freeCodeCamp/curriculum/challenges/chinese/10-coding-interview-prep/rosetta-code/entropy.md

id, title, challengeType, videoUrl, dashedName

id	title	challengeType	videoUrl	dashedName
599d15309e88c813a40baf58	熵	5		entropy

--description--

任务：

计算给定输入字符串的香农熵H.

给定谨慎的随机变量$ X ，它是 N “符号”（总字符）的字符串，由 n $个不同的字符组成（对于二进制，n = 2），位/符号中X的香农熵是：

H*2（X）= - \\ sum* {i = 1} ^ n \\ frac {count_i} {N} \\ log_2 \\ left（\\ frac {count_i} {N} \\ right）

其中$ count_i 是字符 n_i $的计数。

--hints--

entropy是一种功能。

assert(typeof entropy === 'function');

entropy("0")应该返回0

assert.equal(entropy('0'), 0);

entropy("01")应该返回1

assert.equal(entropy('01'), 1);

entropy("0123")应该返回2

assert.equal(entropy('0123'), 2);

entropy("01234567")应该返回3

assert.equal(entropy('01234567'), 3);

entropy("0123456789abcdef")应返回4

assert.equal(entropy('0123456789abcdef'), 4);

entropy("1223334444")应返回1.8464393446710154

assert.equal(entropy('1223334444'), 1.8464393446710154);

--seed--

--seed-contents--

function entropy(s) {

}

--solutions--

function entropy(s) {
    // Create a dictionary of character frequencies and iterate over it.
  function process(s, evaluator) {
    let h = Object.create(null),
      k;
    s.split('').forEach(c => {
      h[c] && h[c]++ || (h[c] = 1); });
    if (evaluator) for (k in h) evaluator(k, h[k]);
    return h;
  }
    // Measure the entropy of a string in bits per symbol.

  let sum = 0,
    len = s.length;
  process(s, (k, f) => {
    const p = f / len;
    sum -= p * Math.log(p) / Math.log(2);
  });
  return sum;
}