def __init__(self,
name='',
display_name=None,
mu=0.0,
sigma=1.0,
alpha=1.0,
beta=1.0,
nchains=1):
self.name = name
if display_name is None:
display_name = self.name
self.display_name = display_name
# save the priors
self._mu = mu
self._sigma = sigma
self._alpha = alpha
self._beta = beta
# save number of chains
self._nchains = nchains
# set initial values
self._mean = normal(mu, sigma).rvs(nchains)
self._std = np.sqrt(invgamma(alpha, beta).rvs(nchains))
def _update_std(self, posts):
"""
Update the current std.
"""
if len(posts) != self._nchains:
raise ValueError("You must update all %d chains simultaneously." % self._nchains)
for i in range(self._nchains):
# update.sigma=function(x,use.core,use.mu,prior){
# require(MCMCpack)
# X=use.core[x,]
X = np.array(posts[i]).flatten()
# n=length(X)
n = len(X)
# alpha=prior$alpha
alpha = self._alpha
# beta=prior$beta
beta = self._beta
# mu=use.mu[x]
mu = self._mean[i]
# a=alpha+n/2
a = alpha + n / 2.0
# b=beta+sum((X-mu)**2)/2
b = beta + ((X - mu) ** 2).sum() / 2.0
# sqrt(rinvgamma(1,a,b))
self._std[i] = np.sqrt(invgamma(a, b).rvs())
return self._std
def _update_std(self, posts):
"""
Update the current std.
"""
if len(posts) != self._nchains:
raise ValueError("You must update all %d chains simultaneously." %
self._nchains)
for i in range(self._nchains):
# update.sigma=function(x,use.core,use.mu,prior){
# require(MCMCpack)
# X=use.core[x,]
X = np.array(posts[i]).flatten()
# n=length(X)
n = len(X)
# alpha=prior$alpha
alpha = self._alpha
# beta=prior$beta
beta = self._beta
# mu=use.mu[x]
mu = self._mean[i]
# a=alpha+n/2
a = alpha + n / 2.
# b=beta+sum((X-mu)**2)/2
b = beta + ((X - mu)**2).sum() / 2.
# sqrt(rinvgamma(1,a,b))
self._std[i] = np.sqrt(invgamma(a, b).rvs())
return self._std
def __init__(self, name="", display_name=None, mu=0.0, sigma=1.0, alpha=1.0, beta=1.0, nchains=1):
self.name = name
if display_name is None:
display_name = self.name
self.display_name = display_name
# save the priors
self._mu = mu
self._sigma = sigma
self._alpha = alpha
self._beta = beta
# save number of chains
self._nchains = nchains
# set initial values
self._mean = normal(mu, sigma).rvs(nchains)
self._std = np.sqrt(invgamma(alpha, beta).rvs(nchains))