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

Here's a Numba implementation of the Levenshtein distance with character swaps costing 2, optimized for NumPy integer arrays: 

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import numpy as np
from numba import njit

@njit(cache=True)
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two NumPy integer arrays
    Character swaps cost 2, insertions/deletions cost 1
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array (integer type)
    arr2 : numpy.ndarray
        Second input array (integer type)
    
    Returns:
    --------
    int
        Levenshtein distance between arr1 and arr2
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters are the same, no cost
            if arr1[i-1] == arr2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Minimum of insert, delete, or substitute
                insert = dp[i, j-1] + 1
                delete = dp[i-1, j] + 1
                substitute = dp[i-1, j-1] + 1
                
                # Check for swap (only if not first elements and characters match swapped)
                swap = float('inf')
                if i > 1 and j > 1 and arr1[i-1] == arr2[j-2] and arr1[i-2] == arr2[j-1]:
                    swap = dp[i-2, j-2] + 2
                
                # Take minimum of all operations
                dp[i, j] = min(insert, delete, substitute, swap)
    
    return dp[m, n]
Example usage: 
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import numpy as np
import time

# Example arrays
a1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
a2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)

# First run to compile
_ = levenshtein(a1, a2)

# Timing the function
start = time.time()
distance = levenshtein(a1, a2)
end = time.time()

print(f"Levenshtein Distance: {distance}")
print(f"Computation Time: {(end-start)*1000:.4f} ms")
Key features: 1. Uses Numba `@njit` decorator for nopython mode compilation 2. Handles character swaps with a cost of 2 3. Works with NumPy integer arrays 4. Cached compilation for faster subsequent runs 5. Efficient dynamic programming approach The implementation follows the standard Levenshtein distance algorithm with an additional check for character swaps. The swap operation is only considered when the surrounding characters match the potential swap condition. Time complexity: O(m*n) Space complexity: O(m*n) Note: This implementation assumes integer arrays. For string comparisons, you'd need to convert strings to integer arrays first.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit(cache=True)
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two NumPy integer arrays
    Character swaps cost 2, insertions/deletions cost 1
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array (integer type)
    arr2 : numpy.ndarray
        Second input array (integer type)
    
    Returns:
    --------
    int
        Levenshtein distance between arr1 and arr2
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters are the same, no cost
            if arr1[i-1] == arr2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Minimum of insert, delete, or substitute
                insert = dp[i, j-1] + 1
                delete = dp[i-1, j] + 1
                substitute = dp[i-1, j-1] + 1
                
                # Check for swap (only if not first elements and characters match swapped)
                swap = float('inf')
                if i > 1 and j > 1 and arr1[i-1] == arr2[j-2] and arr1[i-2] == arr2[j-1]:
                    swap = dp[i-2, j-2] + 2
                
                # Take minimum of all operations
                dp[i, j] = min(insert, delete, substitute, swap)
    
    return dp[m, n]

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(cache=True)
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two NumPy integer arrays
    Character swaps cost 2, insertions/deletions cost 1
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array (integer type)
    arr2 : numpy.ndarray
        Second input array (integer type)
    
    Returns:
    --------
    int
        Levenshtein distance between arr1 and arr2
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters are the same, no cost
            if arr1[i-1] == arr2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Minimum of insert, delete, or substitute
                insert = dp[i, j-1] + 1
                delete = dp[i-1, j] + 1
                substitute = dp[i-1, j-1] + 1
                
                # Check for swap (only if not first elements and characters match swapped)
                swap = float('inf')
                if i > 1 and j > 1 and arr1[i-1] == arr2[j-2] and arr1[i-2] == arr2[j-1]:
                    swap = dp[i-2, j-2] + 2
                
                # Take minimum of all operations
                dp[i, j] = min(insert, delete, substitute, swap)
    
    return dp[m, n]





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: Traceback (most recent call last): File "/usr/src/app/main.py", line 63, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

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

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: False

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: False

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

Here's a Numba implementation of the Levenshtein distance with character swaps costing 2: 

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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays.
    Character swaps cost 2, insertions/deletions cost 1.
    
