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 as input. The function is set to run in nopython mode for better performance. Character swaps are given a cost of 2, as requested. 

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import numpy as np
from numba import njit

@njit
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                # Minimum of insertion, deletion, and substitution
                d[i, j] = min(d[i-1, j] + 1,      # Deletion
                              d[i, j-1] + 1,      # Insertion
                              d[i-1, j-1] + 1)    # Substitution
                
                # Check for transposition (swap)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    d[i, j] = min(d[i, j], d[i-2, j-2] + 2)  # Transposition cost is 2
    
    return d[m, n]

# Example usage
if __name__ == "__main__":
    # Example integer sequences
    seq1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
    seq2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)
    
    distance = levenshtein(seq1, seq2)
    print(f"Levenshtein distance: {distance}")
This implementation has the following features: 1. It uses the `@njit` decorator from Numba to compile the function in nopython mode for better performance. 2. It takes two NumPy integer arrays as input (`seq1` and `seq2`). 3. The algorithm calculates the Levenshtein distance using a dynamic programming approach. 4. Character swaps (transpositions) are given a cost of 2, as requested. 5. The function returns the Levenshtein distance as an integer. To use this function, you need to have Numba and NumPy installed. You can install them using pip: 
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pip install numba numpy
The example usage at the bottom of the script demonstrates how to call the function with two NumPy integer arrays. You can modify these arrays or create your own to test the function with different inputs. Note that this implementation assumes that the input sequences are NumPy arrays of integers. If you need to work with strings, you would need to convert them to integer arrays first (e.g., by using ASCII values or a custom mapping).

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(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                # Minimum of insertion, deletion, and substitution
                d[i, j] = min(d[i-1, j] + 1,      # Deletion
                              d[i, j-1] + 1,      # Insertion
                              d[i-1, j-1] + 1)    # Substitution
                
                # Check for transposition (swap)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    d[i, j] = min(d[i, j], d[i-2, j-2] + 2)  # Transposition cost is 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(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                # Minimum of insertion, deletion, and substitution
                d[i, j] = min(d[i-1, j] + 1,      # Deletion
                              d[i, j-1] + 1,      # Insertion
                              d[i-1, j-1] + 1)    # Substitution
                
                # Check for transposition (swap)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    d[i, j] = min(d[i, j], d[i-2, j-2] + 2)  # Transposition cost is 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 41, 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 function that takes two NumPy integer arrays as input. It's set to run in nopython mode for better performance. Character swaps are given a cost of 2, as requested. 

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import numpy as np
from numba import njit

@njit
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    d[i, j] = min(deletion, insertion, substitution)
    
    return d[m, n]
This implementation has the following features: 1. It uses the `@njit` decorator from Numba to compile the function in nopython mode for better performance. 2. It takes two NumPy integer arrays (`seq1` and `seq2`) as input. 3. It creates a matrix `d` to store the distances, initialized with the base cases. 4. It then fills in the rest of the matrix, considering the following operations: - Insertion (cost 1) - Deletion (cost 1) - Substitution (cost 1) - Swap/Transposition (cost 2) 5. For each cell, it calculates the minimum cost among these operations. 6. The final Levenshtein distance is the value in the bottom-right cell of the matrix. You can use this function like this: 
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# Example usage
seq1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
seq2 = np.array([2, 3, 4, 5, 6], dtype=np.int32)

distance = levenshtein(seq1, seq2)
print(f"Levenshtein distance: {distance}")
This implementation should be fast due to the use of Numba's nopython mode, which compiles the Python function to machine code. Remember to install Numba (`pip install numba`) if you haven't already.

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(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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 43, 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 that takes two NumPy integer arrays as input. The function is called `levenshtein` and is set to run in nopython mode. Character swaps are assigned a cost of 2, as requested. 

