Intel DAAL AI加速——支持从数据预处理到模型预测，数据源必须使用DAAL的底层封装库

# file: low_order_moms_dense_batch.py
#===============================================================================
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## <a name="DAAL-EXAMPLE-PY-LOW_ORDER_MOMENTS_DENSE_BATCH"></a>
## \example low_order_moms_dense_batch.py

import os
import sys

from daal.algorithms import low_order_moments
from daal.data_management import FileDataSource, DataSourceIface

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable

DAAL_PREFIX = os.path.join(‘..‘, ‘data‘)

# Input data set parameters
dataFileName = os.path.join(DAAL_PREFIX, ‘batch‘, ‘covcormoments_dense.csv‘)


def printResults(res):
    printNumericTable(res.get(low_order_moments.minimum),              "Minimum:")
    printNumericTable(res.get(low_order_moments.maximum),              "Maximum:")
    printNumericTable(res.get(low_order_moments.sum),                  "Sum:")
    printNumericTable(res.get(low_order_moments.sumSquares),           "Sum of squares:")
    printNumericTable(res.get(low_order_moments.sumSquaresCentered),   "Sum of squared difference from the means:")
    printNumericTable(res.get(low_order_moments.mean),                 "Mean:")
    printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
    printNumericTable(res.get(low_order_moments.variance),             "Variance:")
    printNumericTable(res.get(low_order_moments.standardDeviation),    "Standard deviation:")
    printNumericTable(res.get(low_order_moments.variation),            "Variation:")

if __name__ == "__main__":

    # Initialize FileDataSource to retrieve input data from .csv file
    dataSource = FileDataSource(
        dataFileName,
        DataSourceIface.doAllocateNumericTable,
        DataSourceIface.doDictionaryFromContext
    )

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

    # Create algorithm for computing low order moments in batch processing mode
    algorithm = low_order_moments.Batch()

    # Set input arguments of the algorithm
    algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())

    # Get computed low order moments
    res = algorithm.compute()

    printResults(res)