Distributed Processing
Distributed processing mode for Principal Component Analysis is not available on GPU.
This mode assumes that data set is split in nblocks blocks across computation nodes.
PCA computation in the distributed processing mode follows the general schema described in Algorithms.
Algorithm Parameters
The PCA algorithm in the distributed processing mode has the following parameters, depending on the computation method parameter method:
Parameter | Method | Default Value | Description |
|---|---|---|---|
computeStep | defaultDense or svdDense | Not applicable | The parameter required to initialize the algorithm. Can be:
|
algorithmFPType | defaultDense or svdDense | float | The floating-point type that the algorithm uses for intermediate computations. Can be float or double . |
method | Not applicable | defaultDense | Available computation methods for PCA computation:
|
covariance | defaultDense | SharedPtr<covariance::Distributed <computeStep, 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. Distributed Processing. |
Use the following two-step schema:
Step 1 - on Local Nodes
Correlation method (
defaultDense
)In this step, 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 |
|---|---|
data | Pointer to the  numeric table that represents the Lmath:i -th data block on the local node.
The input can be an object of any class derived from NumericTable . |
In this step, PCA 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 |
|---|---|
nObservationsCorrelation | Pointer to the  numeric table with the number of observations processed so far on the local node.By default, this result is an object of the HomogenNumericTable class,
but you can define it as an object of any class derived from NumericTable except CSRNumericTable . |
crossProductCorrelation | Pointer to the  numeric table with the cross-product matrix computed so far on the local node.By default, this table is an object of the HomogenNumericTable class,
but you can define it as an object of any class derived from NumericTable
except PackedSymmetricMatrix , PackedTriangularMatrix , and CSRNumericTable . |
sumCorrelation | Pointer to the  numeric table with partial sums computed so far on the local node.By default, this table is an object of the HomogenNumericTable class,
but you can define it as an object of any class derived from NumericTable
except PackedSymmetricMatrix , PackedTriangularMatrix , and CSRNumericTable . |
SVD method (
svdDense
)In this step, 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 |
|---|---|
data | Pointer to the numeric table that represents the Lmath:i -th data block on the local node.
The input can be an object of any class derived from NumericTable . |
In this step, PCA 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 |
|---|---|
nObservationsCorrelation | Pointer to the numeric table with the number of observations processed so far on the local node.By default, this result is an object of the HomogenNumericTable class,
but you can define it as an object of any class derived from NumericTable except CSRNumericTable . |
sumSVD | Pointer to the numeric table with partial sums computed so far on the local node.By default, this table is an object of the HomogenNumericTable class,
but you can define it as an object of any class derived from NumericTable
except PackedSymmetricMatrix , PackedTriangularMatrix , and CSRNumericTable . |
sumSquaresSVD | Pointer to the numeric table with partial sums of squares computed so far on the local node.By default, this table is an object of the HomogenNumericTable class,
but you can define it as an object of any class derived from NumericTable
except PackedSymmetricMatrix , PackedTriangularMatrix , and CSRNumericTable . |
auxiliaryDataSVD | A collection of numeric tables each with the partial result to transmit to the master node for Step 2. The collection can contain objects of any class derived from NumericTable
except the PackedSymmetricMatrix and PackedTriangularMatrix . |
Step 2 - on Master Node
Correlation method (
defaultDense
)In this step, 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 |
|---|---|
partialResults | A collection that contains results computed in Step 1 on local nodes
( nObservationsCorrelation , crossProductCorrelation , and sumCorrelation ).The collection can contain objects of any class derived from NumericTable
except the PackedSymmetricMatrix and PackedTriangularMatrix . |
In this step, PCA 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 |
|---|---|
eigenvalues | Pointer to the numeric table that contains eigenvalues in the descending order. |
eigenvectors | Pointer to the numeric table that contains eigenvectors in the row-major order. |
By default, these results are 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
.SVD method (
svdDense
)In this step, 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 |
|---|---|
partialResults | A collection that contains results computed in Step 1 on local nodes
( nObservationsSVD , sumSVD , sumSquaresSVD , and auxiliaryDataSVD ).The collection can contain objects of any class derived from NumericTable
except the PackedSymmetricMatrix and PackedTriangularMatrix . |
In this step, PCA 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 |
|---|---|
eigenvalues | Pointer to the numeric table that contains eigenvalues in the descending order. |
eigenvectors | Pointer to the numeric table that contains eigenvectors in the row-major order. |
By default, these results are 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
.