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

Certainly! Below is a Numba implementation of the Levenshtein distance algorithm that takes two NumPy integer arrays as input. The cost for character swaps is set to 2, and the function is compiled in `nopython` mode. 

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import numpy as np
import numba
from numba import njit

@njit
def levenshtein(arr1, arr2):
    len1 = len(arr1)
    len2 = len(arr2)
    
    # Create a distance matrix
    dist_matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize the first row and column
    for i in range(len1 + 1):
        dist_matrix[i, 0] = i
    for j in range(len2 + 1):
        dist_matrix[0, j] = j
    
    # Fill the distance matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if arr1[i - 1] == arr2[j - 1]:
                cost = 0
            else:
                cost = 1
            
            dist_matrix[i, j] = min(
                dist_matrix[i - 1, j] + 1,      # Deletion
                dist_matrix[i, j - 1] + 1,      # Insertion
                dist_matrix[i - 1, j - 1] + cost  # Substitution
            )
            
            # Check for swaps (transpositions)
            if i > 1 and j > 1 and arr1[i - 1] == arr2[j - 2] and arr1[i - 2] == arr2[j - 1]:
                dist_matrix[i, j] = min(
                    dist_matrix[i, j],
                    dist_matrix[i - 2, j - 2] + 2  # Swap cost is 2
                )
    
    return dist_matrix[len1, len2]

# Example usage
arr1 = np.array([1, 2, 3, 4], dtype=np.int32)
arr2 = np.array([1, 3, 2, 4], dtype=np.int32)

distance = levenshtein(arr1, arr2)
print("Levenshtein distance:", distance)
### Explanation: - **Initialization**: The distance matrix `dist_matrix` is initialized with the cost of converting an empty array to the other array by inserting or deleting elements. - **Cost Calculation**: The cost of substitution is 1 if the elements are different, and 0 if they are the same. - **Swaps**: If a swap (transposition) is detected, the cost is set to 2. - **Final Distance**: The Levenshtein distance is found in the bottom-right corner of the distance matrix. ### Example Output: For the example arrays `arr1 = [1, 2, 3, 4]` and `arr2 = [1, 3, 2, 4]`, the Levenshtein distance would be `2` because a swap between `2` and `3` is needed, and the cost of a swap is `2`. ### Running in Nopython Mode: The `@njit` decorator ensures that the function is compiled in `nopython` mode, which is the most optimized mode in Numba. This means that the function will run entirely in machine code without falling back to the Python interpreter. This implementation should be efficient and suitable for large arrays.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba
from numba import njit

@njit
def levenshtein(arr1, arr2):
    len1 = len(arr1)
    len2 = len(arr2)
    
    # Create a distance matrix
    dist_matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize the first row and column
    for i in range(len1 + 1):
        dist_matrix[i, 0] = i
    for j in range(len2 + 1):
        dist_matrix[0, j] = j
    
    # Fill the distance matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if arr1[i - 1] == arr2[j - 1]:
                cost = 0
            else:
                cost = 1
            
            dist_matrix[i, j] = min(
                dist_matrix[i - 1, j] + 1,      # Deletion
                dist_matrix[i, j - 1] + 1,      # Insertion
                dist_matrix[i - 1, j - 1] + cost  # Substitution
            )
            
            # Check for swaps (transpositions)
            if i > 1 and j > 1 and arr1[i - 1] == arr2[j - 2] and arr1[i - 2] == arr2[j - 1]:
                dist_matrix[i, j] = min(
                    dist_matrix[i, j],
                    dist_matrix[i - 2, j - 2] + 2  # Swap cost is 2
                )
    
    return dist_matrix[len1, len2]

# Example usage
arr1 = np.array([1, 2, 3, 4], dtype=np.int32)
arr2 = np.array([1, 3, 2, 4], dtype=np.int32)

distance = levenshtein(arr1, arr2)
print("Levenshtein distance:", 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
import numba
from numba import njit

@njit
def levenshtein(arr1, arr2):
    len1 = len(arr1)
    len2 = len(arr2)
    
    # Create a distance matrix
    dist_matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize the first row and column
    for i in range(len1 + 1):
        dist_matrix[i, 0] = i
    for j in range(len2 + 1):
        dist_matrix[0, j] = j
    
    # Fill the distance matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if arr1[i - 1] == arr2[j - 1]:
                cost = 0
            else:
                cost = 1
            
            dist_matrix[i, j] = min(
                dist_matrix[i - 1, j] + 1,      # Deletion
                dist_matrix[i, j - 1] + 1,      # Insertion
                dist_matrix[i - 1, j - 1] + cost  # Substitution
            )
            
            # Check for swaps (transpositions)
            if i > 1 and j > 1 and arr1[i - 1] == arr2[j - 2] and arr1[i - 2] == arr2[j - 1]:
                dist_matrix[i, j] = min(
                    dist_matrix[i, j],
                    dist_matrix[i - 2, j - 2] + 2  # Swap cost is 2
                )
    
    return dist_matrix[len1, len2]

# Example usage
arr1 = np.array([1, 2, 3, 4], dtype=np.int32)
arr2 = np.array([1, 3, 2, 4], dtype=np.int32)

distance = levenshtein(arr1, arr2)
print("Levenshtein distance:", 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: Levenshtein distance: 2 Traceback (most recent call last): File "/usr/src/app/main.py", line 56, 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: True

Substring Evaluation

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

Final Answer: True

Final Answer: False