# main program
random.seed(12345) # seed or the random number generator
# loads data from file
data = loadtxt(os.path.join(datadir,'count.txt'), skiprows = 1)
yvec = data[:, 0]
xmat = data[:, 1:data.shape[1]]
xmat = hstack([ones((data.shape[0], 1)), xmat])
data ={'yvec':yvec, 'xmat':xmat}
# use bayesian regression to initialise
bayesreg = BayesRegression(yvec, xmat)
sig, beta0 = bayesreg.posterior_mean()
init_beta, info = leastsq(minfunc, beta0, args = (yvec, xmat))
data['betaprec'] =-llhessian(data, init_beta)
scale = linalg.inv(data['betaprec'])
# Initialise the random walk MH algorithm
samplebeta = RWMH(posterior, scale, init_beta, 'beta')
ms = MCMC(20000, 4000, data, [samplebeta],
loglike = (logl, xmat.shape[1], 'yvec'))
ms.sampler()
ms.output()
ms.plot('beta')
sum = zeros((kreg, kreg))
for i in xrange(nobs):
sum = sum + lamb[i] * outer(store['xmat'][i], store['xmat'][i])
return -sum
# main program
random.seed(12345) # seed or the random number generator
data = loadtxt(os.path.join(datadir,'count.txt'), skiprows = 1) # loads data from file
yvec = data[:, 0]
xmat = data[:, 1:data.shape[1]]
xmat = hstack([ones((data.shape[0], 1)), xmat])
data ={'yvec':yvec, 'xmat':xmat}
bayesreg = BayesRegression(yvec, xmat) # use bayesian regression to initialise
# nonlinear least squares algorithm
sig, beta0 = bayesreg.posterior_mean()
init_beta, info = leastsq(minfunc, beta0, args = (yvec, xmat))
data['betaprec'] =-llhessian(data, init_beta)
scale = linalg.inv(data['betaprec'])
samplebeta = RWMH(posterior, scale, init_beta, 'beta')
ms = MCMC(20000, 4000, data, [samplebeta], loglike = (logl, xmat.shape[1], 'yvec'))
ms.sampler()
ms.output(filename='example1c.out')
ms.plot('beta', filename='ex_loglinear.pdf')
# ms.CODAoutput('beta')
# ms.plot('beta', elements = [0], plottypes ="trace", filename ="xx.pdf")
# ms.plot('beta', elements = [0], plottypes ="density", filename ="xx.png")
## ms.plot('beta', elements = [0], plottypes ="acf", filename ="yy.ps")
priorreg = ("g_prior", zeros(kreg), 1000.0)
regs = BayesRegression(yvec, xmat, prior=priorreg)
"""A dictionary is set up. The contents of the dictionary will be
available for use for by the functions that make up the MCMC sampler.
Note that we pass in storage space as well as the class intance used
to sample the regression from."""
data = {"yvec": yvec, "xmat": xmat, "regsampler": regs}
U = spmatrix.ll_mat(nobs, nobs, 2 * nobs - 1)
U.put(1.0, range(0, nobs), range(0, nobs))
data["yvectil"] = zeros(nobs)
data["xmattil"] = zeros((nobs, kreg))
data["Upper"] = U
# Use Bayesian regression to initialise MCMC sampler
bayesreg = BayesRegression(yvec, xmat)
sig, beta = bayesreg.posterior_mean()
simsigbeta = CFsampler(WLS, [sig, beta], ["sigma", "beta"])
rho = 0.9
simrho = SliceSampler([post_rho], 0.1, 5, rho, "rho")
blocks = [simrho, simsigbeta]
loglikeinfo = (loglike, kreg + 2, "yvec")
ms = MCMC(10000, 2000, data, blocks, loglike=loglikeinfo)
ms.sampler()
ms.output()
ms.plot("rho")
to sample the regression from."""
data ={'yvec':yvec, 'xmat':xmat, 'regsampler':regs}
U = spmatrix.ll_mat(nobs, nobs, 2 * nobs - 1)
U.put(1.0, range(0, nobs), range(0, nobs))
data['yvectil'] = zeros(nobs)
data['xmattil'] = zeros((nobs, kreg))
data['Upper'] = U
# Use Bayesian regression to initialise MCMC sampler
bayesreg = BayesRegression(yvec, xmat)
sig, beta = bayesreg.posterior_mean()
simsigbeta = CFsampler(WLS, [sig, beta], ['sigma', 'beta'])
scale = 0.002 # tuning parameter for RWMH
rho = 0.9
##rho = [1] ## to test exception handling
# simrho = RWMH(post_rho, scale, rho, 'rho')
simrho = SliceSampler([post_rho], 0.1, 5, rho, 'rho')
#simrho = OBMC(post_rho, 3, scale, rho, 'rho')
# simrho = MH(gencand, post_rho, probcandgprev, probprevgcand, rho, 'rho')
blocks = [simrho, simsigbeta]
loglikeinfo = (loglike, kreg + 2, 'yvec')
ms = MCMC(10000, 2000, data, blocks, loglike = loglikeinfo)
ms.sampler()
ms.output()
#ms.plot('sigbeta')
ms.plot('rho', filename ='rho')
ms.CODAoutput(parameters = ['rho'])
