66 lines
1.5 KiB
JavaScript
66 lines
1.5 KiB
JavaScript
// Test 1
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var training_data = [
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{"color":"blue", "shape":"square", "liked":false},
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{"color":"red", "shape":"square", "liked":false},
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{"color":"blue", "shape":"circle", "liked":true},
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{"color":"red", "shape":"circle", "liked":true},
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{"color":"blue", "shape":"hexagon", "liked":false},
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{"color":"red", "shape":"hexagon", "liked":false},
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{"color":"yellow", "shape":"hexagon", "liked":true},
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{"color":"yellow", "shape":"circle", "liked":true}
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]
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var test_data = [
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{"color":"blue", "shape":"hexagon", "liked":false},
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{"color":"red", "shape":"hexagon", "liked":false},
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{"color":"yellow", "shape":"hexagon", "liked":true},
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{"color":"yellow", "shape":"circle", "liked":true}
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];
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var target = "liked";
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var features = ["color", "shape"];
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var model = ml.learn({
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algorithm:ml.ML.C45,
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data:training_data,
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target:target,
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features:features
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})
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print(ml.print(model))
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print(toJSON(model).length+' Bytes')
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print(ml.classify(model,test_data))
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// Test 2
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var A='A',B='B',C='C',False=false,True=true,CLASS1='CLASS1',CLASS2='CLASS2';
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training_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,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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test_data = [
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[B,71,False],
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[C,70,True],
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]
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model = ml.learn({
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algorithm:ml.ML.C45,
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data:training_data
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})
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print(ml.print(model))
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print(ml.classify(model,test_data))
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