flucoma-sc/release-packaging/HelpSource/Classes/FluidNormalize.schelp

TITLE:: FluidNormalize
summary:: Normalize a FluidDataSet
categories:: Libraries>FluidCorpusManipulation
related:: Classes/FluidStandardize, Classes/FluidRobustScale, Classes/FluidDataSet

DESCRIPTION::
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.

See http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html

CLASSMETHODS::

private:: kr

METHOD:: new
Create a new instance
ARGUMENT:: server
The link::Classes/Server:: on which to run
ARGUMENT:: min
Minimum output value, default 0
ARGUMENT:: max
Maximum output value, default 1
ARGUMENT:: invert
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.


INSTANCEMETHODS::

METHOD:: fit
Compute the normalization factors from a link::Classes/FluidDataSet:: for later.
ARGUMENT:: dataSet
The link::Classes/FluidDataSet:: to normalize
ARGUMENT:: action
A function to run when processing is complete

METHOD:: transform
Normalize a link::Classes/FluidDataSet:: into another link::Classes/FluidDataSet::, using the learned extrema from a previous call to link::Classes/FluidNormalize#fit::
ARGUMENT:: sourceDataSet
The link::Classes/FluidDataSet:: to normalize
ARGUMENT:: destDataSet
The link::Classes/FluidDataSet:: to populate with normalized data
ARGUMENT:: action
A function to run when processing is complete


METHOD:: fitTransform
Normalize a link::Classes/FluidDataSet::
ARGUMENT:: sourceDataSet
The link::Classes/FluidDataSet:: to normalize
ARGUMENT:: destDataSet
The link::Classes/FluidDataSet:: to populate with normalized data
ARGUMENT:: action
A function to run when processing is complete


METHOD:: transformPoint
Normalize a new data point, using the learned extrema from a previous call to link::Classes/FluidNormalize#fit::
ARGUMENT:: sourceBuffer
A link::Classes/Buffer:: with the new data point
ARGUMENT:: destBuffer
A link::Classes/Buffer:: to contain the normalized value
ARGUMENT:: action
A function to run when processing is complete

EXAMPLES::
code::
s.boot;
//Preliminaries: we want some audio, a couple of FluidDataSets, some Buffers and a FluidNormalize
// FluidNormalize.dumpAllMethods
(
~audiofile = FluidFilesPath("Tremblay-ASWINE-ScratchySynth-M.wav");
~raw = FluidDataSet(s);
~norm = FluidDataSet(s);
~pitch_feature = Buffer.new(s);
~stats = Buffer.alloc(s, 7, 2);
~normalizer = FluidNormalize(s);
)

// Load audio and run a pitch analysis, which gives us pitch and pitch confidence (so a 2D datum)
(
~audio = Buffer.read(s,~audiofile);
FluidBufPitch.process(s,~audio, features: ~pitch_feature);
)

// Divide the time series in to 10, and take the mean of each segment and add this as a point to
// the 'raw' FluidDataSet
(
{
	var trig = LocalIn.kr(1, 1);
	var buf =  LocalBuf(2, 1);
    var count = PulseCount.kr(trig) - 1;
	var chunkLen = (~pitch_feature.numFrames / 10).asInteger;
	var stats = FluidBufStats.kr(
			source: ~pitch_feature, startFrame: count * chunkLen,
        numFrames: chunkLen, stats: ~stats, trig: (trig * (count <=9)), blocking:1
	);
	var rd = BufRd.kr(2, ~stats, DC.kr(0), 0, 1);// pick only mean pitch and confidence
	var wr1 = BufWr.kr(rd[0], buf, DC.kr(0));
	var wr2 = BufWr.kr(rd[1], buf, DC.kr(1));
	var dsWr = FluidDataSetWr.kr(~raw, buf: buf, idNumber: count, trig: Done.kr(stats));
	LocalOut.kr( Done.kr(dsWr));
    Poll.kr(trig,count,\count);
	FreeSelf.kr(count - 9);
}.play;
)

// Normalize and load to language-side array
(
~rawarray = Array.new(10);
~normedarray= Array.new(10);
~normalizer.fitTransform(~raw,~norm, {
	~raw.dump{|x| 10.do{|i|
		~rawarray.add(x["data"][i.asString])
	}};
	~norm.dump{|x| 10.do{|i|
		~normedarray.add(x["data"][i.asString])
	}};
});
)

//Plot side by side. Before normalization the two dimensions have radically different scales
//which can be unhelpful in many cases

(
(~rawarray ++ 0).flop.plot("Unnormalized",Rect(0,0,400,400),minval:0,maxval:[5000,1]).plotMode=\bars;
(~normedarray  ++ 0).flop.plot("Normalized",Rect(410,0,400,400)).plotMode=\bars;
)

// single point transform on arbitrary value
~inbuf = Buffer.loadCollection(s,0.5.dup);
~outbuf = Buffer.new(s);
~normalizer.transformPoint(~inbuf,~outbuf,{|x|x.postln;x.getn(0,2,{|y|y.postln;};)});

//Server side queries
(
{
	var audio = BufRd.ar(1,~audio,LFSaw.ar(BufDur.ir(~audio).reciprocal).range(0, BufFrames.ir(~audio)));
	var counter = Stepper.ar(Impulse.ar(ControlRate.ir),max:99);
    var trig = A2K.kr(HPZ1.ar(counter) < 0);
	//average 100 frames: one could use the MovingAverage extension here
	var avg;
    var inputPoint = LocalBuf(2);
    var outputPoint = LocalBuf(2);
    var avgBuf = LocalBuf(100,2);
    //running average of pitch features
	BufWr.kr(FluidPitch.kr(audio),avgBuf,phase:counter);
	avg = Mix.new(BufRd.kr(2, avgBuf, phase:100.collect{|x|x})) * 0.01;
	//assemble data point
	BufWr.kr(avg[0],inputPoint,0);
	BufWr.kr(avg[1],inputPoint,1);
    ~normalizer.kr(trig,inputPoint,outputPoint);
	Poll.kr(trig,BufRd.kr(1,inputPoint,[0,1]),["pitch (raw)", "confidence (raw)"]);
    Poll.kr(trig,BufRd.kr(1,outputPoint,[0,1]),["pitch (normalized)", "confidence (normalized)"])
}.play;
)
::