"""
from __future__ import division, print_function
from collections import OrderedDict
from matplotlib import pyplot as plt
import numpy as np
import seaborn as sb
import particles
from particles import kalman
from particles import state_space_models as ssms
# Define ssm, simulate data
T = 100
my_ssm = kalman.LinearGauss(sigmaX=1., sigmaY=.2, rho=.9)
true_states, data = my_ssm.simulate(T)
# FK models
models = OrderedDict()
models['bootstrap'] = ssms.Bootstrap(ssm=my_ssm, data=data)
models['guided'] = ssms.GuidedPF(ssm=my_ssm, data=data)
models['APF'] = ssms.AuxiliaryPF(ssm=my_ssm, data=data)
# Uncomment line below if you want to include the "Boostrap APF"
# (APF with proposal set to dist. of X_t|X_{t-1}) in the comparison
#models['bootAPF'] = ssm.AuxiliaryBootstrap(ssm=my_ssm, data=data)
# Compute "truth"
kf = kalman.Kalman(ssm=my_ssm, data=data)
kf.filter()
true_loglik = np.cumsum(kf.logpyt)
from matplotlib import pyplot as plt
import numpy as np
import particles
from particles import kalman
from particles import state_space_models
# parameter values
sigmaX = 1.
sigmaY = .2
rho = 0.9
T = 1000
N = 200
# define ss model, simulate data
ssm = kalman.LinearGauss(sigmaX=sigmaX, sigmaY=sigmaY, rho=rho)
true_states, data = ssm.simulate(T)
# computes true log-likelihood
kf = kalman.Kalman(ssm=ssm, data=data)
kf.filter()
true_loglik = np.sum(kf.logpyt)
# FK model
fk_model = state_space_models.GuidedPF(ssm=ssm, data=data)
# Run SMC algorithm for different values of ESS_min
alphas = list(np.linspace(0., .1, 11)) + list(np.linspace(0.15, 1., 18))
results = particles.multiSMC(fk=fk_model,
N=N,
ESSrmin=alphas,
from particles import kalman
# Multivariate model
T = 15
dx, dy = 3, 2
F = np.eye(dx) + 0.01 * random.randn(dx, dx)
G = np.eye(dy, dx) + 0.02 * random.randn(dy, dx)
covX = np.eye(dx) + 0.2 * np.ones((dx, dx))
covY = 0.3 * np.eye(dy)
mu0 = np.ones(dx)
cov0 = 2. * covX
mv_ssm = kalman.MVLinearGauss(F=F, G=G, covX=covX, covY=covY, cov0=cov0, mu0=mu0)
# Univariate model
rho, sigX, sigY, sig0 = 0.9, 1., 0.2, 3.
uni_ssm = kalman.LinearGauss(rho=rho, sigmaX=sigX, sigmaY=sigY, sigma0=sig0)
univariate = False # test univariate or multivariate model?
if univariate:
ssm = uni_ssm
F, G, mu0 = rho, 1., 0.
covX, covY, cov0 = sigX**2, sigY**2, sig0**2
else:
ssm = mv_ssm
# data
x, y = ssm.simulate(T)
# Our Kalman filter
mykf = kalman.Kalman(ssm=ssm, data=y)
mykf.smoother() # this does both filtering and smoothing