[] [] dir_euler Write a function "dir_euler(digraph)" that takes in a connected, directed graph as its input, and then determines whether or not a directed Eulerian circuit exists. After compiling the above cell, you should be able to compile the following cell and obtain the desired outputs. print (dir_euler({"A": ["A", "B", "C"], "B" : ["A"], "C": ["A"]}), dir_euler({"A" : ["B", "D"], "B" : ["A", "D"], "C": ["C", "D"], "D": []})) This should return True False Python Python

[] [] dir_euler Write a function "dir_euler(digraph)" that takes in a connected, directed graph as its input, and then determines whether or not a directed Eulerian circuit exists. After compiling the above cell, you should be able to compile the following cell and obtain the desired outputs. print (dir_euler({"A": ["A", "B", "C"], "B" : ["A"], "C": ["A"]}), dir_euler({"A" : ["B", "D"], "B" : ["A", "D"], "C": ["C", "D"], "D": []})) This should return True False Python Python

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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describe the 2-approximation algorithm's Python source code. The majority of the code is concerned with removing the randomly chosen edge and its surrounding edges from the graph. The incidence matrix B(G) from the graph G is the input to the procedure VC Approx. Every iteration, the endpoints of a chosen edge are added to the list V C and the unmarked edges are kept in the list edges. We input the graph's incidence matrix B and then iterate through the rows of B to find edges that are near the matched edge. The main while loop iterates until this list is empty after each found edge is removed from the list edges.
Write R studio codes to create the folllowing graph for random data vector x.x <- rchisq(1000, df=5)
Coding in Python, we have the function to perform this task, V col_Hdeg whichinputs the adjacency matrix A of the graph and initializes the array vert_color s tohold vertex colors. It then calculates the degree of each vertex and stores it in thearray degrees.The while loop iterates until all vertices are colored and at each iteration, thevertex v with the highest degree is colored. The array temp is used to find the currentcolors of the neighbors of vertex v and an unused smallest color is determined aftersorting this array.
This is done in Perl. Take in a series of positive integers from the user, stopping when they enter a zero, and store them into an array. a) Use a for each loop to print the square of each number if the square is between 100 and 200. b) Use the map and grep functions to do the same as in (a).
In Computer Science a Graph is represented using an adjacency matrix. Ismatrix is a square matrix whose dimension is the total number of vertices.The following example shows the graphical representation of a graph with 5 vertices, its matrixof adjacency, degree of entry and exit of each vertex, that is, the total number ofarrows that enter or leave each vertex (verify in the image) and the loops of the graph, that issay the vertices that connect with themselvesTo program it, use Object Oriented Programming concepts (Classes, objects, attributes, methods), it can be in Java or in Python.-Declare a constant V with value 5-Declare a variable called Graph that is a VxV matrix of integers-Define a MENU procedure with the following textGRAPHS1. Create Graph2.Show Graph3. Adjacency between pairs4.Input degree5.Output degree6.Loops0.exit-Validate MENU so that it receives only valid options (from 0 to 6), otherwise send an error message and repeat the reading-Make the MENU call in the main…
USE PYTHON and ONLY CAN USE THE NUMPY IMPORT In this problem you will implement a function called pagerank which will take as input a numpy array TM, which will represent the transition matrix of a directed graph, and a positive integer n. The output will be a numpy array which gives the page ranks of each vertex in the graph represented by TM. You will iterate the update process n times. THE CODE STARTS imports numpy as np def pagerank(TM, n): C = np.array([[0,1,1],[1,1,1],[0,0,1]]) ------- enter your code -----
Write code for complete application that includes all the code for computing the shortest paths using Dijkstra’s algorithm, along with a program to test the implementation:
Write a c++ program that asks user to enter number of vertices in a directed graph and then the adjacency matrix representing the directed graph. The program, then, must display the node with the lowest indegree. Assume that nodes are named as 0, 1, 2 and so on.
Write a function below that will take a 2d array of booleans representing a map as a parameter which will construct a vgraph as a searchable graph. You many use go, python or java do to this.
The Adjacency Matrix A of the graph is computed from the following rule. A[r,c]={|(r−2c)*(r−3c)|%13, r ≠c 0, r==c Where,r=0,1,2,...,(n-1)andc=0,1,2,...,(n-1)for a nXn matrix. Perform below functions for graphs with n=5 and n=10. 1. Print the Adjacency matrices. 2. Write a program to list Breadth First Search paths starting from each of the vertices. 3. Filter paths in part (2) which visit all the vertices. 4. Write a function to find the path with minimum sum of weights on BFS paths discovered in part (2) individually for n=5 and n= 10 C-++ code
Draw a connected simple graph of 12 vertices and at least 20 edges (Try to make your graph unique). Now write a program in any one of the following languages (C, C++, JAVA or Python) to take the graph as input as:a. Adjacency Listb. Adjacency Matrixc. Incidence MatrixPrint the graph in those formats.
IN PYTHON A tridiagonal matrix is one where the only nonzero elements are the ones on the main diagonal and the ones immediately above and below it.Write a function that solves a linear system whose coefficient matrix is tridiag- onal. In this case, Gauss elimination can be made much more efficient because most elements are already zero and don't need to be modified or added. As an example, consider a linear system Ax = b with 100,000 unknowns and the same number of equations. The coefficient matrix A is tridiagonal, with all elements on the main diagonal equal to 3 and all elements on the diagonals above and below it equal to 1. The vector of constant terms b contains all ones, except that the first and last elements are zero. You can use td to find that x1= −0.10557. The following code format should help: def td(l, m, u, b): '''Solve a linear system Ax = b where A is tridiagonal Inputs: l, lower diagonal of A, n-1 vector m, main diagonal of A, n vector u,…