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

Here's a Python function that uses NumPy's strided trick to create overlapping subarrays from the input array, as per your specification: 

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import numpy as np

def strides(T):
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of subarrays
    n_subarrays = max(0, (len(T) - 50) // 10 + 1)
    
    # If there are no subarrays possible, return an empty list
    if n_subarrays == 0:
        return []
    
    # Create a view of the array with the desired shape and strides
    strided = np.lib.stride_tricks.as_strided(
        T,
        shape=(n_subarrays, 50),
        strides=(T.strides[0] * 10, T.strides[0])
    )
    
    # Convert the strided view to a list of subarrays
    return [subarray for subarray in strided]
This function does the following: 1. It first ensures that the input `T` is a NumPy array using `np.asarray()`. 2. It calculates the number of subarrays that can be created. Each subarray has a length of 50, and they start at indices that are 10 apart. 3. If no subarrays can be created (i.e., if the input array is too short), it returns an empty list. 4. It uses `np.lib.stride_tricks.as_strided()` to create a view of the array with the desired shape and strides. This is the "strided trick" you mentioned: - The shape is `(n_subarrays, 50)`, meaning we want `n_subarrays` subarrays, each of length 50. - The strides are `(T.strides[0] * 10, T.strides[0])`, which means we move 10 elements to start the next subarray, but only 1 element within each subarray. 5. Finally, it converts the strided view to a list of subarrays and returns this list. You can use this function like this: 
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import numpy as np

T = np.arange(100)  # Example input array
result = strides(T)

print(len(result))  # Number of subarrays
print(result[0])    # First subarray
print(result[-1])   # Last subarray
This will create subarrays `[T[0:50], T[10:60], T[20:70], ...]` as requested. Note that this method is memory-efficient as it creates views of the original array rather than copying data.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T):
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of subarrays
    n_subarrays = max(0, (len(T) - 50) // 10 + 1)
    
    # If there are no subarrays possible, return an empty list
    if n_subarrays == 0:
        return []
    
    # Create a view of the array with the desired shape and strides
    strided = np.lib.stride_tricks.as_strided(
        T,
        shape=(n_subarrays, 50),
        strides=(T.strides[0] * 10, T.strides[0])
    )
    
    # Convert the strided view to a list of subarrays
    return [subarray for subarray in strided]

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):
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of subarrays
    n_subarrays = max(0, (len(T) - 50) // 10 + 1)
    
    # If there are no subarrays possible, return an empty list
    if n_subarrays == 0:
        return []
    
    # Create a view of the array with the desired shape and strides
    strided = np.lib.stride_tricks.as_strided(
        T,
        shape=(n_subarrays, 50),
        strides=(T.strides[0] * 10, T.strides[0])
    )
    
    # Convert the strided view to a list of subarrays
    return [subarray for subarray in strided]





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 29, 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

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

Here's a Python function that uses NumPy's strided trick to create overlapping subarrays from an input array of tokens: 

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import numpy as np

def strides(T, window_size=50, step=10):
    """
    Create overlapping subarrays from the input array using the strided trick.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default: 50)
    step (int): Step size between subarrays (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping subarrays
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    num_subarrays = max(0, (T.size - window_size) // step + 1)
    
    # Create a new shape for the strided array
    new_shape = (num_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * step, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array of tokens
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray:", result[0])
    print("Second subarray:", result[1])
    print("Third subarray:", result[2])
This function does the following: 1. It takes an input array `T`, a `window_size` (default 50), and a `step` size (default 10). 2. It calculates the number of valid subarrays based on the input array size, window size, and step size. 3. It creates a new shape and strides for the output array. 4. It uses `np.lib.stride_tricks.as_strided` to create a view of the input array with the new shape and strides, effectively creating the overlapping subarrays without copying data. 5. It returns the resulting array of subarrays. The example usage demonstrates how to use the function and verifies that it produces the expected results. The output will be a 2D NumPy array where each row is a subarray of the original input array, with the specified overlap. This implementation is memory-efficient as it creates a view of the original array rather than copying data. However, be cautious when modifying the resulting array, as changes will affect the original input array due to the shared memory.

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=10):
    """
    Create overlapping subarrays from the input array using the strided trick.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default: 50)
    step (int): Step size between subarrays (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping subarrays
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    num_subarrays = max(0, (T.size - window_size) // step + 1)
    
    # Create a new shape for the strided array
    new_shape = (num_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * step, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array of tokens
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray:", result[0])
    print("Second subarray:", result[1])
    print("Third subarray:", result[2])

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=10):
    """
    Create overlapping subarrays from the input array using the strided trick.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default: 50)
    step (int): Step size between subarrays (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping subarrays
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    num_subarrays = max(0, (T.size - window_size) // step + 1)
    
    # Create a new shape for the strided array
    new_shape = (num_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * step, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array of tokens
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray:", result[0])
    print("Second subarray:", result[1])
    print("Third subarray:", result[2])





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: [[ 0 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] [30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79] [40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89] [50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99]] Shape of result: (6, 50) First subarray: [ 0 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] Second subarray: [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] Third subarray: [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] All tests passed