    Args:
        arr1 (np.ndarray): First input array
        arr2 (np.ndarray): Second input array
    
    Returns:
        int: Levenshtein distance
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    # Adding 1 to each dimension to include empty string case
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters are the same, no cost
            if arr1[i-1] == arr2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Check if swap is possible
                if (i > 1 and j > 1 and 
                    arr1[i-1] == arr2[j-2] and 
                    arr1[i-2] == arr2[j-1]):
                    # Swap operation with cost 2
                    dp[i, j] = min(
                        dp[i-1, j] + 1,      # deletion
                        dp[i, j-1] + 1,      # insertion
                        dp[i-1, j-1] + 1,    # substitution
                        dp[i-2, j-2] + 2     # swap
                    )
                else:
                    # Regular Levenshtein operations
                    dp[i, j] = min(
                        dp[i-1, j] + 1,      # deletion
                        dp[i, j-1] + 1,      # insertion
                        dp[i-1, j-1] + 1     # substitution
                    )
    
    return dp[m, n]

# Example usage
if __name__ == "__main__":
    # Test the function
    a1 = np.array([1, 2, 3, 4], dtype=np.int32)
    a2 = np.array([1, 3, 2, 4], dtype=np.int32)
    
    print(levenshtein(a1, a2))  # Should handle swap efficiently
Key points about this implementation: 1. Uses `@njit` for Numba nopython mode compilation 2. Takes numpy int32 arrays as input 3. Implements standard Levenshtein distance with additional swap operation 4. Swap cost is 2, other edit operations (insert/delete/substitute) cost 1 5. Works with integer arrays (can be easily adapted for other types) The function computes the minimum cost to transform one array into another, with special handling for character swaps. Example use cases: 
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# Basic usage
a1 = np.array([1, 2, 3, 4], dtype=np.int32)
a2 = np.array([1, 3, 2, 4], dtype=np.int32)
print(levenshtein(a1, a2))  # Should return 2 (swap operation)

# Different length arrays
a3 = np.array([1, 2, 3], dtype=np.int32)
a4 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
print(levenshtein(a3, a4))
The implementation provides an efficient, compiled Levenshtein distance calculation with swap support, optimized for numerical arrays.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays.
    Character swaps cost 2, insertions/deletions cost 1.
    
    Args:
        arr1 (np.ndarray): First input array
        arr2 (np.ndarray): Second input array
    
    Returns:
        int: Levenshtein distance
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    # Adding 1 to each dimension to include empty string case
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters are the same, no cost
            if arr1[i-1] == arr2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Check if swap is possible
                if (i > 1 and j > 1 and 
                    arr1[i-1] == arr2[j-2] and 
                    arr1[i-2] == arr2[j-1]):
                    # Swap operation with cost 2
                    dp[i, j] = min(
                        dp[i-1, j] + 1,      # deletion
                        dp[i, j-1] + 1,      # insertion
                        dp[i-1, j-1] + 1,    # substitution
                        dp[i-2, j-2] + 2     # swap
                    )
                else:
                    # Regular Levenshtein operations
                    dp[i, j] = min(
                        dp[i-1, j] + 1,      # deletion
                        dp[i, j-1] + 1,      # insertion
                        dp[i-1, j-1] + 1     # substitution
                    )
    
    return dp[m, n]

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
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays.
    Character swaps cost 2, insertions/deletions cost 1.
    
    Args:
        arr1 (np.ndarray): First input array
        arr2 (np.ndarray): Second input array
    
    Returns:
        int: Levenshtein distance
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    # Adding 1 to each dimension to include empty string case
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters are the same, no cost
            if arr1[i-1] == arr2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Check if swap is possible
                if (i > 1 and j > 1 and 
                    arr1[i-1] == arr2[j-2] and 
                    arr1[i-2] == arr2[j-1]):
                    # Swap operation with cost 2
                    dp[i, j] = min(
                        dp[i-1, j] + 1,      # deletion
                        dp[i, j-1] + 1,      # insertion
                        dp[i-1, j-1] + 1,    # substitution
                        dp[i-2, j-2] + 2     # swap
                    )
                else:
                    # Regular Levenshtein operations
                    dp[i, j] = min(
                        dp[i-1, j] + 1,      # deletion
                        dp[i, j-1] + 1,      # insertion
                        dp[i-1, j-1] + 1     # substitution
                    )
    
    return dp[m, n]





