def test_mogsm(self):
mcgsm = MCGSM(
dim_in=0,
dim_out=3,
num_components=2,
num_scales=2,
num_features=0)
p0 = 0.3
p1 = 0.7
N = 20000
m0 = array([[2], [0], [0]])
m1 = array([[0], [2], [1]])
C0 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
C1 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
input = zeros([0, N])
output = hstack([
dot(cholesky(C0), randn(mcgsm.dim_out, round(p0 * N))) + m0,
dot(cholesky(C1), randn(mcgsm.dim_out, round(p1 * N))) + m1]) * (rand(1, N) + 0.5)
mcgsm.train(input, output, parameters={
'verbosity': 0,
'max_iter': 10,
'train_means': True})
mogsm = MoGSM(3, 2, 2)
# translate parameters from MCGSM to MoGSM
mogsm.priors = sum(exp(mcgsm.priors), 1) / sum(exp(mcgsm.priors))
for k in range(mogsm.num_components):
mogsm[k].mean = mcgsm.means[:, k]
mogsm[k].covariance = inv(dot(mcgsm.cholesky_factors[k], mcgsm.cholesky_factors[k].T))
mogsm[k].scales = exp(mcgsm.scales[k, :])
mogsm[k].priors = exp(mcgsm.priors[k, :]) / sum(exp(mcgsm.priors[k, :]))
self.assertAlmostEqual(mcgsm.evaluate(input, output), mogsm.evaluate(output), 5)
mogsm_samples = mogsm.sample(N)
mcgsm_samples = mcgsm.sample(input)
# generated samples should have the same distribution
for i in range(mogsm.dim):
self.assertTrue(ks_2samp(mogsm_samples[i], mcgsm_samples[0]) > 0.0001)
self.assertTrue(ks_2samp(mogsm_samples[i], mcgsm_samples[1]) > 0.0001)
self.assertTrue(ks_2samp(mogsm_samples[i], mcgsm_samples[2]) > 0.0001)
posterior = mcgsm.posterior(input, mcgsm_samples)
# average posterior should correspond to prior
for k in range(mogsm.num_components):
self.assertLess(abs(1 - mean(posterior[k]) / mogsm.priors[k]), 0.1)
def main(argv):
# load image and turn into grayscale
img = rgb2gray(imread('media/newyork.png'))
# generate data
inputs, outputs = generate_data_from_image(
img, input_mask, output_mask, 220000)
# split data into training, test, and validation sets
inputs = split(inputs, [100000, 200000], 1)
outputs = split(outputs, [100000, 200000], 1)
data_train = inputs[0], outputs[0]
data_test = inputs[1], outputs[1]
data_valid = inputs[2], outputs[2]
# compute normalizing transformation
pre = WhiteningPreconditioner(*data_train)
# intialize model
model = MCGSM(
dim_in=data_train[0].shape[0],
dim_out=data_train[1].shape[0],
num_components=8,
num_scales=4,
num_features=32)
# fit parameters
model.initialize(*pre(*data_train))
model.train(*chain(pre(*data_train), pre(*data_valid)),
parameters={
'verbosity': 1,
'max_iter': 1000,
'threshold': 1e-7,
'val_iter': 5,
'val_look_ahead': 10,
'num_grad': 20,
})
# evaluate model
print 'Average log-likelihood: {0:.4f} [bit/px]'.format(
-model.evaluate(data_test[0], data_test[1], pre))
# synthesize a new image
img_sample = sample_image(img, model, input_mask, output_mask, pre)
imwrite('newyork_sample.png', img_sample,
cmap='gray',
vmin=min(img),
vmax=max(img))
# save model
with open('image_model.pck', 'wb') as handle:
dump({
'model': model,
'input_mask': input_mask,
'output_mask': output_mask}, handle, 1)
return 0
def test_evaluate(self): mcgsm = MCGSM(5, 3, 4, 2, 10) inputs = randn(mcgsm.dim_in, 100) outputs = mcgsm.sample(inputs) pre = WhiteningPreconditioner(inputs, outputs) loglik1 = -mcgsm.evaluate(inputs, outputs, pre) loglik2 = (mcgsm.loglikelihood(*pre(inputs, outputs)).mean() + pre.logjacobian(inputs, outputs).mean()) / log(2.) / mcgsm.dim_out self.assertAlmostEqual(loglik1, loglik2, 8)
