Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

List of all members
Parameter Class Reference

Parameters of the K-Means computation method. More...

Class Constructor

Parameter ( DaalContext  context,
long  nClusters,
long  maxIterations,
double  accuracyThreshold,
double  gamma,
DistanceType  distanceType,
boolean  assignFlag 
)

Constructs a parameter

Parameters
contextContext to manage the parameter of the K-Means algorithm
nClustersNumber of clusters
maxIterationsNumber of iterations
accuracyThresholdThreshold for the termination of the algorithm
gammaWeight used in distance calculation for categorical features
distanceTypeDistance used in the algorithm
assignFlagFlag to enable assignment of observations to clusters; assigns data points
Parameter ( DaalContext  context,
long  nClusters,
long  maxIterations,
double  accuracyThreshold,
double  gamma,
DistanceType  distanceType 
)

Constructs a parameter

Parameters
contextContext to manage the parameter of the K-Means algorithm
nClustersNumber of clusters
maxIterationsNumber of iterations
accuracyThresholdThreshold for the termination of the algorithm
gammaWeight used in distance calculation for categorical features
distanceTypeDistance used in the algorithm
Parameter ( DaalContext  context,
long  nClusters,
long  maxIterations,
double  accuracyThreshold,
double  gamma 
)

Constructs a parameter

Parameters
contextContext to manage the parameter of the K-Means algorithm
nClustersNumber of clusters
maxIterationsNumber of iterations
accuracyThresholdThreshold for the termination of the algorithm
gammaWeight used in distance calculation for categorical features
Parameter ( DaalContext  context,
long  nClusters,
long  maxIterations,
double  accuracyThreshold 
)

Constructs a parameter

Parameters
contextContext to manage the parameter of the K-Means algorithm
nClustersNumber of clusters
maxIterationsNumber of iterations
accuracyThresholdThreshold for the termination of the algorithm
Parameter ( DaalContext  context,
long  nClusters,
long  maxIterations 
)

Constructs a parameter

Parameters
contextContext to manage the parameter of the K-Means algorithm
nClustersNumber of clusters
maxIterationsNumber of iterations

Detailed Description

Member Function Documentation

double getAccuracyThreshold ( )

Retrieves the threshold for the termination of the algorithm

Returns
Threshold for the termination of the algorithm
boolean getAssignFlag ( )

Retrieves the flag for the assignment of data points

Returns
Flag for the assignment of data points
DistanceType getDistanceType ( )

Returns the distance type

Returns
Distance type
double getGamma ( )

Retrieves the weight used in distance calculation for categorical features

Returns
Weight used in distance calculation for categorical features
long getMaxIterations ( )

Retrieves the number of iterations

Returns
Number of iterations
long getNClusters ( )

Retrieves the number of clusters

Returns
Number of clusters
void setAccuracyThreshold ( double  accuracy)

Sets the threshold for the termination of the algorithm

Parameters
accuracyThreshold for the termination of the algorithm
void setAssignFlag ( boolean  assignFlag)

Sets the flag for the assignment of data points

Parameters
assignFlagFlag to enable assignment of observations to clusters
void setGamma ( double  gamma)

Sets the weight used in distance calculation for categorical features

Parameters
gammaWeight used in distance calculation for categorical features
void setMaxIterations ( long  max)

Sets the number of iterations

Parameters
maxNumber of iterations.
void setNClusters ( long  nClusters)

Sets the number of clusters

Parameters
nClustersNumber of clusters

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

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