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 using two numpy int arrays, with character swaps having a cost of 2: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    len_a, len_b = len(a), len(b)
    
    # Initialize the distance matrix with zeros
    dist = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)
    
    # Fill in the first row and column of the distance matrix
    for i in range(len_a + 1):
        dist[i, 0] = i
    for j in range(len_b + 1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 1
            
            # Calculate the minimum cost for insertion, deletion, and substitution
            insertion = dist[i, j - 1] + 1
            deletion = dist[i - 1, j] + 1
            substitution = dist[i - 1, j - 1] + cost
            
            # Check if character swap is possible and calculate the cost
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                swap = dist[i - 2, j - 2] + 2
            else:
                swap = np.inf
            
            # Take the minimum of insertion, deletion, substitution, and swap costs
            dist[i, j] = min(insertion, deletion, substitution, swap)
    
    return dist[len_a, len_b]
Explanation: 1. The function `levenshtein` takes two numpy int arrays `a` and `b` as input. 2. It initializes a distance matrix `dist` with zeros, with dimensions `(len_a + 1, len_b + 1)`. 3. The first row and column of the distance matrix are filled with the corresponding edit distances for empty strings. 4. The main loop iterates over the characters of `a` and `b` and calculates the Levenshtein distance. 5. For each pair of characters, it checks if they are equal. If they are, the cost is 0; otherwise, the cost is 1. 6. It calculates the minimum cost for insertion, deletion, and substitution operations. 7. If character swap is possible (i.e., the current characters and their previous characters are swapped), it calculates the swap cost as `dist[i - 2, j - 2] + 2`. Otherwise, the swap cost is set to infinity. 8. The minimum of insertion, deletion, substitution, and swap costs is taken and stored in the distance matrix. 9. Finally, the function returns the Levenshtein distance, which is the value in the bottom-right cell of the distance matrix. The `@nb.jit(nopython=True)` decorator is used to compile the function using Numba's nopython mode, which can significantly speed up the execution of the function. Note: Make sure you have Numba installed (`pip install numba`) before running this code.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    len_a, len_b = len(a), len(b)
    
    # Initialize the distance matrix with zeros
    dist = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)
    
    # Fill in the first row and column of the distance matrix
    for i in range(len_a + 1):
        dist[i, 0] = i
    for j in range(len_b + 1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 1
            
            # Calculate the minimum cost for insertion, deletion, and substitution
            insertion = dist[i, j - 1] + 1
            deletion = dist[i - 1, j] + 1
            substitution = dist[i - 1, j - 1] + cost
            
            # Check if character swap is possible and calculate the cost
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                swap = dist[i - 2, j - 2] + 2
            else:
                swap = np.inf
            
            # Take the minimum of insertion, deletion, substitution, and swap costs
            dist[i, j] = min(insertion, deletion, substitution, swap)
    
    return dist[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
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    len_a, len_b = len(a), len(b)
    
    # Initialize the distance matrix with zeros
    dist = np.zeros((len_a + 1, len_b + 1), dtype=np.int32)
    
    # Fill in the first row and column of the distance matrix
    for i in range(len_a + 1):
        dist[i, 0] = i
    for j in range(len_b + 1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, len_a + 1):
        for j in range(1, len_b + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 1
            
            # Calculate the minimum cost for insertion, deletion, and substitution
            insertion = dist[i, j - 1] + 1
            deletion = dist[i - 1, j] + 1
            substitution = dist[i - 1, j - 1] + cost
            
            # Check if character swap is possible and calculate the cost
            if i > 1 and j > 1 and a[i - 1] == b[j - 2] and a[i - 2] == b[j - 1]:
                swap = dist[i - 2, j - 2] + 2
            else:
                swap = np.inf
            
            # Take the minimum of insertion, deletion, substitution, and swap costs
            dist[i, j] = min(insertion, deletion, substitution, swap)
    
    return dist[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 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: 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 that takes two NumPy integer arrays and runs in nopython mode. The cost of character swaps is set to 2: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(s1, s2):
    len1, len2 = len(s1), len(s2)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)

