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1. Bubble Sort

'''冒泡排序'''

"""
def BubbleSort(nums):
    listLength = len(nums)
    while listLength > 0:
        for i in range(listLength - 1):
            if nums[i] > nums[i+1]:
                nums[i], nums[i+1] = nums[i+1], nums[i]
        listLength -= 1
    return nums
"""

def BubbleSort(nums):
    for i in range(len(nums)):
        for j in range(len(nums) - i - 1):
            if nums[j] > nums[j+1]:
                nums[j], nums[j+1] = nums[j+1], nums[j]
    return nums

nums = [5, 2, 8, 4, 7, 4, 3, 9, 2, 0, 16,1]
print(BubbleSort(nums))

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2. Quick Sort

'''快速排序'''

def QuickSort(nums, start, end):
    if start < end:
        i, j = start, end
        base = nums[i]
        while i < j:
            while i < j and nums[j] >= base:
                j -= 1
            nums[i] = nums[j]
            while i < j and nums[i] <= base:
                i += 1
            nums[j] = nums[i]
        nums[i] = base
        QuickSort(nums, start, i - 1)
        QuickSort(nums, i+1, end)

nums = [9,4,10,8,13,2,15,7]
QuickSort(nums, 0, len(nums)-1)
print(nums)
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3. Insertion Sort

'''插入排序'''

def InsertSort(nums):
    for i in range(len(nums)-1):
        if nums[i] > nums[i+1]:
            while i>=0 and nums[i] > nums[i+1]:
                nums[i], nums[i+1] = nums[i+1], nums[i]
                i -= 1
    return nums
nums = [9,4,10,8,13,2,15,7]
print(InsertSort(nums))

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4. Shell sort

'''希尔排序'''
#①第一层循环 gap折半 直到gap=1
#②二层三层循环直接插入排序
def ShellSort(nums):
    gap = len(nums) // 2
    while gap >= 1:
        for i in range(gap, len(nums)):
            for j in range(i-gap, -1, -gap):
                if nums[j] > nums[j+gap]:
                    nums[j], nums[j+gap] = nums[j+gap], nums[j]
        gap = gap // 2
    return nums
nums = [9,4,10,8,13,2,15,7]
print(ShellSort(nums))
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5. Selection Sort

'''选择排序'''
def SelectSort(nums):

    for i in range(len(nums)):
        for j in range(i+1, len(nums)):
            if nums[j] < nums[i]:
                nums[j], nums[i] = nums[i], nums[j]
    return nums
nums = [5, 2, 8, 4, 7, 4, 3, 9, 2, 0, 1,16]
print(SelectSort(nums))
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6. Heap Sort

The maximum heap always stores the maximum value in the root node of the tree.
For a minimum heap, the element in the root node is always the minimum value in the tree.
Application Scenario
For example, to find the top 10 largest numbers out of 1 billion numbers, we always build a small top heap with only 10 elements. If the number is smaller than the top of the heap, we will not process it. If the number is larger than the top of the heap, we will replace the top of the heap and then sink it to the appropriate position one by one.
For example, to find the first 10 smallest numbers out of 1 billion numbers, we always build a large top heap with only 10 elements. If the number is larger than the top of the heap, we will not process it; if the number is smaller than the top of the heap, we will replace the top of the heap and then sink it to the appropriate position one by one.
Generally, the top heap is used for ascending order and the top heap is used for descending order

'''构造大顶堆'''
def HeapBuild(nums):
    l = len(nums) - 1
    # 构造大顶堆，从非叶子节点开始倒序遍历，因此是l//2 -1 就是最后一个非叶子节点
    for i in range(len(nums)//2 - 1, -1, -1):
        HeapSort(nums, i, l)

    # 上面的循环完成了大顶堆的构造，那么就开始把根节点跟末尾节点交换，然后重新调整大顶堆
    for j in range(l, -1, -1):
        nums[0], nums[j] = nums[j], nums[0]
        HeapSort(nums, 0, j-1)
    return nums

def HeapSort(nums, i, l):
    left, right = 2 * i + 1, 2 * i + 2  # 左右子节点的下标
    index = i
    # 构造大顶推
    if left <= l and nums[i] < nums[left]:
        index = left

    if right <= l and nums[left] < nums[right] and nums[i] < nums[right]:
        index = right

    if index != i:
        nums[i], nums[index] = nums[index], nums[i]
        HeapSort(nums, index, l)

nums = [17, 13, 40 , 22, 31, 14, 33, 56, 24, 19 ,10, 41, 51, 42, 26]
print('使用大顶堆排序：', HeapBuild(nums))

'''构造小顶堆'''
def SmallHeapBuild(nums):
    l = len(nums) - 1
    # 从非叶子节点开始倒序遍历，因此是l//2 -1 就是最后一个非叶子节点
    for i in range(len(nums)//2 - 1, -1, -1):
        SmallHeapSort(nums, i, l) # 小顶堆构造函数

    # 上面的循环完成了小顶堆的构造，那么就开始把根节点跟末尾节点交换，然后重新调整大顶堆
    # 使用小顶堆进行降序，nums[0]是最小的，放到最后
    for j in range(l, -1 ,-1):
        nums[0], nums[j] = nums[j], nums[0]
        SmallHeapSort(nums, 0, j-1)
    return nums

def SmallHeapSort(nums, i, l):
    left, right = 2 * i + 1, 2 * i + 2  # 左右子节点的下标
    index = i
    # 构建小顶堆
    if left <= l and nums[i] > nums[left]:
        index = left

    if right <= l and nums[left] > nums[right] and nums[i] > nums[right]:
        index = right

    if index != i:
        nums[i], nums[index] = nums[index], nums[i]
        SmallHeapSort(nums, index, l)

nums = [17, 13, 40 , 22, 31, 14, 33, 56, 24, 19 ,10, 41, 51, 42, 26]
print('使用小顶堆倒排序', SmallHeapBuild(nums))


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7. Merge Sort

def MergeBuild(nums):
    if len(nums) == 1:
        return nums
    mid = len(nums) // 2

    left = nums[:mid]
    right = nums[mid:]

    l1 = MergeBuild(left)
    l2 = MergeBuild(right)

    return MergeSort(l1, l2)

def MergeSort(left, right):
    res = []
    while len(left) and len(right):
        if left[0] < right[0]:
            res.append(left.pop(0))
        else:
            res.append(right.pop(0))
    res += left
    res += right
    return res

if __name__ == '__main__':
    nums = [5, 2, 8, 4, 7, 4, 3, 9, 2, 0, 1,16]
    res = MergeBuild(nums)
    print(res)
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8. Binary Search

'''二分查找'''
def BinarySearch(target, nums):
    low = 0
    high = len(nums)-1
    while low <= high:
        mid = (low + high) // 2
        if nums[mid] == target:
            return 'target in nums'
        elif nums[mid] < target:
            low = mid + 1
        else:
            high = mid - 1
    return 'target not in nums'
nums = [2, 4, 7, 8, 9, 10, 13, 15, 19]
print(BinarySearch(13, nums))
print(BinarySearch(20, nums))
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