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import numpy as np
from numba import njit

@njit
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    d[i, j] = min(deletion, insertion, substitution)
    
    return d[m, n]
This implementation has the following characteristics: 1. It uses the `@njit` decorator from Numba to compile the function in nopython mode for improved performance. 2. The function takes two NumPy integer arrays (`seq1` and `seq2`) as input. 3. It creates a matrix `d` to store the distances, initialized with zeros. 4. The first row and column of the matrix are initialized with their respective indices. 5. The rest of the matrix is filled using the Levenshtein distance algorithm, considering insertions, deletions, substitutions, and swaps. 6. Insertions, deletions, and substitutions have a cost of 1, while swaps (transpositions) have a cost of 2. 7. The function returns the Levenshtein distance, which is the value in the bottom-right cell of the matrix. You can use this function like this: 
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import numpy as np

# Example usage
seq1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
seq2 = np.array([2, 3, 4, 5, 6], dtype=np.int32)

distance = levenshtein(seq1, seq2)
print(f"Levenshtein distance: {distance}")
This implementation should provide fast performance due to the use of Numba's nopython mode, which compiles the function to machine code.

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(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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 43, 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 that takes two NumPy integer arrays as input. This implementation includes a cost of 2 for character swaps and runs in nopython mode for better performance: 

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import numpy as np
from numba import njit

@njit
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    d[i, j] = min(deletion, insertion, substitution)
    
    return d[m, n]
This implementation has the following features: 1. It uses the `@njit` decorator from Numba to compile the function in nopython mode for better performance. 2. It takes two NumPy integer arrays (`seq1` and `seq2`) as input. 3. The algorithm calculates the Levenshtein distance using a dynamic programming approach, filling in a matrix of distances. 4. Character swaps (transpositions) are handled with a cost of 2, as specified. 5. The function returns the Levenshtein distance as an integer. You can use this function like this: 
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import numpy as np

# Example usage
seq1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
seq2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)

distance = levenshtein(seq1, seq2)
print(f"Levenshtein distance: {distance}")
This implementation should provide good performance for reasonably sized input arrays due to the use of Numba's just-in-time compilation. Note that the first run of the function might be slower due to the compilation overhead, but subsequent runs will be much faster.

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(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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 43, 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 that takes two NumPy integer arrays as input. The function is designed to run in nopython mode for better performance. Character swaps are given a cost of 2, as requested. 

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import numpy as np
from numba import njit

@njit
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    d[i, j] = min(deletion, insertion, substitution)
    
    return d[m, n]
This implementation has the following features: 1. It uses the `@njit` decorator from Numba to compile the function in nopython mode for improved performance. 2. It takes two NumPy integer arrays (`seq1` and `seq2`) as input. 3. The function calculates the Levenshtein distance using a dynamic programming approach, filling in a matrix of distances. 4. Character swaps (transpositions) are given a cost of 2, as requested. 5. The function returns the final Levenshtein distance as an integer. To use this function, you would call it with two NumPy integer arrays. For example: 
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import numpy as np

# Example usage
seq1 = np.array([1, 2, 3, 4, 5], dtype=np.int32)
seq2 = np.array([1, 2, 4, 3, 5], dtype=np.int32)

distance = levenshtein(seq1, seq2)
print(f"Levenshtein distance: {distance}")
This implementation should provide good performance due to the use of Numba's nopython mode, which compiles the Python function to machine code. The algorithm considers insertions, deletions, substitutions, and swaps (with a cost of 2) when calculating the distance between the two input sequences.

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(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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
def levenshtein(seq1, seq2):
    m, n = len(seq1), len(seq2)
    
    # Create a matrix to store the distances
    d = np.zeros((m+1, n+1), dtype=np.int32)
    
    # Initialize the 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]:
                d[i, j] = d[i-1, j-1]
            else:
                deletion = d[i-1, j] + 1
                insertion = d[i, j-1] + 1
                substitution = d[i-1, j-1] + 1
                
                # Check for swap (transposition)
                if i > 1 and j > 1 and seq1[i-1] == seq2[j-2] and seq1[i-2] == seq2[j-1]:
                    swap = d[i-2, j-2] + 2
                    d[i, j] = min(deletion, insertion, substitution, swap)
                else:
                    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 43, 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