FluidRobustScale: first copying of normalise's code
Pierre Alexandre Tremblay
5 years ago
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bbe715287e
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FluidRobustScale : FluidRealTimeModel {
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var <>min, <>max, <>invert;
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*new {|server, min = 0, max = 1, invert = 0|
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^super.new(server,[min,max,invert])
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.min_(min).max_(max).invert_(invert);
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}
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prGetParams{
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^[this.min,this.max,this.invert,-1,-1];
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}
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fitMsg{|dataSet|
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^this.prMakeMsg(\fit,id,dataSet.id)
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}
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fit{|dataSet, action|
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actions[\fit] = [nil,action];
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this.prSendMsg(this.fitMsg(dataSet));
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}
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transformMsg{|sourceDataSet, destDataSet|
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^this.prMakeMsg(\transform,id,sourceDataSet.id,destDataSet.id);
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}
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transform{|sourceDataSet, destDataSet, action|
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actions[\transform] = [nil,action];
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this.prSendMsg(this.transformMsg(sourceDataSet, destDataSet));
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}
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fitTransformMsg{|sourceDataSet, destDataSet|
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^this.prMakeMsg(\fitTransform,id,sourceDataSet.id,destDataSet.id)
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}
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fitTransform{|sourceDataSet, destDataSet, action|
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actions[\fitTransform] = [nil,action];
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this.prSendMsg(this.fitTransformMsg(sourceDataSet, destDataSet));
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}
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transformPointMsg{|sourceBuffer, destBuffer|
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^this.prMakeMsg(\transformPoint,id,
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this.prEncodeBuffer(sourceBuffer),
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this.prEncodeBuffer(destBuffer),
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["/b_query",destBuffer.asUGenInput]
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);
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}
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transformPoint{|sourceBuffer, destBuffer, action|
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actions[\transformPoint] = [nil,{action.value(destBuffer)}];
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this.prSendMsg(this.transformPointMsg(sourceBuffer, destBuffer));
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}
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kr{|trig, inputBuffer,outputBuffer,min,max,invert|
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min = min ? this.min;
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max = max ? this.max;
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invert = invert ? this.invert;
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this.min_(min).max_(max).invert_(invert);
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^FluidProxyUgen.kr(this.class.name.asString++'/query', K2A.ar(trig),
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id, this.min, this.max, this.invert, this.prEncodeBuffer(inputBuffer), this.prEncodeBuffer(outputBuffer));
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}
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}
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@ -0,0 +1,157 @@
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TITLE:: FluidRobustScale
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summary:: Apply Robust Scaling to FluidDataSet
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categories:: FluidManipulation
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related:: Classes/FluidStandardize, Classes/FluidDataSet
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DESCRIPTION::
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Normalize the entries of a link::Classes/FluidDataSet::, or normalize a data point according to the learned bounds of a data set. On the server.
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See http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html
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CLASSMETHODS::
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private:: kr
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METHOD:: new
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Create a new instance
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ARGUMENT:: server
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The link::Classes/Server:: on which to run
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ARGUMENT:: min
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Minimum output value, default 0
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ARGUMENT:: max
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Maximum output value, default 1
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ARGUMENT:: invert
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The direction in which the normalization will occur for transform and transformpoint. The default 0 is taking in the range of the input used to fit and transforms it towards the normalised range. A value of 1 will expect an input of the normalized range to transform back to the original range.
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INSTANCEMETHODS::
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METHOD:: fit
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Compute the normalization factors from a link::Classes/FluidDataSet:: for later.
