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

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import numpy as np
from numba import njit, int32

@njit(int32(int32[:], int32[:]), nopython=True, cache=True)
def levenshtein(seq1, seq2):
    size_x = seq1.shape[0] + 1
    size_y = seq2.shape[0] + 1
    matrix = np.zeros((size_x, size_y), dtype=np.int32)
    for x in range(size_x):
        matrix[x, 0] = x
    for y in range(size_y):
        matrix[0, y] = y

    for x in range(1, size_x):
        for y in range(1, size_y):
            if seq1[x - 1] == seq2[y - 1]:
                cost = 0
            else:
                cost = 2
            matrix[x, y] = min(
                matrix[x - 1, y] + 1,
                matrix[x, y - 1] + 1,
                matrix[x - 1, y - 1] + cost
            )

    return matrix[size_x - 1, size_y - 1]

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit, int32

@njit(int32(int32[:], int32[:]), nopython=True, cache=True)
def levenshtein(seq1, seq2):
    size_x = seq1.shape[0] + 1
    size_y = seq2.shape[0] + 1
    matrix = np.zeros((size_x, size_y), dtype=np.int32)
    for x in range(size_x):
        matrix[x, 0] = x
    for y in range(size_y):
        matrix[0, y] = y

    for x in range(1, size_x):
        for y in range(1, size_y):
            if seq1[x - 1] == seq2[y - 1]:
                cost = 0
            else:
                cost = 2
            matrix[x, y] = min(
                matrix[x - 1, y] + 1,
                matrix[x, y - 1] + 1,
                matrix[x - 1, y - 1] + cost
            )

    return matrix[size_x - 1, size_y - 1]

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, int32

@njit(int32(int32[:], int32[:]), nopython=True, cache=True)
def levenshtein(seq1, seq2):
    size_x = seq1.shape[0] + 1
    size_y = seq2.shape[0] + 1
    matrix = np.zeros((size_x, size_y), dtype=np.int32)
    for x in range(size_x):
        matrix[x, 0] = x
    for y in range(size_y):
        matrix[0, y] = y

    for x in range(1, size_x):
        for y in range(1, size_y):
            if seq1[x - 1] == seq2[y - 1]:
                cost = 0
            else:
                cost = 2
            matrix[x, y] = min(
                matrix[x - 1, y] + 1,
                matrix[x, y - 1] + 1,
                matrix[x - 1, y - 1] + cost
            )

    return matrix[size_x - 1, size_y - 1]





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: /usr/local/lib/python3.11/dist-packages/numba/core/decorators.py:250: RuntimeWarning: nopython is set for njit and is ignored warnings.warn('nopython is set for njit and is ignored', RuntimeWarning) Traceback (most recent call last): File "/usr/src/app/main.py", line 33, in <module> answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9])) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/numba/core/dispatcher.py", line 703, in _explain_matching_error raise TypeError(msg) TypeError: No matching definition for argument type(s) array(int64, 1d, C), array(int64, 1d, C)

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: True

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: False

Substring Evaluation

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

Final Answer: True

Final Answer: 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

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import numpy as np
from numba import njit

@njit(nogil=True, cache=True)
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    """
    Compute the Levenshtein distance between two strings.

    Args:
        a: The first string.
        b: The second string.

    Returns:
        The Levenshtein distance between the two strings.
    """
    n, m = len(a), len(b)
    d = np.zeros((n + 1, m + 1), dtype=np.int32)

    for i in range(n + 1):
        d[i, 0] = i

    for j in range(m + 1):
        d[0, j] = j

    for j in range(1, m + 1):
        for i in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 2

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

    return d[n, m]

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit

@njit(nogil=True, cache=True)
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    """
    Compute the Levenshtein distance between two strings.

    Args:
        a: The first string.
        b: The second string.

    Returns:
        The Levenshtein distance between the two strings.
    """
    n, m = len(a), len(b)
    d = np.zeros((n + 1, m + 1), dtype=np.int32)

    for i in range(n + 1):
        d[i, 0] = i

    for j in range(m + 1):
        d[0, j] = j

    for j in range(1, m + 1):
        for i in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 2

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

    return d[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(nogil=True, cache=True)
def levenshtein(a: np.ndarray, b: np.ndarray) -> int:
    """
    Compute the Levenshtein distance between two strings.

