A set of results returned by Kinetica.aggregateKMeans(string,IList{string},int,double,IDictionary{string, string}). More...

Inheritance diagram for kinetica.AggregateKMeansResponse:

Collaboration diagram for kinetica.AggregateKMeansResponse:

Properties
IList< IList< double > >	means `[get, set]`
	The k-mean values found. More...

IList< long >	counts = new List<IList<double>>() `[get, set]`
	The number of elements in the cluster closest the corresponding k-means values. More...

IList< double >	rms_dists = new List<long>() `[get, set]`
	The root mean squared distance of the elements in the cluster for each of the k-means values. More...

long	count = new List<double>() `[get, set]`
	The total count of all the clusters - will be the size of the input table. More...

double	rms_dist `[get, set]`
	The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize. More...

double	tolerance `[get, set]`
	The distance between the last two iterations of the algorithm before it quit. More...

int	num_iters `[get, set]`
	The number of iterations the algorithm executed before it quit. More...

IDictionary< string, string >	info `[get, set]`
	Additional information. More...

Properties inherited from kinetica.KineticaData
Schema	Schema `[get]`
	Avro Schema for this class More...

Additional Inherited Members
Public Member Functions inherited from kinetica.KineticaData
	KineticaData (KineticaType type)
	Constructor from Kinetica Type More...

	KineticaData (System.Type type=null)
	Default constructor, with optional System.Type More...

object	Get (int fieldPos)
	Retrieve a specific property from this object More...

void	Put (int fieldPos, object fieldValue)
	Write a specific property to this object More...

Static Public Member Functions inherited from kinetica.KineticaData
static RecordSchema	SchemaFromType (System.Type t, KineticaType ktype=null)
	Create an Avro Schema from a System.Type and a KineticaType. More...

Detailed Description

A set of results returned by Kinetica.aggregateKMeans(string,IList{string},int,double,IDictionary{string, string}).

Definition at line 182 of file AggregateKMeans.cs.

Property Documentation

long kinetica.AggregateKMeansResponse.count = new List<double>()

getset

The total count of all the clusters - will be the size of the input table.

Definition at line 198 of file AggregateKMeans.cs.

IList<long> kinetica.AggregateKMeansResponse.counts = new List<IList<double>>()

getset

The number of elements in the cluster closest the corresponding k-means values.

Definition at line 190 of file AggregateKMeans.cs.

IDictionary<string, string> kinetica.AggregateKMeansResponse.info

getset

Additional information.

Definition at line 213 of file AggregateKMeans.cs.

IList<IList<double> > kinetica.AggregateKMeansResponse.means

getset

The k-mean values found.

Definition at line 186 of file AggregateKMeans.cs.

int kinetica.AggregateKMeansResponse.num_iters

getset

The number of iterations the algorithm executed before it quit.

Definition at line 210 of file AggregateKMeans.cs.

double kinetica.AggregateKMeansResponse.rms_dist

getset

The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.

Definition at line 202 of file AggregateKMeans.cs.

IList<double> kinetica.AggregateKMeansResponse.rms_dists = new List<long>()

getset

The root mean squared distance of the elements in the cluster for each of the k-means values.

Definition at line 194 of file AggregateKMeans.cs.

double kinetica.AggregateKMeansResponse.tolerance

getset

The distance between the last two iterations of the algorithm before it quit.

Definition at line 206 of file AggregateKMeans.cs.

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