C++ API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

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Parameter Struct Reference

Parameter base class for the Adaptive gradient descent algorithm More...

Class Declaration

struct DAAL_EXPORT Parameter : public optimization_solver::iterative_solver::Parameter
{
const sum_of_functions::BatchPtr &function_,
size_t nIterations_ = 100,
double accuracyThreshold_ = 1.0e-05,
data_management::NumericTablePtr batchIndices_ = data_management::NumericTablePtr(),
const size_t batchSize_ = 128,
data_management::NumericTablePtr learningRate_ = data_management::HomogenNumericTable<double>::create(1, 1, data_management::NumericTableIface::doAllocate, 0.01),
double degenerateCasesThreshold_ = 1.0e-08,
size_t seed_ = 777
);
virtual ~Parameter() {}
virtual services::Status check() const DAAL_C11_OVERRIDE;
data_management::NumericTablePtr batchIndices;
data_management::NumericTablePtr learningRate;
double degenerateCasesThreshold;
size_t seed;
engines::EnginePtr engine;
};

Constructor & Destructor Documentation

Parameter ( const sum_of_functions::BatchPtr &  function_,
size_t  nIterations_ = 100,
double  accuracyThreshold_ = 1.0e-05,
data_management::NumericTablePtr  batchIndices_ = data_management::NumericTablePtr(),
const size_t  batchSize_ = 128,
data_management::NumericTablePtr  learningRate_ = data_management::HomogenNumericTable< double >::create(1, 1, data_management::NumericTableIface::doAllocate, 0.01),
double  degenerateCasesThreshold_ = 1.0e-08,
size_t  seed_ = 777 
)

Constructs the parameter base class of the Adaptive gradient descent algorithm

Parameters
[in]function_Objective function represented as sum of functions
[in]nIterations_Maximal number of iterations of the algorithm
[in]accuracyThreshold_Accuracy of the algorithm. The algorithm terminates when this accuracy is achieved
[in]batchIndices_Numeric table that represents 32 bit integer indices of terms in the objective function. If no indices are provided, the implementation will generate random indices.
[in]batchSize_Number of batch indices to compute the stochastic gradient. If batchSize is equal to the number of terms in objective function then no random sampling is performed, and all terms are used to calculate the gradient. This parameter is ignored if batchIndices is provided.
[in]learningRate_Numeric table that contains value of the learning rate
[in]degenerateCasesThreshold_Value needed to avoid degenerate cases in square root computing.
[in]seed_Seed for random generation of 32 bit integer indices of terms in the objective function.
Deprecated:
This item will be removed in a future release. Use engine instead.

Member Function Documentation

virtual services::Status check ( ) const
virtual

Checks the correctness of the parameter

Returns
Status of computations

Member Data Documentation

data_management::NumericTablePtr batchIndices

Numeric table that represents 32 bit integer indices of terms in the objective function. If no indices are provided, the implementation will generate random indices.

double degenerateCasesThreshold

Value needed to avoid degenerate cases in square root computing.

engines::EnginePtr engine

Engine for random generation of 32 bit integer indices of terms in the objective function.

data_management::NumericTablePtr learningRate

Numeric table that contains value of the learning rate

size_t seed

Seed for random generation of 32 bit integer indices of terms in the objective function.

Deprecated:
This item will be removed in a future release. Use engine instead.

The documentation for this struct was generated from the following file:

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