29 lines
1.1 KiB
JavaScript
29 lines
1.1 KiB
JavaScript
var x = [[0, 0, 0], [0, 1, 1], [1, 1, 0], [2, 2, 2], [1, 2, 2], [2, 1, 2]];
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var y = ['A', 'A', 'B', 'B', 'C', 'C'];
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var model = ml.learn({
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algorithm:ml.ML.SVM,
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x:x,
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y:y,
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threshold:false, // no threshold function on output; highest value of svms is winner
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labels:['A','B','C'], // multi-SVM
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C : 15.0, // 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 : 200, // 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: 'rbf', sigma: 0.5 } // { type: "polynomial", c: 1, d: 5}
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});
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print(toJSON(model).length+' Bytes')
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print(model)
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print(model.svms[0])
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var test_data =[[0, 1.2, 0],
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[2.1, 2, 3],
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[2.1,1.1,2.0]
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];
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print(ml.classify(model,x))
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print(ml.classify(model,x.map(function (row) { return row.map(function (col) { return col+random(-0.3,0.3,0.001) })})))
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print(ml.classify(model,test_data))
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print(ml.stats.utils.best(ml.classify(model,[1,2,3])))
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