Question

我一直在努力计算中位数，但我仍然有一些数学问题，因为我无法得到正确的中值，无法找出原因。这是代码;

class StatsCollector {

    constructor() {
        this.inputNumber = 0;
        this.average = 0;

        this.timeout = 19000;

        this.frequencies = new Map();
        for (let i of Array(this.timeout).keys()) {
            this.frequencies.set(i, 0);
        }
    }

    pushValue(responseTimeMs) {
        let req = responseTimeMs;
        if (req > this.timeout) {
            req = this.timeout;
        }

        this.average = (this.average * this.inputNumber + req) / (this.inputNumber + 1);

        console.log(responseTimeMs / 1000)
        let groupIndex = Math.floor(responseTimeMs / 1000);
        this.frequencies.set(groupIndex, this.frequencies.get(groupIndex) + 1);

        this.inputNumber += 1;
    }

    getMedian() {
        let medianElement = 0;
        if (this.inputNumber <= 0) {
            return 0;
        }
        if (this.inputNumber == 1) {
            return this.average
        }
        if (this.inputNumber == 2) {
            return this.average
        }
        if (this.inputNumber > 2) {
            medianElement = this.inputNumber / 2;
        }

        let minCumulativeFreq = 0;
        let maxCumulativeFreq = 0;
        let cumulativeFreq = 0;
        let freqGroup = 0;
        for (let i of Array(20).keys()) {
            if (medianElement <= cumulativeFreq + this.frequencies.get(i)) {
                minCumulativeFreq = cumulativeFreq;
                maxCumulativeFreq = cumulativeFreq + this.frequencies.get(i);
                freqGroup = i;
                break;
            }
            cumulativeFreq += this.frequencies.get(i);
        }

        return (((medianElement - minCumulativeFreq) / (maxCumulativeFreq - minCumulativeFreq)) + (freqGroup)) * 1000;
    }

    getAverage() {
        return this.average;
    }

}

当我输入

的值时，这是结果的快照

342,654,987,1093,2234,6243,7087,20123

enter image description here

应该是正确的结果;

中位数：1663.5

Answer 1

将中位数方法更改为：

function median(values){
  if(values.length ===0) return 0;

  values.sort(function(a,b){
    return a-b;
  });

  var half = Math.floor(values.length / 2);

  if (values.length % 2)
    return values[half];

  return (values[half - 1] + values[half]) / 2.0;
}

fiddle

Answer 2

又矮又甜。

Array.prototype.median = function () {
  return this.slice().sort((a, b) => a - b)[Math.floor(this.length / 2)]; 
};

用法

[4, 5, 7, 1, 33].median()

也可以使用字符串

["a","a","b","b","c","d","e"].median()

Answer 3

这是另一种解决方案：

function median(numbers) {
    const sorted = numbers.slice().sort((a, b) => a - b);
    const middle = Math.floor(sorted.length / 2);

    if (sorted.length % 2 === 0) {
        return (sorted[middle - 1] + sorted[middle]) / 2;
    }

    return sorted[middle];
}

console.log(median([4, 5, 7, 1, 33]));

Answer 4

TypeScript Answer 2020：

// Calculate Median 
const calculateMedian = (array: Array<number>) => {
  // Check If Data Exists
  if (array.length >= 1) {
    // Sort Array
    array = array.sort((a: number, b: number) => {
      return a - b;
    });

    // Array Length: Even
    if (array.length % 2 === 0) {
      // Average Of Two Middle Numbers
      return (array[(array.length / 2) - 1] + array[array.length / 2]) / 2;
    }
    // Array Length: Odd
    else {
      // Middle Number
      return array[(array.length - 1) / 2];
    }
  }
  else {
    // Error
    console.error('Error: Empty Array (calculateMedian)');
  }
};

Answer 5

`

var arr = {  
  max: function(array) {
    return Math.max.apply(null, array);
  },

  min: function(array) {
    return Math.min.apply(null, array);
  },

  range: function(array) {
    return arr.max(array) - arr.min(array);
  },

  midrange: function(array) {
    return arr.range(array) / 2;
  },

  sum: function(array) {
    var num = 0;
    for (var i = 0, l = array.length; i < l; i++) num += array[i];
    return num;
  },

  mean: function(array) {
    return arr.sum(array) / array.length;
  },

  median: function(array) {
    array.sort(function(a, b) {
      return a - b;
    });
    var mid = array.length / 2;
    return mid % 1 ? array[mid - 0.5] : (array[mid - 1] + array[mid]) / 2;
  },

  modes: function(array) {
    if (!array.length) return [];
    var modeMap = {},
      maxCount = 1,
      modes = [array[0]];

