code stringlengths 3 6.57k |
|---|
fhandle.read(1) |
ord(byte) |
struct.unpack('>H', fhandle.read(2) |
ValueError("Invalid JPEG file") |
head.startswith(b'\x00\x00\x00\x0cjP \r\n\x87\n') |
fhandle.seek(32) |
struct.unpack('>L', fhandle.read(4) |
fhandle.seek(4, 1) |
fhandle.read(8) |
struct.unpack('>L', boxHeader[:4]) |
fhandle.seek(boxSize - 8, 1) |
fhandle.read(8) |
struct.unpack(">HHBB", fhandle.read(10) |
_convertToDPI(xDensity, xUnit) |
_convertToDPI(yDensity, yUnit) |
struct.unpack('>L', boxHeader[:4]) |
fhandle.seek(boxSize - 8, 1) |
ValueError("Invalid JPEG2000 file") |
digest_target(target) |
digest_element(target) |
Subsample(self, indicator, labels) |
SubsampleWeights(self, weights, labels) |
subsample_indicator(indicator, num_samples) |
input (indicator) |
tf.where(indicator) |
tf.random.shuffle(indices) |
tf.reshape(indices, [-1]) |
tf.minimum(tf.size(indices) |
tf.slice(indices, [0], tf.reshape(num_samples, [1]) |
ops.indices_to_dense_vector(selected_indices, tf.shape(indicator) |
tf.equal(selected_indicator, 1) |
sample_balanced_positive_negative(indicator, sample_size, labels, positive_fraction=0.5) |
positive(=True) |
negative
(=False) |
examples (scalar in [0,1]) |
tf.logical_not(labels) |
tf.logical_and(labels, indicator) |
tf.logical_and(negative_idx, indicator) |
tf.reduce_sum(tf.cast(positive_idx, dtype=tf.int32) |
int(positive_fraction * sample_size) |
subsample_indicator(positive_idx, max_num_pos) |
tf.reduce_sum(tf.cast(sampled_pos_idx, tf.int32) |
tf.cast(negative_positive_ratio * tf.cast(num_sampled_pos, dtype=tf.float32) |
subsample_indicator(negative_idx, max_num_neg) |
tf.logical_or(sampled_pos_idx, sampled_neg_idx) |
positive(=True) |
negative
(=False) |
examples (scalar in [0,1]) |
_minibatch_subsample_fn(inputs) |
sample_balanced_positive_negative(tf.cast(indicators, tf.bool) |
tf.cast(targets, tf.bool) |
re.compile(r"/\*.*?\*/", re.DOTALL) |
__init__(self, main_path=None, user_path=None) |
__getitem__(self, key) |
__len__(self) |
len(self._cfg_dict) |
_load_json(self, path) |
os.path.exists(path) |
open(path) |
read() |
reg_cmnt.sub("", txt) |
json.loads(txt) |
_set_default(self, cfg) |
cfg.get("path_to_dict", "dict.json") |
cfg.get("path_to_stat", "statistic.json") |
int(cfg.get("words_per_lesson", 5) |
int(cfg.get("CntStudyWords", 50) |
float(cfg.get("MinPercent", 97.0) |
int(cfg.get("MinSuccessCnt", 10) |
int(cfg.get("retry_time", 1800) |
cfg.get("hide_transcription", "no") |
int(cfg.get("start_time_delay", 1) |
int(cfg.get("stat_count_row", 200) |
float(cfg.get("right_answer_percent", 10.0) |
float(cfg.get("wrong_answer_percent", 40.0) |
cfg.get("empty_answer_is_error", "no") |
create_default_user_config(self) |
os.path.isfile(self._user_path) |
open(self._user_path, "wt") |
write(txt) |
reload(self) |
self._cfg_dict.update(self._load_json(self._main_path) |
self._cfg_dict.update(self._load_json(self._user_path) |
self._set_default(self._cfg_dict) |
get_dict(self) |
ConfigTestCase(unittest.TestCase) |
setUp(self) |
os.path.isfile("test_config_user.json") |
os.remove("test_config_user.json") |
tearDown(self) |
os.path.isfile("test_config_user.json") |
os.remove("test_config_user.json") |
equal_cfg(self, cfg, test_dict) |
test_dict.items() |
self.assertEqual(cfg[key], val) |
self.assertEqual(len(cfg) |
test_main(self) |
Config("config.json5", "fake_config_user.json") |
cfg.reload() |
self.equal_cfg(cfg, test_dict) |
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