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

To implement the Levenshtein distance with character swaps costing 2 using Numba in nopython mode, you can follow the code snippet below. This implementation takes two NumPy integer arrays as input and computes the Levenshtein distance between them. First, make sure you have Numba and NumPy installed in your environment. If not, you can install them using pip: 

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pip install numba numpy
Here is the implementation: 
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import numpy as np
from numba import njit

@njit
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    len_a = len(a)
    len_b = len(b)

    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i][0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0][j] = j  # Insertion cost

    # Fill the distance matrix
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i][j] = min(
                dp[i - 1][j] + 1,    # Deletion
                dp[i][j - 1] + 1,    # Insertion
                dp[i - 1][j - 1] + cost # Substitution
            )
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                # Character swap cost
                dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + 2)
    
    return dp[len_a][len_b]

# Example Usage
a = np.array([1, 2, 3, 4])
b = np.array([1, 4, 3, 2])
distance = levenshtein(a, b)
print("Levenshtein distance:", distance)
### Explanation: 1. **Initialization**: We create a 2D distance matrix `dp` where `dp[i][j]` holds the Levenshtein distance between the first `i` characters of `a` and the first `j` characters of `b`. 2. **Base Cases**: The first column represents the cost of deleting all characters from `a`, and the first row represents the cost of inserting all characters from `b`. 3. **Dynamic Programming Fill**: We iterate through the matrix and fill it based on the minimum cost of deletion, insertion, and substitution. Additionally, we account for character swaps by checking if the last two characters of each string can be swapped at a cost of 2. 4. **Return Result**: Finally, we return `dp[len_a][len_b]`, which holds the Levenshtein distance between the entire strings. ### Note: The arrays `a` and `b` should contain integer representations of characters (for example, ASCII values or some other integer mappings). Adjust the input arrays as needed to fit your specific use case.

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(a: np.ndarray, b: np.ndarray) -> int:
    len_a = len(a)
    len_b = len(b)

    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i][0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0][j] = j  # Insertion cost

    # Fill the distance matrix
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i][j] = min(
                dp[i - 1][j] + 1,    # Deletion
                dp[i][j - 1] + 1,    # Insertion
                dp[i - 1][j - 1] + cost # Substitution
            )
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                # Character swap cost
                dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + 2)
    
    return dp[len_a][len_b]

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(a: np.ndarray, b: np.ndarray) -> int:
    len_a = len(a)
    len_b = len(b)

    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i][0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0][j] = j  # Insertion cost

    # Fill the distance matrix
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i][j] = min(
                dp[i - 1][j] + 1,    # Deletion
                dp[i][j - 1] + 1,    # Insertion
                dp[i - 1][j - 1] + cost # Substitution
            )
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                # Character swap cost
                dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + 2)
    
    return dp[len_a][len_b]





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 40, 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

Certainly! Below is a Numba implementation of the Levenshtein distance that takes two NumPy integer arrays as input and runs in nopython mode. The cost for character substitutions (swaps) is set to 2, while deletions and insertions have a cost of 1. Please make sure you have Numba and NumPy installed in your Python environment. You can install them using pip if you haven't already: 

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pip install numba numpy
Here is the code: 
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import numpy as np
from numba import njit

@njit
def levenshtein(arr1, arr2):
    len1 = len(arr1)
    len2 = len(arr2)
    
    # Create a distance matrix
    dp = np.zeros((len1 + 1, len2 + 1), dtype=np.int64)
    
    # Initialize the distance matrix
    for i in range(len1 + 1):
        dp[i][0] = i  # Cost of deletions
    for j in range(len2 + 1):
        dp[0][j] = j  # Cost of insertions

    # Fill the distance matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            cost = 0 if arr1[i - 1] == arr2[j - 1] else 2  # Cost of substitution
            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[len1][len2]

