C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5
Parameter for the Stochastic gradient descent algorithm More...
| Parameter | ( | const sum_of_functions::BatchPtr & | function, |
| size_t | nIterations = 100, |
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| double | accuracyThreshold = 1.0e-05, |
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| data_management::NumericTablePtr | batchIndices = data_management::NumericTablePtr(), |
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| data_management::NumericTablePtr | learningRateSequence = data_management::NumericTablePtr(new data_management::HomogenNumericTable< double >(1, 1, data_management::NumericTableIface::doAllocate, 1.0)), |
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| size_t | seed = 777 |
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| ) |
| [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] | learningRateSequence | Numeric table that contains values of the learning rate sequence |
| [in] | seed | Seed for random generation of 32 bit integer indices of terms in the objective function. |
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virtual |
Checks the correctness of the parameter
Reimplemented from BaseParameter.
For more complete information about compiler optimizations, see our Optimization Notice.