def test_evaluate(self):
mcgsm = MCGSM(5, 3, 4, 2, 10)
inputs = randn(mcgsm.dim_in, 100)
outputs = mcgsm.sample(inputs)
pre = WhiteningPreconditioner(inputs, outputs)
loglik1 = -mcgsm.evaluate(inputs, outputs, pre)
loglik2 = (
mcgsm.loglikelihood(*pre(inputs, outputs)).mean() +
pre.logjacobian(inputs, outputs).mean()) / log(2.) / mcgsm.dim_out
self.assertAlmostEqual(loglik1, loglik2, 8)
def test_mogsm(self):
mcgsm = MCGSM(dim_in=0,
dim_out=3,
num_components=2,
num_scales=2,
num_features=0)
p0 = 0.3
p1 = 0.7
N = 20000
m0 = array([[2], [0], [0]])
m1 = array([[0], [2], [1]])
C0 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
C1 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
input = zeros([0, N])
output = hstack([
dot(cholesky(C0), randn(mcgsm.dim_out, round(p0 * N))) + m0,
dot(cholesky(C1), randn(mcgsm.dim_out, round(p1 * N))) + m1
]) * (rand(1, N) + 0.5)
mcgsm.train(input,
output,
parameters={
'verbosity': 0,
'max_iter': 10,
'train_means': True
})
mogsm = MoGSM(3, 2, 2)
# translate parameters from MCGSM to MoGSM
mogsm.priors = sum(exp(mcgsm.priors), 1) / sum(exp(mcgsm.priors))
for k in range(mogsm.num_components):
mogsm[k].mean = mcgsm.means[:, k]
mogsm[k].covariance = inv(
dot(mcgsm.cholesky_factors[k], mcgsm.cholesky_factors[k].T))
mogsm[k].scales = exp(mcgsm.scales[k, :])
mogsm[k].priors = exp(mcgsm.priors[k, :]) / sum(
exp(mcgsm.priors[k, :]))
self.assertAlmostEqual(mcgsm.evaluate(input, output),
mogsm.evaluate(output), 5)
mogsm_samples = mogsm.sample(N)
mcgsm_samples = mcgsm.sample(input)
# generated samples should have the same distribution
for i in range(mogsm.dim):
self.assertTrue(
ks_2samp(mogsm_samples[i], mcgsm_samples[0]) > 0.0001)
self.assertTrue(
ks_2samp(mogsm_samples[i], mcgsm_samples[1]) > 0.0001)
self.assertTrue(
ks_2samp(mogsm_samples[i], mcgsm_samples[2]) > 0.0001)
posterior = mcgsm.posterior(input, mcgsm_samples)
# average posterior should correspond to prior
for k in range(mogsm.num_components):
self.assertLess(abs(1 - mean(posterior[k]) / mogsm.priors[k]), 0.1)
def main(argv):
# load image and turn into grayscale
img = rgb2gray(imread('media/newyork.png'))
# generate data
inputs, outputs = generate_data_from_image(img, input_mask, output_mask,
220000)
# split data into training, test, and validation sets
inputs = split(inputs, [100000, 200000], 1)
outputs = split(outputs, [100000, 200000], 1)
data_train = inputs[0], outputs[0]
data_test = inputs[1], outputs[1]
data_valid = inputs[2], outputs[2]
# compute normalizing transformation
pre = WhiteningPreconditioner(*data_train)
# intialize model
model = MCGSM(dim_in=data_train[0].shape[0],
dim_out=data_train[1].shape[0],
num_components=8,
num_scales=4,
num_features=32)
# fit parameters
model.initialize(*pre(*data_train))
model.train(*chain(pre(*data_train), pre(*data_valid)),
parameters={
'verbosity': 1,
'max_iter': 1000,
'threshold': 1e-7,
'val_iter': 5,
'val_look_ahead': 10,
'num_grad': 20,
})
# evaluate model
print 'Average log-likelihood: {0:.4f} [bit/px]'.format(
-model.evaluate(data_test[0], data_test[1], pre))
# synthesize a new image
img_sample = sample_image(img, model, input_mask, output_mask, pre)
imwrite('newyork_sample.png',
img_sample,
cmap='gray',
vmin=min(img),
vmax=max(img))
# save model
with open('image_model.pck', 'wb') as handle:
dump(
{
'model': model,
'input_mask': input_mask,
'output_mask': output_mask
}, handle, 1)
return 0
