#Prim's Algorithm Implementation In Python

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Code: from collections import deque def bfs(G, start): # Initialize visited list marked = [False] * len(G) # Use a queue for BFS queue = deque([start]) marked[start] = True while queue: v = queue.popleft() print(v, end=" ") # Visit node # Traverse all neighbors of v for w in G[v]: if not marked[w]: marked[w] = True queue.append(w) # Example usage: # Graph represented as adjacency list G = [ [1, 2], # Neighbors of node 0 [0, 3], # Neighbors of node 1 [0, 3], # Neighbors of node 2 [1, 2] # Neighbors of node 3 ] bfs(G, 0) # Start BFS from node 0 AI #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #Python #Tech #Coding #BigData #Innovation #AITrends #TechTrends #FutureTech#AIArt #AIArtist #Midjourney #StableDiffusion #DigitalArt #GenerativeArt #AICommunity #AIArtworks #CreativeTech

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Both Prim's and Yao's algorithms construct minimum spanning trees, with Prim's being the classic greedy approach found in textbooks and Yao's offering a more advanced, theoretically faster method for large sparse graphs. Complete code for the Visualizer is available in my GitHub repo.

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🚀 Master Quick Sort – The Fast & Efficient Sorting Algorithm! Quick Sort is a divide-and-conquer algorithm that efficiently sorts elements by partitioning the list into smaller sections. Here's how it works: 🔍 How Quick Sort Works: 1️⃣ Choose a Pivot: Select an element (often the first, last, or a random element) as the pivot. 2️⃣ Partition: 🔸 Elements smaller than the pivot are moved to the left. 🔸 Elements larger than the pivot are moved to the right. 🔸 The pivot ends up in its correct sorted position. 3️⃣ Recursion: Apply the same steps recursively to the left and right sub-lists. 4️⃣ The process continues until all sub-lists are sorted. 🔥 Why Use Quick Sort? ✅ Average Time Complexity: O(n log n) – Performs efficiently for most datasets. ✅ In-Place Sorting: No extra memory required (except for recursion). ✅ Widely Used: Commonly used in libraries and frameworks for sorting large arrays. ⚠️ Limitations: 📉 Worst-Case Complexity: O(n²) – Occurs when the pivot choice consistently divides the list poorly. 💡 To reduce this risk, use random pivot selection or median-of-three pivot strategy. 💡 Quick Sort Fun Fact: Despite its name, Quick Sort may not always be the fastest algorithm. For small datasets, Insertion Sort can sometimes outperform it! 📌 Got a question? Drop it in the comments! Don’t forget to like, save, and follow for more algorithm content! 🚀 #Coding #Programming #DataStructures #Algorithms #QuickSort #DeveloperLife #WebDevelopment #Python #JavaScript #LearnToCode #SoftwareDevelopment #ComputerScience #TechContent #BigONotation #SelfTaughtDeveloper #SortingAlgorithms #TechEducation #DivideAndConquer #CodingLife #SortingEfficiency

Implement a Queue with Python!

Insertion Sort (The Simple Helper): Works by placing each element where it belongs, just like arranging cards in your hand. Smooth and elegant on smaller or almost-sorted data, but slows down quickly as the list grows (O(n²)). Heap Sort (The Reliable Worker): Builds a Binary Heap and always picks the maximum efficiently. Stays consistently fast even with large datasets (O(n log n)) and uses very little extra memory (O(1)). Great when stability doesn't matter. ~~~~~~~~~~~~~~~~~~~~~~~ Follow @pycode.hubb For More ~~~~~~~~~~~~~~~~~~~~~~~ #pycode #python3 #coding #coder #programmer #coderlife #learningcode #machinelearning #ML #AI