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

# Numba Implementation of Levenshtein Distance Here's a Numba implementation of the Levenshtein distance algorithm that takes two numpy integer arrays as input and assigns a cost of 2 for character substitutions: 

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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    """
    Calculate the Levenshtein distance between two numpy int arrays.
    
    Parameters:
    -----------
    arr1, arr2 : numpy arrays of integers
    
    Returns:
    --------
    int : the Levenshtein distance
    
    Notes:
    ------
    - Substitutions (character swaps) have a cost of 2
    - Insertions and deletions have a cost of 1
    """
    m, n = len(arr1), len(arr2)
    
    # Create a matrix of size (m+1) x (n+1)
    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
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters match, no cost for substitution
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 2  # Cost of 2 for character swaps
            
            dp[i, j] = min(
                dp[i-1, j] + 1,      # Deletion
                dp[i, j-1] + 1,      # Insertion
                dp[i-1, j-1] + cost  # Substitution
            )
    
    return dp[m, n]
## Example Usage: 
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# Example usage
if __name__ == "__main__":
    # Example arrays
    a = np.array([1, 2, 3, 4, 5], dtype=np.int32)
    b = np.array([1, 3, 4, 5, 6], dtype=np.int32)
    
    # Calculate Levenshtein distance
    distance = levenshtein(a, b)
    print(f"Levenshtein distance: {distance}")
This implementation uses dynamic programming to efficiently calculate the Levenshtein distance. The algorithm builds a matrix where each cell `dp[i,j]` represents the minimum edit distance between the first `i` elements of `arr1` and the first `j` elements of `arr2`. The final result is found at `dp[m,n]`. The `@njit` decorator compiles the function in nopython mode for faster execution.

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):
    """
    Calculate the Levenshtein distance between two numpy int arrays.
    
    Parameters:
    -----------
    arr1, arr2 : numpy arrays of integers
    
    Returns:
    --------
    int : the Levenshtein distance
    
    Notes:
    ------
    - Substitutions (character swaps) have a cost of 2
    - Insertions and deletions have a cost of 1
    """
    m, n = len(arr1), len(arr2)
    
    # Create a matrix of size (m+1) x (n+1)
    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
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters match, no cost for substitution
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 2  # Cost of 2 for character swaps
            
            dp[i, j] = min(
                dp[i-1, j] + 1,      # Deletion
                dp[i, j-1] + 1,      # Insertion
                dp[i-1, j-1] + cost  # 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):
    """
    Calculate the Levenshtein distance between two numpy int arrays.
    
    Parameters:
    -----------
    arr1, arr2 : numpy arrays of integers
    
    Returns:
    --------
    int : the Levenshtein distance
    
    Notes:
    ------
    - Substitutions (character swaps) have a cost of 2
    - Insertions and deletions have a cost of 1
    """
    m, n = len(arr1), len(arr2)
    
    # Create a matrix of size (m+1) x (n+1)
    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
    
    # Fill the matrix
    for i in range(1, m+1):
        for j in range(1, n+1):
            # If characters match, no cost for substitution
            if arr1[i-1] == arr2[j-1]:
                cost = 0
            else:
                cost = 2  # Cost of 2 for character swaps
            
            dp[i, j] = min(
                dp[i-1, j] + 1,      # Deletion
                dp[i, j-1] + 1,      # Insertion
                dp[i-1, j-1] + cost  # 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: All tests passed

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: 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: True

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

# Numba Implementation of Levenshtein Distance Here's a Numba implementation of the Levenshtein distance algorithm that works with two numpy integer arrays. It runs in nopython mode and assigns a cost of 2 to character substitutions (swaps): 

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import numba
import numpy as np

@numba.njit(nopython=True)
def levenshtein(s1, s2):
    """
    Calculate the Levenshtein distance between two numpy integer arrays.
    
