How to use the daal.algorithms.low_order_moments function in daal

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:")

# Read from the distributed HDFS data set at a specified path
    dd = DistributedHDFSDataSet("/Spark/LowOrderMomentsCSR/data/")
    dataRDD = dd.getCSRAsPairRDD(sc)

    # Compute low order moments for dataRDD
    result = runMoments(dataRDD)

    # Print the results
    minimum = result.get(low_order_moments.minimum)
    maximum = result.get(low_order_moments.maximum)
    sum = result.get(low_order_moments.sum)
    sumSquares = result.get(low_order_moments.sumSquares)
    sumSquaresCentered = result.get(low_order_moments.sumSquaresCentered)
    mean = result.get(low_order_moments.mean)
    secondOrderRawMoment = result.get(low_order_moments.secondOrderRawMoment)
    variance = result.get(low_order_moments.variance)
    standardDeviation = result.get(low_order_moments.standardDeviation)
    variation = result.get(low_order_moments.variation)

    # Redirect stdout to a file for correctness verification
    stdout = sys.stdout
    sys.stdout = open('LowOrderMomentsCSR.out', 'w')

    print("Low order moments:")
    printNumericTable(minimum, "Min:")
    printNumericTable(maximum, "Max:")
    printNumericTable(sum, "Sum:")
    printNumericTable(sumSquares, "SumSquares:")
    printNumericTable(sumSquaresCentered, "SumSquaredDiffFromMean:")
    printNumericTable(mean, "Mean:")
    printNumericTable(secondOrderRawMoment, "SecondOrderRawMoment:")
    printNumericTable(variance, "Variance:")

if __name__ == "__main__":

    # Create SparkContext that loads defaults from the system properties and the classpath and sets the name
    sc = SparkContext(conf=SparkConf().setAppName("Spark low_order_moments(dense)").setMaster('local[4]'))

    # Read from the distributed HDFS data set at a specified path
    dd = DistributedHDFSDataSet("/Spark/LowOrderMomentsDense/data/")
    dataRDD = dd.getAsPairRDD(sc)

    # Compute low order moments for dataRDD
    res = runMoments(dataRDD)

    # Print the results
    minimum = res.get(low_order_moments.minimum)
    maximum = res.get(low_order_moments.maximum)
    sum = res.get(low_order_moments.sum)
    sumSquares = res.get(low_order_moments.sumSquares)
    sumSquaresCentered = res.get(low_order_moments.sumSquaresCentered)
    mean = res.get(low_order_moments.mean)
    secondOrderRawMoment = res.get(low_order_moments.secondOrderRawMoment)
    variance = res.get(low_order_moments.variance)
    standardDeviation = res.get(low_order_moments.standardDeviation)
    variation = res.get(low_order_moments.variation)

    # Redirect stdout to a file for correctness verification
    stdout = sys.stdout
    sys.stdout = open('LowOrderMomentsDense.out', 'w')

    print("Low order moments:")
    printNumericTable(minimum, "Min:")

dd = DistributedHDFSDataSet("/Spark/LowOrderMomentsCSR/data/")
    dataRDD = dd.getCSRAsPairRDD(sc)

    # Compute low order moments for dataRDD
    result = runMoments(dataRDD)

    # Print the results
    minimum = result.get(low_order_moments.minimum)
    maximum = result.get(low_order_moments.maximum)
    sum = result.get(low_order_moments.sum)
    sumSquares = result.get(low_order_moments.sumSquares)
    sumSquaresCentered = result.get(low_order_moments.sumSquaresCentered)
    mean = result.get(low_order_moments.mean)
    secondOrderRawMoment = result.get(low_order_moments.secondOrderRawMoment)
    variance = result.get(low_order_moments.variance)
    standardDeviation = result.get(low_order_moments.standardDeviation)
    variation = result.get(low_order_moments.variation)

