# 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')
yvec = data[:, 0]
xmat = data[:, 1:20]
xmat = hstack([ones((xmat.shape[0], 1)), xmat])
"""data is a dictionary whose elements are accessible from the functions
in the MCMC sampler"""
data ={'yvec':yvec, 'xmat':xmat}
prior = ['g_prior',zeros(xmat.shape[1]), 100.]
SSVS = StochasticSearch(yvec, xmat, prior);
data['SS'] = SSVS
"""initialise gamma"""
initgamma = zeros(xmat.shape[1], dtype ='i')
initgamma[0] = 1
simgam = CFsampler(samplegamma, initgamma, 'gamma', store ='all')
# initialise class for MCMC samper
random.seed(12346)
ms = MCMC(20000, 5000, data, [simgam])
ms.sampler()
ms.output()
ms.output(custom = SSVS.output)
txmat = SSVS.extract_regressors(0)
g_prior = ['g_prior', 0.0, 100.]
breg = BayesRegression(yvec,txmat,prior = g_prior)
breg.output()
breg.plot()
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")
data = loadtxt(os.path.join(datadir,'yld2.txt'))
yvec = data[:, 0]
xmat = data[:, 1:20]
xmat = hstack([ones((xmat.shape[0], 1)), xmat])
"""data is a dictionary whose elements are accessible from the functions
in the MCMC sampler"""
data ={'yvec':yvec, 'xmat':xmat}
prior = ['g_prior',zeros(xmat.shape[1]), 100.]
SSVS = StochasticSearch(yvec, xmat, prior);
data['SS'] = SSVS
"""initialise gamma"""
initgamma = zeros(xmat.shape[1], dtype ='i')
initgamma[0] = 1
simgam = CFsampler(samplegamma, initgamma, 'gamma', store ='none')
# initialise class for MCMC samper
ms = MCMC(20000, 5000, data, [simgam])
ms.sampler()
ms.output(filename ='vs.txt')
ms.output(custom = SSVS.output, filename = 'SSVS.out')
ms.output(custom = SSVS.output)
txmat = SSVS.extract_regressors(0)
g_prior = ['g_prior', 0.0, 100.]
breg = BayesRegression(yvec,txmat,prior = g_prior)
breg.output(filename = 'SSVS1.out')
breg.plot()