    array.forEach(function(val) {
      if (!modeMap[val]) modeMap[val] = 1;
      else modeMap[val]++;

      if (modeMap[val] > maxCount) {
        modes = [val];
        maxCount = modeMap[val];
      }
      else if (modeMap[val] === maxCount) {
        modes.push(val);
        maxCount = modeMap[val];
      }
    });
    return modes;
  },

  variance: function(array) {
    var mean = arr.mean(array);
    return arr.mean(array.map(function(num) {
      return Math.pow(num - mean, 2);
    }));
  },

  standardDeviation: function(array) {
    return Math.sqrt(arr.variance(array));
  },

  meanAbsoluteDeviation: function(array) {
    var mean = arr.mean(array);
    return arr.mean(array.map(function(num) {
      return Math.abs(num - mean);
    }));
  },

  zScores: function(array) {
    var mean = arr.mean(array);
    var standardDeviation = arr.standardDeviation(array);
    return array.map(function(num) {
      return (num - mean) / standardDeviation;
    });
  }
};

`

Answer 6

上面的解决方案-排序然后找到中间-很好，但是对于大型数据集来说很慢。首先对数据进行排序的复杂度为n x log（n）。

有一种更快的中值算法，该算法包括根据枢轴将数组分成两部分，然后在较大的集合中查找中值。这是一些JavaScript代码，但是here is a more detailed explanation

// Trying some array
alert(quickselect_median([2300,5,4,0,123,2,76,768,28]));

function quickselect_median(arr) {
   const L = arr.length, halfL = L/2;
   if (L % 2 == 1)
      return quickselect(arr, halfL);
   else
      return 0.5 * (quickselect(arr, halfL - 1) + quickselect(arr, halfL));
}

function quickselect(arr, k) {
   // Select the kth element in arr
   // arr: List of numerics
   // k: Index
   // return: The kth element (in numerical order) of arr
   if (arr.length == 1)
      return arr[0];
   else {
      const pivot = arr[0];
      const lows = arr.filter((e)=>(e<pivot));
      const highs = arr.filter((e)=>(e>pivot));
      const pivots = arr.filter((e)=>(e==pivot));
      if (k < lows.length) // the pivot is too high
         return quickselect(lows, k);
      else if (k < lows.length + pivots.length)// We got lucky and guessed the median
         return pivot;
      else // the pivot is too low
         return quickselect(highs, k - lows.length - pivots.length);
   }
}

精明的读者会注意到一些事情：

我只是将Russel Cohen的Python解决方案音译为JS，所以所有的荣誉给他。
有几个值得进行的小优化这样做，但是值得做并行化，并且代码照原样更快地更改单线程或更快多线程版本。
这是平均线性时间 算法，效率更高的确定时间线性时间版本，请参见Russel's post了解详情，包括性能数据。

Answer 7

要获得更好的时间复杂度，请使用MaxHeap-MinHeap查找数组流的中位数。

Answer 8

更简单，更高效

const median = dataSet => {
  if (dataSet.length === 1) return dataSet[0]
  const sorted = ([ ...dataSet ]).sort()
  const ceil = Math.ceil(sorted.length / 2)
  const floor = Math.floor(sorted.length / 2)
  if (ceil === floor) return sorted[floor]
  return ((sorted[ceil] + sorted[floor]) / 2)
}

Answer 9

简单的解决方案：

function calcMedian(array) {
  const {
    length
  } = array;

  if (length < 1)
    return 0;

  //sort array asc
  array.sort((a, b) => a - b);

  if (length % 2) {
    //length of array is odd
    return array[(length + 1) / 2 - 1];
  } else {
    //length of array is even
    return 0.5 * [(array[length / 2 - 1] + array[length / 2])];
  }
}

console.log(2, calcMedian([1, 2, 2, 5, 6]));
console.log(3.5, calcMedian([1, 2, 2, 5, 6, 7]));
console.log(9, calcMedian([13, 9, 8, 15, 7]));
console.log(3.5, calcMedian([1, 4, 6, 3]));
console.log(5, calcMedian([5, 1, 11, 2, 8]));

Answer 10

更简单、更高效、更易于阅读

克隆数据以避免更改原始数据。
对值列表进行排序。
找到中间点。
从列表中获取中位数。
返回中位数。

function getMedian(data) {
    const values = [...data];
    const v   = values.sort( (a, b) => a - b);
    const mid = Math.floor( v.length / 2);
    const median = (v.length % 2 !== 0) ? v[mid] : (v[mid - 1] + v[mid]) / 2; 
    return median;
}

计算中位数 - javascript

10 个答案: