def test_sample_discrete_log():
assert_equal(sample_discrete_log([-1.]), 0)
assert_equal(sample_discrete_log([-1e3]), 0)
assert_equal(sample_discrete_log([-1e-3]), 0)
assert_equal(sample_discrete_log([-1., -1e3]), 0)
assert_equal(sample_discrete_log([-1e3, -1.]), 1)
assert_raises(Exception, sample_discrete_log, [])
def test_sample_discrete_log():
assert_equal(sample_discrete_log([-1.]), 0)
assert_equal(sample_discrete_log([-1e3]), 0)
assert_equal(sample_discrete_log([-1e-3]), 0)
assert_equal(sample_discrete_log([-1., -1e3]), 0)
assert_equal(sample_discrete_log([-1e3, -1.]), 1)
assert_raises(Exception, sample_discrete_log, [])
def compress_seq_gibbs(passes=PASSES):
'''
Compress image via sequentiall-initialized gibbs sampling.
'''
assert passes >= 1
assert os.path.exists(SAMPLES), 'first create dataset'
print 'seq+gibbs start {} passes'.format(passes)
model = ImageModel()
mixture = ImageModel.Mixture()
mixture.init(model)
scores = numpy.zeros(1, dtype=numpy.float32)
assignments = {}
for i, xy in enumerate(json_stream_load(SAMPLES)):
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
assignments[i] = mixture.id_tracker.packed_to_global(groupid)
print 'seq+gibbs init with {} components'.format(len(mixture))
for _ in xrange(passes - 1):
for i, xy in enumerate(json_stream_load(SAMPLES)):
groupid = mixture.id_tracker.global_to_packed(assignments[i])
mixture.remove_value(model, groupid, xy)
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
assignments[i] = mixture.id_tracker.packed_to_global(groupid)
print 'seq+gibbs found {} components'.format(len(mixture))
image = synthesize_image(model, mixture)
scipy.misc.imsave(os.path.join(RESULTS, 'seq_gibbs.png'), image)
def compress_seq_gibbs(passes=PASSES):
'''
Compress image via sequentiall-initialized gibbs sampling.
'''
assert passes >= 1
assert os.path.exists(SAMPLES), 'first create dataset'
print 'seq+gibbs start {} passes'.format(passes)
model = ImageModel()
mixture = ImageModel.Mixture()
mixture.init(model)
scores = numpy.zeros(1, dtype=numpy.float32)
assignments = {}
for i, xy in enumerate(json_stream_load(SAMPLES)):
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
assignments[i] = mixture.id_tracker.packed_to_global(groupid)
print 'seq+gibbs init with {} components'.format(len(mixture))
for _ in xrange(passes - 1):
for i, xy in enumerate(json_stream_load(SAMPLES)):
groupid = mixture.id_tracker.global_to_packed(assignments[i])
mixture.remove_value(model, groupid, xy)
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
assignments[i] = mixture.id_tracker.packed_to_global(groupid)
print 'seq+gibbs found {} components'.format(len(mixture))
image = synthesize_image(model, mixture)
scipy.misc.imsave(os.path.join(RESULTS, 'seq_gibbs.png'), image)
def compress_annealing(passes=PASSES):
'''
Compress image via subsample annealing.
'''
assert passes >= 1
assert os.path.exists(SAMPLES), 'first create dataset'
print 'annealing start {} passes'.format(passes)
model = ImageModel()
mixture = ImageModel.Mixture()
mixture.init(model)
scores = numpy.zeros(1, dtype=numpy.float32)
assignments = {}
to_add = json_loop_load(SAMPLES)
to_remove = json_loop_load(SAMPLES)
for next_action_is_add in annealing_schedule(passes):
if next_action_is_add:
i, xy = to_add.next()
if i in assignments:
break
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
assignments[i] = mixture.id_tracker.packed_to_global(groupid)
else:
i, xy = to_remove.next()
groupid = mixture.id_tracker.global_to_packed(assignments.pop(i))
mixture.remove_value(model, groupid, xy)
print 'annealing found {} components'.format(len(mixture))
image = synthesize_image(model, mixture)
scipy.misc.imsave(os.path.join(RESULTS, 'annealing.png'), image)
def sample_assignments(self, sample_size):
'''
Sample partial assignment vector
[X_0, ..., X_{n-1}]
where
n = sample_size <= N = dataset_size.
