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155 lines
4.3 KiB
Plaintext
155 lines
4.3 KiB
Plaintext
TITLE:: FluidKMeans
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summary:: Cluster data points with K-Means
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categories:: FluidManipulation
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related:: Classes/FluidDataSet, Classes/FluidLabelSet, Classes/FluidKNN
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DESCRIPTION::
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Uses the K-Means algorithm to learn clusters from a link::Classes/FluidDataSet::
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https://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html#clustering-grouping-observations-together
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CLASSMETHODS::
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METHOD:: new
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Construct a new K Means model on the passed server
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ARGUMENT:: server
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If nil will use Server.default
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INSTANCEMETHODS::
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PRIVATE::k
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METHOD:: fit
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Identify code::k:: clusters in a link::Classes/FluidDataSet::
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ARGUMENT:: dataset
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A link::Classes/FluidDataSet:: of data points
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ARGUMENT:: k
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The number of clusters to identify in the data set
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ARGUMENT:: maxIter
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Maximum number of iterations to use partitioning the data
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ARGUMENT:: buffer
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Seed centroids for clusters WARNING:: Not yet implemented ::
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ARGUMENT:: action
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A function to run when fitting is complete, taking as its argument an array with the number of data points for each cluster
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METHOD:: predict
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Given a trained object, return the cluster ID for each data point in a dataset to a label set.
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ARGUMENT:: dataset
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a link::Classes/FluidDataSet:: containing the data to predict
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ARGUMENT:: labelset
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a link::Classes/FluidLabelSet:: to reveive the predicted clusters
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ARGUMENT:: action
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A function to run when the server responds
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METHOD:: fitPredict
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Run link::Classes/FluidKMeans#*fit:: and link::Classes/FluidKMeans#*predict:: in a single pass: i.e. train the model on the incoming link::Classes/FluidDataSet:: and then return the learned clustering to the passed link::Classes/FluidLabelSet::
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ARGUMENT:: dataset
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a link::Classes/FluidDataSet:: containing the data to fit and predict
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ARGUMENT:: labelset
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a link::Classes/FluidLabelSet:: to reveive the predicted clusters
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ARGUMENT:: k
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The number of clusters to identify in the data set
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ARGUMENT:: maxIter
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Maximum number of iterations to use partitioning the data
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ARGUMENT:: action
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A function to run when the server responds
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METHOD:: predictPoint
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Given a trained object, return the cluster ID for a data point in a link::Classes/Buffer::
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ARGUMENT:: buffer
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a link::Classes/Buffer:: containing a data point
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ARGUMENT:: action
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A function to run when the server responds, taking the ID of the cluser as its argument
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METHOD:: predict
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Report cluster assignments for previously unseen data
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ARGUMENT:: dataset
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A link::Classes/FluidDataSet:: of data points
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ARGUMENT:: labelset
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A link::Classes/FluidLabelSet:: to contain assigments
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ARGUMENT:: action
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A function to run when complete, taking an array of the counts for each catgegory as its argument
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EXAMPLES::
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Server.default.options.outDevice = "Built-in Output"
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code::
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//A dataset for our points, a labelset for cluster labels
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(
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~dataset= FluidDataSet(s,\kdtree_help_rand2d);
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~clusters = FluidLabelSet(s,\kmeans_help_clusters);
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)
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//Make some clumped 2D points and place into a dataset
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(
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~points = (4.collect{64.collect{(1.sum3rand) + [1,-1].choose}.clump(2)}).flatten(1) * 0.5;
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~dataset.clear;
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~tmpbuf = Buffer.alloc(s,2);
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fork{
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s.sync;
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~points.do{|x,i|
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(""++(i+1)++"/128").postln;
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~tmpbuf.setn(0,x);
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~dataset.addPoint(i,~tmpbuf);
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s.sync
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}
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}
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)
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//Make a new k means model, fit it to the dataset and return the discovered clusters to a labelset
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(
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fork{
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~clusters.clear;
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~kmeans = FluidKMeans(s);
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s.sync;
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~kmeans.fitPredict(~dataset,~clusters, 4,action: {|c|
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"Fitted.\n # Points in each cluster:".postln;
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c.do{|x,i|
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("Cluster" + i + "->" + x.asInteger + "points").postln;
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}
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});
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}
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)
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//Dims of kmeans should match dataset
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~kmeans.cols
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//Return labels of clustered points
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(
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~assignments = Array.new(128);
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fork{
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128.do{ |i|
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~clusters.getLabel(i,{|clusterID|
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(i.asString+clusterID).postln;
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~assignments.add(clusterID)
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});
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s.sync;
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}
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}
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)
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//Visualise: we're hoping to see colours neatly mapped to quandrants...
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(
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d = ((~points + 1) * 0.5).flatten(1).unlace;
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// d = [20.collect{1.0.rand}, 20.collect{1.0.rand}];
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w = Window("scatter", Rect(128, 64, 200, 200));
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~colours = [Color.blue,Color.red,Color.green,Color.magenta];
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w.drawFunc = {
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Pen.use {
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d[0].size.do{|i|
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var x = (d[0][i]*200);
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var y = (d[1][i]*200);
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var r = Rect(x,y,5,5);
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Pen.fillColor = ~colours[~assignments[i].asInteger];
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Pen.fillOval(r);
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}
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}
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};
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w.refresh;
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w.front;
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)
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::
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