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