Linux

Linux ricing WTF is ricing Ricing came from the car term “Race Inspired Cosmetic Enhancements” it generally means to customize your software experience tailormade to your usage. In this tutorial we are going to use Arch Linux and Hyprland to completely customize you user experience Prereq

Dual booting arch and win11 Prerequisites Before installing arch we need to do the following things Disk partition We’ll need to make a disk partition in windows 11 before proceeding to make sure both the OS run isolated, this can be done by the following steps Right-click on the Start menu and select Disk Management. Alternatively, you can press Win + X and choose Disk Management from the list. In the Disk Management window, locate the drive you want to partition (usually the C: drive). In my case I’ve installed in D drive on a separate disk Right-click on the drive and select Shrink Volume In the dialog that appears, enter the amount of space to shrink (in MB). This will be the size of the new partition for Arch Linux. Make sure to leave enough space for Windows. In my case I’ve allocated 100 gigs for Arch. Click Shrink. The unallocated space will appear in the Disk Management window. Bios settings Disable Fast Startup Disable secure boot Arch ISO setup Download arch ISO from the official site. Install a tool such as Rufus or Etcher and burn the ISO onto a USB drive. Booting onto Arch Insert the drive into the computer Restart the computer into the BIOS/UEFI/Bootloader menu Select the boot option as the USB drive Installing Arch (under progress) ...

Docker What is docker Docker is a platform for developers and sysadmins to develop, deploy, and run applications with containers. The use of Linux containers to deploy applications is called containerization. Containers are not new, but their use for easily deploying applications is. Why docker Consistency: Docker provides a consistent environment for your application from development all the way through production. Isolation: Docker containers are completely isolated. This means that your application will run the same way no matter where it is deployed. Portability: Docker containers can run on your local development machine, on the cloud, or on-premises. Resource Efficiency: Docker containers share the same OS kernel and are lighter weight than VMs. Productivity: Docker makes it easy to install and run software without worrying about setup or dependencies. Installation of docker I’ll be using Windows 11 for the installation of docker. You can find the installation steps for other operating systems here. ...

Lab expts sem 6 AIML TW1 - DFID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 from collections import defaultdict class Graph: def __init__(self, vertices): self.V = vertices self.graph = defaultdict(list) def addEdge(self, u, v): self.graph[u].append(v) def DLS(self, src, target, maxDepth, visited): visited.append(src) # Add the current node to visited if src == target: return True, visited if maxDepth <= 0: return False, visited for i in self.graph[src]: found, visited = self.DLS(i, target, maxDepth - 1, visited) if found: return True, visited return False, visited def IDDFS(self, src, target, maxDepth): visited = [] # Initialize the visited list for i in range(maxDepth + 1): # Include maxDepth level found, visited = self.DLS(src, target, i, visited) if found: return True, visited return False, visited # Create a graph g = Graph(7) g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(1, 3) g.addEdge(1, 4) g.addEdge(2, 5) g.addEdge(2, 6) target = 6 maxDepth = 3 src = 0 found, visited = g.IDDFS(src, target, maxDepth) if found: print("Target is reachable from source within max depth") print("Visited nodes:", visited) else: print("Target is NOT reachable from source within max depth") print("Visited nodes:", visited) TW2 - BFS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 # Python3 Program to print BFS traversal # from a given source vertex. BFS(int s) # traverses vertices reachable from s. from collections import defaultdict # This class represents a directed graph # using adjacency list representation class Graph: # Constructor def __init__(self): # Default dictionary to store graph self.graph = defaultdict(list) # Function to add an edge to graph def addEdge(self, u, v): self.graph[u].append(v) # Function to print a BFS of graph def BFS(self, s): # Mark all the vertices as not visited visited = [False] * (max(self.graph) + 1) # Create a queue for BFS queue = [] # Mark the source node as # visited and enqueue it queue.append(s) visited[s] = True while queue: # Dequeue a vertex from # queue and print it s = queue.pop(0) print(s, end=" ") # Get all adjacent vertices of the # dequeued vertex s. # If an adjacent has not been visited, # then mark it visited and enqueue it for i in self.graph[s]: if not visited[i]: queue.append(i) visited[i] = True # Driver code if __name__ == '__main__': # Create a graph given in # the above diagram g = Graph() g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(1, 2) g.addEdge(2, 0) g.addEdge(2, 3) g.addEdge(3, 3) print("Following is Breadth First Traversal" " (starting from vertex 2)") g.BFS(2) # This code is contributed by Neelam Yadav # This code is modified by Susobhan Akhuli TW3 - A* algorithm 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 import heapq # tw3 def astar(graph, start, goal): # priority queue to store nodes to be explored open_list = [(0, start)] # dictionary to store parent nodes parents = {} # dictionary to store g values (cost from start node to current node) g_values = {node: float('inf') for node in graph} g_values[start] = 0 # dictionary to store f values (estimated total cost from start to goal) f_values = {node: float('inf') for node in graph} f_values[start] = graph[start][1] iteration = 0 while open_list: # get node with minimum f value current_f, current_node = heapq.heappop(open_list) # check if current node is the goal if current_node == goal: path = [] while current_node in parents: path.append(current_node) current_node = parents[current_node] path.append(start) final_cost = g_values[goal] print(f"\nFinal Cost: {final_cost}") return path[::-1] # explore neighbors for child, cost in graph[current_node][0].items(): # calculate tentative g value tentative_g = g_values[current_node] + cost if tentative_g < g_values[child]: # update parent and g values parents[child] = current_node g_values[child] = tentative_g f_values[child] = tentative_g + graph[child][1] # add child to open list heapq.heappush(open_list, (f_values[child], child)) iteration += 1 print(f"\nIteration {iteration}:") print("Current Path:", reconstruct_path(parents, start, current_node)) print(f"Evaluation Function Value for {current_node}: {f_values[current_node]}") # Function to reconstruct the path from start to goal using parent nodes def reconstruct_path(parents, start, goal): path = [goal] while goal != start: goal = parents[goal] path.append(goal) return path[::-1] # Example usage: start_node = 'A' goal_node = 'G' graph = { 'A': [{'B': 5, 'C': 10}, 10], 'B': [{'D': 5, 'E': 5}, 7], 'C': [{'F': 5}, 7], 'D': [{'G': 10}, 3], 'E': [{'G': 7}, 2], 'F': [{'G': 8}, 1], 'G': [{}, 0] } print("\nA* Search Path:") path = astar(graph, start_node, goal_node) print("Final Path:", path) USP Running a code We shall use fedora to run most of the code. The code will be written in ANSI C and. To run the code, we shall use the following commands: ...

Operating systems Bases of an operating system Execute user programs and make solving user problems easier Make the computer system convenient to use Use the computer hardware in an efficient manner A computer system can be divided roughly into four components: the hardware, the operating system, the application programs, and the users. Use of an operating system The operating system is responsible for the following activities in connection with process management Process creation and deletion Process suspension and resumption Provision of mechanisms for process synchronization Provision of mechanisms for process communication Provision of mechanisms for deadlock handling Operating systems is a resource allocator and it is a control program. ...