# Example usage
if __name__ == "__main__":
    arr1 = np.array([1, 2, 3, 4], dtype=np.int64)
    arr2 = np.array([1, 2, 4, 3], dtype=np.int64)
    distance = levenshtein(arr1, arr2)
    print("Levenshtein distance:", distance)
### Explanation: 1. **Function Signature**: The `levenshtein` function is decorated with `@njit` to compile it in nopython mode. 2. **Distance Matrix**: A 2D array `dp` is created to store the distances. 3. **Initialization**: The first row and column are initialized to represent the cost of deletions and insertions. 4. **Filling the Matrix**: We iterate through both arrays, calculating the cost of substitution (2 if they differ, 0 if they are the same) and filling the matrix with the minimum costs. 5. **Return Value**: Finally, the function returns the value in the bottom-right cell of the matrix, which represents the Levenshtein distance between the two arrays. You can run this code, and it will calculate the Levenshtein distance between the example integer arrays provided. You can replace `arr1` and `arr2` with your own integer numpy arrays to compute their distance.

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):
    len1 = len(arr1)
    len2 = len(arr2)
    
    # Create a distance matrix
    dp = np.zeros((len1 + 1, len2 + 1), dtype=np.int64)
    
    # Initialize the distance matrix
    for i in range(len1 + 1):
        dp[i][0] = i  # Cost of deletions
    for j in range(len2 + 1):
        dp[0][j] = j  # Cost of insertions

    # Fill the distance matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            cost = 0 if arr1[i - 1] == arr2[j - 1] else 2  # Cost of substitution
            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[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 njit

@njit
def levenshtein(arr1, arr2):
    len1 = len(arr1)
    len2 = len(arr2)
    
    # Create a distance matrix
    dp = np.zeros((len1 + 1, len2 + 1), dtype=np.int64)
    
    # Initialize the distance matrix
    for i in range(len1 + 1):
        dp[i][0] = i  # Cost of deletions
    for j in range(len2 + 1):
        dp[0][j] = j  # Cost of insertions

    # Fill the distance matrix
    for i in range(1, len1 + 1):
        for j in range(1, len2 + 1):
            cost = 0 if arr1[i - 1] == arr2[j - 1] else 2  # Cost of substitution
            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[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: 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

Certainly! Below is a Numba implementation of the Levenshtein distance algorithm that accounts for character swaps with a cost of 2. This implementation will utilize Numba's `@njit` decorator to run in nopython mode. The function takes two NumPy integer arrays as input. First, ensure you have Numba and NumPy installed in your environment. You can install them using pip if you haven't already: 

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pip install numba numpy
Here is the `levenshtein` function: 
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import numpy as np
from numba import njit

@njit
def levenshtein(a, b):
    len_a = len(a)
    len_b = len(b)
    
    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i][0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0][j] = j  # Insertion cost

    # Compute the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            
            # Calculate 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

            # Check for character swap
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + 2)  # Swap cost

    return dp[len_a][len_b]

# Example usage
if __name__ == "__main__":
    a = np.array([1, 2, 3, 4])
    b = np.array([1, 3, 2, 4])
    distance = levenshtein(a, b)
    print("Levenshtein distance:", distance)
### Explanation: - The function initializes a dynamic programming matrix `dp` where `dp[i][j]` will hold the Levenshtein distance between the first `i` characters of `a` and the first `j` characters of `b`. - It fills in the base cases for the cost of deletions and insertions. - It then iterates through each character of both strings, calculating the minimum cost of deletions, insertions, and substitutions (with a cost of 1). - It also includes a check for character swaps, which adds a cost of 2 if the characters can be swapped. - Finally, it returns the computed distance. ### Running the Code: You can run the code in a Python environment where Numba is installed. Adjust the example usage with different integer arrays to test the function.

