from __future__ import division, print_function
from matplotlib import pyplot as plt
import numpy as np
# import seaborn as sb
import particles
from particles import distributions as dists
from particles import state_space_models
# set up models, simulate and save data
T = 100
mu0 = 0.
phi0 = 0.9
sigma0 = .5 # true parameters
ssm = state_space_models.DiscreteCox(mu=mu0, phi=phi0, sigma=sigma0)
true_states, data = ssm.simulate(T)
fkmod = state_space_models.Bootstrap(ssm=ssm, data=data)
# run particle filter, compute trajectories
N = 100
pf = particles.SMC(fk=fkmod, N=N, store_history=True)
pf.run()
Bs = pf.hist.compute_trajectories()
# PLOT
# ====
# sb.set_palette("dark")
plt.style.use('ggplot')
savefigs = True # False if you don't want to save plots as pdfs
from __future__ import division, print_function
from matplotlib import pyplot as plt
import numpy as np
# import seaborn as sb
import particles
from particles import distributions as dists
from particles import state_space_models as ssm
# set up models, simulate and save data
T = 100
mu0 = 0.
phi0 = 0.9
sigma0 = .5 # true parameters
my_ssm = ssm.DiscreteCox(mu=mu0, phi=phi0, sigma=sigma0)
true_states, data = my_ssm.simulate(T)
fkmod = ssm.Bootstrap(ssm=my_ssm, data=data)
# run particle filter, compute trajectories
N = 100
pf = particles.SMC(fk=fkmod, N=N, store_history=True)
pf.run()
pf.hist.compute_trajectories()
# PLOT
# ====
# sb.set_palette("dark")
plt.style.use('ggplot')
savefigs = False