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Posted 2021-01-08

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鏁版嵁婧愬姞閫熻瀹樻柟鏂囨。锛堝繀椤讳娇鐢―AAL鑷繁鐨勫簱锛夛細

Data Management

• Numeric Tables
• Tensors
• Data Sources
• Data Dictionaries
• Data Serialization andDeserialization
• Data Compression
• Data Model

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Intel DAAL addresses all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.

Intel DAAL is developed by the same team as the Intel? Math Kernel Library (Intel? MKL)鈥攖he leading math library in the world. This team works closely with Intel? processor architects to squeeze performance from Intel processor-based systems.

Specs at a Glance

 

Processors	Intel Atom?, Intel Core?, Intel? Xeon?, and Intel? Xeon Phi? processors and compatible processors
Languages	Python*, C++, Java*
Development Tools and Environments	Microsoft Visual Studio* (Windows*) Eclipse* and CDT* (Linux*)
Operating Systems	Use the same API for application development on multiple operating systems: Windows, Linux, and macOS*

缁熻鐗瑰緛鐨勮绠楀姞閫熶緥瀛愶細

 

 

# file: low_order_moms_dense_batch.py
#===============================================================================
# Copyright 2014-2018 Intel Corporation.
#
# This software and the related documents are Intel copyrighted  materials,  and
# your use of  them is  governed by the  express license  under which  they were
# provided to you (License).  Unless the License provides otherwise, you may not
# use, modify, copy, publish, distribute,  disclose or transmit this software or
# the related documents without Intel鈥榮 prior written permission.
#
# This software and the related documents  are provided as  is,  with no express
# or implied  warranties,  other  than those  that are  expressly stated  in the
# License.
#===============================================================================

## <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(鈥?.鈥? 鈥榙ata鈥?

# Input data set parameters
dataFileName = os.path.join(DAAL_PREFIX, 鈥榖atch鈥? 鈥榗ovcormoments_dense.csv鈥?


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)銆€銆€