Substring Evaluation

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

Final Answer: True

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

Here's a Python function that uses NumPy's strided trick to create the desired output from an input array of tokens: 

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import numpy as np

def strides(T, window_size=50, step=10):
    """
    Given an array of tokens T, return strided windows of the array.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    step (int): Step size between windows (default: 10)
    
    Returns:
    numpy.ndarray: Array of strided windows
    """
    # Ensure T is a numpy array
    T = np.array(T)
    
    # Calculate the number of valid windows
    num_windows = max(0, (T.size - window_size) // step + 1)
    
    # Use stride_tricks to create the windowed view
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=window_size)[::step][:num_windows]

# Example usage:
if __name__ == "__main__":
    # Create a sample array
    tokens = np.arange(100)
    
    # Get the strided windows
    windows = strides(tokens)
    
    # Print the result
    print(f"Number of windows: {len(windows)}")
    print("First few windows:")
    for i, window in enumerate(windows[:5]):
        print(f"Window {i}: {window}")
This function does the following: 1. It takes an input array `T`, a `window_size` (default 50), and a `step` size (default 10). 2. It ensures that `T` is a NumPy array. 3. It calculates the number of valid windows based on the input array size, window size, and step size. 4. It uses NumPy's `sliding_window_view` function to create a view of the input array with the specified window size. 5. It then uses array slicing to select windows at the specified step intervals. 6. Finally, it returns the resulting array of windows. The function will return an array where each element is a view of the original array, representing the windows [T[0:50], T[10:60], T[20:70], ...] as requested. When you run this script, it will demonstrate the function with a sample array of 100 elements. The output will show the number of windows created and the first few windows. Note that this approach is memory-efficient as it creates views of the original array rather than copying data. However, be cautious about modifying the returned windows, as changes will affect the original array.

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=10):
    """
    Given an array of tokens T, return strided windows of the array.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    step (int): Step size between windows (default: 10)
    
    Returns:
    numpy.ndarray: Array of strided windows
    """
    # Ensure T is a numpy array
    T = np.array(T)
    
    # Calculate the number of valid windows
    num_windows = max(0, (T.size - window_size) // step + 1)
    
    # Use stride_tricks to create the windowed view
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=window_size)[::step][:num_windows]

# Example usage:
if __name__ == "__main__":
    # Create a sample array
    tokens = np.arange(100)
    
    # Get the strided windows
    windows = strides(tokens)
    
    # Print the result
    print(f"Number of windows: {len(windows)}")
    print("First few windows:")
    for i, window in enumerate(windows[:5]):
        print(f"Window {i}: {window}")

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: False

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(T, window_size=50, step=10):
    """
    Given an array of tokens T, return strided windows of the array.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    step (int): Step size between windows (default: 10)
    
    Returns:
    numpy.ndarray: Array of strided windows
    """
    # Ensure T is a numpy array
    T = np.array(T)
    
    # Calculate the number of valid windows
    num_windows = max(0, (T.size - window_size) // step + 1)
    
    # Use stride_tricks to create the windowed view
    return np.lib.stride_tricks.sliding_window_view(T, window_shape=window_size)[::step][:num_windows]

# Example usage:
if __name__ == "__main__":
    # Create a sample array
    tokens = np.arange(100)
    
    # Get the strided windows
    windows = strides(tokens)
    
    # Print the result
    print(f"Number of windows: {len(windows)}")
    print("First few windows:")
    for i, window in enumerate(windows[:5]):
        print(f"Window {i}: {window}")





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: Number of windows: 6 First few windows: Window 0: [ 0 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] Window 1: [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] Window 2: [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] Window 3: [30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79] Window 4: [40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89] All tests passed

Substring Evaluation

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

Final Answer: False

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

Here's a Python function that implements the strided trick to create overlapping subarrays from a given numpy array of tokens: 

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import numpy as np

def strides(T, window_size=50, step=10):
    """
    Create overlapping subarrays from the input array T using the strided trick.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default: 50)
    step (int): Step size between the start of each subarray (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping subarrays
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    n_subarrays = max(0, (T.size - window_size) // step + 1)
    
    # Create a new shape for the strided array
    new_shape = (n_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * step, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array of tokens
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of the result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray (T[0:50]):")
    print(result[0])
    print("\nSecond subarray (T[10:60]):")
    print(result[1])
    print("\nThird subarray (T[20:70]):")
    print(result[2])
This function does the following: 1. It takes an input array `T`, a `window_size` (default 50), and a `step` size (default 10). 2. It calculates the number of valid subarrays based on the input array size, window size, and step size. 3. It uses `np.lib.stride_tricks.as_strided` to create a view of the original array with the desired shape and strides. This is the "strided trick" that allows us to create overlapping subarrays without copying data. 4. The resulting array contains all the overlapping subarrays, where each subarray starts `step` elements after the previous one and has a length of `window_size`. The example usage demonstrates how to use the function and verifies that it produces the expected results. When you run this script, it will create a sample array of 100 tokens and then apply the `strides` function to create the overlapping subarrays. This implementation is memory-efficient because it doesn't create copies of the data. Instead, it creates views of the original array. However, be careful when modifying the resulting array, as changes will affect the original data.

