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 solution to create a function that generates strided slices of a token array using NumPy: 

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import numpy as np

def strides(T, window_size=50, stride=10):
    """
    Generate strided slices of a token array using NumPy.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input token array
    window_size : int, optional (default=50)
        Size of each window slice
    stride : int, optional (default=10)
        Step size between successive windows
    
    Returns:
    --------
    numpy.ndarray
        Array of sliced windows
    """
    # Calculate the number of possible windows
    num_windows = 1 + (len(T) - window_size) // stride
    
    # Use NumPy's stride tricks to create a view of strided slices
    shape = (num_windows, window_size)
    strides = (stride * T.strides[0], T.strides[0])
    
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)
Here's an example of how to use the function: 
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# Example usage
import numpy as np

# Create a sample token array
T = np.arange(100)  # Example array with 100 tokens

# Generate strided windows
windows = strides(T)

# Print the shape and first few windows
print("Windows shape:", windows.shape)
print("\nFirst few windows:")
print(windows[:5])
This might output something like: 
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Windows shape: (6, 50)

First few windows:
[[ 0  1  2 ... 49]
 [10 11 12 ... 59]
 [20 21 22 ... 69]
 [30 31 32 ... 79]
 [40 41 42 ... 89]]
Key points about the implementation: 1. The function uses `np.lib.stride_tricks.as_strided()` to create a view of the original array with strided windows. 2. It calculates the number of possible windows based on the input array length, window size, and stride. 3. The function allows customization of window size and stride. 4. It's memory-efficient as it creates a view rather than copying data. Caution: - `as_strided()` can be unsafe if not used carefully, as it doesn't do bounds checking. - Make sure the window size and stride are compatible with the input array length. You can easily modify the function to handle different types of inputs or add more error checking as needed. Here's an additional example with more complex parameters: 
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# With custom window size and stride
T = np.random.rand(200)
windows = strides(T, window_size=75, stride=15)
print("Custom windows shape:", windows.shape)
This approach is particularly useful for: - Sliding window operations - Preprocessing sequence data - Creating overlapping segments for machine learning tasks

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):
    """
    Generate strided slices of a token array using NumPy.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input token array
    window_size : int, optional (default=50)
        Size of each window slice
    stride : int, optional (default=10)
        Step size between successive windows
    
    Returns:
    --------
    numpy.ndarray
        Array of sliced windows
    """
    # Calculate the number of possible windows
    num_windows = 1 + (len(T) - window_size) // stride
    
    # Use NumPy's stride tricks to create a view of strided slices
    shape = (num_windows, window_size)
    strides = (stride * T.strides[0], T.strides[0])
    
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)

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):
    """
    Generate strided slices of a token array using NumPy.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input token array
    window_size : int, optional (default=50)
        Size of each window slice
    stride : int, optional (default=10)
        Step size between successive windows
    
    Returns:
    --------
    numpy.ndarray
        Array of sliced windows
    """
    # Calculate the number of possible windows
    num_windows = 1 + (len(T) - window_size) // stride
    
    # Use NumPy's stride tricks to create a view of strided slices
    shape = (num_windows, window_size)
    strides = (stride * T.strides[0], T.strides[0])
    
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=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')
And got the output: 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 solution to create the strides using NumPy: 

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import numpy as np

def strides(T, window_size=50, step=10):
    """
    Create strided windows from input array using NumPy striding trick
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    step : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided windows
    """
    # Compute the number of windows
    n_windows = max(0, 1 + (T.shape[0] - window_size) // step)
    
    # Use NumPy's stride tricks to create windows efficiently
    shape = (n_windows, window_size)
    
    # Compute strides
    strides = (T.strides[0] * step, T.strides[0])
    
    # Create strided view
    strided_windows = np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)
    
    return strided_windows

# Example usage
def main():
    # Create a sample array of tokens
    T = np.arange(100)  # Example array from 0 to 99
    
    # Get strided windows
    windows = strides(T)
    
    # Print the shape and first few windows
    print("Windows shape:", windows.shape)
    print("\nFirst few windows:")
    print(windows[:5])

if __name__ == "__main__":
    main()
Key points about this implementation: 1. Uses NumPy's `as_strided()` function from `stride_tricks` to create efficient windows 2. Parameters: - `T`: Input array - `window_size`: Size of each window (default 50) - `step`: Step between windows (default 10) 3. Calculates the number of possible windows based on input array length 4. Uses striding to create views into the original array without copying data Example output might look like: 
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Windows shape: (10, 50)
First few windows:
[[ 0  1  2 ...  49]
 [10 11 12 ...  59]
 [20 21 22 ...  69]
 [30 31 32 ...  79]
 [40 41 42 ...  89]]
Advantages: - Memory efficient (creates views, not copies) - Fast computation - Flexible window and step sizes Note: Be careful with large arrays, as `as_strided()` can potentially create invalid memory access if not used carefully. Alternative with error checking: 
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def strides(T, window_size=50, step=10):
    # Validate inputs
    if window_size <= 0 or step <= 0:
        raise ValueError("Window size and step must be positive")
    
    if window_size > T.shape[0]:
        raise ValueError("Window size cannot exceed array length")
    
