Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 1
The 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 |
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data |
Use when the input data is a normalized or non-normalized data set. Pointer to the n x p numeric table that contains the input data set. This input can be an object of any class derived from NumericTable. |
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correlation |
Use when the input data is a correlation matrix. Pointer to the p x p numeric table that contains the correlation matrix. This input can be an object of any class derived from NumericTable except PackedTriangularMatrix. |
The PCA algorithm has the following parameters, depending on the computation method parameter method:
Parameter |
method |
Default Value |
Description |
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algorithmFPType |
defaultDense or svdDense |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
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method |
Not applicable |
defaultDense |
Available methods for PCA computation:
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covariance |
defaultDense |
SharedPtr<covariance::Batch<algorithmFPType, covariance::defaultDense> > |
The correlation and variance-covariance matrices algorithm to be used for PCA computations with the correlation method. For details, see Correlation and Variance-covariance Matrices. Batch Processing. |
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normalization |
svdDense |
SharedPtr<normalization::zscore::Batch<algorithmFPType,normalization::zscore::defaultDense> > |
The data normalization algorithm to be used for PCA computations with the SVD method. For details, see Normalization. Zscore. Batch Processing. |
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nComponents |
defaultDense, svdDense |
0 |
Number of principal components pr. If it is zero, the algorithm will compute the result for pr = p. |
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isDeterministic |
defaultDense, svdDense |
false |
If true, the algorithm applies the "sign flip" technique to the results. |
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resultsToCompute |
defaultDense, svdDense |
none |
The 64-bit integer flag that specifies which optional result to compute. Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:
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The PCA algorithm calculates the 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.
Result ID |
Result |
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eigenvalues |
Pointer to the 1 x pr numeric table that contains eigenvalues in the descending order. 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 PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable. |
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eigenvectors |
Pointer to the pr x p numeric table that contains eigenvectors in the row-major order. 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 PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable. |
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means |
Pointer to the 1 x pr numeric table that contains mean values for each feature. Optional. If correlation is provided then the vector is filed with zeroes. |
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variances |
Pointer to the 1 x pr numeric table that contains mean values for each feature. Optional. If correlation is provided then the vector is filed with zeroes. |
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dataForTransform |
Pointer to key value data collection containing the aggregated data for normalization and whitening with the following key value pairs:
If resultsToCompute does not contain mean, the dataForTransform means table is NULL. If resultsToCompute does not contain variances, the dataForTransform variances table is NULL. If resultsToCompute does not contain eigenvalues, the dataForTransform eigenvalues table is NULL. |