter
thetaweights = thetaweights / sum(
thetaweights) #first transform to normalized ones
xNormConst = sum(xweights, axis=0)
grad = zeros(Ntheta)
lap = zeros(Ntheta)
hScore_theta = zeros(Ntheta) #store simple H score for each theta particle
compositeHScore = zeros(1)
simpleHScore = zeros(1)
weave.inline(code,['states', 'y', 'parameters', 'thetaweights', 'xweights', 'xNormConst', 'hScore_theta', \
'Nx', 'Ntheta', 'Dparam', 'grad', 'lap', 'compositeHScore', 'simpleHScore'], \
type_converters=weave.converters.blitz, libraries = ["m"])
return {"simpleHScore": simpleHScore, "compositeHScore": compositeHScore}
modelx = SSM(name="Simplest model x",
xdimension=1,
ydimension=1,
HScoreInference=HScoreInference,
continuousObs=continuousObs)
modelx.setComputeHscore(computeHscore)
modelx.setFirstStateGenerator(firstStateGenerator)
modelx.setObservationGenerator(observationGenerator)
modelx.setTransitionAndWeight(transitionAndWeight)
# Values used to generate the synthetic dataset when needed:
# (untransformed parameters)
modelx.setParameters(array([0.9]))
modelx.addStateFiltering()
modelx.addStatePrediction()
modelx.addObsPrediction()
