answer:d) Olumsuz pekiştirme ile açıklanır.

answer:Sure! Here's a simple implementation of a hash table in Python: ``` class HashTable(): def __init__(self, size=10): self.size = size self.keys = [None] * self.size self.values = [None] * self.size def hash_function(self, key): return hash(key) % self.size def insert(self, key, value): i = self.hash_function(key) while self.keys[i] is not None: if self.keys[i] == key: self.values[i] = value return i = (i + 1) % self.size self.keys[i] = key self.values[i] = value def get(self, key): i = self.hash_function(key) while self.keys[i] is not None: if self.keys[i] == key: return self.values[i] i = (i + 1) % self.size return None def __str__(self): items = [] for k, v in zip(self.keys, self.values): if k is not None: items.append(f"{k}: {v}") return "{" + ", ".join(items) + "}" ``` Here's how you can use it: ``` ht = HashTable() ht.insert("a", 1) ht.insert("b", 2) ht.insert("c", 3) print(ht.get("a")) # Output: 1 print(ht.get("d")) # Output: None print(ht) # Output: {b: 2, c: 3, a: 1} ``` In this implementation, we use a hash function to map each key to an index in the `keys` and `values` arrays. If there are collisions (i.e., multiple keys map to the same index), we use linear probing to find the next available index. The `insert` method inserts a key-value pair into the table, the `get` method retrieves the value associated with a key, and the `__str__` method provides a string representation of the table.

answer:Sure! Here's a basic implementation of Breadth-first traversal in Python: ```python from collections import deque graph = { 'A': ['B', 'C'], 'B': ['D', 'E'], 'C': ['F'], 'D': [], 'E': ['F'], 'F': [] } def BFS(start_node, graph): visited = set() queue = deque([start_node]) while queue: node = queue.popleft() if node not in visited: print(node, end=' ') visited.add(node) neighbours = graph[node] for neighbour in neighbours: queue.append(neighbour) BFS('A', graph) ``` In this implementation, we first define a graph as a dictionary of nodes and their adjacent nodes. We then define a `BFS` function which takes a starting node and the graph as input. We initialize a set called `visited` to keep track of the nodes we have already visited. We also create a `deque` called `queue` to hold the nodes that we want to visit next, starting with the starting node. We then enter a loop that continues until there are no more nodes in the queue. We use the `popleft` method to remove the first node from the queue and assign it to `node`. We then check if `node` is in `visited`. If it is not, we print `node`, add it to the `visited` set, and get its neighbours from the graph. We then iterate over these neighbours and add them to the `queue`. The final output of the `BFS` function is a breadth-first traversal of the graph starting from the starting node.

answer:Sure! Here's a basic implementation of Depth-first traversal in Python: ```python graph = { 'A': ['B', 'C'], 'B': ['D', 'E'], 'C': ['F'], 'D': [], 'E': ['F'], 'F': [] } def DFS(node, graph, visited=None): if visited is None: visited = set() visited.add(node) print(node, end=' ') for neighbour in graph[node]: if neighbour not in visited: DFS(neighbour, graph, visited) DFS('A', graph) ``` In this implementation, we first define a graph as a dictionary of nodes and their adjacent nodes. We then define a recursive `DFS` function which takes a node, the graph, and a set called `visited` (which keeps track of the nodes we have already visited) as input. If `visited` is not already initialized (if it is None), we initialize it to an empty set. We then add the current node to the `visited` set and print it. We then loop over the neighbours of the current node from the graph and call the `DFS` function recursively on any neighbours that have not already been visited. This ensures that we visit all the nodes in the graph. The final output of the `DFS` function is a depth-first traversal of the graph starting from the specified node.