def main():
"""Run a Markov chain Monte Carlo to fit a high-dimensional pdf"""
# fit the data -- sigma sampled in log
np.random.seed()
p0 = []
for i in range(dim):
p0.append([-2.,2.])
samps,post,frac = mcmc.mcfit(logpost,p0,burnin=5000,N=3000,outfile='test.mcmc')
# pl.figure()
# pl.plot(post)
vtens_est = np.cov(samps.T)
# print np.sum(vtens_est-vtensor)
for i in range(dim):
pl.figure().add_subplot(111)
# x = np.dot(samps,evecs[:,i])
# pl.hist(x,100,normed=True,histtype="step",color="g")
x = samps
x = x[x < 3.*np.sqrt(evals[i])]
x = x[x > -3.*np.sqrt(evals[i])]
pl.hist(x,100,normed=True,histtype="step",color="k")
xs = np.linspace(-3.*np.sqrt(evals[i]),3.*np.sqrt(evals[i]),500)
pl.plot(xs,np.exp(-xs**2/evals[i]/2)/np.sqrt(2*np.pi*evals[i]))
pl.xlim([-3.*np.sqrt(evals[i]),3.*np.sqrt(evals[i])])
pl.show()
def sampleAlpha(x):
nStep = 30
pars = -2. * np.zeros(nStep)
if len(x) > 0:
imax = np.ceil(np.max(x) * nStep).astype('int')
pars[imax:] = pars[imax:] - 2.
p0 = []
for i in range(len(pars)):
p0.append([pars[i]-0.001,pars[i]+0.001])
parsChain,lnPChain,afrac = mcmc.mcfit(lnPosterior,p0,burnin=0,N=3000,
args=(x,),outfile='foo.mcmc')
return parsChain
