Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

Public Member Functions | Static Public Attributes | List of all members
Batch Class Reference

Provides methods for model-based training in the batch processing mode. More...

Public Member Functions

def __init__
 
def parameter
 
def getInput
 
def getMethod
 
def getResult
 
def resetResult
 
def clone
 
def compute
 
- Public Member Functions inherited from Batch
def getInput
 
def setResult
 
def resetResult
 
def clone
 
def getResult
 
def compute
 
- Public Member Functions inherited from AlgorithmImpl
def computeNoThrow
 
def compute
 
def checkComputeParams
 
def checkResult
 
def setupCompute
 
def resetCompute
 
def enableResetOnCompute
 
def hostApp
 
def setHostApp
 
- Public Member Functions inherited from Algorithm
def checkComputeParams
 
def getBaseParameter
 
- Public Member Functions inherited from AlgorithmIfaceImpl
def enableChecks
 
def isChecksEnabled
 
- Public Member Functions inherited from AlgorithmIface
def checkComputeParams
 
def checkResult
 
def getMethod
 

Static Public Attributes

 input = ...
 

Detailed Description

Parameters
fptypeData type to use in intermediate computations for model-based training, double or float
methodgradient boosted trees training method, Method
Enumerations
  • Method Computation methods
References
Aliases
  • Batch_Float64Xboost is an alias of Batch(fptype=float64, method=daal.algorithms.gbt.regression.training.xboost)
  • Batch_Float32Xboost is an alias of Batch(fptype=float32, method=daal.algorithms.gbt.regression.training.xboost)

Constructor & Destructor Documentation

def __init__ (   self,
  args 
)

Variant 1
Default constructor

Variant 2

Constructs a gradient boosted trees training algorithm by copying input objects and parameters of another gradient boosted trees training algorithm in the batch processing mode

Parameters
otherAlgorithm to use as the source to initialize the input objects and parameters of the algorithm

Member Function Documentation

def clone (   self)

Returns a pointer to a newly allocated gradient boosted trees training algorithm with a copy of the input objects and parameters for this gradient boosted trees training algorithm in the batch processing mode

Returns
Pointer to the newly allocated algorithm
def compute (   self)

Invokes computations

def getInput (   self)

Get input objects for the algorithm

Returns
input objects of the algorithm
def getMethod (   self)

Returns the method of the algorithm

Returns
Method of the algorithm
def getResult (   self)

Returns the structure that contains the result of model-based training

Returns
Structure that contains the result of model-based training
def parameter (   self,
  args 
)

Variant 1

Gets parameter of the algorithm

Returns
parameter of the algorithm

Variant 2

Gets parameter of the algorithm

Returns
parameter of the algorithm
def resetResult (   self)

resetResult(Batch self) -> Status

Member Data Documentation

input = ...
static

Input data structure


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

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