Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 2
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. |
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 pr ≤ p 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 nFeatures ≠ p, the algorithm will return non-relevant results. |
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. |
C++: pca_metrics_dense_batch.cpp
Java*: PCAMetricsDenseBatch.java
Python*: pca_metrics_dense_batch.py