wolverinn / iridescent Goto Github PK
View Code? Open in Web Editor NEWSolid data structure and algorithms
License: GNU General Public License v3.0
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选择排序python算法写错
def select_sort(lst):
if not lst:
return []
for i in range(len(lst) - 1):
smallest = i
for j in range(i, len(lst)):
if lst[j] < lst[smallest]:
smallest = j
lst[i], lst[smallest] = lst[smallest], lst[i] #已更正
return lst
Hash表的问题
def insert(self, key, value):
index = self.hash_code(key)
head = self.table[index]
if not head: # 如果哈希表对应位置还是空的
self.table[index] = listNode(key, value)
else:
while head.next:
head = head.next
head.next = listNode(key, value)
这里insert重复的key，是有问题的
关于B树第一点最后一段话的内容纠正
首先非常感谢作者的分享，但我在阅读过程中发现了几处难以理解的地方。
在B树那一部分内容第一点的最后一段中，作者写道：
在这类应用中，磁盘IO的速度已经远远超过了读取到数据之后在内存中进行处理的速度，所以提升速度的关键是减小磁盘IO。
以上是文章原话，个人认为应该改成“磁盘IO的时间已经远远超过了读取到数据之后在内存中进行处理的时间”。
如果我的理解出现错误，希望作者能帮我具体指出。谢谢。
红黑树部分的补充
作者写得不错，很感谢笔记的分享，这对复习和快速的准备很有帮助。
(个人看法)感觉红黑树那里可以建议看Algorithms第4版，因为我对比了一下Introduction to Algorithm和Algorithms第四版的引入，感觉后者那种把左倾红黑树作为2-3树(3阶B树)的二叉树表现形式的观点似乎更容易记忆；把3节点的2个key拆开，左边为红色右边为黑色，因此红色的节点表明和parent节点等black height；新插入的节点作为红色是为了假设它和parent节点等高, 插入之后的一系列操作可以理解是3节点变成4节点之后的分裂和中间节点的上升等等。
当然，这也可能是是因为我对经典红黑树理解不能...感觉四条性质放下来很抽象。
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