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 algorithm that takes two numpy integer arrays and treats character swaps with a cost of 2: 

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import numpy as np
from numba import njit

@njit(nogil=True)
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    # Create distance matrix
    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):
            if seq1[i-1] == seq2[j-1]:
                substitution_cost = 0
            else:
                # Check if it's a swap
                if (i > 1 and j > 1 and 
                    seq1[i-1] == seq2[j-2] and 
                    seq1[i-2] == seq2[j-1]):
                    substitution_cost = 2  # Swap cost
                else:
                    substitution_cost = 1  # Regular substitution
                    
            # Calculate minimum cost operation
            deletion = d[i-1, j] + 1
            insertion = d[i, j-1] + 1
            substitution = d[i-1, j-1] + substitution_cost
            
            # Take the minimum of the three operations
            d[i, j] = min(deletion, insertion, substitution)
            
    return d[m, n]
You can use it like this: 
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# Example usage
seq1 = np.array([1, 2, 3, 4], dtype=np.int32)
seq2 = np.array([1, 2, 4, 3], dtype=np.int32)

distance = levenshtein(seq1, seq2)
print(f"Levenshtein distance: {distance}")
This implementation: 1. Uses the `@njit` decorator to compile the function in nopython mode for better performance 2. Takes two numpy integer arrays as input 3. Creates a distance matrix using dynamic programming 4. Handles three types of operations: - Insertion (cost 1) - Deletion (cost 1) - Substitution (cost 1) - Swaps (cost 2) 5. Returns the minimum edit distance between the two sequences The algorithm checks for swaps by looking at adjacent characters and applies a cost of 2 when a swap is detected. Regular substitutions still cost 1, and insertions and deletions also cost 1. Note that the first time you run this function, there will be a compilation overhead, but subsequent calls will be much faster due to Numba's JIT compilation. If you need to handle larger sequences, you might want to adjust the dtype of the distance matrix to `np.int64` to avoid potential overflow: 
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d = np.zeros((m + 1, n + 1), dtype=np.int64)
This implementation assumes the input arrays contain integers. If you need to handle different types, you'll need to modify the function accordingly.

Extract Code

I extracted the following code from that output: 

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import numpy as np 
from numba import njit

@njit(nogil=True)
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    # Create distance matrix
    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):
            if seq1[i-1] == seq2[j-1]:
                substitution_cost = 0
            else:
                # Check if it's a swap
                if (i > 1 and j > 1 and 
                    seq1[i-1] == seq2[j-2] and 
                    seq1[i-2] == seq2[j-1]):
                    substitution_cost = 2  # Swap cost
                else:
                    substitution_cost = 1  # Regular substitution
                    
            # Calculate minimum cost operation
            deletion = d[i-1, j] + 1
            insertion = d[i, j-1] + 1
            substitution = d[i-1, j-1] + substitution_cost
            
            # Take the minimum of the three operations
            d[i, j] = min(deletion, insertion, substitution)
            
    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(nogil=True)
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    # Create distance matrix
    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):
            if seq1[i-1] == seq2[j-1]:
                substitution_cost = 0
            else:
                # Check if it's a swap
                if (i > 1 and j > 1 and 
                    seq1[i-1] == seq2[j-2] and 
                    seq1[i-2] == seq2[j-1]):
                    substitution_cost = 2  # Swap cost
                else:
                    substitution_cost = 1  # Regular substitution
                    
            # Calculate minimum cost operation
            deletion = d[i-1, j] + 1
            insertion = d[i, j-1] + 1
            substitution = d[i-1, j-1] + substitution_cost
            
            # Take the minimum of the three operations
            d[i, j] = min(deletion, insertion, substitution)
            
    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 47, 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 algorithm with character swaps costing 2: 

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import numba
import numpy as np

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    # Get lengths of both sequences
    len1 = len(s1)
    len2 = len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j
        
    # Fill in the rest of the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if s1[i-1] == s2[j-1]:
                cost = 0
            else:
                cost = 1
                
            # Regular Levenshtein operations (insertion, deletion, substitution)
            matrix[i, j] = min(
                matrix[i-1, j] + 1,      # deletion
                matrix[i, j-1] + 1,      # insertion
                matrix[i-1, j-1] + cost  # substitution
            )
            
