Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3

Batch Processing

Algorithm Input

The Quality Metrics for PCA 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

eigenvalues

p eigenvalues (explained variances), numeric table of size 1 x p. You can define it as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Algorithm Parameters

The quality metric algorithm has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

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

nComponents

0

Number of principal components prp to compute metrics for. If it is zero, the algorithm will compute the result for p.

nFeatures

0

Number of features in the data set used as input in PCA algorithm. If it is zero, the algorithm will compute the result for p. Note: if nFeaturesp, the algorithm will return non-relevant results.

Algorithm Output

The quality metric for PCA algorithm calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm.

Result ID

Result

explainedVariances

Pointer to the 1 x pr numeric table that contains a reduced eigenvalues array.

explainedVariancesRatios

Pointer to the 1 x pr numeric table that contains an array of reduced explained variances ratios.

noiseVariance

Pointer to the 1 x 1 numeric table that contains noise variance.

Note

By default, each numeric table specified by the collection elements is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable, except for PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Examples

C++: pca_metrics_dense_batch.cpp

Java*: PCAMetricsDenseBatch.java

Python*: pca_metrics_dense_batch.py