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(a, b):
    len_a = len(a)
    len_b = len(b)
    
    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i][0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0][j] = j  # Insertion cost

    # Compute the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            
            # Calculate 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

            # Check for character swap
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + 2)  # Swap cost

    return dp[len_a][len_b]

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(a, b):
    len_a = len(a)
    len_b = len(b)
    
    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i][0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0][j] = j  # Insertion cost

    # Compute the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            
            # Calculate 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

            # Check for character swap
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + 2)  # Swap cost

    return dp[len_a][len_b]





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

You can use Numba to implement the Levenshtein distance with the specified requirements. The following code provides a Numba implementation of the Levenshtein distance algorithm, taking two numpy integer arrays as input. It operates in nopython mode and considers character swaps to have a cost of 2. Here's the implementation: 

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import numpy as np
from numba import njit

@njit
def levenshtein(s1: np.ndarray, s2: np.ndarray) -> int:
    len_s1 = s1.shape[0]
    len_s2 = s2.shape[0]
    
    # Create a distance matrix
    dp = np.zeros((len_s1 + 1, len_s2 + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_s1 + 1):
        dp[i][0] = i
    for j in range(len_s2 + 1):
        dp[0][j] = j

    # Compute the Levenshtein distance
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            cost = 0 if s1[i - 1] == s2[j - 1] else 1
            
            dp[i][j] = min(
                dp[i - 1][j] + 1,      # Deletion
                dp[i][j - 1] + 1,      # Insertion
                dp[i - 1][j - 1] + cost # Substitution
            )
            
            # Check for character 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)  # Swap cost

    return dp[len_s1][len_s2]

# Example usage
s1 = np.array([1, 2, 3, 4])  # Example input as integer arrays
s2 = np.array([1, 3, 2, 4])
distance = levenshtein(s1, s2)
print("Levenshtein distance:", distance)
### Explanation: - The function `levenshtein` takes two numpy integer arrays as inputs. - A distance matrix `dp` is created to store the computed distances between substrings of `s1` and `s2`. - The matrix is initialized based on the lengths of the input arrays, filling in the base cases for deletions and insertions. - The main loop iterates through the length of both arrays and computes the minimum distance based on the operations (deletion, insertion, substitution, and character swap). - Finally, the function returns the computed Levenshtein distance. ### Note: - This code assumes the integer arrays represent characters (for example, ASCII values), and it computes the distance based on the operations defined. - Make sure you have Numba and NumPy installed in your Python environment to run this 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(s1: np.ndarray, s2: np.ndarray) -> int:
    len_s1 = s1.shape[0]
    len_s2 = s2.shape[0]
    
    # Create a distance matrix
    dp = np.zeros((len_s1 + 1, len_s2 + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_s1 + 1):
        dp[i][0] = i
    for j in range(len_s2 + 1):
        dp[0][j] = j

    # Compute the Levenshtein distance
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            cost = 0 if s1[i - 1] == s2[j - 1] else 1
            
            dp[i][j] = min(
                dp[i - 1][j] + 1,      # Deletion
                dp[i][j - 1] + 1,      # Insertion
                dp[i - 1][j - 1] + cost # Substitution
            )
            
            # Check for character 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)  # Swap cost

    return dp[len_s1][len_s2]

# Example usage
s1 = np.array([1, 2, 3, 4])  # Example input as integer arrays
s2 = np.array([1, 3, 2, 4])
distance = levenshtein(s1, s2)
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
from numba import njit

@njit
def levenshtein(s1: np.ndarray, s2: np.ndarray) -> int:
    len_s1 = s1.shape[0]
    len_s2 = s2.shape[0]
    
    # Create a distance matrix
    dp = np.zeros((len_s1 + 1, len_s2 + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_s1 + 1):
        dp[i][0] = i
    for j in range(len_s2 + 1):
        dp[0][j] = j

    # Compute the Levenshtein distance
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            cost = 0 if s1[i - 1] == s2[j - 1] else 1
            
            dp[i][j] = min(
                dp[i - 1][j] + 1,      # Deletion
                dp[i][j - 1] + 1,      # Insertion
                dp[i - 1][j - 1] + cost # Substitution
            )
            