    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j

    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 = 2 if s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1] and i > 1 and j > 1 else 1

            matrix[i, j] = min(matrix[i - 1, j] + 1,       # deletion
                               matrix[i, j - 1] + 1,       # insertion
                               matrix[i - 1, j - 1] + cost) # substitution

    return matrix[len1, len2]
Explanation: - The function `levenshtein` takes two NumPy integer arrays `s1` and `s2` as input. - It creates a matrix of size `(len1 + 1, len2 + 1)` to store the Levenshtein distances. - The first row and column of the matrix are initialized with the corresponding edit distances for empty strings. - The main loop iterates over the characters of both strings and fills the matrix. - If the characters at the current positions are the same, the cost is 0. - If the characters at the current positions are different, it checks if a character swap is possible (i.e., the characters at the previous positions are the same and the current positions are greater than 1). If a swap is possible, the cost is set to 2; otherwise, the cost is set to 1. - The minimum cost is calculated by considering deletion, insertion, and substitution operations. - Finally, the Levenshtein distance is returned as the value in the bottom-right cell of the matrix. Note: The `@nb.jit(nopython=True)` decorator is used to compile the function in nopython mode for better performance. You can use this function as follows: 
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s1 = np.array([1, 2, 3, 4, 5])
s2 = np.array([1, 2, 4, 3, 5])

distance = levenshtein(s1, s2)
print("Levenshtein distance:", distance)
Output: 
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Levenshtein distance: 2
In this example, the Levenshtein distance between the arrays `s1` and `s2` is 2 because two character swaps are required to transform `s1` into `s2`.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(s1, s2):
    len1, len2 = len(s1), len(s2)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)

    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j

    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 = 2 if s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1] and i > 1 and j > 1 else 1

            matrix[i, j] = min(matrix[i - 1, j] + 1,       # deletion
                               matrix[i, j - 1] + 1,       # insertion
                               matrix[i - 1, j - 1] + cost) # substitution

    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 numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(s1, s2):
    len1, len2 = len(s1), len(s2)
    matrix = np.zeros((len1 + 1, len2 + 1), dtype=np.int32)

    for i in range(len1 + 1):
        matrix[i, 0] = i
    for j in range(len2 + 1):
        matrix[0, j] = j

    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 = 2 if s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1] and i > 1 and j > 1 else 1

            matrix[i, j] = min(matrix[i - 1, j] + 1,       # deletion
                               matrix[i, j - 1] + 1,       # insertion
                               matrix[i - 1, j - 1] + cost) # substitution

    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 34, 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 calculates the Levenshtein distance between them, with character swaps having a cost of 2: 

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import numpy as np
from numba import njit

@njit
def levenshtein(a, b):
    n = len(a)
    m = len(b)
    
    # Initialize the distance matrix with zeros
    dist = np.zeros((n+1, m+1), dtype=np.int64)
    
    # Fill in the first row and column of the distance matrix
    for i in range(1, n+1):
        dist[i, 0] = i
    for j in range(1, m+1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, n+1):
        for j in range(1, m+1):
            if a[i-1] == b[j-1]:
                cost = 0
            else:
                cost = 1
            
            deletion = dist[i-1, j] + 1
            insertion = dist[i, j-1] + 1
            substitution = dist[i-1, j-1] + cost
            
            if i > 1 and j > 1 and a[i-1] == b[j-2] and a[i-2] == b[j-1]:
                swap = dist[i-2, j-2] + 2
            else:
                swap = np.inf
            
            dist[i, j] = min(deletion, insertion, substitution, swap)
    
    return dist[n, m]
Explanation: 1. The function `levenshtein` takes two NumPy integer arrays `a` and `b` as input. 2. It initializes a distance matrix `dist` with zeros, where the number of rows is `len(a)+1` and the number of columns is `len(b)+1`. The extra row and column are used for the base cases. 3. The first row and column of the distance matrix are filled with the corresponding edit distances, assuming that the other string is empty. 4. The main loop calculates the Levenshtein distance by iterating over the characters of both strings. 5. For each pair of characters, it calculates the cost of different operations: - If the characters are the same, the cost is 0. - If the characters are different, the cost is 1. - The deletion cost is the distance of deleting the current character from string `a`. - The insertion cost is the distance of inserting the current character into string `b`. - The substitution cost is the distance of substituting the current character in string `a` with the current character in string `b`. - The swap cost is the distance of swapping the current character in string `a` with the previous character in string `b`, with a cost of 2. This is only considered if both strings have at least two characters and the current character in `a` matches the previous character in `b`, and vice versa. 6. The minimum of the deletion, insertion, substitution, and swap costs is stored in the distance matrix. 7. Finally, the Levenshtein distance is returned, which is the value at the bottom-right cell of the distance matrix. The `@njit` decorator is used to compile the function in nopython mode using Numba, which can significantly speed up the execution of the function. Note: Make sure you have Numba installed (`pip install numba`) before running 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(a, b):
    n = len(a)
    m = len(b)
    
