Online Processing
Online processing computation mode assumes that data arrives in blocks
.
.Computation of correlation and variance-covariance matrices in the online processing mode follows
the general computation schema for online processing described in Algorithms.
Algorithm Input
The correlation and variance-covariance matrices algorithm in the online processing mode 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
that represents the current data block.While the input for defaultDense , singlePassDense , or sumDense method can be an object of any class derived
from NumericTable , the input for fastCSR , singlePassCSR , or sumCSR method can only be an object of
the CSRNumericTable class. |
Algorithm Parameters
The correlation and variance-covariance matrices algorithm has the following parameters in the online processing mode:
Parameter | Default Valude | Description |
|---|---|---|
algorithmFPType | float | The floating-point type that the algorithm uses for intermediate computations. Can be float or double . |
method | defaultDense | Available methods for computation of correlation and variance-covariance matrices:
|
outputMatrixType | covarianceMatrix | The type of the output matrix. Can be:
|
initializationProcedure | Not applicable | The procedure for setting initial parameters of the algorithm in the online processing mode.
By default, the algorithm sets the nObservations , sum , and crossProduct parameters to zero. |
Partial Results
The correlation and variance-covariance matrices algorithm in the online processing mode calculates partial 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 |
|---|---|
nObservations | Pointer to the
numeric table that contains the number of observations processed so far.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 CSRNumericTable . |
crossProduct | Pointer to
numeric table with the cross-product matrix computed so far.By default, this table 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 . |
sum | Pointer to
numeric table with partial sums computed so far.By default, this table 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 . |
Algorithm Output
The correlation and variance-covariance matrices 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 |
|---|---|
covariance | Use when outputMatrixType``=``covarianceMatrix . Pointer to the numeric table with the
variance-covariance matrix.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 PackedTriangularMatrix and CSRNumericTable . |
correlation | Use when outputMatrixType``=``correlationMatrix . Pointer to the numeric table with the
correlation matrix.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 PackedTriangularMatrix and CSRNumericTable . |
mean | Pointer to the
numeric table with means.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 PackedTriangularMatrix , PackedSymmetricMatrix , and CSRNumericTable . |
Optimization Notice |
|---|
Intel’s compilers may or may not optimize to the same degree for
non-Intel microprocessors for optimizations that are not unique to
Intel microprocessors. These optimizations include SSE2, SSE3, and
SSSE3 instruction sets and other optimizations. Intel does not
guarantee the availability, functionality, or effectiveness of any
optimization on microprocessors not manufactured by Intel.
Microprocessor-dependent optimizations in this product are intended
for use with Intel microprocessors. Certain optimizations not
specific to Intel microarchitecture are reserved for Intel
microprocessors. Please refer to the applicable product User and
Reference Guides for more information regarding the specific
instruction sets covered by this notice. Notice revision #20110804 |