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| Topics Covered | Turnaround Time | Price (USD) |
|---|---|---|
| Algorithm Design & Analysis | 48 hours | $120 |
| Data Structures & Algorithms | 72 hours | $150 |
| Advanced Java Programming | 48 hours | $130 |
| Database Management Systems | 96 hours | $140 |
| Software Engineering Principles | 72 hours | $160 |
| Machine Learning Algorithms | 48 hours | $180 |
| Web Development & Frameworks | 96 hours | $170 |
- Graduate Programming Assignment Help
- Implementation of A* Algorithm for Finding Path
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Implementation of A* Algorithm for Finding Path
import heapq
def a_star_algorithm(start, goal, graph):
open_set = []
heapq.heappush(open_set, (0, start))
came_from = {}
g_score = {node: float('inf') for node in graph}
g_score[start] = 0
f_score = {node: float('inf') for node in graph}
f_score[start] = heuristic(start, goal)
while open_set:
current = heapq.heappop(open_set)[1]
if current == goal:
return reconstruct_path(came_from, current)
for neighbor in graph[current]:
tentative_g_score = g_score[current] + graph[current][neighbor]
if tentative_g_score < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = tentative_g_score
f_score[neighbor] = g_score[neighbor] + heuristic(neighbor, goal)
if neighbor not in [i[1] for i in open_set]:
heapq.heappush(open_set, (f_score[neighbor], neighbor))
return None
def heuristic(a, b):
# Placeholder heuristic function; use appropriate heuristic for your problem
return abs(a - b)
def reconstruct_path(came_from, current):
total_path = [current]
while current in came_from:
current = came_from[current]
total_path.append(current)
The A* algorithm is a popular path finding and graph traversal technique used in graduate-level programming assignments, particularly in fields such as artificial intelligence and game development. This algorithm efficiently finds the shortest path from a start node to a goal node by combining aspects of Dijkstra’s Algorithm and Greedy Best-First Search.
Implementing the A* algorithm allows students to grasp important concepts such as heuristic functions, priority queues, and graph traversal strategies. This deepens their understanding of how algorithms can be optimized for real-world problems.
Knowledge of the A* algorithm prepares students for careers in fields like robotics, game development, and navigation systems, where efficient path finding and decision-making are crucial.
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