opt_context_numbes = []
opt_image_sample = []
opt_out = []
opt_ind_arr = []
for image_number in range(max_number):
label, image = transformers.get_image(df, image_number)
print(image_number, label)
start = minicolumn.count_clusters
for subimage_number in range(0, count_subimages_for_image):
codes, context_numbes, image_sample = transforms.get_sample_codes(
image)
if codes is None:
continue
opt_ind, out_code, status = minicolumn.learn(codes,
ind,
in_controversy=20,
out_controversy=5)
if opt_ind is None:
continue
opt_context_numbes.append(context_numbes[opt_ind])
opt_image_sample.append(image_sample)
opt_out.append(out_code)
opt_ind_arr.append(opt_ind)
if status == MINICOLUMN.LEARN:
print('№', ind, 'Всего кластеров:', minicolumn.count_clusters)
means.append(np.mean([len(p.clusters) for p in minicolumn.space]))
elif status == MINICOLUMN.SLEEP:
sleep__(minicolumn)
ind += 1
print('Изменение кол-ва кластеров', minicolumn.count_clusters - start)
class TestFunctionalMinicolumn(unittest.TestCase):
class Answer(Enum):
CONT_NUM = 0
OPT_IND = 1
OPT_OUT = 2
STATUS = 3
MEANS = 4
EMPTY_CODE = 5
@classmethod
def setUpClass(cls):
print('\n')
print('#########')
print('The executing of these tests can be a bit long')
print('#########')
def setUp(self):
window_size = [4, 4]
width_angle = np.pi / 2
strength = 0
non_zeros_bits = 5
space_size = 500
directs = 4
self.minicolumn = Minicolumn(space_size=space_size,
max_clusters=900,
in_dimensions=64,
in_random_bits=25,
out_dimensions=20,
out_random_bits=15,
seed=42,
code_alignment=5,
in_point_activate=5,
out_point_activate=4,
in_cluster_modify=6,
out_cluster_modify=3,
lr=0.3,
binarization=0.1)
self.transformer = ContextTransformer(directs=directs,
window_size=window_size,
width_angle=width_angle,
strength=strength,
non_zeros_bits=non_zeros_bits)
self.answer = [{
self.Answer.STATUS:
MINICOLUMN.LEARN,
self.Answer.OPT_OUT:
np.array(
[0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0]),
self.Answer.MEANS:
0.628,
self.Answer.CONT_NUM:
314,
self.Answer.OPT_IND:
7,
self.Answer.EMPTY_CODE:
False
}, {
self.Answer.STATUS:
MINICOLUMN.LEARN,
self.Answer.OPT_OUT:
np.array(
[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]),
self.Answer.MEANS:
1.154,
self.Answer.CONT_NUM:
577,
self.Answer.OPT_IND:
20,
self.Answer.EMPTY_CODE:
False
}, {
self.Answer.STATUS:
MINICOLUMN.LEARN,
self.Answer.OPT_OUT:
np.array(
[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]),
self.Answer.MEANS:
1.408,
self.Answer.CONT_NUM:
704,
self.Answer.OPT_IND:
14,
self.Answer.EMPTY_CODE:
False
}, {
self.Answer.STATUS:
MINICOLUMN.LEARN,
self.Answer.OPT_OUT:
np.array(
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]),
self.Answer.MEANS:
1.928,
self.Answer.CONT_NUM:
964,
self.Answer.OPT_IND:
29,
self.Answer.EMPTY_CODE:
False
}, {
self.Answer.STATUS: MINICOLUMN.SLEEP,
self.Answer.OPT_OUT: None,
self.Answer.MEANS: 1.928,
self.Answer.CONT_NUM: 964,
self.Answer.OPT_IND: None,
self.Answer.EMPTY_CODE: False
}, {
self.Answer.STATUS: MINICOLUMN.SLEEP,
self.Answer.OPT_OUT: None,
self.Answer.MEANS: 1.928,
self.Answer.CONT_NUM: 964,
self.Answer.OPT_IND: None,
self.Answer.EMPTY_CODE: False
}]
def __add_to_dict(self, dict_var, key, value):
dict_tmp = dict_var[-1]
dict_tmp[key] = value
dict_var[-1] = dict_tmp
def __init_answ(self, answ):
answ.append({
self.Answer.EMPTY_CODE: None,
self.Answer.CONT_NUM: None,
self.Answer.OPT_IND: None,
self.Answer.OPT_OUT: None,
self.Answer.STATUS: None,
self.Answer.MEANS: None
})
def test_minicolumn(self):
df = pd.read_csv('../../data/test/test_image.csv',
header=None,
nrows=1)
answer = []
label, image = transformers.get_image(df, 0)
self.assertEqual(9, label)
for im_num in range(0, 6):
self.__init_answ(answer)
codes, context_numbes, image_sample = self.transformer.get_sample_codes(
image)
if codes is None:
self.__add_to_dict(answer, self.Answer.EMPTY_CODE, True)
continue
else:
self.__add_to_dict(answer, self.Answer.EMPTY_CODE, False)
opt_ind, out_code, status = self.minicolumn.learn(
codes, im_num, in_controversy=20, out_controversy=5)
self.__add_to_dict(answer, self.Answer.OPT_IND, opt_ind)
self.__add_to_dict(answer, self.Answer.OPT_OUT, out_code)
self.__add_to_dict(answer, self.Answer.CONT_NUM,
self.minicolumn.count_clusters)
self.__add_to_dict(answer, self.Answer.STATUS, status)
self.__add_to_dict(
answer, self.Answer.MEANS,
np.mean([len(p.clusters) for p in self.minicolumn.space]))
for item in zip(self.answer, answer):
for key in item[0]:
np.testing.assert_equal(item[0][key], item[1][key])