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

Public Member Functions | List of all members
Model Class Reference

Model of the classifier trained by the stump.training.Batch algorithm. More...

Public Member Functions

def serializationTag
 
def getSerializationTag
 
def downCast
 
def getSplitFeature
 
def setSplitFeature
 
def getNumberOfFeatures
 
def __init__
 
def getLeftSubsetAverage_{Float64|Float32}
 
def getRightSubsetAverage_{Float64|Float32}
 
def getSplitValue_{Float64|Float32}
 
def setLeftSubsetAverage_{Float64|Float32}
 
def setRightSubsetAverage_{Float64|Float32}
 
def setSplitValue_{Float64|Float32}
 
- Public Member Functions inherited from Model
def getNFeatures
 
def getNumberOfFeatures
 
def setNFeatures
 
- Public Member Functions inherited from Model
def __init__
 
def getSerializationTag
 
- Public Member Functions inherited from SerializationIface
def serialize
 
def deserialize
 
def getSerializationTag
 
- Public Member Functions inherited from Base
def __init__
 

Detailed Description

References

Constructor & Destructor Documentation

def __init__ (   self,
  args 
)

Variant 1

Empty constructor for deserialization


Variant 2

Constructs the decision stump model

Parameters
nFeaturesNumber of features in the dataset
dummyDummy variable for the templated constructor
Deprecated:
This item will be removed in a future release. Use Model.create instead.
Allowed Types
  • float64
  • float32

Member Function Documentation

def downCast (   r)

downCast(daal.services.SharedPtr< daal.algorithms.classifier.Model > const & r) -> daal.services.SharedPtr< daal.algorithms.stump.Model >

def getLeftSubsetAverage_{Float64|Float32} (   self)

Returns an average of the weighted responses for the "left" subset

Returns
Average of the weighted responses for the "left" subset
Full Names
  • getLeftSubsetAverage_Float64 is for float64
  • getLeftSubsetAverage_Float32 is for float32
def getNumberOfFeatures (   self)

Retrieves the number of features in the dataset was used on the training stage

Returns
Number of features in the dataset was used on the training stage
def getRightSubsetAverage_{Float64|Float32} (   self)

Returns an average of the weighted responses for the "right" subset

Returns
Average of the weighted responses for the "right" subset
Full Names
  • getRightSubsetAverage_Float64 is for float64
  • getRightSubsetAverage_Float32 is for float32
def getSerializationTag (   self)

getSerializationTag(Model self) -> int

def getSplitFeature (   self)

Returns the split feature

Returns
Index of the feature over which the split is made
def getSplitValue_{Float64|Float32} (   self)

Returns a value of the feature that defines the split

Returns
Value of the feature over which the split is made
Full Names
  • getSplitValue_Float64 is for float64
  • getSplitValue_Float32 is for float32
def serializationTag ( )
def setLeftSubsetAverage_{Float64|Float32} (   self,
  leftSubsetAverage 
)

Sets an average of the weighted responses for the "left" subset

Parameters
leftSubsetAverageAn average of the weighted responses for the "left" subset
Full Names
  • setLeftSubsetAverage_Float64 is for float64
  • setLeftSubsetAverage_Float32 is for float32
def setRightSubsetAverage_{Float64|Float32} (   self,
  rightSubsetAverage 
)

Sets an average of the weighted responses for the "right" subset

Parameters
rightSubsetAverageAn average of the weighted responses for the "right" subset
Full Names
  • setRightSubsetAverage_Float64 is for float64
  • setRightSubsetAverage_Float32 is for float32
def setSplitFeature (   self,
  splitFeature 
)

Sets the split feature

Parameters
splitFeatureIndex of the split feature
def setSplitValue_{Float64|Float32} (   self,
  splitValue 
)

Sets a value of the feature that defines the split

Parameters
splitValueValue of the split feature
Full Names
  • setSplitValue_Float64 is for float64
  • setSplitValue_Float32 is for float32

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

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