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152 lines
3.3 KiB
Plaintext
152 lines
3.3 KiB
Plaintext
TITLE:: FluidMLPClassifier
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summary:: Classification with a neural network
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categories:: Undocumented classes
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related:: Classes/FluidMLPRegressor, Classes/FluidDataSet
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DESCRIPTION::
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CLASSMETHODS::
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METHOD:: new
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(describe method here)
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ARGUMENT:: server
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(describe argument here)
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ARGUMENT:: hidden
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(describe argument here)
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ARGUMENT:: activation
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(describe argument here)
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ARGUMENT:: maxIter
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(describe argument here)
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ARGUMENT:: learnRate
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(describe argument here)
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ARGUMENT:: momentum
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(describe argument here)
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ARGUMENT:: batchSize
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(describe argument here)
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ARGUMENT:: validation
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(describe argument here)
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returns:: (describe returnvalue here)
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INSTANCEMETHODS::
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METHOD:: predictPoint
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(describe method here)
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ARGUMENT:: sourceBuffer
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(describe argument here)
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ARGUMENT:: action
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(describe argument here)
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returns:: (describe returnvalue here)
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METHOD:: fit
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(describe method here)
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ARGUMENT:: sourceDataSet
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(describe argument here)
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ARGUMENT:: targetLabelSet
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(describe argument here)
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ARGUMENT:: action
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(describe argument here)
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returns:: (describe returnvalue here)
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METHOD:: predict
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(describe method here)
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ARGUMENT:: sourceDataSet
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(describe argument here)
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ARGUMENT:: targetLabelSet
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(describe argument here)
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ARGUMENT:: action
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(describe argument here)
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returns:: (describe returnvalue here)
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EXAMPLES::
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code::
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(
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~classifier = FluidMLPClassifier(s,hidden:[6],validation:0,momentum:0.1,learnRate:0.01);
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~sourcedata= FluidDataSet(s,\mlpclassify_help_examples);
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~labels = FluidLabelSet(s,\mlpclassify_help_labels);
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~testdata = FluidDataSet(s,\mlpclassify_help_test);
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~predictedlabels = FluidLabelSet(s,\mlpclassify_help_mapping);
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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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~centroids = [[0.5,0.5],[-0.5,0.5],[0.5,-0.5],[-0.5,-0.5]];
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~categories = [\red,\orange,\green,\blue];
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~trainingset = Dictionary();
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~labeldata = Dictionary();
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4.do{ |i|
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64.do{ |j|
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~trainingset.put("mlpclass"++i++\_++j, ~centroids[i].collect{|x| x.gauss(0.5/3)});
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~labeldata.put("mlpclass"++i++\_++j,[~categories[i]]);
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}
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};
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~sourcedata.load(Dictionary.with(*[\cols->2,\data->~trainingset]));
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~labels.load(Dictionary.with(*[\cols->1,\data->~labeldata]));
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)
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//Fit the classifier to the example DataSet and labels, and then run prediction on the test data into our mapping label set
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~classifier.fit(~sourcedata,~labels,action:{|loss| ("Trained"+loss).postln});
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//make some test data
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(
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~testset = Dictionary();
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4.do{ |i|
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64.do{ |j|
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~testset.put("mlpclass_test"++i++\_++j, ~centroids[i].collect{|x| x.gauss(0.5/3)});
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}
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};
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~testdata.load(Dictionary.with(*[\cols->2,\data->~testset]));
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)
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//Run the test data through the network, into the predicted labelset
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~classifier.predict(~testdata,~predictedlabels,action:{"Test complete".postln});
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//get labels from server
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~predictedlabels.dump(action:{|d| ~labelsdict = d["data"]});
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//Visualise: we're hoping to see colours neatly mapped to quandrants...
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(
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c = Dictionary();
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c.add("red"->Color.red);
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c.add("blue"->Color.blue);
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c.add("green"->Color.green);
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c.add("orange"->Color.new255(255, 127, 0));
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e = 200 * ((~centroids + 1) * 0.5).flatten(1).unlace;
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w = Window("scatter", Rect(128, 64, 200, 200));
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w.drawFunc = {
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Pen.use {
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~testset.keysValuesDo{|k,v|
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var x = v[0].linlin(-1,1,200,0).asInteger;
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var y = v[1].linlin(-1,1,200,0).asInteger;
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var r = Rect(x,y,5,5);
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Pen.fillColor = c.at(~labelsdict[k][0]);
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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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:: |