python实现顺序查找和哈希查找

def sequence_search(array, num):
    for i in range(len(array)):
        if array[i] == num:
            return i
    return False

array_0 = [23, 43, 12, 54, 65, 48]
print(sequence_search(array_0, 12))
>>> 2

class HashSearch:
    def __init__(self, num):
        if isinstance(num, int):
            self.num = abs(num)
            self.empty = self.num
            self._list = [None] * self.num
        else:
            raise TypeError(‘num must be a int number‘)
    def __hash(self, key):
        return key % self.num
    def put(self, key):
        assert self.empty > 0, ‘this array is full‘
        index = self.__hash(key)
        while self._list[index]:
            index = self.__hash(index+1)
        self._list[index] = key
        self.empty -= 1
    def find(self, key):
        index = self.__hash(key)
        temp = indexwhile self._list[index] != key:
            index = self.__hash(index+1)
　　　　　　　if index == temp:
                return False
        return index 

class HashTable:
    def __init__(self):
        # 哈希表的初始大小已经被选择为 11。尽管这是任意的，但是重要的是，
        # 大小是质数，使得冲突解决算法可以尽可能高效。
        self.size = 11
        self.slots = [None] * self.size
        self.data = [None] * self.size
 
    # hash 函数实现简单的余数方法
    def hash(self, key, size):
        return key % size
 
    # 冲突解决技术是  加1  rehash 函数的线性探测
    def rehash(self, old_hash, size):
        return (old_hash+1) % size
 
    # 假定最终将有一个空槽，除非 key 已经存在于  self.slots  中。 它计算原始
    # 哈希值，如果该槽不为空，则迭代 rehash 函数，直到出现空槽。如果非空槽已经包含 key，
    # 则旧数据值将替换为新数据值。
    def put(self, key, data):
        hash_value = self.hash(key, len(self.slots))
 
        if self.slots[hash_value] is None:
            self.slots[hash_value] = key
            self.data[hash_value] = data
 
        else:
            if self.slots[hash_value] == key:
                self.data[hash_value] = data
            else:
                next_slot = self.rehash(hash_value, len(self.slots))
                while self.slots[next_slot] is not None and                         self.slots[next_slot] != key:
                    next_slot = self.rehash(next_slot, len(self.slots))
 
                if self.slots[next_slot] is None:
                    self.slots[next_slot] = key
                    self.data[next_slot] = data
                else:
                    self.data[next_slot] = data
 
    # 从计算初始哈希值开始。如果值不在初始槽中，则 rehash 用
    # 于定位下一个可能的位置。
    def get(self, key):
        start_slot = self.hash(key, len(self.slots))
 
        data = None
        stop = False
        found = False
        pos = start_slot
 
        while self.slots[pos] is not  None and not found and not stop:
            if self.slots[pos] == key:
                found = True
                data = self.data[pos]
            else:
                pos = self.rehash(pos, len(self.slots))
                if pos == start_slot:
                    stop = True
        return data
 
    # 我们重载  __getitem__  和 __setitem__  方法以允许使
    # 用  []  访问。 这意味着一旦创建了HashTable，索引操作符将可用。
    def __getitem__(self, item):
        return self.get(item)
 
    def __setitem__(self, key, value):
        self.put(key, value)