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

References | Namespaces | Classes | Enumerations
Mean Squared Error Algorithm

Contains classes for computing the Mean squared error objective function. More...

References

 Batch
 

Namespaces

 daal::algorithms::optimization_solver::mse
 Contains classes for computing the Mean squared error objective function.
 
 daal::algorithms::optimization_solver::mse::interface1
 Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface.
 
 daal::algorithms::optimization_solver::mse::interface2
 Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface.
 

Classes

struct  Parameter
 Parameter for Mean squared error objective function More...
 
class  Input
 Input objects for the Mean squared error objective function More...
 

Enumerations

enum  InputId { argument = (int)sum_of_functions::argument, data, dependentVariables }
 
enum  OptionalInputId { optionalArgument = lastInputId + 1 }
 
enum  OptionalDataId { weights, gramMatrix }
 
enum  Method { defaultDense = 0 }
 

Enumeration Type Documentation

enum InputId

Available identifiers of input objects of the Mean squared error objective function

Enumerator
argument 

Numeric table of size 1 x p with input argument of the objective function

data 

Numeric table of size n x p with data

dependentVariables 

Numeric table of size n x 1 with dependent variables

enum Method

Available methods for computing results of Mean squared error objective function

Enumerator
defaultDense 

Default: performance-oriented method.

enum OptionalDataId

Available identifiers of optional input for the iterative solver

Enumerator
weights 

NumericTable of size 1 x n with samples weights. Applied for all method

gramMatrix 

NumericTable of size p x p with last iteration number. Applied for all method

enum OptionalInputId

Available identifiers of optional input for the iterative solver

Enumerator
optionalArgument 

Algorithm-specific input data, can be generated by previous runs of the algorithm

For more complete information about compiler optimizations, see our Optimization Notice.