    # Initialize the distance matrix with zeros
    dist = np.zeros((n+1, m+1), dtype=np.int64)
    
    # Fill in the first row and column of the distance matrix
    for i in range(1, n+1):
        dist[i, 0] = i
    for j in range(1, m+1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, n+1):
        for j in range(1, m+1):
            if a[i-1] == b[j-1]:
                cost = 0
            else:
                cost = 1
            
            deletion = dist[i-1, j] + 1
            insertion = dist[i, j-1] + 1
            substitution = dist[i-1, j-1] + cost
            
            if i > 1 and j > 1 and a[i-1] == b[j-2] and a[i-2] == b[j-1]:
                swap = dist[i-2, j-2] + 2
            else:
                swap = np.inf
            
            dist[i, j] = min(deletion, insertion, substitution, swap)
    
    return dist[n, m]

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):
    n = len(a)
    m = len(b)
    
    # Initialize the distance matrix with zeros
    dist = np.zeros((n+1, m+1), dtype=np.int64)
    
    # Fill in the first row and column of the distance matrix
    for i in range(1, n+1):
        dist[i, 0] = i
    for j in range(1, m+1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, n+1):
        for j in range(1, m+1):
            if a[i-1] == b[j-1]:
                cost = 0
            else:
                cost = 1
            
            deletion = dist[i-1, j] + 1
            insertion = dist[i, j-1] + 1
            substitution = dist[i-1, j-1] + cost
            
            if i > 1 and j > 1 and a[i-1] == b[j-2] and a[i-2] == b[j-1]:
                swap = dist[i-2, j-2] + 2
            else:
                swap = np.inf
            
            dist[i, j] = min(deletion, insertion, substitution, swap)
    
    return dist[n, m]





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 46, 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 the Numba implementation of the Levenshtein distance, taking two NumPy integer arrays and running in nopython mode with character swap cost of 2: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    m, n = len(a), len(b)
    
    # Initialize the distance matrix
    dist = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Fill the first row and column of the distance matrix
    for i in range(m + 1):
        dist[i, 0] = i
    for j in range(n + 1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 2  # Character swap cost
            dist[i, j] = min(dist[i - 1, j] + 1,      # Deletion
                             dist[i, j - 1] + 1,      # Insertion
                             dist[i - 1, j - 1] + cost)  # Substitution
    
    return dist[m, n]
Explanation: 1. We import the necessary libraries, NumPy for array manipulation and Numba for JIT compilation. 2. We define the `levenshtein` function and decorate it with `@nb.jit(nopython=True)` to enable Numba's nopython mode for optimal performance. 3. Inside the function, we get the lengths of the input arrays `a` and `b` and store them in variables `m` and `n`, respectively. 4. We initialize a distance matrix `dist` with shape `(m + 1, n + 1)` filled with zeros. The extra row and column are used to handle empty strings. 5. We fill the first row and column of the distance matrix with increasing values, representing the cost of transforming an empty string to the corresponding prefix of the other string. 6. We iterate over the remaining cells of the distance matrix using nested loops. 7. For each cell, we check if the characters at the corresponding positions in `a` and `b` are equal. If they are equal, the cost is 0; otherwise, the cost is set to 2 (character swap cost). 8. We update the value of the current cell by taking the minimum of three possibilities: - Deletion: The cost of deleting a character from `a` (moving to the cell above and adding 1). - Insertion: The cost of inserting a character into `b` (moving to the cell on the left and adding 1). - Substitution: The cost of substituting a character (moving to the cell diagonally and adding the cost). 9. Finally, we return the value in the bottom-right cell of the distance matrix, which represents the Levenshtein distance between the two input arrays. You can call this function by passing two NumPy integer arrays as arguments, and it will return the Levenshtein distance between them. Note: Make sure you have Numba installed (`pip install numba`) before running this code.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    m, n = len(a), len(b)
    
    # Initialize the distance matrix
    dist = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Fill the first row and column of the distance matrix
    for i in range(m + 1):
        dist[i, 0] = i
    for j in range(n + 1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 2  # Character swap cost
            dist[i, j] = min(dist[i - 1, j] + 1,      # Deletion
                             dist[i, j - 1] + 1,      # Insertion
                             dist[i - 1, j - 1] + cost)  # Substitution
    
    return dist[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
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    m, n = len(a), len(b)
    
    # Initialize the distance matrix
    dist = np.zeros((m + 1, n + 1), dtype=np.int32)
    
    # Fill the first row and column of the distance matrix
    for i in range(m + 1):
        dist[i, 0] = i
    for j in range(n + 1):
        dist[0, j] = j
    
    # Calculate the Levenshtein distance
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 2  # Character swap cost
            dist[i, j] = min(dist[i - 1, j] + 1,      # Deletion
                             dist[i, j - 1] + 1,      # Insertion
                             dist[i - 1, j - 1] + cost)  # Substitution
    
    return dist[m, n]





