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104 lines
2.5 KiB
Python
104 lines
2.5 KiB
Python
FluidMLPRegressor : FluidRTDataClient {
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const <identity = 0;
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const <sigmoid = 1;
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const <relu = 2;
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const <tanh = 3;
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*new {|server, hidden = #[3,3] , activation = 2, outputActivation = 0, tapIn = 0, tapOut = -1,maxIter = 1000, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
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var hiddenCtrlLabels;
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hidden = [hidden.size]++hidden;
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hiddenCtrlLabels = hidden.collect{|x,i| \hidden++i};
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^super.new1(server,
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[hiddenCtrlLabels,hidden].lace ++
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[
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\activation,activation,
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\outputActivation, outputActivation,
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\tapIn, tapIn,
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\tapOut, tapOut,
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\maxIter, maxIter,
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\learnRate,learnRate,
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\momentum, momentum,
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\batchSize,batchSize,
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\validation,validation,
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])
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}
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clear{ |action|
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this.prSendMsg(\clear,action:action);
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}
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fit{|sourceDataSet, targetDataSet, action|
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this.prSendMsg(\fit,
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[sourceDataSet.asSymbol, targetDataSet.asSymbol],
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action,numbers(FluidMessageResponse,_,1,_)
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);
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}
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predict{ |sourceDataSet, targetDataSet, action|
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this.prSendMsg(\predict,
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[sourceDataSet.asSymbol, targetDataSet.asSymbol],
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action);
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}
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predictPoint { |sourceBuffer, targetBuffer, action|
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sourceBuffer = this.prEncodeBuffer(sourceBuffer);
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targetBuffer = this.prEncodeBuffer(targetBuffer);
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this.prSendMsg(\predictPoint,
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[sourceBuffer.asUGenInput, targetBuffer.asUGenInput], action,outputBuffers:[targetBuffer]);
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}
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}
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FluidMLPClassifier : FluidRTDataClient {
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const <identity = 0;
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const <sigmoid = 1;
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const <relu = 2;
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const <tanh = 3;
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*new {|server, hidden = #[3,3] , activation = 2, maxIter = 1000, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
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var hiddenCtrlLabels;
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hidden = [hidden.size]++hidden;
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hiddenCtrlLabels = hidden.collect{|x,i| \hidden++i};
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^super.new1(server,
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[hiddenCtrlLabels,hidden].lace ++
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[
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\activation,activation,
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\maxIter, maxIter,
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\learnRate,learnRate,
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\momentum, momentum,
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\batchSize,batchSize,
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\validation,validation,
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])
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}
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clear{ |action|
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this.prSendMsg(\clear,action:action);
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}
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fit{|sourceDataSet, targetLabelSet, action|
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this.prSendMsg(\fit,
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[sourceDataSet.asSymbol, targetLabelSet.asSymbol],
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action,numbers(FluidMessageResponse,_,1,_)
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);
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}
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predict{ |sourceDataSet, targetLabelSet, action|
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this.prSendMsg(\predict,
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[sourceDataSet.asSymbol, targetLabelSet.asSymbol],
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action);
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}
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predictPoint { |sourceBuffer, action|
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sourceBuffer = this.prEncodeBuffer(sourceBuffer);
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this.prSendMsg(\predictPoint,
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[sourceBuffer], action, string(FluidMessageResponse,_,_));
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}
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}
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