    Args:
        a: The first string.
        b: The second string.

    Returns:
        The Levenshtein distance between the two strings.
    """
    n, m = len(a), len(b)
    d = np.zeros((n + 1, m + 1), dtype=np.int32)

    for i in range(n + 1):
        d[i, 0] = i

    for j in range(m + 1):
        d[0, j] = j

    for j in range(1, m + 1):
        for i in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                cost = 0
            else:
                cost = 2

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

    return d[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: 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

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import numpy as np
from numba import njit, uint32, int32

@njit(uint32(uint32[:], uint32[:]), nogil=True, cache=True)
def levenshtein(s1, s2):
    n, m = s1.shape[0], s2.shape[0]
    cost = np.zeros((n+1, m+1), dtype=int32)
    for i in range(1, n+1):
        cost[i,0] = i*2
    for j in range(1, m+1):
        cost[0,j] = j*2
    for i in range(1, n+1):
        for j in range(1, m+1):
            if s1[i-1] == s2[j-1]:
                cost[i,j] = cost[i-1,j-1]
            else:
                cost[i,j] = min(cost[i-1,j]+2, cost[i,j-1]+2)
    return cost[n,m]

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit, uint32, int32

@njit(uint32(uint32[:], uint32[:]), nogil=True, cache=True)
def levenshtein(s1, s2):
    n, m = s1.shape[0], s2.shape[0]
    cost = np.zeros((n+1, m+1), dtype=int32)
    for i in range(1, n+1):
        cost[i,0] = i*2
    for j in range(1, m+1):
        cost[0,j] = j*2
    for i in range(1, n+1):
        for j in range(1, m+1):
            if s1[i-1] == s2[j-1]:
                cost[i,j] = cost[i-1,j-1]
            else:
                cost[i,j] = min(cost[i-1,j]+2, cost[i,j-1]+2)
    return cost[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, uint32, int32

@njit(uint32(uint32[:], uint32[:]), nogil=True, cache=True)
def levenshtein(s1, s2):
    n, m = s1.shape[0], s2.shape[0]
    cost = np.zeros((n+1, m+1), dtype=int32)
    for i in range(1, n+1):
        cost[i,0] = i*2
    for j in range(1, m+1):
        cost[0,j] = j*2
    for i in range(1, n+1):
        for j in range(1, m+1):
            if s1[i-1] == s2[j-1]:
                cost[i,j] = cost[i-1,j-1]
            else:
                cost[i,j] = min(cost[i-1,j]+2, cost[i,j-1]+2)
    return cost[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 25, in <module> answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9])) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/numba/core/dispatcher.py", line 703, in _explain_matching_error raise TypeError(msg) TypeError: No matching definition for argument type(s) array(int64, 1d, C), array(int64, 1d, C)

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

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import numpy as np
from numba import njit, int32, optional
from numba.typed import List

@njit(int32(int32[:], int32[:]), cache=True, nogil=True, parallel=True)
def levenshtein(s1, s2):
    len_s1 = len(s1)
    len_s2 = len(s2)
    matrix = np.zeros((len_s1 + 1, len_s2 + 1), dtype=int32)

    # Initialize first row and column
    for i in range(len_s1 + 1):
        matrix[i, 0] = i
    for j in range(len_s2 + 1):
        matrix[0, j] = j

    # Populate the rest of the matrix
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            cost = 1 if s1[i - 1] != s2[j - 1] else 0
            matrix[i, j] = min(matrix[i - 1, j] + 1, matrix[i, j - 1] + 1, matrix[i - 1, j - 1] + cost)

    return matrix[len_s1, len_s2]

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import njit, int32, optional
from numba.typed import List

@njit(int32(int32[:], int32[:]), cache=True, nogil=True, parallel=True)
def levenshtein(s1, s2):
    len_s1 = len(s1)
    len_s2 = len(s2)
    matrix = np.zeros((len_s1 + 1, len_s2 + 1), dtype=int32)

    # Initialize first row and column
    for i in range(len_s1 + 1):
        matrix[i, 0] = i
    for j in range(len_s2 + 1):
        matrix[0, j] = j

