Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]
Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]
Below is one solution that uses NumPy’s stride tricks. In this example, the function creates sliding windows of size 50 with a step of 10 from a 1D NumPy array T. Note: The as_strided method creates a new view on the original data with the given shape and strides—it does not copy data. Use it with caution since improper use can lead to unexpected memory errors. Here’s the code: ------------------------------------------------------------ import numpy as np def strides(T, window_size=50, step=10): # Ensure input is a numpy array T = np.asarray(T) # Calculate the number of windows that can be formed n_windows = (len(T) - window_size) // step + 1 # New shape for the sliding window view: # Each window is of size 'window_size' and there are 'n_windows' windows. new_shape = (n_windows, window_size) # The new strides: # - Move 'step' elements forward in the array for the start of each new window. # - Normal stride for getting an element within a window. new_strides = (T.strides[0] * step, T.strides[0]) # Use numpy's as_strided to create the sliding-window view. return np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides) # Example usage: if __name__ == "__main__": # Create a sample array of tokens (for example, integers from 0 to 99) tokens = np.arange(100) windows = strides(tokens) # Each row is a window. The first window should be tokens[0:50], the second tokens[10:60], etc. for i, w in enumerate(windows): print(f"Window {i}: {w}") ------------------------------------------------------------ Explanation: 1. We first determine how many full windows can be produced given the token array length. 2. We set the new shape to be (n_windows, window_size) so that each row is a sliding window. 3. The new stride for rows is the original stride times the step size (10 tokens), while the stride within a window remains the same. 4. The as_strided function then creates a view on T without copying data. This function will return an array where each row corresponds to one of the windows: T[0:50], T[10:60], T[20:70] and so on.
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import numpy as np def strides(T, window_size=50, step=10): # Ensure input is a numpy array T = np.asarray(T) # Calculate the number of windows that can be formed n_windows = (len(T) - window_size) // step + 1 # New shape for the sliding window view: # Each window is of size 'window_size' and there are 'n_windows' windows. new_shape = (n_windows, window_size) # The new strides: # - Move 'step' elements forward in the array for the start of each new window. # - Normal stride for getting an element within a window. new_strides = (T.strides[0] * step, T.strides[0]) # Use numpy's as_strided to create the sliding-window view. return np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides) |
Testing if the previous output contains the string as_strided: True
Running the following program:
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 | import numpy as np def strides(T, window_size=50, step=10): # Ensure input is a numpy array T = np.asarray(T) # Calculate the number of windows that can be formed n_windows = (len(T) - window_size) // step + 1 # New shape for the sliding window view: # Each window is of size 'window_size' and there are 'n_windows' windows. new_shape = (n_windows, window_size) # The new strides: # - Move 'step' elements forward in the array for the start of each new window. # - Normal stride for getting an element within a window. new_strides = (T.strides[0] * step, T.strides[0]) # Use numpy's as_strided to create the sliding-window view. return np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides) answer = strides(np.arange(100)).shape expected = (6, 50) assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' answer = strides(np.arange(100)).sum() expected = 14850 assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' print('All tests passed') |
All tests passed
Testing if the previous output contains the string All tests passed: True
Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]
Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]
Here’s one way to do it using NumPy’s stride trick. Suppose you want to extract windows of length 50 starting every 10 tokens. The idea is to use np.lib.stride_tricks.as_strided to “view” the input array T as a two‐dimensional array where each row is one of the sliding windows. Below is the complete function: ------------------------------------------------------------ import numpy as np def strides(T): window_size = 50 step = 10 n = (len(T) - window_size) // step + 1 # number of windows we can get if n < 1: raise ValueError("Input array is too short to extract any window of the specified size.") # Calculate new shape and strides new_shape = (n, window_size) new_strides = (T.strides[0] * step, T.strides[0]) # Use as_strided to create the sliding windows view return np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides) # Example usage: if __name__ == "__main__": # Create a sample token array from 0 to 99 (so we have plenty of tokens) T = np.arange(100) windows = strides(T) for i, window in enumerate(windows): print(f"Window {i}: {window}") ------------------------------------------------------------ How the function works: 1. We set window_size = 50 and step = 10. 2. The number of windows n is calculated as (len(T) - window_size) // step + 1. 3. For the underlying memory layout, the new shape is (n, window_size) and the new strides are (T.strides[0] * step, T.strides[0]), meaning that the start of each new window is 10 elements further along. 4. np.lib.stride_tricks.as_strided is then used to create a view that represents the desired sliding windows. Note: Be careful when using as_strided since it creates a view with overlapping memory. If you modify the windows, you might inadvertently modify the original array T.
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | import numpy as np def strides(T): window_size = 50 step = 10 n = (len(T) - window_size) // step + 1 # number of windows we can get if n < 1: raise ValueError("Input array is too short to extract any window of the specified size.") # Calculate new shape and strides new_shape = (n, window_size) new_strides = (T.strides[0] * step, T.strides[0]) # Use as_strided to create the sliding windows view return np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides) |
Testing if the previous output contains the string as_strided: True
Running the following program:
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 | import numpy as np def strides(T): window_size = 50 step = 10 n = (len(T) - window_size) // step + 1 # number of windows we can get if n < 1: raise ValueError("Input array is too short to extract any window of the specified size.") # Calculate new shape and strides new_shape = (n, window_size) new_strides = (T.strides[0] * step, T.strides[0]) # Use as_strided to create the sliding windows view return np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides) answer = strides(np.arange(100)).shape expected = (6, 50) assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' answer = strides(np.arange(100)).sum() expected = 14850 assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' print('All tests passed') |
All tests passed
Testing if the previous output contains the string All tests passed: True