def test_gaussian_1D_1mode_train():
# create some example data
num = 10000
mu = 3
sigma = 1
samples = be.randn((num, 1)) * sigma + mu
# set up the reader to get minibatches
batch_size = 100
samples_train, samples_validate = batch.split_tensor(samples, 0.9)
data = batch.Batch({
'train':
batch.InMemoryTable(samples_train, batch_size),
'validate':
batch.InMemoryTable(samples_validate, batch_size)
})
# parameters
learning_rate = schedules.PowerLawDecay(initial=0.1, coefficient=0.1)
mc_steps = 1
num_epochs = 10
num_sample_steps = 100
# set up the model and initialize the parameters
vis_layer = layers.GaussianLayer(1)
hid_layer = layers.OneHotLayer(1)
rbm = BoltzmannMachine([vis_layer, hid_layer])
rbm.initialize(data, method='hinton')
# modify the parameters to shift the initialized model from the data
# this forces it to train
rbm.layers[0].params = layers.ParamsGaussian(
rbm.layers[0].params.loc - 3, rbm.layers[0].params.log_var - 1)
# set up the optimizer and the fit method
opt = optimizers.ADAM(stepsize=learning_rate)
cd = fit.SGD(rbm, data)
# fit the model
print('training with persistent contrastive divergence')
cd.train(opt, num_epochs, method=fit.pcd, mcsteps=mc_steps)
# sample data from the trained model
model_state = \
samplers.SequentialMC.generate_fantasy_state(rbm, num, num_sample_steps)
pts_trained = model_state[0]
percent_error = 10
mu_trained = be.mean(pts_trained)
assert numpy.abs(mu_trained / mu - 1) < (percent_error / 100)
sigma_trained = numpy.sqrt(be.var(pts_trained))
assert numpy.abs(sigma_trained / sigma - 1) < (percent_error / 100)
def test_mean_variance():
# create some random data
s = be.rand((100000, ))
# reference result
ref_mean = be.mean(s)
ref_var = be.var(s)
# do the online calculation
mv = math_utils.MeanVarianceCalculator()
for i in range(10):
mv.update(s[i * 10000:(i + 1) * 10000])
assert be.allclose(be.float_tensor(np.array([ref_mean])),
be.float_tensor(np.array([mv.mean])))
assert be.allclose(be.float_tensor(np.array([ref_var])),
be.float_tensor(np.array([mv.var])),
rtol=1e-4,
atol=1e-7)
def test_mean_variance_2d():
# create some random data
num = 10000
dim2 = 10
num_steps = 10
stepsize = num // num_steps
s = be.rand((num,dim2))
# reference result
ref_mean = be.mean(s, axis=0)
ref_var = be.var(s, axis=0)
# do the online calculation
mv = math_utils.MeanVarianceArrayCalculator()
for i in range(num_steps):
mv.update(s[i*stepsize:(i+1)*stepsize])
assert be.allclose(ref_mean, mv.mean)
assert be.allclose(ref_var, mv.var, rtol=1e-3, atol=1e-5)
def test_mean_variance():
# create some random data
num = 100000
num_steps = 10
stepsize = num // num_steps
s = be.rand((num,))
# reference result
ref_mean = be.mean(s)
ref_var = be.var(s)
# do the online calculation
mv = math_utils.MeanVarianceCalculator()
for i in range(num_steps):
mv.update(s[i*stepsize:(i+1)*stepsize])
assert be.allclose(be.float_tensor(np.array([ref_mean])),
be.float_tensor(np.array([mv.mean])))
assert be.allclose(be.float_tensor(np.array([ref_var])),
be.float_tensor(np.array([mv.var])),
rtol=1e-3, atol=1e-5)