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=10):
    """
    Create overlapping subarrays from the input array T using the strided trick.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default: 50)
    step (int): Step size between the start of each subarray (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping subarrays
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    n_subarrays = max(0, (T.size - window_size) // step + 1)
    
    # Create a new shape for the strided array
    new_shape = (n_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * step, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array of tokens
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of the result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray (T[0:50]):")
    print(result[0])
    print("\nSecond subarray (T[10:60]):")
    print(result[1])
    print("\nThird subarray (T[20:70]):")
    print(result[2])

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=10):
    """
    Create overlapping subarrays from the input array T using the strided trick.
    
    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default: 50)
    step (int): Step size between the start of each subarray (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping subarrays
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    n_subarrays = max(0, (T.size - window_size) // step + 1)
    
    # Create a new shape for the strided array
    new_shape = (n_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * step, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array of tokens
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of the result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray (T[0:50]):")
    print(result[0])
    print("\nSecond subarray (T[10:60]):")
    print(result[1])
    print("\nThird subarray (T[20:70]):")
    print(result[2])





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: [[ 0 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] [30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79] [40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89] [50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99]] Shape of the result: (6, 50) First subarray (T[0:50]): [ 0 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] Second subarray (T[10:60]): [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] Third subarray (T[20:70]): [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] All tests passed

Substring Evaluation

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

Final Answer: True

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

Here's a Python function that implements the strided trick to create overlapping subarrays from a given numpy array of tokens: 

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import numpy as np

def strides(T, window_size=50, stride=10):
    """
    Given an array of tokens T as numpy array, using strided trick,
    returns [T[0:50], T[10:60], T[20:70], ...]

    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default 50)
    stride (int): Step size between start of each subarray (default 10)

    Returns:
    numpy.ndarray: A 2D array where each row is a subarray
    """
    
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    n_subarrays = max(0, (T.size - window_size) // stride + 1)
    
    # Create a new shape for the strided array
    new_shape = (n_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * stride, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray (T[0:50]):", result[0])
    print("Second subarray (T[10:60]):", result[1])
    print("Third subarray (T[20:70]):", result[2])
This function does the following: 1. It takes three parameters: - `T`: The input numpy array of tokens. - `window_size`: The size of each subarray (default is 50). - `stride`: The step size between the start of each subarray (default is 10). 2. It ensures that `T` is a numpy array using `np.asarray()`. 3. It calculates the number of valid subarrays that can be created. 4. It creates a new shape for the strided array based on the number of subarrays and the window size. 5. It calculates the strides for the new array. The first stride is the original stride times the step size, and the second stride is the original stride. 6. It uses `np.lib.stride_tricks.as_strided()` to create the strided array efficiently without copying data. 7. Finally, it returns the strided array. The advantage of this method is that it's very memory-efficient. Instead of creating multiple copies of the data for each subarray, it creates a view of the original data with different strides. This means it uses much less memory and is much faster for large arrays. When you run this code, it will create a sample array and demonstrate how the function works. You can modify the `window_size` and `stride` parameters to get different results.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(T, window_size=50, stride=10):
    """
    Given an array of tokens T as numpy array, using strided trick,
    returns [T[0:50], T[10:60], T[20:70], ...]

    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default 50)
    stride (int): Step size between start of each subarray (default 10)

    Returns:
    numpy.ndarray: A 2D array where each row is a subarray
    """
    
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    n_subarrays = max(0, (T.size - window_size) // stride + 1)
    
    # Create a new shape for the strided array
    new_shape = (n_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * stride, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray (T[0:50]):", result[0])
    print("Second subarray (T[10:60]):", result[1])
    print("Third subarray (T[20:70]):", result[2])

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, stride=10):
    """
    Given an array of tokens T as numpy array, using strided trick,
    returns [T[0:50], T[10:60], T[20:70], ...]

    Parameters:
    T (numpy.ndarray): Input array of tokens
    window_size (int): Size of each subarray (default 50)
    stride (int): Step size between start of each subarray (default 10)

    Returns:
    numpy.ndarray: A 2D array where each row is a subarray
    """
    
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Calculate the number of valid subarrays
    n_subarrays = max(0, (T.size - window_size) // stride + 1)
    
    # Create a new shape for the strided array
    new_shape = (n_subarrays, window_size)
    
    # Calculate the strides for the new array
    new_strides = (T.strides[0] * stride, T.strides[0])
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(T, shape=new_shape, strides=new_strides)
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array
    tokens = np.arange(100)
    
    # Get the strided subarrays
    result = strides(tokens)
    
    # Print the result
    print(result)
    print(f"Shape of result: {result.shape}")
    
    # Verify a few subarrays
    print("\nFirst subarray (T[0:50]):", result[0])
    print("Second subarray (T[10:60]):", result[1])
    print("Third subarray (T[20:70]):", result[2])





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: [[ 0 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] [30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79] [40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89] [50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99]] Shape of result: (6, 50) First subarray (T[0:50]): [ 0 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] Second subarray (T[10:60]): [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] Third subarray (T[20:70]): [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] All tests passed

Substring Evaluation

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

Final Answer: True