def main(num_samples, burn, lag, w):
alpha = 1.0
N = 25
Z, Nk, K = utils.crp_gen(N, alpha)
# crp with gamma prior
log_prior_fun = lambda a: -a
log_pdf_lambda = lambda a : utils.unorm_lcrp_post(a, N, K, log_prior_fun)
proposal_fun = lambda : gamrnd(1.0,1.0)
D = (0, float('Inf'))
samples = su.slice_sample(proposal_fun, log_pdf_lambda, D, num_samples=num_samples, burn=burn, lag=lag, w=w)
minval = min(samples)
maxval = max(samples)
xvals = numpy.linspace(minval, maxval, 100)
yvals = numpy.array([ math.exp(log_pdf_lambda(x)) for x in xvals])
yvals /= trapz(xvals, yvals)
ax=pylab.subplot(2,1,1)
pylab.hist(X,50,normed=True)
ax_1=pylab.subplot(2,1,2)
pylab.hist(samples,100,normed=True)
pylab.plot(xvals,-yvals,c='red',lw=3, alpha=.8)
pylab.xlim(ax.get_xlim())
pylab.ylim(ax.get_ylim())
pylab.show()
def __transition_state_alpha(self):
logps = numpy.zeros(self.n_grid)
for i in range(self.n_grid):
alpha = self.alpha_grid[i]
logps[i] = utils.unorm_lcrp_post(alpha, self.n_cols, self.V, lambda x: 0)
# log_pdf_lambda = lambda a : utils.lcrp(self.n_cols, self.Nv, a) + self.alpha_prior_lambda(a)
index = utils.log_pflip(logps)
self.alpha = self.alpha_grid[index]
def transition_alpha(self):
"""
Calculate CRP alpha conditionals over grid and transition
"""
logps = numpy.zeros(len(self.alpha_grid))
for i in range(len(self.alpha_grid)):
alpha = self.alpha_grid[i]
logps[i] = utils.unorm_lcrp_post(alpha, self.N, self.K, lambda x: 0)
# log_pdf_lambda = lambda a : utils.lcrp(self.n_cols, self.Nv, a) + self.alpha_prior_lambda(a)
index = utils.log_pflip(logps)
self.alpha = self.alpha_grid[index]
def transition_alpha(self):
"""
Calculate CRP alpha conditionals over grid and transition
"""
logps = numpy.zeros(len(self.alpha_grid))
for i in range(len(self.alpha_grid)):
alpha = self.alpha_grid[i]
logps[i] = utils.unorm_lcrp_post(alpha, self.N, self.K,
lambda x: 0)
# log_pdf_lambda = lambda a : utils.lcrp(self.n_cols, self.Nv, a) + self.alpha_prior_lambda(a)
index = utils.log_pflip(logps)
self.alpha = self.alpha_grid[index]
