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74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
FluidBaseMLP : FluidDataClient {
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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 = 0, maxIter = 100, 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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}
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FluidMLPRegressor : FluidBaseMLP {
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*new {|server, hidden = #[3,3] , activation = 0, maxIter = 100, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
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^super.new(server,hidden,activation, maxIter,learnRate, momentum, batchSize,validation)
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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, layer, action|
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this.prSendMsg(\predict,
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[sourceDataSet.asSymbol, targetDataSet.asSymbol,layer],
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action);
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}
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predictPoint { |sourceBuffer, targetBuffer, layer action|
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this.prSendMsg(\predictPoint,
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[sourceBuffer.asUGenInput, targetBuffer.asUGenInput, layer], action);
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}
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}
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FluidMLPClassifier : FluidBaseMLP {
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*new {|server, hidden = #[3,3] , activation = 0, maxIter = 100, learnRate = 0.0001, momentum = 0.9, batchSize = 50, validation = 0.2|
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^super.new(server,hidden,activation, maxIter,learnRate, momentum, batchSize,validation)
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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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this.prSendMsg(\predictPoint,
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[sourceBuffer.asUGenInput], action,string(FluidMessageResponse,_,_));
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}
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}
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