3 ms·
Here's how you do it: focus on the simple case, solve it, then ask Copilot to vectorize the code. Life is too short.
by credit_guy 1y ago
Here's how you do it: focus on the simple case, solve it, then ask Copilot to vectorize the code. Life is too short.
- gus_massa 1y agoI never tried that. Can you use this method with the example in the article and post it here? Does it work of Copilot hallucinates magical functions? (I'd be worry if I ever have to fix the code.)
- credit_guy 1y ago"Here’s how you can create a test case to compare the performance of the for-loop method and the vectorized method using numpy.linalg.solve. I will provide the Python code for you to test this." import numpy as np import time # Create random test data np.random.seed(42) # For reproducibility A = np.random.rand(100, 5, 5) # 100 random 5x5 matrices x = np.random.rand(100, 5) # 100 random vectors of length 5 # Method 1: For-loop approach start_time = time.time() y_loop = np.empty_like(x) for i in range(100): y_loop[i, :] = np.linalg.solve(A[i, :, :], x[i, :]) loop_time = time.time() - start_time # Method 2: Vectorized approach start_time = time.time() y_vectorized = np.linalg.solve(A, x) vectorized_time = time.time() - start_time # Verify correctness correctness = np.allclose(y_loop, y_vectorized) # Print results print(f"For-loop method time: {loop_time:.6f} seconds") print(f"Vectorized method time: {vectorized_time:.6f} seconds") print(f"Results are {'correct' if correctness else 'incorrect'}") For-loop method time: 0.001004 seconds Vectorized method time: 0.000000 seconds Results are correct