            # Handle transposition (swap) if possible
            if (i > 1 and j > 1 and 
                s1[i-1] == s2[j-2] and 
                s1[i-2] == s2[j-1]):
                matrix[i, j] = min(
                    matrix[i, j],
                    matrix[i-2, j-2] + 2  # swap cost is 2
                )
    
    return matrix[len1, len2]
This implementation: 1. Uses the `@numba.jit(nopython=True)` decorator to compile the function in nopython mode for better performance 2. Takes two numpy integer arrays as input 3. Creates a dynamic programming matrix to compute the minimum edit distance 4. Includes four types of operations: - Insertion (cost 1) - Deletion (cost 1) - Substitution (cost 1) - Transposition/Swap (cost 2) You can use it like this: 
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# Example usage
s1 = np.array([1, 2, 3, 4])
s2 = np.array([1, 4, 3, 2])

distance = levenshtein(s1, s2)
print(f"Levenshtein distance: {distance}")
The function will return the minimum edit distance between the two sequences, where each edit operation has a cost of 1 except for swaps which cost 2. Note that this implementation considers adjacent character transpositions (swaps) as a special case with a cost of 2, which is slightly different from the traditional Levenshtein distance that only considers insertions, deletions, and substitutions.

Extract Code

I extracted the following code from that output: 

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import numba
import numpy as np

@numba.jit(nopython=True)  
def levenshtein(s1, s2):
    # Get lengths of both sequences
    len1 = len(s1)
    len2 = len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j
        
    # Fill in the rest of the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if s1[i-1] == s2[j-1]:
                cost = 0
            else:
                cost = 1
                
            # Regular Levenshtein operations (insertion, deletion, substitution)
            matrix[i, j] = min(
                matrix[i-1, j] + 1,      # deletion
                matrix[i, j-1] + 1,      # insertion
                matrix[i-1, j-1] + cost  # substitution
            )
            
            # Handle transposition (swap) if possible
            if (i > 1 and j > 1 and 
                s1[i-1] == s2[j-2] and 
                s1[i-2] == s2[j-1]):
                matrix[i, j] = min(
                    matrix[i, j],
                    matrix[i-2, j-2] + 2  # swap cost is 2
                )
    
    return matrix[len1, len2]

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.jit(nopython=True)  
def levenshtein(s1, s2):
    # Get lengths of both sequences
    len1 = len(s1)
    len2 = len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j
        
    # Fill in the rest of the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if s1[i-1] == s2[j-1]:
                cost = 0
            else:
                cost = 1
                
            # Regular Levenshtein operations (insertion, deletion, substitution)
            matrix[i, j] = min(
                matrix[i-1, j] + 1,      # deletion
                matrix[i, j-1] + 1,      # insertion
                matrix[i-1, j-1] + cost  # substitution
            )
            
            # Handle transposition (swap) if possible
            if (i > 1 and j > 1 and 
                s1[i-1] == s2[j-2] and 
                s1[i-2] == s2[j-1]):
                matrix[i, j] = min(
                    matrix[i, j],
                    matrix[i-2, j-2] + 2  # swap cost is 2
                )
    
    return matrix[len1, len2]





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 52, 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: False

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: 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 algorithm with character swaps costing 2: 

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import numba
import numpy as np

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    # Get lengths of both sequences
    len1, len2 = len(s1), len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j
    
    # Fill in the rest of the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if s1[i-1] == s2[j-1]:
                # If characters match, no additional cost
                matrix[i, j] = matrix[i-1, j-1]
            else:
                # Calculate costs for different operations
                deletion = matrix[i-1, j] + 1
                insertion = matrix[i, j-1] + 1
                substitution = matrix[i-1, j-1] + 1
                
                # Handle transposition (swap) with cost 2
                if (i > 1 and j > 1 and 
                    s1[i-1] == s2[j-2] and 
                    s1[i-2] == s2[j-1]):
                    swap = matrix[i-2, j-2] + 2
                    matrix[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    matrix[i, j] = min(deletion, insertion, substitution)
    
    # Return the bottom-right cell of the matrix
    return matrix[len1, len2]
This implementation: 1. Uses the `@numba.jit(nopython=True)` decorator to compile the function in nopython mode for better performance 2. Takes two numpy integer arrays as input (`s1` and `s2`) 3. Creates a dynamic programming matrix to compute the minimum edit distance 4. Considers four types of operations: - Insertion (cost 1) - Deletion (cost 1) - Substitution (cost 1) - Transposition/Swap (cost 2) 5. Returns the minimum edit distance between the two sequences You can use it like this: 
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# Example usage
s1 = np.array([1, 2, 3, 4])
s2 = np.array([1, 2, 4, 3])

distance = levenshtein(s1, s2)
print(f"Levenshtein distance: {distance}")
The function will handle arrays of any length and will treat transpositions (character swaps) as having a cost of 2, while other operations (insertions, deletions, and substitutions) have a cost of 1. Some key points: - The function expects numpy integer arrays as input - It's optimized with Numba's nopython mode - The implementation uses dynamic programming for efficiency - It properly handles all edge cases including empty arrays - The swap operation is considered when two adjacent characters are swapped

Extract Code

I extracted the following code from that output: 

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import numba
import numpy as np

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    # Get lengths of both sequences
    len1, len2 = len(s1), len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j
    
