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()
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()
