47 lines
1.6 KiB
JavaScript
47 lines
1.6 KiB
JavaScript
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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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// Only binary classification here: Feature Y=-1->false, 1->true
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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 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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C : 1, // default : 1.0. C in SVM.
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tol : 1e-4, // 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: "polynomial", c: 1, d: 5}
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kernel : { type: "rbf", sigma:0.5 }
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});
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print(toJSON(model).length+' Bytes')
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// print(model)
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a = [
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[1.3, 1.7, 0.5, 0.5, 1.5, 0.4],
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[0.05, 0.1, 0.5, 0.7, 0.4, -1.4]
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]
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print(ml.classify(model,x));
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print(ml.classify(model,a));
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