堆排序

• Runtime: 84 ms, faster than 69.47%
• Memory Usage: 37.7 MB, less than 47.67%

时间复杂度
空间复杂度

var findKthLargest = function(nums, k) {
    let len = nums.length
    let mid = Math.floor(len/2)
    for(let i = mid; i-- ; i >= 0) {
        heapSort(nums, i, len)
    }
    for(let i = len - 1; i > len - k; i--) {
        swap(nums, 0, i)
        heapSort(nums, 0, i)
    }
    return nums[0]
};

var swap = function(nums, a, b) {
    let temp = nums[a]
    nums[a] = nums[b]
    nums[b] = temp
}

var heapSort = function(nums, index, boundary) {
    let max = index
    let left = 2 * index + 1
    let right = 2 * index + 2
    if(left < boundary && nums[left] > nums[max]) max = left
    if(right < boundary && nums[right] > nums[max]) max = right
    if(max > index) {
        swap(nums, max, index)
        heapSort(nums, max, boundary)
    }
}

部分排序实现方法

期望为线性时间的选择算法，将输入数组进行递归划分，快速排序会处理递归的两边，randomized-select只处理划分的一边，这一差异会在性能分析中体现出来，快排 O(nlogn)，随机化排序O(n)

• Runtime: 104 ms, faster than 43.58%
• Memory Usage: 37.8 MB, less than 45.16%

时间复杂度
空间复杂度

var findKthLargest = function(nums, k) {
    return partQuickSort(nums, 0, nums.length - 1, k - 1)
};

var partition = function(nums, start, end) {
    let flag = nums[end]
    let index = start
    for(let i = start; i < end; i++) {
        if(nums[i] > flag) {
            swap(nums, index, i)
            //[nums[index], nums[i]] = [nums[i], nums[index]]
            index++
        }
    }
    swap(nums, index, end)
    //[nums[index], nums[end]] = [nums[end], nums[index]]
    return index
}

var partQuickSort = function(nums, start, end, k) {
    let p = partition(nums, start, end)
    if(p === k) return nums[p]
    return p < k ? partQuickSort(nums, p + 1, end, k) : partQuickSort(nums, start, p - 1, k) 
}

var swap = function(nums, m, n){ 
    let temp = nums[m]
    nums[m] = nums[n]
    nums[n] = temp

}

部分快排

• Runtime: 76 ms, faster than 94.62%
• Memory Usage: 39.2 MB, less than 80.84%

/**
 * @param {number[]} nums
 * @param {number} k
 * @return {number}
 */
var findKthLargest = function(nums, k) {
    let len = nums.length
    return partQuickSort(nums, 0, len - 1, len - k)
};

var partQuickSort = function(nums, i, j, k) {
    if(j - i < 1) return nums[i]
    let flag = nums[i]
    let start = i + 1
    let end = j
    while(start <= end) {
        if(nums[start] <= flag) {
            start++
        } else {
            swap(nums, start, end)
            end--
        }
    }
    swap(nums, i, start - 1)
    if(k === start - 1) return nums[k]
    return k > start - 1 ? partQuickSort(nums, start, j, k) : partQuickSort(nums, i, start - 2, k)
}

var swap = function(nums, i, j) {
    let temp = nums[i]
    nums[i] = nums[j]
    nums[j] = temp
}

奇奇怪怪的方法

• Runtime: 176 ms, faster than 12.23%
• Memory Usage: 37.8 MB, less than 43.73%

var findKthLargest = function(nums, k) {
    let len = nums.length
    for(let i = 0; i < k - 1; i++) {
        let max = Math.max.apply(this, nums)
        let index = nums.indexOf(max)
        nums.splice(index, 1)
    }
    return Math.max.apply(this, nums)
};

快速排序实现

• Runtime: 1076 ms, faster than 5.09%
• Memory Usage: 172.1 MB, less than 5.04%

var findKthLargest = function(nums, k) {
    nums = quickSort(nums)
    return nums[k - 1]
};

var quickSort = function(nums) {
    if(nums.length < 2) return nums
    let flag = nums.pop()
    let less = quickSort(nums.filter(item => item < flag))
    let more = quickSort(nums.filter(item => item >= flag))
    return more.concat([flag], less)
}

基于快速排序进行改进

• Runtime: 104 ms, faster than 34.25%
• Memory Usage: 39.5 MB, less than 54.93%

/**
 * @param {number[]} nums
 * @param {number} k
 * @return {number}
 */
var findKthLargest = function(nums, k) {
    let len = nums.length
    let res = partSort(nums, 0, len - 1, len - k)
    if(res === undefined) res = nums[len - k]
    return res
};

function partSort(nums, i, j, index) {
    if(j - i < 1) return
    let flag = nums[i]
    let start = i + 1
    let end = j
    while(start <= end ) {
        if(nums[start] <= flag) {
            start++
        } else {
            swap(nums, start, end)
            end--
        }
    }
    swap(nums, i, start - 1)
    if(start - 1 === index) {
      return nums[start - 1]
    }
    // debugger
    if(start - 2 < j && start - 2 > i) partSort(nums, i, start - 2, index)
    if (start < j)partSort(nums, start, j, index)
    
}

var swap = function(nums, i, j) {
    let temp = nums[i]
    nums[i] = nums[j]
    nums[j] = temp
}