From 6a4d4115e875e0e438aa60015a28f5a961a9e863 Mon Sep 17 00:00:00 2001
From: sbosse <sbosse@uni-bremen.de>
Date: Mon, 21 Jul 2025 23:07:28 +0200
Subject: [PATCH] Mon 21 Jul 22:43:21 CEST 2025

---
 js/ml/lr.js | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 js/ml/lr.js

diff --git a/js/ml/lr.js b/js/ml/lr.js
new file mode 100644
index 0000000..f4c0b82
--- /dev/null
+++ b/js/ml/lr.js
@@ -0,0 +1,66 @@
+/**
+ * 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;
+}