def ais_nodata(fname, do_exact=True):
rbm_params = load_rbm_params(fname)
# ais estimate using tempered models as intermediate distributions
t1 = time.time()
(logz, log_var_dz), aisobj = rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123)
print 'AIS logZ : %f' % logz
print ' log_variance : %f' % log_var_dz
print 'Elapsed time: ', time.time() - t1
if do_exact:
exact_logz = compute_logz(rbm_params)
print 'Exact logZ = %f' % exact_logz
numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
def ais_nodata(fname, do_exact=True):
rbm_params = load_rbm_params(fname)
# ais estimate using tempered models as intermediate distributions
t1 = time.time()
(logz, log_var_dz), aisobj = rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123)
print "AIS logZ : %f" % logz
print " log_variance : %f" % log_var_dz
print "Elapsed time: ", time.time() - t1
if do_exact:
exact_logz = compute_logz(rbm_params)
print "Exact logZ = %f" % exact_logz
# accept less than 1% error
assert abs(exact_logz - logz) < 0.01 * exact_logz
def ais_nodata(fname, do_exact=True):
rbm_params = load_rbm_params(fname)
# ais estimate using tempered models as intermediate distributions
t1 = time.time()
(logz, log_var_dz), aisobj = rbm_tools.rbm_ais(rbm_params,
n_runs=100,
seed=123)
print 'AIS logZ : %f' % logz
print ' log_variance : %f' % log_var_dz
print 'Elapsed time: ', time.time() - t1
if do_exact:
exact_logz = compute_logz(rbm_params)
print 'Exact logZ = %f' % exact_logz
numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
def ais_nodata(fname, do_exact=True, betas=None):
rbm_params = load_rbm_params(fname)
# ais estimate using tempered models as intermediate distributions
t1 = time.time()
(logz, log_var_dz), aisobj = \
rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, betas=betas)
print 'AIS logZ : %f' % logz
print ' log_variance : %f' % log_var_dz
print 'Elapsed time: ', time.time() - t1
if do_exact:
exact_logz = compute_logz(rbm_params)
print 'Exact logZ = %f' % exact_logz
# accept less than 1% error
assert abs(exact_logz - logz) < 0.01*exact_logz
def ais_data(fname, do_exact=True):
rbm_params = load_rbm_params(fname)
# load data to set visible biases to ML solution
from pylearn.datasets import MNIST
dataset = MNIST.train_valid_test()
data = numpy.asarray(dataset.train.x, dtype=config.floatX)
# run ais using B=0 model with ML visible biases
t1 = time.time()
(logz, log_var_dz), aisobj = rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, data=data)
print 'AIS logZ : %f' % logz
print ' log_variance : %f' % log_var_dz
print 'Elapsed time: ', time.time() - t1
if do_exact:
exact_logz = compute_logz(rbm_params)
print 'Exact logZ = %f' % exact_logz
numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
def ais_data(fname, do_exact=True):
rbm_params = load_rbm_params(fname)
# load data to set visible biases to ML solution
from pylearn2.datasets.mnist import MNIST
dataset = MNIST(which_set='train', one_hot=True)
data = numpy.asarray(dataset.X, dtype=config.floatX)
# run ais using B=0 model with ML visible biases
t1 = time.time()
(logz, log_var_dz), aisobj = \
rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, data=data)
print 'AIS logZ : %f' % logz
print ' log_variance : %f' % log_var_dz
print 'Elapsed time: ', time.time() - t1
if do_exact:
exact_logz = compute_logz(rbm_params)
print 'Exact logZ = %f' % exact_logz
numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
def ais_data(fname, do_exact=True, betas=None):
rbm_params = load_rbm_params(fname)
# load data to set visible biases to ML solution
from pylearn2.datasets.mnist import MNIST
dataset = MNIST(which_set='train')
data = numpy.asarray(dataset.X, dtype=config.floatX)
# run ais using B=0 model with ML visible biases
t1 = time.time()
(logz, log_var_dz), aisobj = \
rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, data=data,
betas=betas)
print('AIS logZ : %f' % logz)
print(' log_variance : %f' % log_var_dz)
print('Elapsed time: ', time.time() - t1)
if do_exact:
exact_logz = compute_logz(rbm_params)
print('Exact logZ = %f' % exact_logz)
numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
