code stringlengths 3 6.57k |
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skl.gaussian_process.kernels.DotProduct(sigma_0=0, sigma_0_bounds="fixed") |
GaussianProcessRegressionSklearn(kernel=kernel, optimizer=None, rng=1) |
smlb.TabularData(data=np.array([[-1], [1]]) |
np.array([-1, 1]) |
smlb.TabularData(data=np.array([[-2], [-1], [0], [1], [2]]) |
gpr.fit(train_data) |
apply(valid_data) |
np.allclose(mean, [-2, -1, 0, 1, 2]) |
test_GaussianProcessRegressionSklearn_2() |
skl.gaussian_process.kernels.WhiteKernel(noise_level=0.1, noise_level_bounds=(1e-5, 1e-5) |
GaussianProcessRegressionSklearn(kernel=kernel, rng=1) |
np.ones(shape=(n, 1) |
np.ones(shape=n) |
smlb.TabularData(data=train_data.samples() |
gpr.fit(train_data) |
apply(valid_data) |
np.allclose(conf.mean, train_data.labels() |
np.allclose(conf.stddev, np.ones(n) |
np.sqrt(1e-5) |
assert (preds.mean == conf.mean) |
all() |
np.allclose(preds.stddev, np.ones(n) |
np.sqrt(np.square(conf.stddev) |
np.allclose(noise.mean, np.zeros(shape=n) |
np.allclose(noise.stddev, np.sqrt(1e-5) |
test_GaussianProcessRegressionSklearn_3() |
skl.gaussian_process.kernels.WhiteKernel(noise_level=1, noise_level_bounds=(1e-5, 1e5) |
GaussianProcessRegressionSklearn(kernel=kernel, rng=1) |
smlb.TabularData(data=np.ones(shape=(n, 1) |
np.ones(shape=n) |
smlb.LabelNoise(noise=smlb.NormalNoise(stddev=nlsd, rng=1) |
fit(data) |
apply(data) |
gpr.fit(data) |
apply(data) |
np.allclose(conf.mean, np.ones(n) |
np.allclose(conf.stddev, np.ones(n) |
assert (preds.mean == conf.mean) |
all() |
np.allclose(preds.stddev, np.sqrt(np.square(conf.stddev) |
np.square(nlsd) |
np.allclose(noise.mean, np.zeros(shape=n) |
np.allclose(noise.stddev, nlsd, atol=1e-1) |
picking_number(n, arr) |
range(n) |
arr.count(arr[i]) |
arr.count(arr[i] + 1) |
int(input() |
strip() |
int(a_temp) |
input() |
strip() |
split(' ') |
print (picking_number(n, a) |
print_usage() |
print("usage: " + os.path.basename(sys.argv[0]) |
print("quality_type:\n\t" + "\n\t".join(quality_runner_types) |
main() |
len(sys.argv) |
print_usage() |
print_usage() |
import_python_file(dataset_filepath) |
print("Error: " + str(e) |
QualityRunner.find_subclass(quality_type) |
print_usage() |
FileSystemResultStore() |
run_remove_results_for_dataset(result_store, dataset, runner_class) |
main() |
exit(ret) |
logging.getLogger(__name__) |
mkstemp(suffix=df_name_suffix, prefix=df_name_prefix, dir=dir_name) |
os.path.join(dir_name, filename) |
logger.info("Creating %s file %s from dataframe.", export_type, filepath) |
dataframe.to_parquet(path=filepath, index=index) |
dataframe.to_csv(filepath, index=index, header=header) |
pd_colupdate(dataframe: pd.DataFrame, coldict: dict) |
pd.DataFrame.rename(columns=dict) |
logger.info("Renaming and filtering dataframe columns using coldict key:values.") |
dataframe.rename(columns=coldict) |
coldict.items() |
copy() |
argparse.ArgumentParser () |
parser.add_argument ('-v', '--verbose', help = 'Enable Verbose Mode', action = 'store_true') |
parser.add_argument ('-ip', help = 'IP Address to Test') |
parser.parse_args () |
format(args.ip) |
print ('Retrieving location information ...') |
json.loads ((urllib.request.urlopen (location_url) |
read () |
decode ("utf-8") |
print ('All done.') |
_model(jimi.db._document) |
str() |
str() |
str() |
str() |
bool() |
dict() |
new(self,name,className,classType,location,hidden) |
super(_model, self) |
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