def test_simulateParam2(self):
'''
Stochastic ode under the interpretation that the parameters follow
some sort of distribution. In this case, a function handle which
has the same name as R
'''
t0 = 0
# the initial state, normalized to zero one
x0 = [1, 1.27e-6, 0]
# set the time sequence that we would like to observe
t = numpy.linspace(0, 150, 100)
# Standard. Find the solution.
ode = common_models.SIR()
ode.setParameters([0.5, 1.0 / 3.0])
ode.setInitialValue(x0, t0)
solutionReference = ode.integrate(t[1::], full_output=False)
# now we need to define our ode explicitly
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma']
transitionList = [
Transition(origState='S',
destState='I',
equation='beta*S*I',
transitionType=TransitionType.T),
Transition(origState='I',
destState='R',
equation='gamma*I',
transitionType=TransitionType.T)
]
# our stochastic version
odeS = SimulateOdeModel(stateList,
paramList,
transitionList=transitionList)
# define our parameters in terms of two gamma distributions
# where the expected values are the same as before [0.5,1.0/3.0]
d = dict()
d['beta'] = (rgamma, {'shape': 100.0, 'rate': 200.0})
d['gamma'] = (rgamma, (100.0, 300.0))
odeS.setParameters(d).setInitialValue(x0, t0)
# now we generate the solutions
solutionDiff = odeS.simulateParam(t[1::], 1000) - solutionReference
# test :)
if numpy.any(abs(solutionDiff) >= 0.2):
raise Exception("Possible problem with simulating the parameters")
def test_simulateParam2(self):
'''
Stochastic ode under the interpretation that the parameters follow
some sort of distribution. In this case, a function handle which
has the same name as R
'''
t0 = 0
# the initial state, normalized to zero one
x0 = [1,1.27e-6,0]
# set the time sequence that we would like to observe
t = numpy.linspace(0, 150, 100)
# Standard. Find the solution.
ode = common_models.SIR()
ode.setParameters([0.5,1.0/3.0])
ode.setInitialValue(x0,t0)
solutionReference = ode.integrate(t[1::],full_output=False)
# now we need to define our ode explicitly
stateList = ['S','I','R']
paramList = ['beta','gamma']
transitionList = [
Transition(origState='S',destState='I',equation='beta * S * I',transitionType=TransitionType.T),
Transition(origState='I',destState='R',equation='gamma * I',transitionType=TransitionType.T)
]
# our stochastic version
odeS = SimulateOdeModel(stateList,
paramList,
transitionList=transitionList)
# define our parameters in terms of two gamma distributions
# where the expected values are the same as before [0.5,1.0/3.0]
d = dict()
d['beta'] = (rgamma,{'shape':100.0,'rate':200.0})
d['gamma'] = (rgamma,(100.0,300.0))
odeS.setParameters(d).setInitialValue(x0,t0)
# now we generate the solutions
solutionDiff = odeS.simulateParam(t[1::],1000) - solutionReference
# test :)
if numpy.any(abs(solutionDiff)>=0.2):
raise Exception("Possible problem with simulating the parameters")
