Mon 21 Jul 22:43:21 CEST 2025
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js/demo/ml1.js
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159
js/demo/ml1.js
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var Ml = require('/opt/JAM/lib/libml');
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// KNN
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console.log(Ml)
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var data = [
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[0, 0, 0], [0, 1, 1], [1, 1, 0], [2, 2, 2], [1, 2, 2], [3, 2, 2], [3, 5, 6]
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]
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var results = [
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'a', 'a', 'a', 'b', 'b', 'c', 'c'
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]
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var model1 = Ml.learn({x:data,y:results,algorithm:Ml.ML.KNN,distance:Ml.ML.EUCL});
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console.log(model1)
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var prediction1 = Ml.classify(model1,[[1.81, 1.81, 1.81], [0.5, 0.5, 0.5]]);
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console.log(Ml.ML.KNN,prediction1)
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// DTI
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var model2 = Ml.learn({x:data,y:results,algorithm:Ml.ML.DTI,eps:0.2});
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console.log(Ml.print(model2))
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var prediction2 = Ml.classify(model2,[[1.81, 1.81, 1.81], [0.5, 0.5, 0.5]]);
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console.log(Ml.ML.DTI,prediction2)
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// MLP
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var x = [[0.4, 0.5, 0.5, 0., 0., 0.],
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[0.5, 0.3, 0.5, 0., 0., 0.],
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[0.4, 0.5, 0.5, 0., 0., 0.],
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[0., 0., 0.5, 0.3, 0.5, 0.],
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[0., 0., 0.5, 0.4, 0.5, 0.],
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[0., 0., 0.5, 0.5, 0.5, 0.]];
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var y = [[1, 0],
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[1, 0],
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[1, 0],
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[0, 1],
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[0, 1],
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[0, 1]];
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var mlp1 = new Ml.learn({
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x : x,
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y : y,
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labels:['a','b'],
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hidden_layers : [4,4,5],
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lr : 0.6,
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epochs : 20000,
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algorithm:Ml.ML.MLP,
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verbose:1
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});
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a = [[0.5, 0.5, 0., 0., 0., 0.],
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[0., 0., 0., 0.5, 0.5, 0.],
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[0.5, 0.5, 0.5, 0.5, 0.5, 0.]];
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console.log(Ml.ML.MLP,Ml.classify(mlp1,a));
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console.log(Ml.ML.MLP,Ml.classify(mlp1,a[0]));
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var x = [[0.004, 0.005, 0.005, 0., 0., 0.],
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[0.005, 0.003, 0.005, 0., 0., 0.],
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[0.004, 0.005, 0.005, 0., 0., 0.],
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[0., 0., 0.005, 0.003, 0.005, 0.],
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[0., 0., 0.005, 0.004, 0.005, 0.],
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[0., 0., 0.005, 0.005, 0.005, 0.]];
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var y = ['a',
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'a',
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'a',
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'b',
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'b',
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'b'];
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var mlp2 = new Ml.learn({
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x : x,
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y : y,
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features:['f1','f2','f3','f4','f5','f6'],
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labels:['a','b'],
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hidden_layers : [4,4,5],
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lr : 0.6,
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epochs : 20000,
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algorithm:Ml.ML.MLP,
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normalize:true,
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bipolar:true,
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verbose:1
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});
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console.log(Ml.ML.MLP,Ml.classify(mlp2,{f1:0.005,f2:0.005,f3:0,f4:0,f5:0,f6:0}));
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// SVM
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var x = [[0.4, 0.5, 0.5, 0., 0., 0.],
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[0.5, 0.3, 0.5, 0., 0., 0.01],
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[0.4, 0.8, 0.5, 0., 0.1, 0.2],
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[1.4, 0.5, 0.5, 0., 0., 0.],
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[1.5, 0.3, 0.5, 0., 0., 0.],
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[0., 0.9, 1.5, 0., 0., 0.],
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[0., 0.7, 1.5, 0., 0., 0.],
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[0.5, 0.1, 0.9, 0., -1.8, 0.],
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[0.8, 0.8, 0.5, 0., 0., 0.],
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[0., 0.9, 0.5, 0.3, 0.5, 0.2],
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[0., 0., 0.5, 0.4, 0.5, 0.],
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[0., 0., 0.5, 0.5, 0.5, 0.],
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[0.3, 0.6, 0.7, 1.7, 1.3, -0.7],
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[0., 0., 0.5, 0.3, 0.5, 0.2],
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[0., 0., 0.5, 0.4, 0.5, 0.1],
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[0., 0., 0.5, 0.5, 0.5, 0.01],
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[0.2, 0.01, 0.5, 0., 0., 0.9],
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[0., 0., 0.5, 0.3, 0.5, -2.3],
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[0., 0., 0.5, 0.4, 0.5, 4],
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[0., 0., 0.5, 0.5, 0.5, -2]];
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var y = [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1];
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var svm = Ml.learn({
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x : x,
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y : y,
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C : 1.1, // default : 1.0. C in SVM.
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tol : 1e-5, // default : 1e-4. Higher tolerance --> Higher precision
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max_passes : 20, // default : 20. Higher max_passes --> Higher precision
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alpha_tol : 1e-5, // default : 1e-5. Higher alpha_tolerance --> Higher precision
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kernel : { type: "polynomial", c: 1, d: 5},
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// default : {type : "gaussian", sigma : 1.0}
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// {type : "gaussian", sigma : 0.5}
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// {type : "linear"} // x*y
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// {type : "polynomial", c : 1, d : 8} // (x*y + c)^d
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// Or you can use your own kernel.
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// kernel : function(vecx,vecy) { return dot(vecx,vecy);}
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algorithm:Ml.ML.SVM
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});
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console.log(svm.algorithm,Ml.classify(svm,[1.3, 1.7, 0.5, 0.5, 1.5, 0.4]));
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// C4.5
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var CLASS1='CL1',CLASS2='CL2',A='A',B='B',C='C',True=true,False=false;
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var data = [
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[A,70,True,CLASS1],
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[A,90,True,CLASS2],
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[A,85,False,CLASS2],
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[A,95,False,CLASS2],
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[A,70,False,CLASS1],
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[B,90,True,CLASS1],
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[B,78,False,CLASS1],
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[B,65,True,CLASS1],
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[B,75,False,CLASS1],
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[C,85,True,CLASS1],
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[C,80,True,CLASS2],
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[C,70,True,CLASS2],
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[C,80,False,CLASS1],
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[C,80,False,CLASS1],
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[C,96,False,CLASS1],
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];
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var c45 = Ml.learn({
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data:data,
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algorithm:Ml.ML.C45
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});
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var testData = [
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[B,71,False],
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[C,70,True],
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[C,75,True],
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[C,95,True],
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];
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console.log(c45.algorithm,Ml.classify(c45,testData));
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