Kinetica   C#   API  Version 7.2.3.0
AggregateKMeans.cs
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1 /*
2  * This file was autogenerated by the Kinetica schema processor.
3  *
4  * DO NOT EDIT DIRECTLY.
5  */
6 
7 using System.Collections.Generic;
8 
9 namespace kinetica
10 {
25  public class AggregateKMeansRequest : KineticaData
26  {
30  public struct Options
31  {
34  public const string WHITEN = "whiten";
35 
38  public const string MAX_ITERS = "max_iters";
39 
44  public const string NUM_TRIES = "num_tries";
45 
61  public const string CREATE_TEMP_TABLE = "create_temp_table";
62 
63  public const string TRUE = "true";
64  public const string FALSE = "false";
65 
74  public const string RESULT_TABLE = "result_table";
75 
92  public const string RESULT_TABLE_PERSIST = "result_table_persist";
93 
97  public const string TTL = "ttl";
98  } // end struct Options
99 
106  public string table_name { get; set; }
107 
113  public IList<string> column_names { get; set; } = new List<string>();
114 
117  public int k { get; set; }
118 
121  public double tolerance { get; set; }
122 
213  public IDictionary<string, string> options { get; set; } = new Dictionary<string, string>();
214 
218 
326  IList<string> column_names,
327  int k,
328  double tolerance,
329  IDictionary<string, string> options = null)
330  {
331  this.table_name = table_name ?? "";
332  this.column_names = column_names ?? new List<string>();
333  this.k = k;
334  this.tolerance = tolerance;
335  this.options = options ?? new Dictionary<string, string>();
336  } // end constructor
337  } // end class AggregateKMeansRequest
338 
342  public class AggregateKMeansResponse : KineticaData
343  {
347  public struct Info
348  {
351  public const string QUALIFIED_RESULT_TABLE_NAME = "qualified_result_table_name";
352  } // end struct Info
353 
355  public IList<IList<double>> means { get; set; } = new List<IList<double>>();
356 
359  public IList<long> counts { get; set; } = new List<long>();
360 
363  public IList<double> rms_dists { get; set; } = new List<double>();
364 
367  public long count { get; set; }
368 
371  public double rms_dist { get; set; }
372 
375  public double tolerance { get; set; }
376 
379  public int num_iters { get; set; }
380 
393  public IDictionary<string, string> info { get; set; } = new Dictionary<string, string>();
394  } // end class AggregateKMeansResponse
395 } // end namespace kinetica
const string QUALIFIED_RESULT_TABLE_NAME
The fully qualified name of the result table (i.e.
IDictionary< string, string > info
Additional information.
const string WHITEN
When set to 1 each of the columns is first normalized by its stdv - default is not to whiten.
AggregateKMeansRequest()
Constructs an AggregateKMeansRequest object with default parameters.
const string RESULT_TABLE
The name of a table used to store the results, in [schema_name.
long count
The total count of all the clusters - will be the size of the input table.
const string TTL
Sets the TTL of the table specified in RESULT_TABLE.
double rms_dist
The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.
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.
IDictionary< string, string > options
Optional parameters.
int num_iters
The number of iterations the algorithm executed before it quit.
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 ...
IList< string > column_names
List of column names on which the operation would be performed.
int k
The number of mean points to be determined by the algorithm.
IList< double > rms_dists
The root mean squared distance of the elements in the cluster for each of the k-means values.
string table_name
Name of the table on which the operation will be performed.
IList< IList< double > > means
The k-mean values found.
const string NUM_TRIES
Number of times to run the k-means algorithm with a different randomly selected starting points - hel...
IList< long > counts
The number of elements in the cluster closest the corresponding k-means values.
double tolerance
Stop iterating when the distances between successive points is less than the given tolerance.
const string MAX_ITERS
Number of times to try to hit the tolerance limit before giving up - default is 10.
const string RESULT_TABLE_PERSIST
If TRUE, then the result table specified in RESULT_TABLE will be persisted and will not expire unless...
double tolerance
The distance between the last two iterations of the algorithm before it quit.