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: 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: 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, taking two numpy integer arrays as input and running in nopython mode. In this implementation, character swaps have a cost of 2: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    n = len(a)
    m = len(b)
    
    # Initialize the distance matrix
    dist = np.zeros((n+1, m+1), dtype=np.int64)
    
    # Fill the first row and column of the distance matrix
    for i in range(1, n+1):
        dist[i, 0] = i
    for j in range(1, m+1):
        dist[0, j] = j
    
    # Fill the rest of the distance matrix
    for i in range(1, n+1):
        for j in range(1, m+1):
            if a[i-1] == b[j-1]:
                cost = 0
            else:
                cost = 2 if i > 1 and j > 1 and a[i-1] == b[j-2] and a[i-2] == b[j-1] else 1
            
            dist[i, j] = min(dist[i-1, j] + 1,      # Deletion
                             dist[i, j-1] + 1,      # Insertion
                             dist[i-1, j-1] + cost) # Substitution or swap
    
    return dist[n, m]
This implementation follows the dynamic programming approach to calculate the Levenshtein distance between two sequences. Here's how it works: 1. We define the `levenshtein` function using the `@nb.jit` decorator with `nopython=True` to enable Numba's just-in-time compilation and run the function in nopython mode for better performance. 2. We initialize a distance matrix `dist` with dimensions `(n+1, m+1)`, where `n` and `m` are the lengths of the input arrays `a` and `b`, respectively. The extra row and column are used for handling empty strings. 3. We fill the first row and column of the distance matrix with the values `0` to `n` and `0` to `m`, respectively. These values represent the cost of transforming an empty string to the corresponding prefix of the other string. 4. We iterate over the remaining cells of the distance matrix using nested loops. For each cell, we calculate the cost of substitution or swapping characters based on the following conditions: - If the characters at the current positions (`a[i-1]` and `b[j-1]`) are equal, the cost is 0. - If the characters are not equal and a swap is possible (i.e., `i > 1`, `j > 1`, `a[i-1] == b[j-2]`, and `a[i-2] == b[j-1]`), the cost is 2. - Otherwise, the cost is 1 (for substitution). 5. We update the value of the current cell in the distance matrix by taking the minimum of three values: - The value of the cell above plus 1 (deletion operation). - The value of the cell to the left plus 1 (insertion operation). - The value of the cell diagonally up and to the left plus the cost (substitution or swap operation). 6. Finally, we return the value in the bottom-right cell of the distance matrix, which represents the Levenshtein distance between the two input arrays. You can use this function by passing two numpy integer arrays as arguments. The function will return the Levenshtein distance between the arrays, considering character swaps with a cost of 2.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    n = len(a)
    m = len(b)
    
    # Initialize the distance matrix
    dist = np.zeros((n+1, m+1), dtype=np.int64)
    
    # Fill the first row and column of the distance matrix
    for i in range(1, n+1):
        dist[i, 0] = i
    for j in range(1, m+1):
        dist[0, j] = j
    
    # Fill the rest of the distance matrix
    for i in range(1, n+1):
        for j in range(1, m+1):
            if a[i-1] == b[j-1]:
                cost = 0
            else:
                cost = 2 if i > 1 and j > 1 and a[i-1] == b[j-2] and a[i-2] == b[j-1] else 1
            
            dist[i, j] = min(dist[i-1, j] + 1,      # Deletion
                             dist[i, j-1] + 1,      # Insertion
                             dist[i-1, j-1] + cost) # Substitution or swap
    
    return dist[n, m]

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numpy as np
import numba as nb

@nb.jit(nopython=True)
def levenshtein(a, b):
    n = len(a)
    m = len(b)
    
    # Initialize the distance matrix
    dist = np.zeros((n+1, m+1), dtype=np.int64)
    
    # Fill the first row and column of the distance matrix
    for i in range(1, n+1):
        dist[i, 0] = i
    for j in range(1, m+1):
        dist[0, j] = j
    
    # Fill the rest of the distance matrix
    for i in range(1, n+1):
        for j in range(1, m+1):
            if a[i-1] == b[j-1]:
                cost = 0
            else:
                cost = 2 if i > 1 and j > 1 and a[i-1] == b[j-2] and a[i-2] == b[j-1] else 1
            
            dist[i, j] = min(dist[i-1, j] + 1,      # Deletion
                             dist[i, j-1] + 1,      # Insertion
                             dist[i-1, j-1] + cost) # Substitution or swap
    
    return dist[n, m]





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 39, 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