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## <a name="DAAL-EXAMPLE-PY-OUTLIER_DETECTION_UNIVARIATE_BATCH"></a>
## \example out_detect_uni_dense_batch.py

from daal.algorithms.univariate_outlier_detection import InitIface, Batch_Float64DefaultDense, data, weights
from daal.data_management import FileDataSource, writeOnly, DataSourceIface, BlockDescriptor_Float64
import os, inspect, sys
from os.path import join as jp
utils_folder = os.path.realpath(os.path.abspath(jp(os.path.split(inspect.getfile(inspect.currentframe()))[0], "..")))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable

DAAL_PREFIX = jp('..', 'data')

# Input data set parameters
datasetFileName = jp(DAAL_PREFIX, 'batch', 'outlierdetection.csv')

if __name__ == "__main__":

    # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
    dataSource = FileDataSource(
        datasetFileName, DataSourceIface.doAllocateNumericTable,
        DataSourceIface.doDictionaryFromContext
    )

    # Retrieve the data from the input file
    dataSource.loadDataBlock()

    nFeatures = dataSource.getNumberOfColumns()

    algorithm = Batch_Float64DefaultDense()

    algorithm.input.set(data, dataSource.getNumericTable())

    # Compute outliers and get the computed results
    res = algorithm.compute()

    printNumericTable(dataSource.getNumericTable(), "Input data")
    printNumericTable(res.get(weights), "Outlier detection result (univariate)")
