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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 
10 
11 namespace kinetica
12 {
13 
26  {
27 
50  public struct Options
51  {
52 
55  public const string WHITEN = "whiten";
56 
59  public const string MAX_ITERS = "max_iters";
60 
64  public const string NUM_TRIES = "num_tries";
65  } // end struct Options
66 
67 
70  public string table_name { get; set; }
71 
76  public IList<string> column_names { get; set; } = new List<string>();
77 
80  public int k { get; set; }
81 
84  public double tolerance { get; set; }
85 
106  public IDictionary<string, string> options { get; set; } = new Dictionary<string, string>();
107 
108 
112 
149  IList<string> column_names,
150  int k,
151  double tolerance,
152  IDictionary<string, string> options = null)
153  {
154  this.table_name = table_name ?? "";
155  this.column_names = column_names ?? new List<string>();
156  this.k = k;
157  this.tolerance = tolerance;
158  this.options = options ?? new Dictionary<string, string>();
159  } // end constructor
160 
161  } // end class AggregateKMeansRequest
162 
163 
164 
167  {
168 
170  public IList<IList<double>> means { get; set; } = new List<IList<double>>();
171 
174  public IList<long> counts { get; set; } = new List<long>();
175 
178  public IList<double> rms_dists { get; set; } = new List<double>();
179 
182  public long count { get; set; }
183 
186  public double rms_dist { get; set; }
187 
190  public double tolerance { get; set; }
191 
194  public int num_iters { get; set; }
195 
196  } // end class AggregateKMeansResponse
197 
198 
199 
200 
201 } // end namespace kinetica
A set of results returned by /aggregate/kmeans.
int k
The number of mean points to be determined by the algorithm.
long count
The total count of all the clusters - will be the size of the input table.
IList< string > column_names
List of column names on which the operation would be performed.
double tolerance
The distance between the last two iterations of the algorithm before it quit.
AggregateKMeansRequest()
Constructs an AggregateKMeansRequest object with default parameters.
const string WHITEN
When set to 1 each of the columns is first normalized by its stdv - default is not to whiten...
int num_iters
The number of iterations the algorithm executed before it quit.
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.
const string MAX_ITERS
Number of times to try to hit the tolerance limit before giving up - default is 10.
const string NUM_TRIES
Number of times to run the k-means algorithm with a different randomly selected starting points - hel...
IDictionary< string, string > options
Optional parameters.
string table_name
Name of the table on which the operation will be performed.
A set of parameters for /aggregate/kmeans.
IList< IList< double > > means
The k-mean values found.
double rms_dist
The sum of all the rms_dists - the value the k-means algorithm is attempting to minimize.
IList< double > rms_dists
The root mean squared distance of the elements in the cluster for each of the k-means values...
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...
KineticaData - class to help with Avro Encoding for Kinetica
Definition: KineticaData.cs:14