def test_early_winner_bias():
particle_size = (5,5)
max_num_particles = 50
feature_size = (5,5)
image_shape = (1,20,20)
image_iter = GridImageGenerator(
num_features = 5,
feature_size = feature_size,
image_shape = image_shape,
num_images = 100,
sparsity = 0.6,
random_seed = int(time.time() % 100000)
)
f1 = [[[0,0,1,0,0],
[0,0,1,0,0],
[1,1,1,1,1],
[0,0,1,0,0],
[0,0,1,0,0]]]
f2 = [[[1,0,0,0,1],
[0,1,0,1,0],
[0,0,1,0,0],
[0,1,0,1,0],
[1,0,0,0,1]]]
f3 = [[[0,0,0,0,0],
[0,1,1,1,0],
[0,1,1,1,0],
[0,1,1,1,0],
[0,0,0,0,0]]]
f4 = [[[0,0,1,1,0],
[0,0,1,0,0],
[1,0,1,0,1],
[0,0,1,0,0],
[0,1,1,0,0]]]
eq_area_feats = [ numpy.array(f1), numpy.array(f2),
numpy.array(f3),
numpy.array(f4)
]
f5 = numpy.array([[[0,1,0,0,0],
[1,0,0,0,1],
[1,0,0,0,1],
[1,0,0,0,1],
[0,0,0,1,0]]])
image_iter.features = eq_area_feats
image_iter.show_features()
particles = ParticleList(particle_size, image_shape,
max_num_particles=max_num_particles)
t1 = time.time()
while image_iter.has_next():
image = image_iter.next()
particles.update(image)
particles.show_winners(50)
eq_area_feats.append(f5)
image_iter.features = eq_area_feats
image_iter.show_features()
image_iter.reset()
while image_iter.has_next():
image = image_iter.next()
particles.update(image)
particles.show_winners(50)
t2 = time.time()
learning_time = t2 - t1
# Validate results
features = particles.get_features()
target_features = image_iter.features[:]
target_num_features = len(target_features)
num_targets_learned = 0
for learned_feat in features:
for i in range(len(target_features)):
target = target_features[i]
if (learned_feat == target).all():
del target_features[i]
num_targets_learned += 1
break
print "Learned %s of the %s target features." % (num_targets_learned, target_num_features)
print "Learned %s features in total." % len(features)
print "Learning time: %.06f seconds" % learning_time
print
def basic_test_particle_learning():
particle_size = (5,5)
num_particles = 40
feature_size = (5,5)
image_shape = (1,20,20)
image_iter = GridImageGenerator(
num_features = 5,
feature_size = feature_size,
image_shape = image_shape,
num_images = 50,
sparsity = 0.6,
random_seed = int(time.time() % 100000)
)
f1 = [[[0,0,1,0,0],
[0,0,1,0,0],
[1,1,1,1,1],
[0,0,1,0,0],
[0,0,1,0,0]]]
f2 = numpy.array([[[1,0,0,0,1],
[0,1,0,1,0],
[0,0,1,0,0],
[0,1,0,1,0],
[1,0,0,0,1]]])
f3 = numpy.array([[[0,0,0,0,0],
[0,1,1,1,0],
[0,1,1,1,0],
[0,1,1,1,0],
[0,0,0,0,0]]])
f4 = [[[0,0,1,1,0],
[0,0,1,0,0],
[1,0,1,0,1],
[0,0,1,0,0],
[0,1,1,0,0]]]
eq_area_feats = [ numpy.array(f1), f2,
f3,
numpy.array(f4)
]
subsume_feats = [ f2, f3 ]
f1 = [[[0,0,1,0,0],
[0,0,1,0,0],
[0,0,1,0,0],
[0,0,1,0,0],
[0,0,1,0,0]]]
f3 = numpy.array([[[0,0,0,0,0],
[0,0,0,1,0],
[0,0,1,0,0],
[0,1,0,0,0],
[0,0,0,0,0]]])
f2 = numpy.array([[[0,0,0,0,0],
[0,1,0,0,0],
[0,0,1,0,0],
[0,0,0,1,0],
[0,0,0,0,0]]])
f4 = [[[0,1,0,0,0],
[1,0,0,0,1],
[1,0,0,0,1],
[1,0,0,0,1],
[0,0,0,1,0]]]
subsume_feats.extend([f2, f3])
line_feats = [ numpy.array(f1), f2,
f3,
numpy.array(f4)
]
image_iter.features = subsume_feats #eq_area_feats + line_feats
image_iter.show_features()
particles = ParticleList(particle_size, image_shape,
max_num_particles=40)
t1 = time.time()
while image_iter.has_next():
image = image_iter.next()
particles.update(image)
#toimage(image[0]).show()
particles.show_winners(500)
#from IPython.Debugger import Pdb; Pdb(color_scheme='Linux').set_trace()
t2 = time.time()
learning_time = t2 - t1
# Validate results
features = particles.get_features()
target_features = image_iter.features[:]
target_num_features = len(target_features)
num_targets_learned = 0
for learned_feat in features:
for i in range(len(target_features)):
target = target_features[i]
if (learned_feat == target).all():
del target_features[i]
num_targets_learned += 1
break
print "Learned %s of the %s target features." % (num_targets_learned, target_num_features)
print "Learned %s features in total." % len(features)
print "Learning time: %.06f seconds" % learning_time
print