            # Check for character 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)  # Swap cost

    return dp[len_s1][len_s2]

# Example usage
s1 = np.array([1, 2, 3, 4])  # Example input as integer arrays
s2 = np.array([1, 3, 2, 4])
distance = levenshtein(s1, s2)
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 48, 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

Certainly! Below is a Numba implementation of the Levenshtein distance algorithm that takes two NumPy integer arrays as input and runs in nopython mode. In this implementation, character swaps have a cost of 2, while insertions and deletions have a cost of 1. 

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import numpy as np
from numba import njit

@njit
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    # Lengths of the input arrays
    len_a = a.shape[0]
    len_b = b.shape[0]
    
    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i, 0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0, j] = j  # Insertion cost

    # Compute the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i, j] = min(
                dp[i - 1, j] + 1,    # Deletion
                dp[i, j - 1] + 1,    # Insertion
                dp[i - 1, j - 1] + cost  # Substitution
            )
            # Check for swap (only if i and j are greater than 1)
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                dp[i, j] = min(dp[i, j], dp[i - 2, j - 2] + 2)  # Swap cost

    return dp[len_a, len_b]

# Example usage
if __name__ == "__main__":
    arr1 = np.array([1, 2, 3, 4])
    arr2 = np.array([1, 3, 2, 4])
    
    distance = levenshtein(arr1, arr2)
    print("Levenshtein distance:", distance)
### Explanation: - The function `levenshtein` takes two NumPy integer arrays `a` and `b`. - It initializes a distance matrix `dp` to store the intermediate distances. - It fills the first row and column of the matrix with the costs of deletions and insertions. - It iterates through each character in both arrays, computing the minimum cost for deletions, insertions, substitutions, and swaps. - The swap operation checks if two characters can be swapped to match the other string, adding a cost of 2 if they can be swapped. ### Running the Code To execute this code, you'll need to have Numba and NumPy installed in your Python environment. You can run the script, and it will calculate the Levenshtein distance between the two example arrays provided. Adjust the arrays as needed for your specific cases.

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(a: np.ndarray, b: np.ndarray) -> int:
    # Lengths of the input arrays
    len_a = a.shape[0]
    len_b = b.shape[0]
    
    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i, 0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0, j] = j  # Insertion cost

    # Compute the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i, j] = min(
                dp[i - 1, j] + 1,    # Deletion
                dp[i, j - 1] + 1,    # Insertion
                dp[i - 1, j - 1] + cost  # Substitution
            )
            # Check for swap (only if i and j are greater than 1)
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                dp[i, j] = min(dp[i, j], dp[i - 2, j - 2] + 2)  # Swap cost

    return dp[len_a, len_b]

# Example usage
if __name__ == "__main__":
    arr1 = np.array([1, 2, 3, 4])
    arr2 = np.array([1, 3, 2, 4])
    
    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
from numba import njit

@njit
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    # Lengths of the input arrays
    len_a = a.shape[0]
    len_b = b.shape[0]
    
    # Create a distance matrix
    dp = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)

    # Initialize the distance matrix
    for i in range(len_a + 1):
        dp[i, 0] = i  # Deletion cost
    for j in range(len_b + 1):
        dp[0, j] = j  # Insertion cost

    # Compute the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i, j] = min(
                dp[i - 1, j] + 1,    # Deletion
                dp[i, j - 1] + 1,    # Insertion
                dp[i - 1, j - 1] + cost  # Substitution
            )
            # Check for swap (only if i and j are greater than 1)
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                dp[i, j] = min(dp[i, j], dp[i - 2, j - 2] + 2)  # Swap cost

    return dp[len_a, len_b]

# Example usage
if __name__ == "__main__":
    arr1 = np.array([1, 2, 3, 4])
    arr2 = np.array([1, 3, 2, 4])
    
    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 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: 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