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 achieve the desired functionality using NumPy's strided tricks, you can use the `numpy.lib.stride_tricks.as_strided` function. This function allows you to create a view of the array with a different shape and strides, which can be used to efficiently create overlapping windows of the array. Here's how you can implement the `strides()` function: 

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import numpy as np

def strides(T, window_size=50, step_size=10):
    # Calculate the number of windows
    num_windows = (len(T) - window_size) // step_size + 1
    
    # Use as_strided to create the overlapping windows
    strided_view = np.lib.stride_tricks.as_strided(
        T,
        shape=(num_windows, window_size),
        strides=(T.strides[0] * step_size, T.strides[0])
    )
    
    return strided_view

# Example usage:
T = np.arange(100)  # Example array
result = strides(T)
print(result)
### Explanation: - **`window_size=50`**: This is the size of each window (50 elements in this case). - **`step_size=10`**: This is the step size between the start of each window (10 elements in this case). - **`num_windows`**: This calculates the number of windows that can be created given the array length, window size, and step size. - **`as_strided`**: This function creates a view of the array with the specified shape and strides. The strides are calculated such that each window starts `step_size` elements apart. ### Example Output: For `T = np.arange(100)`, the output will be: 
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[[ 0  1  2 ... 47 48 49]
 [10 11 12 ... 57 58 59]
 [20 21 22 ... 67 68 69]
 ...
 [70 71 72 ... 97 98 99]]
This output corresponds to the windows `[T[0:50], T[10:60], T[20:70], ...]`. ### Important Notes: - **Memory Safety**: `as_strided` creates a view, not a copy, so modifying the returned array will modify the original array `T`. If you need a copy, you can use `.copy()` on the result. - **Edge Cases**: Ensure that the array `T` is long enough to create at least one window. If `len(T) < window_size`, the function will raise an error.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T, window_size=50, step_size=10):
    # Calculate the number of windows
    num_windows = (len(T) - window_size) // step_size + 1
    
    # Use as_strided to create the overlapping windows
    strided_view = np.lib.stride_tricks.as_strided(
        T,
        shape=(num_windows, window_size),
        strides=(T.strides[0] * step_size, T.strides[0])
    )
    
    return 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

def strides(T, window_size=50, step_size=10):
    # Calculate the number of windows
    num_windows = (len(T) - window_size) // step_size + 1
    
    # Use as_strided to create the overlapping windows
    strided_view = np.lib.stride_tricks.as_strided(
        T,
        shape=(num_windows, window_size),
        strides=(T.strides[0] * step_size, T.strides[0])
    )
    
    return 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: All tests passed

Substring Evaluation

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

Final Answer: True