API ML

Contents

• mlt_perceptron: Perceptron interface.
  param:
  *data: Perceptron data.
  ex:*

  var test_data = [
  [1, 2, 1],
  [2, 3, 1],
  [2, 1, 1],
  [2, 6, -1],
  [3, 5, -1]
  ];

  var data = {
  eta: 0.5,
  max_iter_num: 0,
  row: test_data.length,
  col: test_data[0].length,
  data: test_data
  };

  var res = mlt_perceptron(data);
  mlt_page_console_log(res);

• mlt_knn: K-nearest neighbor interface.
  param:
  *data: K-nearest neighbor data.
  ex:*

  var test_data = [
  [1, 2, 1], [1, 1, 1], [2, 2, 1], [2, 1, 1], [1.5, 1.5, 1],
  [10, 11, 2], [10, 10, 2], [11, 11, 2], [11, 10, 2], [10.5, 10.5, 2], [11.5, 11.5, 2],
  [10, 2, 3], [10, 1, 3], [11, 2, 3], [11, 1, 3]
  ];

  var data = {
  k: 3,
  norm_dist_num: 2,
  category: 3,
  dim: 2,
  in_data: [4, 5],
  data_len: test_data.length,
  data: test_data
  };

  var ret = mlt_knn(data);
  mlt_page_console_log(ret);

• mlt_naive_bayes: Naive Bayes interface.
  param:
  *data: Naive Bayes data.
  ex:*

  var data = {
  have_den: 1,
  cat_len: 3,
  chara_len: 4,
  pr_cat: [(3 / 8), (3 / 8), (2 / 8)],
  pr_chara: [(3 / 8), (4 / 8), (4 / 8), (3 / 8)],
  pr_cond_chara: [
  [(1 / 3), (1 / 3), (1 / 3), (1 / 3)],
  [(1 / 3), (2 / 3), (1 / 3), (1 / 3)],
  [(1 / 2), (1 / 2), (2 / 2), (1 / 2)]
  ]
  };

  var p = mlt_naive_bayes(data);
  mlt_page_console_log(p, '\n');
  mlt_page_console_log(p[3] + 1, '\n');