7 using System.Collections.Generic;
34 public const string WHITEN =
"whiten";
63 public const string TRUE =
"true";
64 public const string FALSE =
"false";
97 public const string TTL =
"ttl";
117 public int k {
get;
set; }
212 public IDictionary<string, string>
options {
get;
set; } =
new Dictionary<string, string>();
326 IDictionary<string, string>
options =
null)
352 public IList<IList<double>>
means {
get;
set; } =
new List<IList<double>>();
356 public IList<long>
counts {
get;
set; } =
new List<long>();
360 public IList<double>
rms_dists {
get;
set; } =
new List<double>();
389 public IDictionary<string, string>
info {
get;
set; } =
new Dictionary<string, string>();
IList< double > rms_dists
The root mean squared distance of the elements in the cluster for each of the k-means values.
IList< string > column_names
List of column names on which the operation would be performed.
const string CREATE_TEMP_TABLE
If TRUE, a unique temporary table name will be generated in the sys_temp schema and used in place of ...
const string TTL
Sets the TTL of the table specified in RESULT_TABLE.
AggregateKMeansRequest(string table_name, IList< string > column_names, int k, double tolerance, IDictionary< string, string > options=null)
Constructs an AggregateKMeansRequest object with the specified parameters.
KineticaData - class to help with Avro Encoding for Kinetica
IDictionary< string, string > info
Additional information.
int k
The number of mean points to be determined by the algorithm.
double tolerance
Stop iterating when the distances between successive points is less than the given tolerance.
A set of results returned by Kinetica.aggregateKMeans.
const string MAX_ITERS
Number of times to try to hit the tolerance limit before giving up - default is 10.
string table_name
Name of the table on which the operation will be performed.
const string RESULT_TABLE
The name of a table used to store the results, in [schema_name.
A set of string constants for the parameter options.
const string WHITEN
When set to 1 each of the columns is first normalized by its stdv - default is not to whiten.
A set of parameters for Kinetica.aggregateKMeans.
IList< IList< double > > means
The k-mean values found.
int num_iters
The number of iterations the algorithm executed before it quit.
long count
The total count of all the clusters - will be the size of the input table.
double tolerance
The distance between the last two iterations of the algorithm before it quit.
double rms_dist
The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.
const string RESULT_TABLE_PERSIST
If TRUE, then the result table specified in RESULT_TABLE will be persisted and will not expire unless...
IList< long > counts
The number of elements in the cluster closest the corresponding k-means values.
const string NUM_TRIES
Number of times to run the k-means algorithm with a different randomly selected starting points - hel...
AggregateKMeansRequest()
Constructs an AggregateKMeansRequest object with default parameters.
IDictionary< string, string > options
Optional parameters.
const string QUALIFIED_RESULT_TABLE_NAME
The fully qualified name of the result table (i.e.
A set of string constants for the parameter info.