    # Rest of the implementation remains the same
    ...

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 strided windows from input array using NumPy striding trick
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    step : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided windows
    """
    # Compute the number of windows
    n_windows = max(0, 1 + (T.shape[0] - window_size) // step)
    
    # Use NumPy's stride tricks to create windows efficiently
    shape = (n_windows, window_size)
    
    # Compute strides
    strides = (T.strides[0] * step, T.strides[0])
    
    # Create strided view
    strided_windows = np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)
    
    return strided_windows

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 strided windows from input array using NumPy striding trick
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    step : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided windows
    """
    # Compute the number of windows
    n_windows = max(0, 1 + (T.shape[0] - window_size) // step)
    
    # Use NumPy's stride tricks to create windows efficiently
    shape = (n_windows, window_size)
    
    # Compute strides
    strides = (T.strides[0] * step, T.strides[0])
    
    # Create strided view
    strided_windows = np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)
    
    return strided_windows





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

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 function that uses NumPy's striding capabilities to create overlapping windows of tokens: 

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import numpy as np

def strides(T, window_size=50, stride=10):
    """
    Create strided windows from a token array.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided windows
    """
    # Calculate the number of windows based on array length and stride
    n_windows = 1 + (len(T) - window_size) // stride
    
    # Create view of strided windows using as_strided
    shape = (n_windows, window_size)
    strides = (stride * T.itemsize, T.itemsize)
    
    # Use np.lib.stride_tricks.as_strided to create the windows
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)
Example usage: 
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# Create a sample token array
T = np.arange(100)  # Example: tokens from 0 to 99

# Get strided windows
windows = strides(T)

print(windows.shape)  # Will show (10, 50) - 10 windows of size 50
print(windows[0])     # First window: [0, 1, 2, ..., 49]
print(windows[1])     # Second window: [10, 11, 12, ..., 59]
Key points: - Uses `np.lib.stride_tricks.as_strided()` for efficient window creation - Allows customization of window size and stride - Creates a view, not a copy, so memory-efficient - Works with any NumPy array of tokens Note: Be cautious with `as_strided()` as it can cause segmentation faults if not used carefully. Always ensure windows are within array bounds.

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):
    """
    Create strided windows from a token array.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided windows
    """
    # Calculate the number of windows based on array length and stride
    n_windows = 1 + (len(T) - window_size) // stride
    
    # Create view of strided windows using as_strided
    shape = (n_windows, window_size)
    strides = (stride * T.itemsize, T.itemsize)
    
    # Use np.lib.stride_tricks.as_strided to create the windows
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)

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):
    """
    Create strided windows from a token array.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided windows
    """
    # Calculate the number of windows based on array length and stride
    n_windows = 1 + (len(T) - window_size) // stride
    
    # Create view of strided windows using as_strided
    shape = (n_windows, window_size)
    strides = (stride * T.itemsize, T.itemsize)
    
    # Use np.lib.stride_tricks.as_strided to create the windows
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=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')
And got the output: 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 using NumPy that implements the strided trick you described: 

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import numpy as np

def strides(T, window_size=50, stride=10):
    """
    Create windowed views of a token array using strided trick
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of windowed views
    """
    # Ensure T is a NumPy array
    T = np.asarray(T)
    
    # Calculate the number of windows
    n_windows = 1 + (len(T) - window_size) // stride
    
    # Create strided view
    shape = (n_windows, window_size)
    strides = (stride * T.strides[0], T.strides[0])
    
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)
Example usage: 
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# Example 1: Basic usage
tokens = np.arange(100)  # Example array of tokens
windowed_tokens = strides(tokens)
print(windowed_tokens.shape)  # Will show (6, 50) windows
print(windowed_tokens[0])     # First window: [0, 1, ..., 49]
print(windowed_tokens[1])     # Second window: [10, 11, ..., 59]

# Example 2: Custom window and stride
tokens = np.random.rand(200)
windowed_tokens = strides(tokens, window_size=75, stride=15)
print(windowed_tokens.shape)
Key points: - Uses NumPy's `as_strided()` for efficient view creation - Allows custom window size and stride - Returns a 2D array where each row is a window - Very memory efficient as it creates views, not copies Caution: `as_strided()` can be unsafe if not used carefully, as it doesn't do bounds checking. Alternative with less risk: 
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def safe_strides(T, window_size=50, stride=10):
    """Safer version with explicit windowing"""
    return np.array([T[i:i+window_size] 
                     for i in range(0, len(T)-window_size+1, stride)])
This alternative method is slightly slower but safer, as it creates actual copies instead of views.

