|  | ## Sample (Kennard-Stone)

#### Synopsis

Creates a sample from an example set using the Kennard-Stone algorithm.

#### Description

This operator performs a Kennard-Stone Sampling. This sampling Algorithm works as follows: First find the two points most separated in the training set. For each candidate point, find the smallest distance to any object already selected. Select that point for the training set which has the largest of these smallest distances As described above, this algorithm always gives the same result, due to the two starting points which are always the same. This implementation reduces number of iterations by holding a list with candidates of the largest smallest distances. The parameters controll the number of examples in the sample

#### Input

- example set input: expects: ExampleSetMetaData: #examples: = 0; #attributes: 0

#### Output

- example set output:
- original:

#### Parameters

- sample: Determines how the amount of data is specified.
- sample size: The number of examples which should be sampled
- sample ratio: The fraction of examples which should be sampled

#### ExampleProcess