    # Redirect stdout to a file for correctness verification
    stdout = sys.stdout
    sys.stdout = open('LowOrderMomentsCSR.out', 'w')

    print("Low order moments:")
    printNumericTable(minimum, "Min:")
    printNumericTable(maximum, "Max:")
    printNumericTable(sum, "Sum:")
    printNumericTable(sumSquares, "SumSquares:")
    printNumericTable(sumSquaresCentered, "SumSquaredDiffFromMean:")
    printNumericTable(mean, "Mean:")
    printNumericTable(secondOrderRawMoment, "SecondOrderRawMoment:")
    printNumericTable(variance, "Variance:")
    printNumericTable(standardDeviation, "StandartDeviation:")

if __name__ == "__main__":

    comm = MPI.COMM_WORLD
    rankId = comm.Get_rank()

    # Initialize FileDataSource to retrieve the input data from a .csv file
    dataSource = FileDataSource(datasetFileNames[rankId],
                                DataSourceIface.doAllocateNumericTable,
                                DataSourceIface.doDictionaryFromContext)

    # Retrieve the input data
    dataSource.loadDataBlock()

    # Create an algorithm to compute low order moments on local nodes
    localAlgorithm = low_order_moments.Distributed(step=step1Local)

    # Set the input data set to the algorithm
    localAlgorithm.input.set(low_order_moments.data, dataSource.getNumericTable())

    # Compute low order moments
    pres = localAlgorithm.compute()

    # Serialize partial results required by step 2
    dataArch = InputDataArchive()
    pres.serialize(dataArch)

    nodeResults = dataArch.getArchiveAsArray()

    # Transfer partial results to step 2 on the root node
    serializedData = comm.gather(nodeResults)

jp(DATA_PREFIX, 'covcormoments_csr_1.csv'),
    jp(DATA_PREFIX, 'covcormoments_csr_2.csv'),
    jp(DATA_PREFIX, 'covcormoments_csr_3.csv'),
    jp(DATA_PREFIX, 'covcormoments_csr_4.csv')
]

if __name__ == "__main__":

    comm = MPI.COMM_WORLD
    rankId = comm.Get_rank()

    # Retrieve the input data from a file
    dataTable = createSparseTable(datasetFileNames[rankId])

    # Create an algorithm to compute low order moments on local nodes
    localAlgorithm = low_order_moments.Distributed(step1Local, method=low_order_moments.fastCSR)

    # Set the input data set to the algorithm
    localAlgorithm.input.set(low_order_moments.data, dataTable)

    # Compute low order moments
    pres = localAlgorithm.compute()

    # Serialize partial results required by step 2
    dataArch = InputDataArchive()
    pres.serialize(dataArch)

    nodeResults = dataArch.getArchiveAsArray()

    # Transfer partial results to step 2 on the root node
    serializedData = comm.gather(nodeResults)

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:")

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:")

return momentsMaster.finalizeCompute()

if __name__ == "__main__":

    # Create SparkContext that loads defaults from the system properties and the classpath and sets the name
    sc = SparkContext(conf=SparkConf().setAppName("Spark low_order_moments(sparse)").setMaster('local[4]'))

    # Read from the distributed HDFS data set at a specified path
    dd = DistributedHDFSDataSet("/Spark/LowOrderMomentsCSR/data/")
    dataRDD = dd.getCSRAsPairRDD(sc)

    # Compute low order moments for dataRDD
    result = runMoments(dataRDD)

    # Print the results
    minimum = result.get(low_order_moments.minimum)
    maximum = result.get(low_order_moments.maximum)
    sum = result.get(low_order_moments.sum)
    sumSquares = result.get(low_order_moments.sumSquares)
    sumSquaresCentered = result.get(low_order_moments.sumSquaresCentered)
    mean = result.get(low_order_moments.mean)
    secondOrderRawMoment = result.get(low_order_moments.secondOrderRawMoment)
    variance = result.get(low_order_moments.variance)
    standardDeviation = result.get(low_order_moments.standardDeviation)
    variation = result.get(low_order_moments.variation)

    # Redirect stdout to a file for correctness verification
    stdout = sys.stdout
    sys.stdout = open('LowOrderMomentsCSR.out', 'w')

    print("Low order moments:")
    printNumericTable(minimum, "Min:")

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:")