    # Fill in the rest of the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if s1[i-1] == s2[j-1]:
                # If characters match, no additional cost
                matrix[i, j] = matrix[i-1, j-1]
            else:
                # Calculate costs for different operations
                deletion = matrix[i-1, j] + 1
                insertion = matrix[i, j-1] + 1
                substitution = matrix[i-1, j-1] + 1
                
                # Handle transposition (swap) with cost 2
                if (i > 1 and j > 1 and 
                    s1[i-1] == s2[j-2] and 
                    s1[i-2] == s2[j-1]):
                    swap = matrix[i-2, j-2] + 2
                    matrix[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    matrix[i, j] = min(deletion, insertion, substitution)
    
    # Return the bottom-right cell of the matrix
    return matrix[len1, len2]

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.jit(nopython=True)
def levenshtein(s1, s2):
    # Get lengths of both sequences
    len1, len2 = len(s1), len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j
    
    # Fill in the rest of the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            if s1[i-1] == s2[j-1]:
                # If characters match, no additional cost
                matrix[i, j] = matrix[i-1, j-1]
            else:
                # Calculate costs for different operations
                deletion = matrix[i-1, j] + 1
                insertion = matrix[i, j-1] + 1
                substitution = matrix[i-1, j-1] + 1
                
                # Handle transposition (swap) with cost 2
                if (i > 1 and j > 1 and 
                    s1[i-1] == s2[j-2] and 
                    s1[i-2] == s2[j-1]):
                    swap = matrix[i-2, j-2] + 2
                    matrix[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    matrix[i, j] = min(deletion, insertion, substitution)
    
    # Return the bottom-right cell of the matrix
    return matrix[len1, len2]





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 49, 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: False

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: 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 algorithm that takes two numpy integer arrays and uses a cost of 2 for character swaps: 

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import numpy as np
from numba import jit

@jit(nopython=True)
def levenshtein(s1, s2):
    """
    Calculate the Levenshtein distance between two integer arrays.
    Character swaps cost 2, insertions and deletions cost 1.
    
    Parameters:
    s1, s2 : numpy arrays of integers
    
    Returns:
    int : The Levenshtein distance
    """
    # Get lengths of both arrays
    len1, len2 = len(s1), len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    dp = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        dp[i, 0] = i
    for j in range(len2 + 1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            # If characters are same, no operation needed
            if s1[i-1] == s2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Calculate costs for different operations
                deletion = dp[i-1, j] + 1
                insertion = dp[i, j-1] + 1
                substitution = dp[i-1, j-1] + 2  # Swap cost is 2
                
                # Take minimum of all operations
                dp[i, j] = min(deletion, insertion, substitution)
    
    # Return the bottom-right cell of the matrix
    return dp[len1, len2]

# Example usage:
if __name__ == "__main__":
    # Example arrays
    arr1 = np.array([1, 2, 3, 4], dtype=np.int32)
    arr2 = np.array([1, 2, 4, 3], dtype=np.int32)
    
    # Calculate distance
    distance = levenshtein(arr1, arr2)
    print(f"Levenshtein distance: {distance}")
This implementation: 1. Uses the `@jit(nopython=True)` decorator to compile the function in nopython mode for better performance 2. Takes two numpy integer arrays as input 3. Creates a dynamic programming matrix to calculate the minimum edit distance 4. Uses the following costs: - Insertion: 1 - Deletion: 1 - Substitution (swap): 2 5. Returns the final Levenshtein distance as an integer The algorithm works by: 1. Creating a matrix with dimensions (len1+1) × (len2+1) 2. Initializing the first row and column 3. Filling the matrix using dynamic programming 4. For each cell, calculating the minimum cost of operations needed to transform one prefix into another You can use it like this: 
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# Example usage
arr1 = np.array([1, 2, 3, 4], dtype=np.int32)
arr2 = np.array([1, 2, 4, 3], dtype=np.int32)
distance = levenshtein(arr1, arr2)
print(distance)  # Will output 2 because one swap is needed (cost of 2)
Note that the first time you run this function, it will be compiled by Numba, which might take a moment. Subsequent calls will be much faster. The function assumes that the input arrays contain integers and will work with any numpy arrays of integers. If you need to work with different data types, you'll need to modify the dtype accordingly.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import jit

@jit(nopython=True)
def levenshtein(s1, s2):
    """
    Calculate the Levenshtein distance between two integer arrays.
    Character swaps cost 2, insertions and deletions cost 1.
    