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ARGUMENT:: dataSet
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The link::Classes/FluidDataSet:: to normalize
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ARGUMENT:: action
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A function to run when processing is complete
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METHOD:: transform
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Normalize a link::Classes/FluidDataSet:: into another link::Classes/FluidDataSet::, using the learned extrema from a previous call to link::Classes/FluidRobustScale#fit::
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ARGUMENT:: sourceDataSet
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The link::Classes/FluidDataSet:: to normalize
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ARGUMENT:: destDataSet
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The link::Classes/FluidDataSet:: to populate with normalized data
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ARGUMENT:: action
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A function to run when processing is complete
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METHOD:: fitTransform
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Normalize a link::Classes/FluidDataSet::
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ARGUMENT:: sourceDataSet
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The link::Classes/FluidDataSet:: to normalize
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ARGUMENT:: destDataSet
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The link::Classes/FluidDataSet:: to populate with normalized data
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ARGUMENT:: action
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A function to run when processing is complete
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METHOD:: transformPoint
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Normalize a new data point, using the learned extrema from a previous call to link::Classes/FluidRobustScale#fit::
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ARGUMENT:: sourceBuffer
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A link::Classes/Buffer:: with the new data point
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ARGUMENT:: destBuffer
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A link::Classes/Buffer:: to contain the normalized value
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ARGUMENT:: action
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A function to run when processing is complete
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EXAMPLES::
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code::
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s.boot;
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//Preliminaries: we want some audio, a couple of FluidDataSets, some Buffers and a FluidRobustScale
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// FluidRobustScale.dumpAllMethods
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(
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~audiofile = File.realpath(FluidBufPitch.class.filenameSymbol).dirname +/+ "../AudioFiles/Tremblay-ASWINE-ScratchySynth-M.wav";
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~raw = FluidDataSet(s,\norm_help_raw);
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~norm = FluidDataSet(s,\norm_help_normd);
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~pitch_feature = Buffer.new(s);
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~stats = Buffer.alloc(s, 7, 2);
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~normalizer = FluidRobustScale(s);
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)
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// Load audio and run a pitch analysis, which gives us pitch and pitch confidence (so a 2D datum)
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(
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~audio = Buffer.read(s,~audiofile);
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FluidBufPitch.process(s,~audio, features: ~pitch_feature);
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)
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// Divide the time series in to 10, and take the mean of each segment and add this as a point to
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// the 'raw' FluidDataSet
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(
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{
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var trig = LocalIn.kr(1, 1);
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var buf = LocalBuf(2, 1);
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var count = PulseCount.kr(trig) - 1;
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var chunkLen = (~pitch_feature.numFrames / 10).asInteger;
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var stats = FluidBufStats.kr(
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source: ~pitch_feature, startFrame: count * chunkLen,
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numFrames: chunkLen, stats: ~stats, trig: (trig * (count <=9)), blocking:1
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);
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var rd = BufRd.kr(2, ~stats, DC.kr(0), 0, 1);// pick only mean pitch and confidence
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var wr1 = BufWr.kr(rd[0], buf, DC.kr(0));
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var wr2 = BufWr.kr(rd[1], buf, DC.kr(1));
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var dsWr = FluidDataSetWr.kr(~raw, buf: buf, trig: Done.kr(stats));
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LocalOut.kr( Done.kr(dsWr));
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Poll.kr(trig,count,\count);
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FreeSelf.kr(count - 9);
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}.play;
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)
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// Normalize and load to language-side array
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(
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~rawarray = Array.new(10);
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~normedarray= Array.new(10);
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~normalizer.fitTransform(~raw,~norm, {
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~raw.dump{|x| 10.do{|i|
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~rawarray.add(x["data"][i.asString])
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}};
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~norm.dump{|x| 10.do{|i|
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~normedarray.add(x["data"][i.asString])
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}};
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});
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)
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//Plot side by side. Before normalization the two dimensions have radically different scales
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//which can be unhelpful in many cases
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(
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~rawarray.flatten(1).unlace.plot("Unnormalized",Rect(0,0,400,400),minval:0,maxval:[5000,1]).plotMode=\bars;
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~plot2 = ~normedarray.flatten(1).unlace.plot("Normalized",Rect(410,0,400,400)).plotMode=\bars;
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)
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// single point transform on arbitrary value
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~inbuf = Buffer.loadCollection(s,0.5.dup);
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~outbuf = Buffer.new(s);
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~normalizer.transformPoint(~inbuf,~outbuf,{|x|x.postln;x.getn(0,2,{|y|y.postln;};)});
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OSCFunc.trace(false,true)
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//Server side queries
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(
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{
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var audio = BufRd.ar(1,~audio,LFSaw.ar(BufDur.ir(~audio).reciprocal).range(0, BufFrames.ir(~audio)));
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var counter = Stepper.ar(Impulse.ar(ControlRate.ir),max:99);
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var trig = A2K.kr(HPZ1.ar(counter) < 0);
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//average 100 frames: one could use the MovingAverage extension here
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var avg;
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var inputPoint = LocalBuf(2);
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var outputPoint = LocalBuf(2);
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var avgBuf = LocalBuf(100,2);
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//running average of pitch features
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BufWr.kr(FluidPitch.kr(audio),avgBuf,phase:counter);
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avg = Mix.new(BufRd.kr(2, avgBuf, phase:100.collect{|x|x})) * 0.01;
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//assemble data point
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BufWr.kr(avg[0],inputPoint,0);
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BufWr.kr(avg[1],inputPoint,1);
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~normalizer.kr(trig,inputPoint,outputPoint);
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Poll.kr(trig,BufRd.kr(1,inputPoint,[0,1]),["pitch (raw)", "confidence (raw)"]);
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Poll.kr(trig,BufRd.kr(1,outputPoint,[0,1]),["pitch (normalized)", "confidence (normalized)"])
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}.play;
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)
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::
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