Online Processing¶

Online processing computation mode assumes that data arrives in blocks \(i = 1, 2, 3, \ldots \text{nblocks}\).

Computation of low order moments in the online processing mode follows the general computation schema for online processing described in Algorithms.

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 (Online Processing)¶
Input ID	Input
`data`	Pointer to the numeric table of size \(n_i \times p\) that represents the current data block. 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 (Online 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: 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
`initializationProcedure`	Not applicable	The procedure for setting initial parameters of the algorithm in the online processing mode. By default, the algorithm does the following initialization: Sets `nObservations`, `partialSum`, and `partialSumSquares` to zero. Sets `partialMinimum` and `partialMaximum` to the first row of the input table.
`estimatesToCompute`	`estimatesAll`	Estimates to be computed by the algorithm: `estimatesAll` - all supported moments `estimatesMinMax` - minimum and maximum `estimatesMeanVariance` - mean and variance

Partial Results¶

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

Partial Results for Low Order Moments (Online Processing)¶
Result ID	Result
`nObservations`	Pointer to the \(1 \times 1\) numeric table that contains the number of rows processed so far. By default, this result is an object of the `HomogenNumericTable` class, but you can define the result as an object of any class derived from `NumericTable` except `CSRNumericTable`.

Partial characteristics computed so far, each in a \(1 \times p\) numeric table. By default, each table is an object of the HomogenNumericTable class, but you can define the tables as objects of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Partial Characteristics for Low Order Moments (Online Processing)¶
Result ID	Result
`partialMinimum`	Partial minimums
`partialMaximum`	Partial maximums
`partialSum`	Partial sums
`partialSumSquares`	Partial sums of squares
`partialSumSquaresCentered`	Partial sums of squared differences from the means

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 \(1 \times p\) 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 (Online 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

Batch Processing

Distributed Processing