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: Traceback (most recent call last): File "/usr/src/app/main.py", line 65, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

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

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: False

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: False

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

Here's a Numba implementation of the Levenshtein distance with character swaps costing 2, optimized for running in nopython mode: 

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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays with swap cost of 2.
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array of integers
    arr2 : numpy.ndarray
        Second input array of integers
    
    Returns:
    --------
    int
        Levenshtein distance between the two arrays
    """
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # Cost of substitution/replacement
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 1
            
            # Check for possible swap
            swap_cost = float('inf')
            if i > 1 and j > 1 and arr1[i-1] == arr2[j-2] and arr1[i-2] == arr2[j-1]:
                swap_cost = dp[i-2, j-2] + 2
            
            # Compute minimum cost
            dp[i, j] = min(
                dp[i-1, j] + 1,        # deletion
                dp[i, j-1] + 1,        # insertion
                dp[i-1, j-1] + cost,   # substitution
                swap_cost              # swap
            )
    
    return dp[m, n]
You can use this function like this: 
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# Example usage
import numpy as np

# Test the function
a1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
a2 = np.array([1, 3, 2, 4, 5], dtype=np.int32)

dist = levenshtein(a1, a2)
print(f"Levenshtein distance: {dist}")
A few key points about this implementation: 1. It's decorated with `@njit` to run in nopython mode 2. It uses a dynamic programming approach to compute Levenshtein distance 3. Swaps have a cost of 2 4. It works with numpy int arrays 5. It handles deletion, insertion, substitution, and swapping operations The time complexity is O(mn) where m and n are the lengths of the input arrays, and the space complexity is also O(mn). The swap detection works by checking if the current and previous characters can be swapped to potentially reduce the edit distance. Example outputs: 
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# For [1,2,3,4,5] and [1,3,2,4,5], the distance would be 2 (one swap)
# For [1,2,3,4,5] and [1,2,3,4,6], the distance would be 1 (one substitution)

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays with swap cost of 2.
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array of integers
    arr2 : numpy.ndarray
        Second input array of integers
    
    Returns:
    --------
    int
        Levenshtein distance between the two arrays
    """
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # Cost of substitution/replacement
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 1
            
            # Check for possible swap
            swap_cost = float('inf')
            if i > 1 and j > 1 and arr1[i-1] == arr2[j-2] and arr1[i-2] == arr2[j-1]:
                swap_cost = dp[i-2, j-2] + 2
            
            # Compute minimum cost
            dp[i, j] = min(
                dp[i-1, j] + 1,        # deletion
                dp[i, j-1] + 1,        # insertion
                dp[i-1, j-1] + cost,   # substitution
                swap_cost              # swap
            )
    
    return dp[m, n]

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
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays with swap cost of 2.
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array of integers
    arr2 : numpy.ndarray
        Second input array of integers
    
    Returns:
    --------
    int
        Levenshtein distance between the two arrays
    """
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        dp[i, 0] = i
    for j in range(n+1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # Cost of substitution/replacement
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 1
            
            # Check for possible swap
            swap_cost = float('inf')
            if i > 1 and j > 1 and arr1[i-1] == arr2[j-2] and arr1[i-2] == arr2[j-1]:
                swap_cost = dp[i-2, j-2] + 2
            
            # Compute minimum cost
            dp[i, j] = min(
                dp[i-1, j] + 1,        # deletion
                dp[i, j-1] + 1,        # insertion
                dp[i-1, j-1] + cost,   # substitution
                swap_cost              # swap
            )
    
    return dp[m, n]





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: Traceback (most recent call last): File "/usr/src/app/main.py", line 63, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

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

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: False

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: False

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

Here's a Numba implementation of the Levenshtein distance with character swaps costing 2, optimized for NumPy int arrays: 

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import numpy as np
from numba import njit

@njit(cache=True)
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays.
    Character swaps cost 2, insertions/deletions cost 1.
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array 
    arr2 : numpy.ndarray
        Second input array
    
    Returns:
    --------
    int
        Levenshtein distance
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix 
    # Add 1 to handle empty string case
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        d[i, 0] = i
    for j in range(n+1):
        d[0, j] = j
    