    # Populate the rest of the matrix
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            cost = 1 if s1[i - 1] != s2[j - 1] else 0
            matrix[i, j] = min(matrix[i - 1, j] + 1, matrix[i, j - 1] + 1, matrix[i - 1, j - 1] + cost)

    return matrix[len_s1, len_s2]

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, int32, optional
from numba.typed import List

@njit(int32(int32[:], int32[:]), cache=True, nogil=True, parallel=True)
def levenshtein(s1, s2):
    len_s1 = len(s1)
    len_s2 = len(s2)
    matrix = np.zeros((len_s1 + 1, len_s2 + 1), dtype=int32)

    # Initialize first row and column
    for i in range(len_s1 + 1):
        matrix[i, 0] = i
    for j in range(len_s2 + 1):
        matrix[0, j] = j

    # Populate the rest of the matrix
    for i in range(1, len_s1 + 1):
        for j in range(1, len_s2 + 1):
            cost = 1 if s1[i - 1] != s2[j - 1] else 0
            matrix[i, j] = min(matrix[i - 1, j] + 1, matrix[i, j - 1] + 1, matrix[i - 1, j - 1] + cost)

    return matrix[len_s1, len_s2]





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 30, in <module> answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9])) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/numba/core/dispatcher.py", line 703, in _explain_matching_error raise TypeError(msg) TypeError: No matching definition for argument type(s) array(int64, 1d, C), array(int64, 1d, C)

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

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import numpy as np
from numba import jit

@jit(nopython=True)
def levenshtein(a, b):
    """
    Levenshtein distance between two numpy int arrays.
    Character swaps are cost 2.
    """
    n, m = len(a), len(b)

    # Create a matrix to store the distances
    D = np.zeros((n + 1, m + 1), dtype=np.int64)

    # Initialize the first row and column
    for i in range(n + 1):
        D[i, 0] = i

    for j in range(m + 1):
        D[0, j] = j

    # Fill in the rest of the matrix
    for i in range(1, n + 1):
        for j in range(1, m + 1):
            cost = 2 if a[i - 1] != b[j - 1] else 0
            D[i, j] = min(
                D[i - 1, j] + 1,  # Deletion
                D[i, j - 1] + 1,  # Insertion
                D[i - 1, j - 1] + cost,  # Substitution
            )

    # Return the distance
    return D[n, m]

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numba import jit

@jit(nopython=True)
def levenshtein(a, b):
    """
    Levenshtein distance between two numpy int arrays.
    Character swaps are cost 2.
    """
    n, m = len(a), len(b)

    # Create a matrix to store the distances
    D = np.zeros((n + 1, m + 1), dtype=np.int64)

    # Initialize the first row and column
    for i in range(n + 1):
        D[i, 0] = i

    for j in range(m + 1):
        D[0, j] = j

    # Fill in the rest of the matrix
    for i in range(1, n + 1):
        for j in range(1, m + 1):
            cost = 2 if a[i - 1] != b[j - 1] else 0
            D[i, j] = min(
                D[i - 1, j] + 1,  # Deletion
                D[i, j - 1] + 1,  # Insertion
                D[i - 1, j - 1] + cost,  # Substitution
            )

    # Return the distance
    return D[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 jit

@jit(nopython=True)
def levenshtein(a, b):
    """
    Levenshtein distance between two numpy int arrays.
    Character swaps are cost 2.
    """
    n, m = len(a), len(b)

    # Create a matrix to store the distances
    D = np.zeros((n + 1, m + 1), dtype=np.int64)

    # Initialize the first row and column
    for i in range(n + 1):
        D[i, 0] = i

    for j in range(m + 1):
        D[0, j] = j

    # Fill in the rest of the matrix
    for i in range(1, n + 1):
        for j in range(1, m + 1):
            cost = 2 if a[i - 1] != b[j - 1] else 0
            D[i, j] = min(
                D[i - 1, j] + 1,  # Deletion
                D[i, j - 1] + 1,  # Insertion
                D[i - 1, j - 1] + cost,  # Substitution
            )

    # Return the distance
    return D[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: All tests passed

Substring Evaluation

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

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string njit: False

Substring Evaluation

Testing if the previous output contains the string nopython: True

Final Answer: True

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string import numba: False

Substring Evaluation

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

Final Answer: True

Final Answer: True