3. If you are successful, the result of print(get_optimal_route( L9, Ll )) will be ['L9', L8', L5', L2', 'Ll']. Explain why this is the case. 4. Review the code to make sure you understand it. In the writeup, you will need to provide an overview of the code using your own words. Do not copy the description from Sayak Paul's article. 5. Run with different levels of hyperparameters gamma and alpha. What are these parameters for? For example, try 0.05 for both. What happens now? Write your findings and explanation. Remember to set back gamma = 0.9 and alpha = 0.75 after this before you proceed with the next questions. 6. When you run print(get_optimal_route(L?', L1')) how many times does the while loop in the get_optimal_route() get executed and why it is this number? Hint: one way to check is to add steps =0 before the while loop.

Step 3: Understand the question

The question is asking why the result of print(get_optimal_route( L9, Ll )) is ['L9', 'L8', 'L5', 'L2', 'Ll'].

Step 4: Analyze the statement

The get_optimal_route function is likely a pathfinding function that finds the shortest or most optimal route between two points, in this case, L9 and Ll. The result ['L9', 'L8', 'L5', 'L2', 'Ll'] is the sequence of points that the function has determined to be the optimal route. This could be due to factors such as distance, cost, or other weights associated with the points.

Step 5: Review the code

Without the actual code, it's hard to provide an overview. However, generally, the code likely includes a function to calculate the optimal route, and variables or data structures to store the points and their associated weights or costs. It may also include a loop or recursive function to iterate through the points and calculate the optimal route.

Step 6: Experiment with hyperparameters

Gamma and alpha are hyperparameters that are often used in machine learning algorithms. Gamma can control the balance between exploration and exploitation, while alpha is often the learning rate. Changing these values can affect the performance of the algorithm. For example, setting both to 0.05 may result in the algorithm taking longer to find the optimal route, or it may not find the optimal route at all.

Step 7: Understand the loop execution

The number of times the while loop in the get_optimal_route() function gets executed depends on the conditions set in the loop. For example, the loop may continue until it has found the optimal route, or until it has iterated through all the points. Adding steps = 0 before the loop and incrementing it within the loop can help track the number of iterations.

Note: The specific answers to these questions will depend on the actual code, which I don't have access to. The steps provided should guide you to understand and answer the questions.