    Parameters:
        s1 (numpy.ndarray): First array of integers
        s2 (numpy.ndarray): Second array of integers
    
    Returns:
        int: Levenshtein distance with substitutions costing 2
    """
    # Get lengths of both arrays
    len_s1 = len(s1)
    len_s2 = len(s2)
    
    # Create a distance matrix
    dp = np.zeros((len_s1 + 1, len_s2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len_s1 + 1):
        dp[i, 0] = i
    for j in range(len_s2 + 1):
        dp[0, j] = j
    
    # Fill in the distance matrix
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            if s1[i-1] == s2[j-1]:
                # No change needed if elements are the same
                dp[i, j] = dp[i-1, j-1]
            else:
                # Calculate costs for operations:
                deletion = dp[i-1, j] + 1      # Delete from s1
                insertion = dp[i, j-1] + 1     # Insert into s1
                substitution = dp[i-1, j-1] + 2  # Substitute (costs 2)
                
                # Choose the minimum cost operation
                dp[i, j] = min(deletion, insertion, substitution)
    
    # Return the final distance
    return dp[len_s1, len_s2]
### Example usage: 
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# Example arrays
a = np.array([1, 2, 3, 4], dtype=np.int32)
b = np.array([1, 2, 5, 4], dtype=np.int32)

# Calculate distance
distance = levenshtein(a, b)
print(f"Levenshtein distance: {distance}")  # Output: 2 (one substitution at cost 2)
This implementation uses dynamic programming to build the distance matrix, where each cell represents the minimum edit distance between prefixes of the two input arrays. For character substitutions (swaps), it assigns a cost of 2 as requested, while insertions and deletions cost 1.

Extract Code

I extracted the following code from that output: 

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import numba
import numpy as np

@numba.njit(nopython=True)
def levenshtein(s1, s2):
    """
    Calculate the Levenshtein distance between two numpy integer arrays.
    
    Parameters:
        s1 (numpy.ndarray): First array of integers
        s2 (numpy.ndarray): Second array of integers
    
    Returns:
        int: Levenshtein distance with substitutions costing 2
    """
    # Get lengths of both arrays
    len_s1 = len(s1)
    len_s2 = len(s2)
    
    # Create a distance matrix
    dp = np.zeros((len_s1 + 1, len_s2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len_s1 + 1):
        dp[i, 0] = i
    for j in range(len_s2 + 1):
        dp[0, j] = j
    
    # Fill in the distance matrix
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            if s1[i-1] == s2[j-1]:
                # No change needed if elements are the same
                dp[i, j] = dp[i-1, j-1]
            else:
                # Calculate costs for operations:
                deletion = dp[i-1, j] + 1      # Delete from s1
                insertion = dp[i, j-1] + 1     # Insert into s1
                substitution = dp[i-1, j-1] + 2  # Substitute (costs 2)
                
                # Choose the minimum cost operation
                dp[i, j] = min(deletion, insertion, substitution)
    
    # Return the final distance
    return dp[len_s1, len_s2]

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numba
import numpy as np

@numba.njit(nopython=True)
def levenshtein(s1, s2):
    """
    Calculate the Levenshtein distance between two numpy integer arrays.
    
    Parameters:
        s1 (numpy.ndarray): First array of integers
        s2 (numpy.ndarray): Second array of integers
    
    Returns:
        int: Levenshtein distance with substitutions costing 2
    """
    # Get lengths of both arrays
    len_s1 = len(s1)
    len_s2 = len(s2)
    
    # Create a distance matrix
    dp = np.zeros((len_s1 + 1, len_s2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len_s1 + 1):
        dp[i, 0] = i
    for j in range(len_s2 + 1):
        dp[0, j] = j
    
    # Fill in the distance matrix
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            if s1[i-1] == s2[j-1]:
                # No change needed if elements are the same
                dp[i, j] = dp[i-1, j-1]
            else:
                # Calculate costs for operations:
                deletion = dp[i-1, j] + 1      # Delete from s1
                insertion = dp[i, j-1] + 1     # Insert into s1
                substitution = dp[i-1, j-1] + 2  # Substitute (costs 2)
                
                # Choose the minimum cost operation
                dp[i, j] = min(deletion, insertion, substitution)
    
    # Return the final distance
    return dp[len_s1, len_s2]





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: 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: True

Substring Evaluation

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

Final Answer: True

Final Answer: True