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Author: Anfany

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+#  # -*- coding：utf-8 -*-
+# &Author  AnFany
+
+
+
+# 本文提供8种排序算法
+import numpy as np
+from prettytable import PrettyTable # 用表格输出时间对比
+import time
+
+
+paidict = {'Bubble': ['冒泡'], 'Insert': ['直接插入'],'Shell': ['希尔'],
+           'Select': ['直接选择'],'Quick': ['快速'], 'Heap': ['堆'],
+           'Merge': ['归并'], 'sorted': ['Python自带'], 'Radix': ['基数']}
+
+# 直接插入排序
+def Insert(listdata):
+    for i in range(1, len(listdata)):
+        # 设置当前值前一个元素的标识
+        j = i - 1
+        # 如果当前值小于前一个元素,则将当前值作为一个临时变量存储,将前一个元素后移一位
+        if listdata[i] < listdata[j]:
+            temp, listdata[i] = listdata[i], listdata[j]
+            # 继续往前寻找,如果有比临时变量大的数字,则后移一位,直到找到比临时变量小的元素或者达到列表第一个元素
+            j = j - 1
+            while j >= 0 and listdata[j] > temp:
+                listdata[j + 1] = listdata[j]
+                j = j - 1
+            # 将临时变量赋值给合适位置
+            listdata[j + 1] = temp
+    return listdata
+
+# 希尔排序
+def Shell(listdata):
+    n = len(listdata)
+    # 希尔增量
+    gap = int(n / 2)
+    while gap > 0:
+        # 按增量进行直接插入排序
+        for i in range(gap, n):
+            j = i
+            # 直接插入排序
+            while j >= gap and listdata[j - gap] > listdata[j]:
+                listdata[j - gap], listdata[j] = listdata[j], listdata[j - gap]
+                j -= gap
+        # 得到新的增量
+        gap = int(gap / 2)
+    return listdata
+
+# 直接选择排序
+def Select(listdata):
+    for i in range(len(listdata) - 1):
+        minnum = i
+        for j in range(i + 1, len(listdata)):
+            if listdata[j] < listdata[minnum]:
+                minnum = j
+        listdata[i], listdata[minnum] = listdata[minnum], listdata[i]
+    return listdata
+
+# 堆排序
+def adjust_heap(lists, i, size):
+    lchild = 2 * i + 1
+    rchild = 2 * i + 2
+    max = i
+    if i < size / 2:
+        if lchild < size and lists[lchild] > lists[max]:
+            max = lchild
+        if rchild < size and lists[rchild] > lists[max]:
+            max = rchild
+        if max != i:
+            lists[max], lists[i] = lists[i], lists[max]
+            adjust_heap(lists, max, size)
+
+# 创建堆
+def build_heap(lists, size):
+    for i in range(0, (int(size/2)))[::-1]:
+        adjust_heap(lists, i, size)
+
+# 堆排序
+def Heap(lists):
+    size = len(lists)
+    build_heap(lists, size)
+    for i in range(0, size)[::-1]:
+        lists[0], lists[i] = lists[i], lists[0]
+        adjust_heap(lists, 0, i)
+    return lists
+
+# 基数排序
+def Radix(listdata):
+    k = len(str(max(listdata)))  # k获取最大位数
+    for k in range(k):  # 遍历位数，从低到高
+        s = [[] for i in range(10)]  # 生成存放数的十个桶
+        for i in listdata:  # 遍历元素
+            s[i // (10 ** k) % 10].append(i)  # 分桶
+        listdata = [a for b in s for a in b]  # 合并桶
+    return listdata
+
+# 归并排序
+def Merge(listdata):
+    n = len(listdata)
+
+    if n == 1:
+        return listdata
+    mid = n // 2
+
+    # 对分割的左半部分进行归并排序
+    leftdata = Merge(listdata[:mid])
+    # 对分割的右半部分进行归并排序
+    rightdata = Merge(listdata[mid:])
+
+    # 对排序之后的两部分进行合并
+    # 定义两个游标
+    left, right = 0, 0
+
+    merge_result_li = []
+    left_n = len(leftdata)
+    right_n = len(rightdata)
+
+    while left < left_n and right < right_n:
+        if leftdata[left] <= rightdata[right]:
+            merge_result_li.append(leftdata[left])
+            left += 1
+        else:
+            merge_result_li.append(rightdata[right])
+            right += 1
+
+    merge_result_li += leftdata[left:]
+    merge_result_li += rightdata[right:]
+
+    # 将合并后的结果返回
+    return merge_result_li
+
+# 冒泡排序
+def Bubble(listdata):
+    for i in range(len(listdata) - 1):  # 这个循环负责设置冒泡排序进行的次数
+        for j in range(len(listdata) - i - 1):  # ｊ为列表下标
+            if listdata[j] > listdata[j + 1]:
+                listdata[j], listdata[j + 1] = listdata[j + 1], listdata[j]
+    return listdata
+
+# 快速排序
+def Quick(listdata):
+    if len(listdata) == 0:
+        return []
+    pivots = [x for x in listdata if x == listdata[0]]
+    lesser = Quick([x for x in listdata if x < listdata[0]])
+    greater = Quick([x for x in listdata if x > listdata[0]])
+    return lesser + pivots + greater
+
+# 最终的主程序
+if __name__ == "__main__":
+    numpdata = np.arange(1, 100000)
+    count = [5, 50, 500, 5000, 50000]
+    # 不同数组长度，每种排序算法运行50次的平均值得对比
+    colu = ['排序'] + ['%s条' % f for f in count]
+    x = PrettyTable(colu)
+    x.title = '不同算法用时(毫秒)对比，随机取数区间[1, 100000]'
+    for pa in paidict:
+        print(pa)
+        timep = []
+        for ci in count:
+            np.random.shuffle(numpdata)
+            ldata = list(np.random.choice(numpdata, ci))
+            start = time.clock()
+            result = eval(pa)(ldata)
+            end = time.clock()
+            if result == sorted(ldata):
+                timep.append('%.5f' % ((end - start) * 1000))
+            else:
+                print('排序算法：%s错误' % pa)
+        x.add_row([paidict[pa][0]] + timep)
+    print(x)
+
+
+
+
+
+