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utils.ts 1.7 kB

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  1. // 分类算法
  2. export const classifierAlgorithms = [
  3. {
  4. label: 'logistic_regression(逻辑回归)',
  5. value: 'logistic_regression',
  6. },
  7. {
  8. label: 'decision_tree(决策树)',
  9. value: 'decision_tree',
  10. },
  11. {
  12. label: 'random_forest(随机森林)',
  13. value: 'random_forest',
  14. },
  15. {
  16. label: 'SVM(支持向量机)',
  17. value: 'SVM',
  18. },
  19. {
  20. label: 'naive_bayes(朴素贝叶斯)',
  21. value: 'naive_bayes',
  22. },
  23. {
  24. label: 'GBM(梯度提升树)',
  25. value: 'GBM',
  26. },
  27. ];
  28. // 回归算法
  29. export const regressorAlgorithms = [
  30. {
  31. label: 'bayesian_ridge(岭回归)',
  32. value: 'bayesian_ridge',
  33. },
  34. {
  35. label: 'ARD_regression(自动相关性确定回归)',
  36. value: 'ARD_regression',
  37. },
  38. {
  39. label: 'gaussian_process(高斯回归)',
  40. value: 'gaussian_process',
  41. },
  42. ];
  43. // 框架类型
  44. export enum FrameworkType {
  45. Sklearn = 'sklearn',
  46. Keras = 'keras',
  47. Pytorch = 'pytorch',
  48. }
  49. // 框架类型选项
  50. export const frameworkTypeOptions = [
  51. {
  52. label: FrameworkType.Sklearn,
  53. value: FrameworkType.Sklearn,
  54. },
  55. {
  56. label: FrameworkType.Keras,
  57. value: FrameworkType.Keras,
  58. },
  59. {
  60. label: FrameworkType.Pytorch,
  61. value: FrameworkType.Pytorch,
  62. },
  63. ];
  64. // 查询策略
  65. export const queryStrategies = [
  66. {
  67. label: 'uncertainty_sampling',
  68. value: 'uncertainty_sampling',
  69. },
  70. {
  71. label: 'uncertainty_batch_sampling',
  72. value: 'uncertainty_batch_sampling',
  73. },
  74. {
  75. label: 'max_std_sampling',
  76. value: 'max_std_sampling',
  77. },
  78. {
  79. label: 'expected_improvement',
  80. value: 'expected_improvement',
  81. },
  82. {
  83. label: 'upper_confidence_bound',
  84. value: 'upper_confidence_bound',
  85. },
  86. ];