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):
    """
    Create windowed views of a token array using strided trick
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of windowed views
    """
    # Ensure T is a NumPy array
    T = np.asarray(T)
    
    # Calculate the number of windows
    n_windows = 1 + (len(T) - window_size) // stride
    
    # Create strided view
    shape = (n_windows, window_size)
    strides = (stride * T.strides[0], T.strides[0])
    
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=strides)

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):
    """
    Create windowed views of a token array using strided trick
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array of tokens
    window_size : int, optional (default=50)
        Size of each window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of windowed views
    """
    # Ensure T is a NumPy array
    T = np.asarray(T)
    
    # Calculate the number of windows
    n_windows = 1 + (len(T) - window_size) // stride
    
    # Create strided view
    shape = (n_windows, window_size)
    strides = (stride * T.strides[0], T.strides[0])
    
    return np.lib.stride_tricks.as_strided(T, shape=shape, strides=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')
And got the output: 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 solution using NumPy's striding capabilities: 

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import numpy as np

def strides(T, window_size=50, stride=10):
    """
    Create strided views of an array using numpy's advanced indexing.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array to be strided
    window_size : int, optional (default=50)
        Size of each strided window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided views
    """
    # Calculate number of possible windows
    n_windows = 1 + (T.shape[0] - window_size) // stride
    
    # Use np.lib.stride_tricks.as_strided for efficient view creation
    from numpy.lib.stride_tricks import as_strided
    
    # Create strided view
    strided_views = as_strided(
        T, 
        shape=(n_windows, window_size), 
        strides=(T.strides[0] * stride, T.strides[0])
    )
    
    return strided_views

# Example usage
if __name__ == "__main__":
    # Create a sample array
    T = np.arange(100)  # array from 0 to 99
    
    # Get strided views
    result = strides(T)
    
    print("Original array shape:", T.shape)
    print("Strided views shape:", result.shape)
    print("\nFirst few strided views:")
    print(result[:5])  # Print first 5 strided windows
Key points about this implementation: 1. Uses `numpy.lib.stride_tricks.as_strided()` for efficient memory view creation 2. Allows customizable window size and stride 3. Creates views, not copies, so it's memory efficient 4. Calculates number of possible windows based on input array length Example output might look like: 
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Original array shape: (100,)
Strided views shape: (6, 50)

First few strided views:
[[ 0  1  2 ...  49]
 [10 11 12 ...  59]
 [20 21 22 ...  69]
 [30 31 32 ...  79]
 [40 41 42 ...  89]]
Alternative implementation using NumPy's `lib.stride_tricks`: 
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import numpy as np
from numpy.lib.stride_tricks import sliding_window_view

def strides_alt(T, window_size=50, stride=10):
    """
    Alternative implementation using sliding_window_view
    """
    return sliding_window_view(T, window_size)[::stride]
Notes: - Be cautious with `as_strided()` as it can create views that might cause out-of-bounds access - The function assumes the input can be fully strided - You might want to add error checking for input array size vs window size

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, stride=10):
    """
    Create strided views of an array using numpy's advanced indexing.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array to be strided
    window_size : int, optional (default=50)
        Size of each strided window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided views
    """
    # Calculate number of possible windows
    n_windows = 1 + (T.shape[0] - window_size) // stride
    
    # Create strided view
    strided_views = as_strided(
        T, 
        shape=(n_windows, window_size), 
        strides=(T.strides[0] * stride, T.strides[0])
    )
    
    return strided_views

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, stride=10):
    """
    Create strided views of an array using numpy's advanced indexing.
    
    Parameters:
    -----------
    T : numpy.ndarray
        Input array to be strided
    window_size : int, optional (default=50)
        Size of each strided window
    stride : int, optional (default=10)
        Step size between windows
    
    Returns:
    --------
    numpy.ndarray
        Array of strided views
    """
    # Calculate number of possible windows
    n_windows = 1 + (T.shape[0] - window_size) // stride
    
    # Create strided view
    strided_views = as_strided(
        T, 
        shape=(n_windows, window_size), 
        strides=(T.strides[0] * stride, T.strides[0])
    )
    
    return strided_views





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