Initial Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

LLM Generation

Query

Write me a numba implementation of levensthin distance, taking two numpy int arrays. Call it levenshtein. Run it in nopython mode. Character swaps are cost 2.

Output

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import numpy as np
from numba import njit

@njit(nopython=True)
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    """
    Calculate the Levenshtein distance between two numpy int arrays.
    
    Parameters:
    a (np.ndarray): The first array.
    b (np.ndarray): The second array.
    
    Returns:
    int: The Levenshtein distance between the two arrays.
    """
    
    # Get the lengths of the arrays
    m, n = len(a), len(b)
    
    # Create a 2D array to store the distances
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Initialize the first row and column
    for i in range(m + 1):
        dp[i, 0] = i
    for j in range(n + 1):
        dp[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # If the current elements are equal, there is no cost
            if a[i - 1] == b[j - 1]:
                cost = 0
            # If the current elements are not equal, the cost is 1 (insertion or deletion) or 2 (substitution)
            else:
                cost = 2  # Character swaps are cost 2
            
            # Calculate the minimum distance
            dp[i, j] = min(
                dp[i - 1, j] + 1,  # Deletion
                dp[i, j - 1] + 1,  # Insertion
                dp[i - 1, j - 1] + cost  # Substitution or no operation
            )
    
    # Return the Levenshtein distance
    return dp[m, n]

# Example usage
a = np.array([1, 2, 3])
b = np.array([1, 3, 2])
distance = levenshtein(a, b)
print(f"The Levenshtein distance between the two arrays is: {distance}")
This code calculates the Levenshtein distance between two numpy int arrays using dynamic programming. The `levenshtein` function takes two numpy int arrays as input and returns the Levenshtein distance between them. The distance is calculated using a 2D array `dp` where `dp[i, j]` represents the Levenshtein distance between the first `i` elements of the first array and the first `j` elements of the second array. The function uses a nested loop to fill up the `dp` array and finally returns the Levenshtein distance between the two arrays. The example usage demonstrates how to use the `levenshtein` function with two sample arrays.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit(nopython=True)
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    """
    Calculate the Levenshtein distance between two numpy int arrays.
    
    Parameters:
    a (np.ndarray): The first array.
    b (np.ndarray): The second array.
    
    Returns:
    int: The Levenshtein distance between the two arrays.
    """
    
    # Get the lengths of the arrays
    m, n = len(a), len(b)
    
    # Create a 2D array to store the distances
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Initialize the first row and column
    for i in range(m + 1):
        dp[i, 0] = i
    for j in range(n + 1):
        dp[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # If the current elements are equal, there is no cost
            if a[i - 1] == b[j - 1]:
                cost = 0
            # If the current elements are not equal, the cost is 1 (insertion or deletion) or 2 (substitution)
            else:
                cost = 2  # Character swaps are cost 2
            
            # Calculate the minimum distance
            dp[i, j] = min(
                dp[i - 1, j] + 1,  # Deletion
                dp[i, j - 1] + 1,  # Insertion
                dp[i - 1, j - 1] + cost  # Substitution or no operation
            )
    
    # Return the Levenshtein distance
    return dp[m, n]

# Example usage
a = np.array([1, 2, 3])
b = np.array([1, 3, 2])
distance = levenshtein(a, b)
print(f"The Levenshtein distance between the two arrays is: {distance}")

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numpy as np
from numba import njit

@njit(nopython=True)
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    """
    Calculate the Levenshtein distance between two numpy int arrays.
    
    Parameters:
    a (np.ndarray): The first array.
    b (np.ndarray): The second array.
    
    Returns:
    int: The Levenshtein distance between the two arrays.
    """
    
    # Get the lengths of the arrays
    m, n = len(a), len(b)
    
    # Create a 2D array to store the distances
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Initialize the first row and column
    for i in range(m + 1):
        dp[i, 0] = i
    for j in range(n + 1):
        dp[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # If the current elements are equal, there is no cost
            if a[i - 1] == b[j - 1]:
                cost = 0
            # If the current elements are not equal, the cost is 1 (insertion or deletion) or 2 (substitution)
            else:
                cost = 2  # Character swaps are cost 2
            
            # Calculate the minimum distance
            dp[i, j] = min(
                dp[i - 1, j] + 1,  # Deletion
                dp[i, j - 1] + 1,  # Insertion
                dp[i - 1, j - 1] + cost  # Substitution or no operation
            )
    
    # Return the Levenshtein distance
    return dp[m, n]

# Example usage
a = np.array([1, 2, 3])
b = np.array([1, 3, 2])
distance = levenshtein(a, b)
print(f"The Levenshtein distance between the two arrays is: {distance}")





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: The Levenshtein distance between the two arrays is: 2 All tests passed /usr/local/lib/python3.12/dist-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)

Substring Evaluation

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

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: True

Substring Evaluation

Testing if the previous output contains the string nopython: True

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: False

Substring Evaluation

Testing if the previous output contains the string from numba: True

Final Answer: True

Final Answer: True