t, series, steps, events = M.getStats()
ser = st.nanmean(series, axis=0)
# print series.shape, ser.shape
return ser
d = runModel([beta, alpha, sigma])
# ~ import pylab as P
# ~ P.plot(d)
# ~ P.show()
dt = {'S': d[:, 0], 'E': d[:, 1], 'I': d[:, 2], 'A': d[:, 3], 'R': d[:, 4]}
F = FitModel(900,
runModel,
inits,
tf, ['beta', 'alpha', 'sigma'], ['S', 'E', 'I', 'A', 'R'],
wl=140,
nw=1,
verbose=0,
burnin=100)
F.set_priors(tdists=[st.uniform] * 3,
tpars=[(0.00001, .0006), (.1, .5), (0.0006, 1)],
tlims=[(0, .001), (.001, 1), (0, 1)],
pdists=[st.uniform] * 5,
ppars=[(0, 500)] * 5,
plims=[(0, 500)] * 5)
F.run(dt, 'MCMC', likvar=1e1, pool=0, monitor=[])
# ~ print F.optimize(data=dt,p0=[0.1,.5,.1], optimizer='oo',tol=1e-55, verbose=1, plot=1)
# ==Uncomment the line below to see plots of the results
F.plot_results()
wl = fim - inicio
nw = 1
tpars = [(1, 1), (0, 5e-6), (.9999, .0002)]
tlims = [(0, 1), (0, 5e-6), (.9999, 1.0001)]
inits = [1 - dt['I'][0], dt['I'][0], 0]
dt2 = copy.deepcopy(dt)
# print inits
F = FitModel(5000,
model,
inits,
fim - inicio,
tnames,
pnames,
wl,
nw,
verbose=1,
burnin=1000,
constraints=[])
F.set_priors(tdists=[st.beta, st.uniform, st.uniform],
tpars=tpars,
tlims=tlims,
pdists=[st.beta] * nph,
ppars=[(1, 1)] * nph,
plims=[(0, 1)] * nph)
F.run(dt,
'DREAM',
likvar=1e-9,
P.vlines(t0s, 0, top, colors='g')
P.vlines(tfs, 0, top, colors='r')
P.legend([pnames[1]])
P.show()
#Priors and limits for all countries
tpars = [
(2, 1)
] * nt #[(1, 2),(1, 2),(1, 2),(1, 2),(1, 2), (2, 1),(1, 2),(1, 2), (2, 1), (1, 2),]
tlims = [(0, 1)] * nt
F = FitModel(1000,
model,
inits,
tf,
tnames,
pnames,
wl,
nw,
verbose=1,
burnin=200,
constraints=[])
F.set_priors(tdists=nt * [st.beta],
tpars=tpars,
tlims=tlims,
pdists=[st.beta] * nph,
ppars=[(1, 1)] * nph,
plims=[(0, 1)] * nph)
F.run(dt,
'DREAM',
likvar=1e-4,
'''ODE model'''
S, I, R = y
return [
-beta * I * S, #dS/dt
beta * I * S - tau * I, #dI/dt
tau * I
] #dR/dt
y = odeint(sir, inits, np.arange(0, tf, 1)) #np.arange(t0,tf,step))
return y
F = FitModel(300,
model,
y0,
tf, ['beta'], ['S', 'I', 'R'],
wl=36,
nw=1,
verbose=1,
burnin=100)
F.set_priors(tdists=[st.norm],
tpars=[(1.1, .2)],
tlims=[(0.5, 1.5)],
pdists=[st.uniform] * 3,
ppars=[(0, .1), (0, .1), (.8, .2)],
plims=[(0, 1)] * 3)
d = model([1.0]) #simulate some data
noise = st.norm(0, 0.01).rvs(36)
dt = {'I': d[:, 1] + noise} # add noise
F.run(dt, 'MCMC', likvar=1e-5, pool=True, monitor=[])
#==Uncomment the line below to see plots of the results
F.plot_results()
propf = (f1, f2, f3, f4, f5)
tmat = np.array([[-1, 0, 0, 0, 0],
[1, -1, 0, -1, 0],
[0, 1, -1, 0, 0],
[0, 0, 0, 1, -1],
[0, 0, 1, 0, 1]
])
M = Model(vnames=vnames, rates=r, inits=inits, tmat=tmat, propensity=propf)
#t0 = time.time()
M.run(tmax=tf, reps=1, viz=0, serial=True)
t, series, steps, events = M.getStats()
ser = st.nanmean(series, axis=0)
#print series.shape
return ser
d = runModel([beta, alpha, sigma])
dt = {'S': d[:, 0], 'E': d[:, 1], 'I': d[:, 2], 'A': d[:, 3], 'R': d[:, 4]}
F = FitModel(300, runModel, inits, tf, ['beta', 'alpha', 'sigma'], ['S', 'E', 'I', 'A', 'R'],
wl=7, nw=20, verbose=True, burnin=100)
F.set_priors(tdists=[st.uniform] * 3, tpars=[(0.00001, .0006), (.1, .5), (0.0006, 1)],
tlims=[(0, .001), (.001, 1), (0, 1)],
pdists=[st.uniform] * 5, ppars=[(0, 500)] * 5, plims=[(0, 500)] * 5)
F.run(dt, 'MCMC', likvar=1e1, pool=0, monitor=[])
#~ print F.optimize(data=dt,p0=[0.1,.5,.1], optimizer='oo',tol=1e-55, verbose=1, plot=1)
#==Uncomment the line below to see plots of the results
F.plot_results()
