Write Algorithm for Negamax. Negamax(v) in: node v out: utility value of node v
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Write
Negamax(v)
in: node v
out: utility value of node v
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- c) After the first iteration of the following analysis write the in and out sets for each node. 1: x = a 2: y = b 3: if (x = 0) 4: y = y + z 5: y = y 6:z = y ZQuestion 5: Add Leaves Implement add_d_leaves, a function that takes in a Tree instance t and mutates it so that at each depth d in the tree, d leaves with labels v are added to each node at that depth. For example, we want to add 1 leaf with v in it to each node at depth 1, 2 leaves to each node at depth 2, and so on. Recall that the depth of a node is the number of edges from that node to the root, so the depth of the root is 0. The leaves should be added to the end of the list of branches. def add_d_leaves(t, v): """Add d leaves containing v to each node at every depth d. > > > t1 = Tree(1, [ Tree(3)]) > > > add_d_leaves(t1, 4) > > > t1 Tree(1, [Tree(3, [Tree(4)])]) > > > t2 = Tree(2, [Tree(5), Tree (6)]) > > > t3 = Tree(3, [t1, Tree(0), t2]) > > > add_d_leaves (t3, 10) > > > print(t 3) 3 1 3 4 10 10 10 10 10 10 0 10 2 5 10 10 6 10 10 10 """ def add_leaves(t, d): "*** YOUR CODE HERE **** add_leaves (t, 0)Write is the node arrangement based on postorder traversal. Left Data Right X E X X F X
- All of the following are true EXCEPT: An empty tree is defined to have a height of 1. The depth of a node p is the number of ancestors of p, other than p itself. The height of a tree to be equal to the maximum of the depths of its nodes (or zero, if the tree is empty). The height of a node p in a tree T: • If p is a leaf, then the height of p is 0. • Otherwise, the height of p is one more than the maximum of the heights of p's children.Write is the node arrangement based on inorder traversal. Left Data Right BX X E XKRUSKAL’S MINIMUM SPANNING TREE ALGORITHM Task 1: Implement heap_sort() function for using Kruskal’s MST algorithm. This function also needs heapify() function. You can add your utility functions as many as you want. Task 2: Implement Kruskals_MST() function. This function needs another function for detecting the cycles on the graph which name is is_cycle(). You can add your utility functions as many as you want.
- Kruskal's Algorithm: Kruskal's algorithm is a minimum-spanning-tree algorithm which finds an edge of the least possible weight that connects any two trees in the forest. This algorithm is a Greedy Algorithm. The steps to find a MST using this algorithm are as follows: Sort all the edges in non-decreasing order of their weight. Pick the smallest edge. Check if it forms a cycle with the spanning tree formed so far. If cycle is not formed, include this edge. Else, discard it. Repeat step2 until there are (V-1) edges in the spanning tree. Example with 10 vertices and 20 weighted edges:Write code for finding the successor to a deleted node:using c++ and without vector. you will implement the dijkstra’s shortest path algorithm on an undirected weighted graph. Use a min-heap to store the node labels and their current costs. Tasks:1- Create a struct to store the node label and its cost:struct Node{char label;int cost;}; 2- Implement a class template MinHeap that has the following declaration:3- Create a class WeightedGraph, which stores a graph using an adjacency matrixwith the following declaration: class WeightedGraph{ int** g; int nVertices;public:int getNVertices();//returns the number of vertices int getWeight(char,char);//returns weight of the edge connecting the given vertices int* returnNeighbors(int v);// returns the indices of the neighbors of the vertex v as an int array int numNeighbors(int v);//returns the number of neighbors of the vertex v void loadGraphFromFile(ifstream&);//allocates the adjacency matrix & initializes edge weights from the specified file void dijkstra(char startVertex, char* prev,…
- bool DelOddCopEven(Node* headPtr) { if (headPtr == nullptr) { return false; } Node* prev = nullptr; Node* current = headPtr; while (current != nullptr) { if (current->data % 2 != 0) { // Odd-valued node, delete it if (prev != nullptr) { prev->link = current->link; } else { headPtr = current->link; } Node* temp = current; current = current->link; delete temp; } else { // Even-valued node, create a copy and insert it after the original node Node* newNode = new Node; newNode->data = current->data; newNode->link = current->link; current->link = newNode; // Update the previous and current pointers prev = newNode; current = newNode->link; } } return true;} int FindListLength(Node* headPtr){ int length = 0;…Suppose we have a tree type and a variable t defined as follows: datatype tree = Node of tree * int * tree| Leafval t = Node(Node(Leaf, 1, Leaf), 2, Node(Leaf, 3, Leaf))Note that t is a tree of the following form:Node 2/ \Node 1 Node 3/ \ / \L L L L Write a function maxTree that finds the maximum values within a tree.For example, maxTree t returns 3. Note that this is NOT a searchtree. This function assumes that the input tree is not a Leaf. IN SML SHOW OUTPUT WORKINGWrite Algorithm for Alpha-beta pruning using negamax.Negamax-Alpha-Beta(v, α, β)in: node v; alpha value α; beta value βout: utility value of node v