TITLE:: FluidKMeans
summary:: Cluster data points with K-Means
categories:: FluidManipulation
related:: Classes/FluidDataSet, Classes/FluidLabelSet, Classes/FluidKNN

DESCRIPTION::
Uses the K-Means algorithm to learn clusters from a link::Classes/FluidDataSet::

https://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html#clustering-grouping-observations-together

CLASSMETHODS::

INSTANCEMETHODS::

PRIVATE::k

METHOD:: predictPoint
Given a trained object, return the cluster ID for a data point in a link::Classes/Buffer::

ARGUMENT:: buffer
a link::Classes/Buffer:: containing a data point

ARGUMENT:: action
A function to run when the server responds, taking the ID of the cluser as its argument

METHOD:: fit
Identify code::k:: clusters in a link::Classes/FluidDataSet::

ARGUMENT:: dataset
A link::Classes/FluidDataSet:: of data points

ARGUMENT:: k
The number of clusters to identify in the data set

ARGUMENT:: maxIter
Maximum number of iterations to use partitioning the data

ARGUMENT:: buffer
Seed centroids for clusters WARNING:: Not yet implemented ::

ARGUMENT:: action
A function to run when fitting is complete, taking as its argument an array with the number of data points for each cluster

METHOD:: write
write learned clusters to disk as a JSON file. Will not overwrite existing files

ARGUMENT:: filename
Absolute path for file

ARGUMENT:: action
A function to run when the file is written

METHOD:: read
Read a learned clustering of a data set from a JSON file

ARGUMENT:: filename
Absolute path of the JSON file

ARGUMENT:: action
Function to run when the file has been read

METHOD:: getClusters
Fill a link::Classes/FluidLabelSet:: with the assignments for each point in the passed link::Classes/FluidDataSet:: that was used to train this instance

ARGUMENT:: dataset
The link::Classes/FluidDataSet:: used to train this instance

ARGUMENT:: labelset
A link::Classes/FluidLabelSet:: to fill with assignments

ARGUMENT:: action
A function to run when the operation is complete

METHOD:: cols
Retreive the dimentionality of the dataset this instance is trained on

ARGUMENT:: action
A function to run when the server responds, taking the dimensionality as its argument

METHOD:: predict
Report cluster assignments for previously unseen data

ARGUMENT:: dataset
A link::Classes/FluidDataSet:: of data points

ARGUMENT:: labelset
A link::Classes/FluidLabelSet:: to contain assigments

ARGUMENT:: action
A function to run when complete, taking an array of the counts for each catgegory as its argument

EXAMPLES::

code::
(some example code)
::