    # Fill in the rest of the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # Cost of substitution (or 0 if characters match)
            if arr1[i-1] == arr2[j-1]:
                cost_subst = 0
            else:
                cost_subst = 1
            
            # Check if swap is possible and cheaper
            swap_cost = 2
            if (i > 1 and j > 1 and 
                arr1[i-1] == arr2[j-2] and 
                arr1[i-2] == arr2[j-1]):
                swap_cost = 2
            else:
                swap_cost = float('inf')
            
            # Compute minimum of different operations
            d[i, j] = min(
                d[i-1, j] + 1,      # deletion
                d[i, j-1] + 1,      # insertion
                d[i-1, j-1] + cost_subst,  # substitution
                swap_cost + d[i-2, j-2] if swap_cost != float('inf') else float('inf')  # swap
            )
    
    return d[m, n]

# Example usage
if __name__ == "__main__":
    # Test the function
    arr1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
    arr2 = np.array([1, 2, 5, 4, 3], dtype=np.int32)
    
    print(levenshtein(arr1, arr2))
Key features: - Uses `@njit(cache=True)` for Numba nopython mode compilation - Handles character swaps with a cost of 2 - Works with NumPy int arrays - Computes the minimum edit distance - Supports insertion, deletion, substitution, and swapping operations The implementation follows the standard dynamic programming approach to Levenshtein distance, with an additional check for character swaps. The swap operation is only considered when the two characters can be swapped to match the corresponding characters in the other array. Time complexity is O(m*n), where m and n are the lengths of the input arrays. You can test it with different input arrays. The function returns the minimum number of operations (with specified costs) to transform one array into another.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit(cache=True)
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays.
    Character swaps cost 2, insertions/deletions cost 1.
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array 
    arr2 : numpy.ndarray
        Second input array
    
    Returns:
    --------
    int
        Levenshtein distance
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix 
    # Add 1 to handle empty string case
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        d[i, 0] = i
    for j in range(n+1):
        d[0, j] = j
    
    # Fill in the rest of the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # Cost of substitution (or 0 if characters match)
            if arr1[i-1] == arr2[j-1]:
                cost_subst = 0
            else:
                cost_subst = 1
            
            # Check if swap is possible and cheaper
            swap_cost = 2
            if (i > 1 and j > 1 and 
                arr1[i-1] == arr2[j-2] and 
                arr1[i-2] == arr2[j-1]):
                swap_cost = 2
            else:
                swap_cost = float('inf')
            
            # Compute minimum of different operations
            d[i, j] = min(
                d[i-1, j] + 1,      # deletion
                d[i, j-1] + 1,      # insertion
                d[i-1, j-1] + cost_subst,  # substitution
                swap_cost + d[i-2, j-2] if swap_cost != float('inf') else float('inf')  # swap
            )
    
    return d[m, n]

# Example usage
if __name__ == "__main__":
    # Test the function
    arr1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
    arr2 = np.array([1, 2, 5, 4, 3], dtype=np.int32)
    
    print(levenshtein(arr1, arr2))

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(cache=True)
def levenshtein(arr1, arr2):
    """
    Compute Levenshtein distance between two numpy int arrays.
    Character swaps cost 2, insertions/deletions cost 1.
    
    Parameters:
    -----------
    arr1 : numpy.ndarray
        First input array 
    arr2 : numpy.ndarray
        Second input array
    
    Returns:
    --------
    int
        Levenshtein distance
    """
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix 
    # Add 1 to handle empty string case
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m+1):
        d[i, 0] = i
    for j in range(n+1):
        d[0, j] = j
    
    # Fill in the rest of the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # Cost of substitution (or 0 if characters match)
            if arr1[i-1] == arr2[j-1]:
                cost_subst = 0
            else:
                cost_subst = 1
            
            # Check if swap is possible and cheaper
            swap_cost = 2
            if (i > 1 and j > 1 and 
                arr1[i-1] == arr2[j-2] and 
                arr1[i-2] == arr2[j-1]):
                swap_cost = 2
            else:
                swap_cost = float('inf')
            
            # Compute minimum of different operations
            d[i, j] = min(
                d[i-1, j] + 1,      # deletion
                d[i, j-1] + 1,      # insertion
                d[i-1, j-1] + cost_subst,  # substitution
                swap_cost + d[i-2, j-2] if swap_cost != float('inf') else float('inf')  # swap
            )
    
    return d[m, n]

