Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 2

Batch Processing

Algorithm Input

The Z-score normalization 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.

Input ID

Input

data

Pointer to the numeric table of size n x p. This table can be an object of any class derived from NumericTable.

Algorithm Parameters

The Z-score normalization algorithm has the following parameters. Some of them are required only for specific values of the computation method parameter method:

Parameter

method

Default Value

Description

algorithmFPType

defaultDense

or

sumDense

double

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

Not applicable

defaultDense

Available computation methods:

  • defaultDense - performance-oriented method.
  • 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.

moments

defaultDense

SharedPtr<low_order_moments::Batch<algorithmFPType, low_order_moments::defaultDense> >

Pointer to the low order moments algorithm that computes means and standard deviations to be used for Z-score normalization with the defaultDense method. For details, see Moments of Low Order. Batch Processing.

resultsToCompute

defaultDense

or

sumDense

none

The 64-bit integer flag that specifies which characteristics of the objective function to compute.

Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:

mean

mean

variance

variance

Algorithm Output

The Z-score normalization algorithm calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

normalizedData

Pointer to the n x p numeric table that stores the result of normalization. By default, the result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

means

Pointer to the 1 x p numeric table that contains mean values for each feature.

Optional.

variances

Pointer to the 1 x p numeric table that contains variance values for each feature.

Optional.

Note

Examples

C++: zscore_dense_batch.cpp

Java*: ZScoreDenseBatch.java

Python*: zscore_dense_batch.py

See Also