def get_prior():
prior_dict = {
'log_eta': dists.LogD(dists.Gamma(a=.1, b=.1)),
'logit_sigma': dists.LogitD(dists.Beta(a=1., b=1.)),
'log_c': dists.LogD(dists.Gamma(a=.1, b=.1))
}
return dists.StructDist(prior_dict)
def get_prior():
prior_dict = {
'lam': dists.Normal(loc=0., scale=1.),#W
'log_alpha': dists.LogD(dists.Gamma(a=.1, b=.1)),#W
'log_delta': dists.LogD(dists.Gamma(a=.1, b=.1)),
}
return dists.StructDist(prior_dict)
def get_prior():
prior_dict = {
'log_eta': dists.LogD(dists.Gamma(a=.1, b=.1)), #W
'log_tau_minus_one': dists.LogD(dists.Uniform(a=0., b=4.)), #W
'logit_sigma': dists.LogitD(dists.Beta(a=1., b=1.)),
'log_c': dists.LogD(dists.Gamma(a=.1, b=.1)) #W
}
return dists.StructDist(prior_dict)
def get_prior():
prior_dict = {
'log_eta': dists.LogD(dists.Gamma(a=0.1, b=0.1)),
'log_tau_minus_one': dists.LogD(dists.Gamma(a=1.0, b=1.0)),
'log_c': dists.LogD(dists.Gamma(a=0.1, b=0.1)),
'log_lam': dists.LogD(dists.Gamma(a=0.1, b=0.1))
}
#prior_dict = {
# 'log_eta':dists.LogD(dists.Gamma(a=1.0, b=1.0)),
# 'log_tau_minus_one':dists.LogD(dists.Gamma(a=1.0, b=1.0)),
# 'log_c':dists.LogD(dists.Gamma(a=1.0, b=1.0)),
# 'log_lam':dists.LogD(dists.Gamma(a=1.0, b=1.0))}
return dists.StructDist(prior_dict)
self.sigmaY = 1. / np.sqrt(self.precY)
ssm_cls = ThetaLogisticReparametrised
# data
data = np.loadtxt('../../datasets/nutria.txt')
# prior
dict_prior = OrderedDict()
dict_prior['tau0'] = dists.TruncNormal(b=3.)
dict_prior['tau1'] = dists.TruncNormal(b=3.)
dict_prior['tau2'] = dists.TruncNormal(b=3.)
dict_prior['precX'] = dists.Gamma(a=2., b=1.)
dict_prior['precY'] = dists.Gamma(a=2., b=1.)
prior = dists.StructDist(dict_prior)
# Particle Gibbs
class PGibbs(mcmc.ParticleGibbs):
def update_theta(self, theta, x):
new_theta = theta.copy()
ax = np.array(x)
dax = np.diff(ax)
ay = np.array(data).flatten()
tau0 = theta['tau0']
tau1 = theta['tau1']
tau2 = theta['tau2']
# update precisions
deltaX = dax - tau0 + tau1 * np.exp(tau2 * ax[:-1])
if dataset_name == 'sonar':
N = 10**4
Ms = [10, 20, 30, 40, 50, 60]
typM = 50
elif dataset_name == 'pima':
N = 10**3
Ms = [1, 3, 5]
typM = 3
elif dataset_name == 'eeg':
N = 10**3
Ms = [1, 3, 5, 7, 10, 15, 20]
typM = 5
# prior & model
prior = dists.StructDist({'beta': dists.MvNormal(scale=5., cov=np.eye(p))})
class LogisticRegression(smc_samplers.StaticModel):
def logpyt(self, theta, t):
# log-likelihood factor t, for given theta
lin = np.matmul(theta['beta'], data[t, :])
return -np.logaddexp(0., -lin)
# algorithms
# N and values of M set above according to dataset
ESSrmin = 0.5
nruns = 16
results = []
sigma, rho = 0.2, 0.95 # fixed values
xlag = np.array(x[1:] + [
0.,
])
dx = (x - rho * xlag) / (1. - rho)
s = sigma / (1. - rho)**2
new_theta['mu'] = self.prior.laws['mu'].posterior(dx, sigma=s).rvs()
return new_theta
prior_dict = {
'mu': dists.Normal(scale=2.),
'rho': dists.Uniform(a=-1., b=1.),
'sigma': dists.Gamma()
}
my_prior = dists.StructDist(prior_dict)
# real data
raw_data = np.loadtxt('data.txt', skiprows=2, usecols=(3, ), comments='(C)')
full_data = np.diff(np.log(raw_data))
data = full_data[:50]
pg = PGStochVol(ssm_cls=StochVol,
data=data,
prior=my_prior,
Nx=200,
niter=1000)
pg.run() # may take several seconds...
# plt.plot(pg.chain.theta['mu'])
# plt.xlabel('iter')
def get_prior():
prior_dict = {
'log_alpha': dists.LogD(dists.Gamma(a=.1, b=.1)),
'log_lambda': dists.LogD(dists.Gamma(a=.1, b=.1))
}
return dists.StructDist(prior_dict)
def get_prior():
prior_dict = {
'log_half_nu_minus_one': dists.LogD(dists.Gamma(a=1., b=1.)),
'log_sigma': dists.LogD(dists.Gamma(a=.1, b=.1))
}
return dists.StructDist(prior_dict)
def get_prior():
prior_dict = {
'log_eta':dists.LogD(dists.Gamma(a=0.1, b=0.1)),
'log_c':dists.LogD(dists.Gamma(a=0.1, b=0.1)),
'log_lam':dists.LogD(dists.Gamma(a=0.1, b=0.1))}
return dists.StructDist(prior_dict)
import warnings
warnings.simplefilter('ignore') # hide warnings
from matplotlib import pyplot as plt
import numpy as np
from particles import distributions as dists
prior_dict = {
'mu': dists.Normal(scale=2.),
'rho': dists.Uniform(a=-1., b=1.),
'sigma': dists.Gamma()
}
my_prior = dists.StructDist(prior_dict)
theta = my_prior.rvs(size=500) # sample 500 theta-parameters
plt.style.use('ggplot')
plt.hist(theta['sigma'], 30)
plt.xlabel('sigma')
plt.figure()
z = my_prior.logpdf(theta)
plt.hist(z, 30)
plt.xlabel('log-pdf')
plt.figure()
another_prior_dict = {
'rho': dists.Uniform(a=-1., b=1.),
'log_sigma': dists.LogD(dists.Gamma())
