code stringlengths 3 6.57k |
|---|
lower() |
in ("yes", "true", "t", "1") |
validate_market_trading_pair_tuple(value: str) |
dev_5_vwap_config_map.get("exchange") |
validate_market_trading_pair(market, value) |
order_percent_of_volume_prompt() |
dev_5_vwap_config_map.get("percent_slippage") |
required_exchanges.append(value) |
order (limit/market) |
quantity (denominated in the base asset, default is 1) |
order (default is Buy Order) |
dev_5_vwap_config_map.get("is_vwap") |
dev_5_vwap_config_map.get("is_vwap") |
dev_5_vwap_config_map.get("is_vwap") |
dev_5_vwap_config_map.get("order_type") |
order (in seconds) |
dev_5_vwap_config_map.get("order_type") |
ReactAppView(View) |
get(self, request) |
open(os.path.join(str(settings.ROOT_DIR) |
HttpResponse(file.read() |
Rule(LinkExtractor(allow = ['/[a-zA-Z0-9-]+-\d+\.html$']) |
Rule(LinkExtractor(deny = ['/ban-tin'], allow = ['/[a-zA-Z0-9-]+-b+\d+\.html']) |
Rule(LinkExtractor() |
Copyright (c) |
Copyright (c) |
NamedArgumentTest(BayemcoinTestFramework) |
set_test_params(self) |
run_test(self) |
node.help(command='getinfo') |
assert(h.startswith('getinfo\n') |
assert_raises_jsonrpc(-8, 'Unknown named parameter', node.help, random='getinfo') |
node.getblockhash(height=0) |
node.getblock(blockhash=h) |
assert_equal(node.echo() |
assert_equal(node.echo(arg0=0,arg9=9) |
assert_equal(node.echo(arg1=1) |
assert_equal(node.echo(arg9=None) |
assert_equal(node.echo(arg0=0,arg3=3,arg9=9) |
NamedArgumentTest() |
main() |
np.random.seed(1207) |
pd.read_json('./input/train.json') |
get_scaled_imgs(df) |
df.iterrows() |
np.array(row['band_1']) |
np.array(row['band_2']) |
band_1.reshape(75, 75) |
band_2.reshape(75, 75) |
log(x*y) |
log(x) |
log(y) |
band_1.mean() |
band_1.max() |
band_1.min() |
band_2.mean() |
band_2.max() |
band_2.min() |
band_3.mean() |
band_3.max() |
band_3.min() |
imgs.append(np.dstack((a, b) |
np.array(imgs) |
get_more_images(imgs) |
range(0,imgs.shape[0]) |
cv2.flip(a,1) |
cv2.flip(a,0) |
cv2.flip(b,1) |
cv2.flip(b,0) |
cv2.flip(c,1) |
cv2.flip(c,0) |
vert_flip_imgs.append(np.dstack((av, bv, cv) |
hori_flip_imgs.append(np.dstack((ah, bh, ch) |
vert_flip_imgs.append(np.dstack((av, bv) |
hori_flip_imgs.append(np.dstack((ah, bh) |
np.array(vert_flip_imgs) |
np.array(hori_flip_imgs) |
np.concatenate((imgs,v,h) |
getModel() |
Sequential() |
model.add(Conv2D(64, kernel_size=(3, 3) |
model.add(Conv2D(64, kernel_size=(3, 3) |
model.add(Conv2D(64, kernel_size=(3, 3) |
model.add(MaxPooling2D(pool_size=(3, 3) |
model.add(Conv2D(128, kernel_size=(3, 3) |
model.add(Conv2D(128, kernel_size=(3, 3) |
model.add(Conv2D(128, kernel_size=(3, 3) |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Dropout(0.2) |
model.add(Conv2D(128, kernel_size=(3, 3) |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Dropout(0.2) |
model.add(Conv2D(256, kernel_size=(3, 3) |
model.add(MaxPooling2D(pool_size=(2, 2) |
model.add(Flatten() |
model.add(Dense(1024, activation='relu') |
model.add(Dropout(0.5) |
model.add(Dense(256, activation='relu') |
model.add(Dropout(0.2) |
model.add(Dense(1, activation="sigmoid") |
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