Getting Started Guide

Contents

Batch Processing

LASSO algorithm follows the general workflow described in Usage Model: Training and Prediction.

Training

For a description of common input and output parameters, refer to Usage Model: Training and Prediction. The LASSO algorithm has the following input parameters in addition to the common input parameters:
Input ID
Input
weights
Optional input.
Pointer to the 1 x
n
numeric table with weights of samples. The input can be an object of any class derived from
NumericTable
except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
By default, all weights are equal to 1.
gramMatrix
Optional input.
Pointer to the
p
x
p
numeric table with pre-computed Gram Matrix. The input can be an object of any class derived from
NumericTable
except for
CSRNumericTable
.
By default, the table is set to an empty numeric table. It is used only when the number of features is less than the number of observations.
The LASSO batch training 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.
method
defaultDense
The computation method used by the LASSO regression. The only training method supported so far is the default dense method.
interceptFlag
True
A flag that indicates whether or not to compute
lassoParameters
Numeric table of size 1 x 1 that contains the default LASSO parameter equal to 0.1.
L
1
coefficients:
A numeric table of size 1 x
k
(where
k
is the number of dependent variables) or 1 x 1. The contents of the table depend on its size:
  • For the table of size 1 x
    k
    , use the values of LASSO parameters
    for
    j
    = 1, ...,
    k
  • For the table of size 1 x 1, use the value of LASSO parameter for each dependant variable
This parameter can be an object of any class derived from
NumericTable
, except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
optimizationSolver
Coordinate Descent solver
Optimization procedure used at the training stage.
optResultToCompute
0
The 64-bit integer flag that specifies which extra characteristics of the LASSO regression to compute.
Provide the following value to request a characteristic:
  • computeGramMatrix
    for Computation Gram matrix
dataUseInComputation
doNotUse
A flag that indicates a permission to overwrite input data. Provide the following value to restrict or allow modification of input data:
  • doNotUse
    – restricts modification
  • doUse
    – allows modification
In additional to linear regression result LASSO algorithm has the following optional results:
Result ID
Result
gramMatrix
Pointer to the computed Gram Matrix with size
p
x
p
.

Prediction

For a description of the input and output, refer to Usage Model: Training and Prediction.
At the prediction stage, LASSO 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.
method
defaultDense
Default performance-oriented computation method, the only method supported by the regression-based prediction.
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Product and Performance Information

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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 reservered 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