    Parameters:
    s1, s2 : numpy arrays of integers
    
    Returns:
    int : The Levenshtein distance
    """
    # Get lengths of both arrays
    len1, len2 = len(s1), len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    dp = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        dp[i, 0] = i
    for j in range(len2 + 1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            # If characters are same, no operation needed
            if s1[i-1] == s2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Calculate costs for different operations
                deletion = dp[i-1, j] + 1
                insertion = dp[i, j-1] + 1
                substitution = dp[i-1, j-1] + 2  # Swap cost is 2
                
                # Take minimum of all operations
                dp[i, j] = min(deletion, insertion, substitution)
    
    # Return the bottom-right cell of the matrix
    return dp[len1, len2]

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 jit

@jit(nopython=True)
def levenshtein(s1, s2):
    """
    Calculate the Levenshtein distance between two integer arrays.
    Character swaps cost 2, insertions and deletions cost 1.
    
    Parameters:
    s1, s2 : numpy arrays of integers
    
    Returns:
    int : The Levenshtein distance
    """
    # Get lengths of both arrays
    len1, len2 = len(s1), len(s2)
    
    # Create matrix of size (len1+1) x (len2+1)
    dp = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)
    
    # Initialize first row and column
    for i in range(len1 + 1):
        dp[i, 0] = i
    for j in range(len2 + 1):
        dp[0, j] = j
    
    # Fill the matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            # If characters are same, no operation needed
            if s1[i-1] == s2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Calculate costs for different operations
                deletion = dp[i-1, j] + 1
                insertion = dp[i, j-1] + 1
                substitution = dp[i-1, j-1] + 2  # Swap cost is 2
                
                # Take minimum of all operations
                dp[i, j] = min(deletion, insertion, substitution)
    
    # Return the bottom-right cell of the matrix
    return dp[len1, len2]





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: False

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

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 algorithm with character swaps costing 2: 

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import numba
import numpy as np

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    # Create matrix of size (len(s1) + 1) x (len(s2) + 1)
    m, n = len(s1), len(s2)
    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 s1[i-1] == s2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Regular operations (insertion, deletion, substitution)
                dp[i, j] = min(
                    dp[i-1, j] + 1,    # deletion
                    dp[i, j-1] + 1,    # insertion
                    dp[i-1, j-1] + 1   # substitution
                )
                
                # Check for transposition (swap)
                if (i > 1 and j > 1 and 
                    s1[i-1] == s2[j-2] and 
                    s1[i-2] == s2[j-1]):
                    dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    return dp[m, n]
You can use it like this: 
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# Example usage
s1 = np.array([1, 2, 3, 4, 5])
s2 = np.array([1, 2, 4, 3, 5])
distance = levenshtein(s1, s2)
print(f"Levenshtein distance: {distance}")
This implementation: 1. Uses Numba's `@numba.jit(nopython=True)` decorator for performance optimization 2. Takes two numpy integer arrays as input 3. Creates a dynamic programming matrix to compute the minimum edit distance 4. Handles basic operations (insertion, deletion, substitution) with cost 1 5. Handles transpositions (character swaps) with cost 2 6. Returns the minimum edit distance The algorithm considers four types of operations: - Insertion (cost 1) - Deletion (cost 1) - Substitution (cost 1) - Transposition/Swap (cost 2) For adjacent characters that are swapped, it will use the transposition cost of 2 if that leads to a lower total cost than using other operations. Note that this implementation is optimized for Numba compilation and uses numpy arrays instead of strings. If you need to work with strings, you'll need to convert them to integer arrays first.

Extract Code

I extracted the following code from that output: 

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import numba
import numpy as np

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    # Create matrix of size (len(s1) + 1) x (len(s2) + 1)
    m, n = len(s1), len(s2)
    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 s1[i-1] == s2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Regular operations (insertion, deletion, substitution)
                dp[i, j] = min(
                    dp[i-1, j] + 1,    # deletion
                    dp[i, j-1] + 1,    # insertion
                    dp[i-1, j-1] + 1   # substitution
                )
                
                # Check for transposition (swap)
                if (i > 1 and j > 1 and 
                    s1[i-1] == s2[j-2] and 
                    s1[i-2] == s2[j-1]):
                    dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    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 numba
import numpy as np

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    # Create matrix of size (len(s1) + 1) x (len(s2) + 1)
    m, n = len(s1), len(s2)
    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 s1[i-1] == s2[j-1]:
                dp[i, j] = dp[i-1, j-1]
            else:
                # Regular operations (insertion, deletion, substitution)
                dp[i, j] = min(
                    dp[i-1, j] + 1,    # deletion
                    dp[i, j-1] + 1,    # insertion
                    dp[i-1, j-1] + 1   # substitution
                )
                
                # Check for transposition (swap)
                if (i > 1 and j > 1 and 
                    s1[i-1] == s2[j-2] and 
                    s1[i-2] == s2[j-1]):
                    dp[i, j] = min(dp[i, j], dp[i-2, j-2] + 2)
    
    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 42, 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: False

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: False