# Example usage
if __name__ == "__main__":
    # Test the function
    arr1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
    arr2 = np.array([1, 2, 5, 4, 3], dtype=np.int32)
    
    print(levenshtein(arr1, arr2))





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: 2 Traceback (most recent call last): File "/usr/src/app/main.py", line 78, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

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

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: False

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: False

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

Here's a Numba implementation of the Levenshtein distance with character swaps costing 2, designed to work with NumPy integer arrays and compiled in nopython mode: 

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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    # Add 1 to dimensions to include initial base cases
    d = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m + 1):
        d[i, 0] = i
    for j in range(n + 1):
        d[0, j] = j
    
    # Compute Levenshtein distance with swap operation
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # Cost of substitution
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 1
            
            # Standard operations: deletion, insertion, substitution
            d[i, j] = min(
                d[i-1, j] + 1,      # deletion
                d[i, j-1] + 1,      # insertion
                d[i-1, j-1] + cost  # substitution
            )
            
            # Swap operation (transposition)
            if (i > 1 and j > 1 and 
                arr1[i-1] == arr2[j-2] and 
                arr1[i-2] == arr2[j-1]):
                d[i, j] = min(d[i, j], d[i-2, j-2] + 2)
    
    return d[m, n]

# Example usage
if __name__ == "__main__":
    # Test the function
    a1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
    a2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)
    
    print(levenshtein(a1, a2))  # Should show lower cost due to swap
Key points about this implementation: 1. Uses `@njit` decorator for Numba's nopython mode compilation 2. Works with NumPy integer arrays 3. Implements standard Levenshtein operations (insertion, deletion, substitution) 4. Adds a special swap/transposition operation with a cost of 2 5. Returns the total edit distance The swap operation is detected when: - There are at least 2 characters in both arrays - Swapping would make those 2 characters match Time complexity is O(mn), where m and n are the lengths of the input arrays. Example use cases: 
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# Different types of comparisons
a1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
a2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)  # Swap of 3 and 4
a3 = np.array([1, 2, 3, 5, 4], dtype=np.int32)  # Another swap
a4 = np.array([1, 2, 3, 4, 6], dtype=np.int32)  # Substitution
This implementation provides a flexible, performant Levenshtein distance calculation for integer arrays with an added swap operation.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    # Add 1 to dimensions to include initial base cases
    d = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m + 1):
        d[i, 0] = i
    for j in range(n + 1):
        d[0, j] = j
    
    # Compute Levenshtein distance with swap operation
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # Cost of substitution
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 1
            
            # Standard operations: deletion, insertion, substitution
            d[i, j] = min(
                d[i-1, j] + 1,      # deletion
                d[i, j-1] + 1,      # insertion
                d[i-1, j-1] + cost  # substitution
            )
            
            # Swap operation (transposition)
            if (i > 1 and j > 1 and 
                arr1[i-1] == arr2[j-2] and 
                arr1[i-2] == arr2[j-1]):
                d[i, j] = min(d[i, j], d[i-2, j-2] + 2)
    
    return d[m, n]

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
def levenshtein(arr1, arr2):
    # Get lengths of input arrays
    m, n = len(arr1), len(arr2)
    
    # Create distance matrix
    # Add 1 to dimensions to include initial base cases
    d = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(m + 1):
        d[i, 0] = i
    for j in range(n + 1):
        d[0, j] = j
    
    # Compute Levenshtein distance with swap operation
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # Cost of substitution
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 1
            
            # Standard operations: deletion, insertion, substitution
            d[i, j] = min(
                d[i-1, j] + 1,      # deletion
                d[i, j-1] + 1,      # insertion
                d[i-1, j-1] + cost  # substitution
            )
            
            # Swap operation (transposition)
            if (i > 1 and j > 1 and 
                arr1[i-1] == arr2[j-2] and 
                arr1[i-2] == arr2[j-1]):
                d[i, j] = min(d[i, j], d[i-2, j-2] + 2)
    
    return d[m, n]





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: Traceback (most recent call last): File "/usr/src/app/main.py", line 50, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got 2 instead of 3

Substring Evaluation

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

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: False

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: False