Batch Processing

Intel® oneAPI Data Analytics Library Developer Guide and Reference

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ID 772611

Date 7/13/2023

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Batch Processing

Algorithm Input

The low order moments algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Algorithm Input for Low Order Moments (Batch Processing)
Input ID	Input
`data`	Pointer to the numeric table of size $n i m e s p$ to compute moments for. While the input for `defaultDense`, `singlePassDense`, or `sumDense` method can be an object of any class derived from `NumericTable`, the input for `fastCSR`, `singlePassCSR`, or `sumCSR` method can only be an object of the `CSRNumericTable` class.

Algorithm Parameters

The low order moments algorithm has the following parameters:

Algorithm Parameters for Low Order Moments (Batch Processing)
Parameter	Default Valude	Description
`algorithmFPType`	`float`	The floating-point type that the algorithm uses for intermediate computations. Can be `float` or `double`.
`method`	`defaultDense`	Available methods for computation of low order moments: For CPU: `defaultDense` - default performance-oriented method `singlePassDense` - implementation of the single-pass algorithm proposed by D.H.D. West `sumDense` - implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; returns an error if pre-computed sums are not defined `fastCSR` - performance-oriented method for CSR numeric tables `singlePassCSR` - implementation of the single-pass algorithm proposed by D.H.D. West; optimized for CSR numeric tables `sumCSR` - implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; optimized for CSR numeric tables; returns an error if pre-computed sums are not defined For GPU: `defaultDense` - default performance-oriented method
`estimatesToCompute`	`estimatesAll`	Estimates to be computed by the algorithm: `estimatesAll` - all supported moments `estimatesMinMax` - minimum and maximum `estimatesMeanVariance` - mean and variance

Algorithm Output

The low order moments algorithm calculates the results described in the following table. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

NOTE:

Each result is a pointer to the $1 i m e s p$ numeric table that contains characteristics for each feature in the data set. By default, the tables are objects of the HomogenNumericTable class, but you can define each table as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Algorithm Output for Low Order Moments (Batch Processing)
Result ID	Characteristic
`minimum`	Minimums
`maximum`	Maximums
`sum`	Sums
`sumSquares`	Sums of squares
`sumSquaresCentered`	Sums of squared differences from the means
`mean`	Estimates for the means
`secondOrderRawMoment`	Estimates for the second order raw moments
`variance`	Estimates for the variances
`standardDeviation`	Estimates for the standard deviations
`variation`	Estimates for the variations

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex. Notice revision #20201201

Product and Performance Information

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.

Notice revision #20201201

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Intel® oneAPI Data Analytics Library Developer Guide and Reference

Batch Processing

Algorithm Input

Algorithm Parameters

Algorithm Output