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
数据源加速见官方文档（必须使用DAAL自己的库）：
可以看到支持的数据源：同数据类型的table（matrix），不同类型的table，以及从DB文件取数据、数据序列化、压缩等。
在这些定制的数据源上，Intel DAAL使用自己底层的CPU进行硬件加速！下面摘自其官方：
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)—the 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* |
统计特征的计算加速例子：
# 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's 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('..', '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)