'''
assert sample_size <= self.dataset_size
assignments = []
counts = []
scores = []
bogus = 0
for size in xrange(sample_size):
score_empty = self.score_add_value(0, bogus, size)
if len(counts) == 0 or counts[-1] != 0:
counts.append(0)
scores.append(score_empty)
else:
scores[-1] = score_empty
assign = sample_discrete_log(scores)
counts[assign] += 1
size += 1
scores[assign] = self.score_add_value(counts[assign], bogus, bogus)
assignments.append(assign)
return assignments
def sample_assignments(self, sample_size):
'''
Sample partial assignment vector
[X_0, ..., X_{n-1}]
where
n = sample_size <= N = dataset_size.
'''
assert sample_size <= self.dataset_size
assignments = []
counts = []
scores = []
bogus = 0
for size in xrange(sample_size):
score_empty = self.score_add_value(0, bogus, size)
if len(counts) == 0 or counts[-1] != 0:
counts.append(0)
scores.append(score_empty)
else:
scores[-1] = score_empty
assign = sample_discrete_log(scores)
counts[assign] += 1
size += 1
scores[assign] = self.score_add_value(counts[assign], bogus, bogus)
assignments.append(assign)
return assignments
def compress_annealing(passes=PASSES):
'''
Compress image via subsample annealing.
'''
assert passes >= 1
assert os.path.exists(SAMPLES), 'first create dataset'
print 'annealing start {} passes'.format(passes)
model = ImageModel()
mixture = ImageModel.Mixture()
mixture.init(model)
scores = numpy.zeros(1, dtype=numpy.float32)
assignments = {}
to_add = json_loop_load(SAMPLES)
to_remove = json_loop_load(SAMPLES)
for next_action_is_add in annealing_schedule(passes):
if next_action_is_add:
i, xy = to_add.next()
if i in assignments:
break
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
assignments[i] = mixture.id_tracker.packed_to_global(groupid)
else:
i, xy = to_remove.next()
groupid = mixture.id_tracker.global_to_packed(assignments.pop(i))
mixture.remove_value(model, groupid, xy)
print 'annealing found {} components'.format(len(mixture))
image = synthesize_image(model, mixture)
scipy.misc.imsave(os.path.join(RESULTS, 'annealing.png'), image)
def compress_sequential():
'''
Compress image via sequential initialization.
'''
assert os.path.exists(SAMPLES), 'first create dataset'
print 'sequential start'
model = ImageModel()
mixture = ImageModel.Mixture()
mixture.init(model)
scores = numpy.zeros(1, dtype=numpy.float32)
for xy in json_stream_load(SAMPLES):
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
print 'sequential found {} components'.format(len(mixture))
image = synthesize_image(model, mixture)
scipy.misc.imsave(os.path.join(RESULTS, 'sequential.png'), image)
def compress_sequential():
'''
Compress image via sequential initialization.
'''
assert os.path.exists(SAMPLES), 'first create dataset'
print 'sequential start'
model = ImageModel()
mixture = ImageModel.Mixture()
mixture.init(model)
scores = numpy.zeros(1, dtype=numpy.float32)
for xy in json_stream_load(SAMPLES):
scores.resize(len(mixture))
mixture.score_value(model, xy, scores)
groupid = sample_discrete_log(scores)
mixture.add_value(model, groupid, xy)
print 'sequential found {} components'.format(len(mixture))
image = synthesize_image(model, mixture)
scipy.misc.imsave(os.path.join(RESULTS, 'sequential.png'), image)
def test_sample_discrete_log_underflow():
sample_discrete_log([-1e3])
sample_discrete_log([-1e3, -1e-3])
def test_sample_discrete_log_underflow():
sample_discrete_log([-1e3])
sample_discrete_log([-1e3, -1e-3])
