【JavaScript】Support Vector Machine in Javascript

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ml-svm

Support Vector Machines in Javascript

Implementation of this simplified Sequential Minimization Optimization algorithm

Installation

npm install ml-svm

API

API documentation

本帖隐藏的内容

svm-master.zip (1.17 MB)

2017-4-16 04:04:44 上传

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关键词：Javascript machine Support script Vector Vector

1. Example
   
2. 
   
3. // Instantiate the svm classifier
   
4. var SVM = require('ml-svm');
   
5. 
   
6. var options = {
   
7.   C: 0.01,
   
8.   tol: 10e-4,
   
9.   maxPasses: 10,
   
10.   maxIterations: 10000,
    
11.   kernel: 'rbf',
    
12.   kernelOptions: {
    
13.     sigma: 0.5
    
14.   }
    
15. };
    
16. 
    
17. var svm = new SVM(options);
    
18. 
    
19. // Train the classifier - we give him an xor
    
20. var features = [[0,0],[0,1],[1,1],[1,0]];
    
21. var labels = [1, -1, 1, -1];
    
22. svm.train(features, labels);
    
23. 
    
24. // Let's see how narrow the margin is
    
25. var margins = svm.margin(features);
    
26. 
    
27. // Let's see if it is separable by testing on the training data
    
28. svm.predict(features); // [1, -1, 1, -1]
    
29. 
    
30. // I want to see what my support vectors are
    
31. var supportVectors = svm.supportVectors();
    
32. 
    
33. // Now we want to save the model for later use
    
34. var model = svm.toJSON();
    
35. 
    
36. /// ... later, you can make predictions without retraining the model
    
37. var importedSvm = SVM.load(model);
    
38. importedSvm.predict(features); // [1, -1, 1, -1]

复制代码

1. 'use strict';
   
2. 
   
3. const iris = require('ml-dataset-iris');
   
4. const CV = require('ml-cross-validation');
   
5. const SVM = require('..');
   
6. 
   
7. 
   
8. var labels = iris.getClasses();
   
9. const features = iris.getNumbers();
   
10. 
    
11. // Separate in 2 categories
    
12. labels = labels.map(label => {
    
13.     if(label === labels[0]) return 1;
    
14.     return -1;
    
15. });
    
16. //
    
17. // const features = [[20, 10],[30,30]];
    
18. // var labels = [-1, 1];
    
19. 
    
20. // var svm = new SVM();
    
21. // svm.train(features, labels);
    
22. 
    
23. console.time('cv');
    
24. const result = CV.leaveOneOut(SVM, features, labels, {
    
25.     kernel: 'rbf',
    
26.     C: 1,
    
27.     kernelOptions: {
    
28.         sigma: 0.2,
    
29.     }
    
30. });
    
31. console.timeEnd('cv');
    
32. console.log(result);

复制代码