diff --git a/js/demo/ml1.js b/js/demo/ml1.js
new file mode 100644
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--- /dev/null
+++ b/js/demo/ml1.js
@@ -0,0 +1,159 @@
+var Ml = require('/opt/JAM/lib/libml');
+
+// KNN
+console.log(Ml)
+var data = [
+[0, 0, 0], [0, 1, 1], [1, 1, 0], [2, 2, 2], [1, 2, 2], [3, 2, 2], [3, 5, 6]
+]
+var results = [
+'a', 'a', 'a', 'b', 'b', 'c', 'c'
+]
+var model1 = Ml.learn({x:data,y:results,algorithm:Ml.ML.KNN,distance:Ml.ML.EUCL});
+console.log(model1)
+var prediction1 = Ml.classify(model1,[[1.81, 1.81, 1.81], [0.5, 0.5, 0.5]]);
+console.log(Ml.ML.KNN,prediction1)
+
+// DTI
+var model2 = Ml.learn({x:data,y:results,algorithm:Ml.ML.DTI,eps:0.2});
+console.log(Ml.print(model2))
+var prediction2 = Ml.classify(model2,[[1.81, 1.81, 1.81], [0.5, 0.5, 0.5]]);
+console.log(Ml.ML.DTI,prediction2)
+
+// MLP
+var x = [[0.4, 0.5, 0.5, 0.,  0.,  0.],
+         [0.5, 0.3,  0.5, 0.,  0.,  0.],
+         [0.4, 0.5, 0.5, 0.,  0.,  0.],
+         [0.,  0.,  0.5, 0.3, 0.5, 0.],
+         [0.,  0.,  0.5, 0.4, 0.5, 0.],
+         [0.,  0.,  0.5, 0.5, 0.5, 0.]];
+var y = [[1, 0],
+         [1, 0],
+         [1, 0],
+         [0, 1],
+         [0, 1],
+         [0, 1]];
+
+var mlp1 = new Ml.learn({
+    x : x,
+    y : y,
+    labels:['a','b'],
+    hidden_layers : [4,4,5],
+    lr : 0.6,
+    epochs : 20000,
+    algorithm:Ml.ML.MLP,
+    verbose:1
+});
+a = [[0.5, 0.5, 0., 0., 0., 0.],
+     [0., 0., 0., 0.5, 0.5, 0.],
+     [0.5, 0.5, 0.5, 0.5, 0.5, 0.]];
+
+console.log(Ml.ML.MLP,Ml.classify(mlp1,a));
+console.log(Ml.ML.MLP,Ml.classify(mlp1,a[0]));
+
+var x = [[0.004, 0.005, 0.005, 0.,  0.,  0.],
+         [0.005, 0.003, 0.005, 0.,  0.,  0.],
+         [0.004, 0.005, 0.005, 0.,  0.,  0.],
+         [0.,  0.,  0.005, 0.003, 0.005, 0.],
+         [0.,  0.,  0.005, 0.004, 0.005, 0.],
+         [0.,  0.,  0.005, 0.005, 0.005, 0.]];
+var y = ['a',
+         'a',
+         'a',
+         'b',
+         'b',
+         'b'];
+
+var mlp2 = new Ml.learn({
+    x : x,
+    y : y,
+    features:['f1','f2','f3','f4','f5','f6'],
+    labels:['a','b'],
+    hidden_layers : [4,4,5],
+    lr : 0.6,
+    epochs : 20000,
+    algorithm:Ml.ML.MLP,
+    normalize:true,
+    bipolar:true,
+    verbose:1
+});
+
+
+
+console.log(Ml.ML.MLP,Ml.classify(mlp2,{f1:0.005,f2:0.005,f3:0,f4:0,f5:0,f6:0}));
+
+// SVM
+var x = [[0.4, 0.5, 0.5, 0.,  0.,  0.],
+         [0.5, 0.3,  0.5, 0.,  0.,  0.01],
+         [0.4, 0.8, 0.5, 0.,  0.1,  0.2],
+         [1.4, 0.5, 0.5, 0.,  0.,  0.],
+         [1.5, 0.3,  0.5, 0.,  0.,  0.],
+         [0., 0.9, 1.5, 0.,  0.,  0.],
+         [0., 0.7, 1.5, 0.,  0.,  0.],
+         [0.5, 0.1,  0.9, 0.,  -1.8,  0.],
+         [0.8, 0.8, 0.5, 0.,  0.,  0.],
+         [0.,  0.9,  0.5, 0.3, 0.5, 0.2],
+         [0.,  0.,  0.5, 0.4, 0.5, 0.],
+         [0.,  0.,  0.5, 0.5, 0.5, 0.],
+         [0.3, 0.6, 0.7, 1.7,  1.3, -0.7],
+         [0.,  0.,  0.5, 0.3, 0.5, 0.2],
+         [0.,  0.,  0.5, 0.4, 0.5, 0.1],
+         [0.,  0.,  0.5, 0.5, 0.5, 0.01],
+         [0.2, 0.01, 0.5, 0.,  0.,  0.9],
+         [0.,  0.,  0.5, 0.3, 0.5, -2.3],
+         [0.,  0.,  0.5, 0.4, 0.5, 4],
+         [0.,  0.,  0.5, 0.5, 0.5, -2]];
+
+var y =  [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1];
+
+var svm = Ml.learn({
+    x : x,
+    y : y,
+    C : 1.1, // default : 1.0. C in SVM.
+    tol : 1e-5, // default : 1e-4. Higher tolerance --> Higher precision
+    max_passes : 20, // default : 20. Higher max_passes --> Higher precision
+    alpha_tol : 1e-5, // default : 1e-5. Higher alpha_tolerance --> Higher precision
+
+    kernel : { type: "polynomial", c: 1, d: 5},
+    // default : {type : "gaussian", sigma : 1.0}
+    // {type : "gaussian", sigma : 0.5}
+    // {type : "linear"} // x*y
+    // {type : "polynomial", c : 1, d : 8} // (x*y + c)^d
+    // Or you can use your own kernel.
+    // kernel : function(vecx,vecy) { return dot(vecx,vecy);}
+    algorithm:Ml.ML.SVM
+});
+
+console.log(svm.algorithm,Ml.classify(svm,[1.3,  1.7,  0.5, 0.5, 1.5, 0.4]));
+
+
+// C4.5
+var CLASS1='CL1',CLASS2='CL2',A='A',B='B',C='C',True=true,False=false;
+var data = [
+[A,70,True,CLASS1],
+[A,90,True,CLASS2],
+[A,85,False,CLASS2],
+[A,95,False,CLASS2],
+[A,70,False,CLASS1],
+[B,90,True,CLASS1],
+[B,78,False,CLASS1],
+[B,65,True,CLASS1],
+[B,75,False,CLASS1],
+[C,85,True,CLASS1],
+[C,80,True,CLASS2],
+[C,70,True,CLASS2],
+[C,80,False,CLASS1],
+[C,80,False,CLASS1],
+[C,96,False,CLASS1],
+];
+var c45 = Ml.learn({
+    data:data,
+    algorithm:Ml.ML.C45
+});
+
+var testData = [
+        [B,71,False],
+        [C,70,True],
+        [C,75,True],
+        [C,95,True],
+      ];
+console.log(c45.algorithm,Ml.classify(c45,testData));