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64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
FluidKNNRegressor : FluidModelObject {
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var <>numNeighbours, <>weight;
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*new {|server, numNeighbours = 3, weight = 1|
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^super.new(server,[numNeighbours,weight])
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.numNeighbours_(numNeighbours)
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.weight_(weight);
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}
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prGetParams{^[this.id,this.numNeighbours,this.weight,-1,-1];}
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fitMsg{|sourceDataSet, targetDataSet|
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^this.prMakeMsg(\fit,this.id,sourceDataSet.id,targetDataSet.id)
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}
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fit{|sourceDataSet, targetDataSet, action|
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actions[\fit] = [nil,action];
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this.prSendMsg(this.fitMsg(sourceDataSet, targetDataSet));
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}
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predictMsg{ |sourceDataSet, targetDataSet|
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^this.prMakeMsg(\predict,this.id,sourceDataSet.id,targetDataSet.id)
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}
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predict{ |sourceDataSet, targetDataSet, action|
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actions[\predict] = [nil, action];
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this.prSendMsg(this.predictMsg(sourceDataSet, targetDataSet));
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}
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predictPointMsg { |sourceBuffer, targetBuffer|
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^this.prMakeMsg(\predictPoint,id,
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this.prEncodeBuffer(sourceBuffer),
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this.prEncodeBuffer(targetBuffer),
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["/b_query", targetBuffer.asUGenInput]);
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}
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predictPoint { |sourceBuffer, targetBuffer, action|
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actions[\predictPoint] = [nil,{action.value(targetBuffer)}];
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this.predictPointMsg(sourceBuffer, targetBuffer);
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this.prSendMsg(this.predictPointMsg(sourceBuffer, targetBuffer));
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}
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kr{|trig, inputBuffer, outputBuffer, numNeighbours, weight|
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^FluidKNNRegressorQuery.kr(K2A.ar(trig),
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this, numNeighbours??{this.numNeighbours}, weight??{this.weight},
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this.prEncodeBuffer(inputBuffer),
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this.prEncodeBuffer(outputBuffer));
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}
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}
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FluidKNNRegressorQuery : FluidRTMultiOutUGen {
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*kr{ |trig, model,numNeighbours = 3, weight = 1,inputBuffer, outputBuffer |
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^this.multiNew('control',trig, model.asUGenInput,
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numNeighbours,weight,
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inputBuffer.asUGenInput, outputBuffer.asUGenInput)
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}
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init { arg ... theInputs;
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inputs = theInputs;
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^this.initOutputs(1, rate);
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}
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} |