KMeansRT:

Filter: Extension

k-means classification in real time

Source: SOMTrain.sc

Performs online k-means classification, classifying each datapoint that comes in and updating the k-means centroids.

The method is described in Algorithm B.1 of Brian McFee's 2012 thesis (http://cseweb.ucsd.edu/~bmcfee/papers/bmcfee_dissertation.pdf ). McFee calls it "online Hartigan k-means".

bufnum |
a Buffer with "k" frames and "d + 1" channels (where d is the number of dimensions of input data. Each frame of the Buffer will hold data for a centroid (the centroid location, plus the final channel holds the effective number of points associated with the centroid). |

inputdata |
An array representing the input point. Num channels must match the dimensionality of the points in the dataset. |

k |
k is the number of centroids. |

gate |
The unit is active while gate > 0. While <=0, no search is performed and output is held steady |

reset |
If reset is greater than 0, the "flexibility" of the centroids is reset back to initial values (as if no data had been received). |

learn |
Controls whether the unit is learning from its input. If you set this to zero it will not learn, but will still output decisions. This is useful for applying a previously-learned clusterer without modifying it. This argument cannot be modulated: each time you use KMeansRT it is either learning from scratch, or using a fixed pre-learnt buffer. |

the cluster index with which the input datapoint has been associated.

A convenience method (just a wrapper round BufRd really) that lets you access the centroid location, given a cluster index.

bufnum |
the same buffer as is passed to .kr |

classif |
the classification index, i.e. the output from .kr |

ndims |
the number of dimensions ("d" in the above description) |

the "d"-dimensional current location of the centroid corresponding to index "classif".

This example clusters every frame of a sound sample, using the spectral centroid of each frame, and sonifies the result:

This example clusters onsets:

helpfile source: /Library/Application Support/SuperCollider/Extensions/sc3-plugins-3.13.0/SC3plugins/MCLDUGens/HelpSource/Classes/KMeansRT.schelp

link::Classes/KMeansRT::

link::Classes/KMeansRT::