67 lines
2.5 KiB
JavaScript
67 lines
2.5 KiB
JavaScript
/**
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* Created by joonkukang on 2014. 1. 12..
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*/
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var math = require('./utils').math;
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let LogisticRegression = module.exports = function (settings) {
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var self = this;
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self.x = settings['input'];
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self.y = settings['label'];
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self.W = math.zeroMat(settings['n_in'],settings['n_out']);
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self.b = math.zeroVec(settings['n_out']);
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self.settings = {
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'log level' : 1 // 0 : nothing, 1 : info, 2: warn
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};
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};
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LogisticRegression.prototype.train = function (settings) {
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var self = this;
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var lr = 0.1, epochs = 200;
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if(typeof settings['input'] !== 'undefined')
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self.x = settings['input'];
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if(typeof settings['lr'] !== 'undefined')
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lr = settings['lr'];
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if(typeof settings['epochs'] !== 'undefined')
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epochs = settings['epochs'];
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var i;
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var currentProgress = 1;
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for(i=0;i<epochs;i++) {
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var probYgivenX = math.softmaxMat(math.addMatVec(math.mulMat(self.x,self.W),self.b));
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var deltaY = math.minusMat(self.y,probYgivenX);
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var deltaW = math.mulMat(math.transpose(self.x),deltaY);
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var deltaB = math.meanMatAxis(deltaY,0);
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self.W = math.addMat(self.W,math.mulMatScalar(deltaW,lr));
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self.b = math.addVec(self.b,math.mulVecScalar(deltaB,lr));
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if(self.settings['log level'] > 0) {
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var progress = (1.*i/epochs)*100;
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if(progress > currentProgress) {
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console.log("LogisticRegression",progress.toFixed(0),"% Completed.");
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currentProgress++;
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}
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}
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}
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if(self.settings['log level'] > 0)
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console.log("LogisticRegression Final Cross Entropy : ",self.getReconstructionCrossEntropy());
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};
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LogisticRegression.prototype.getReconstructionCrossEntropy = function () {
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var self = this;
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var probYgivenX = math.softmaxMat(math.addMatVec(math.mulMat(self.x,self.W),self.b));
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var a = math.mulMatElementWise(self.y, math.activateMat(probYgivenX,Math.log));
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var b = math.mulMatElementWise(math.mulMatScalar(math.addMatScalar(self.y,-1),-1),
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math.activateMat(math.mulMatScalar(math.addMatScalar(probYgivenX,-1),-1),Math.log));
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var crossEntropy = -math.meanVec(math.sumMatAxis(math.addMat(a,b),1));
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return crossEntropy;
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};
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LogisticRegression.prototype.predict = function (x) {
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var self = this;
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return math.softmaxMat(math.addMatVec(math.mulMat(x,self.W),self.b));
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};
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LogisticRegression.prototype.set = function(property,value) {
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var self = this;
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self.settings[property] = value;
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}
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