Initial Query

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] ... ]

LLM Generation

Query

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] ... ]

Output

To solve this problem, we need to generate overlapping windows from a given numpy array of tokens using strided tricks. Each window should start at intervals of 10 elements and have a length of 50 elements. The goal is to efficiently create these windows using numpy's strided functionality to avoid unnecessary memory usage. ### Approach 1. **Window Size and Step**: The window size is fixed at 50 elements, and each subsequent window starts 10 elements after the previous one. 2. **Check Validity**: If the array length is less than the window size, return an empty list since no valid window can be formed. 3. **Calculate Number of Windows**: Determine the number of valid windows using the formula `(len(T) - window_size) // step + 1`. 4. **Strided Array**: Use numpy's `as_strided` function to create a view of the array with the calculated shape and strides. This allows us to efficiently generate overlapping windows without copying data. ### Solution Code 

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import numpy as np
from numpy.lib.stride_tricks import as_strided

def strides(T):
    window_size = 50
    step = 10
    if len(T) < window_size:
        return []
    num_windows = (len(T) - window_size) // step + 1
    new_shape = (num_windows, window_size)
    new_strides = (step * T.strides[0], T.strides[0])
    strided_view = as_strided(T, shape=new_shape, strides=new_strides)
    return list(strided_view)
### Explanation - **Window Size and Step**: The window size is 50, and each new window starts 10 elements after the previous one. This means the first window is from index 0 to 49, the next from 10 to 59, and so on. - **Number of Windows**: The formula `(len(T) - window_size) // step + 1` ensures we calculate the exact number of valid windows that fit within the array bounds. - **Strided View**: Using `as_strided`, we create a 2D view of the array where each row is a window. The strides parameter is set such that moving to the next row skips 10 elements in the original array, and each element within a row is consecutive. This method efficiently creates overlapping windows without copying data, leveraging numpy's memory management for optimal performance. This approach ensures that we generate the required windows efficiently using numpy's strided tricks, which is both memory efficient and computationally optimal.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numpy.lib.stride_tricks import as_strided

def strides(T):
    window_size = 50
    step = 10
    if len(T) < window_size:
        return []
    num_windows = (len(T) - window_size) // step + 1
    new_shape = (num_windows, window_size)
    new_strides = (step * T.strides[0], T.strides[0])
    strided_view = as_strided(T, shape=new_shape, strides=new_strides)
    return list(strided_view)

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: True

Run Code Interpreter

Running the following program: 

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import numpy as np
from numpy.lib.stride_tricks import as_strided

def strides(T):
    window_size = 50
    step = 10
    if len(T) < window_size:
        return []
    num_windows = (len(T) - window_size) // step + 1
    new_shape = (num_windows, window_size)
    new_strides = (step * T.strides[0], T.strides[0])
    strided_view = as_strided(T, shape=new_shape, strides=new_strides)
    return list(strided_view)





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')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 20, in <module> answer = strides(np.arange(100)).shape ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'list' object has no attribute 'shape'

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Final Answer: False