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 is a possible implementation of the Levenshtein distance using numba and numpy: 

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import numpy as np
import numba

@numba.jit(nopython=True)
def levenshtein(a, b):
  # Create a matrix to store the distances
  dist = np.zeros((a.size + 1, b.size + 1), dtype=np.int32)

  # Initialize the first row and column
  dist[: ,0] = np.arange(a.size + 1)
  dist[0, :] = np.arange(b.size + 1)

  # Iterate over the matrix, computing the distances
  for i in range(1, a.size + 1):
    for j in range(1, b.size + 1):
      cost = 0 if a[i-1] == b[j-1] else 1
      dist[i, j] = min(dist[i-1, j] + 1,
                       dist[i, j-1] + 1,
                       dist[i-1, j-1] + cost + (cost == 1))

  return dist[a.size, b.size]
This implementation uses numba's `jit` decorator to compile the function in nopython mode, which should result in faster performance. The function takes two numpy int arrays, `a` and `b`, as input and returns the Levenshtein distance between them. The cost of a character swap is set to 2, as specified in the prompt.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba

@numba.jit(nopython=True)
def levenshtein(a, b):
  # Create a matrix to store the distances
  dist = np.zeros((a.size + 1, b.size + 1), dtype=np.int32)

  # Initialize the first row and column
  dist[: ,0] = np.arange(a.size + 1)
  dist[0, :] = np.arange(b.size + 1)

  # Iterate over the matrix, computing the distances
  for i in range(1, a.size + 1):
    for j in range(1, b.size + 1):
      cost = 0 if a[i-1] == b[j-1] else 1
      dist[i, j] = min(dist[i-1, j] + 1,
                       dist[i, j-1] + 1,
                       dist[i-1, j-1] + cost + (cost == 1))

  return dist[a.size, b.size]

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

@numba.jit(nopython=True)
def levenshtein(a, b):
  # Create a matrix to store the distances
  dist = np.zeros((a.size + 1, b.size + 1), dtype=np.int32)

  # Initialize the first row and column
  dist[: ,0] = np.arange(a.size + 1)
  dist[0, :] = np.arange(b.size + 1)

  # Iterate over the matrix, computing the distances
  for i in range(1, a.size + 1):
    for j in range(1, b.size + 1):
      cost = 0 if a[i-1] == b[j-1] else 1
      dist[i, j] = min(dist[i-1, j] + 1,
                       dist[i, j-1] + 1,
                       dist[i-1, j-1] + cost + (cost == 1))

  return dist[a.size, b.size]





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

Sure, I can help you write a Numba implementation of the Levenshtein distance algorithm, which is a string metric for measuring the difference between two sequences. Here's a function that should work for your requirements: 

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import numba
import numpy as np

@numba.njit(fastmath=True)
def levenshtein(a, b):
    m = len(a)
    n = len(b)

    # Initialize matrix of zeros
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)

    for i in range(m + 1):
        dp[i, 0] = i

    for j in range(n + 1):
        dp[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 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
            )

            # Transposition (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)

    return dp[m, n]
This Numba implementation of the Levenshtein distance algorithm works with two numpy int arrays as input, runs in nopython mode, and considers character swaps as a cost of 2.

Extract Code

I extracted the following code from that output: 

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import numba
import numpy as np

@numba.njit(fastmath=True)
def levenshtein(a, b):
    m = len(a)
    n = len(b)

    # Initialize matrix of zeros
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)

    for i in range(m + 1):
        dp[i, 0] = i

    for j in range(n + 1):
        dp[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 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
            )

            # Transposition (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)

    return dp[m, n]

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numba
import numpy as np

@numba.njit(fastmath=True)
def levenshtein(a, b):
    m = len(a)
    n = len(b)

    # Initialize matrix of zeros
    dp = np.zeros((m + 1, n + 1), dtype=np.int64)

    for i in range(m + 1):
        dp[i, 0] = i

    for j in range(n + 1):
        dp[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 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
            )

            # Transposition (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)

    return dp[m, n]





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 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: 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

Sure, I can help you write a NumBA implementation of the Levenshtein distance function. Here it is: 

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import numpy as np
import numba

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    m, n = s1.shape[0], s2.shape[0]

    # Create a matrix to store the edit distances.
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)

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

    # Fill in the rest of the matrix.
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 2 if s1[i - 1] != s2[j - 1] else 0

            # Minimum of insertion, deletion, and substitution.
            dp[i, j] = min(dp[i - 1, j] + 1, dp[i, j - 1] + 1, dp[i - 1, j - 1] + cost)

            # Transposition cost if the characters match and the previous ones don't.
            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] + cost)

    return dp[m, n]
This function takes two NumPy int arrays `s1` and `s2`, and returns the Levenshtein distance between them. The function is decorated with `@numba.jit(nopython=True)` to ensure it is compiled in Nopython mode for optimal performance. The cost of character swaps is set to 2, as per your request.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    m, n = s1.shape[0], s2.shape[0]

    # Create a matrix to store the edit distances.
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)

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

    # Fill in the rest of the matrix.
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 2 if s1[i - 1] != s2[j - 1] else 0

            # Minimum of insertion, deletion, and substitution.
            dp[i, j] = min(dp[i - 1, j] + 1, dp[i, j - 1] + 1, dp[i - 1, j - 1] + cost)

            # Transposition cost if the characters match and the previous ones don't.
            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] + cost)

    return dp[m, n]

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import numpy as np
import numba

@numba.jit(nopython=True)
def levenshtein(s1, s2):
    m, n = s1.shape[0], s2.shape[0]

    # Create a matrix to store the edit distances.
    dp = np.zeros((m + 1, n + 1), dtype=np.int32)

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

    # Fill in the rest of the matrix.
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 2 if s1[i - 1] != s2[j - 1] else 0

            # Minimum of insertion, deletion, and substitution.
            dp[i, j] = min(dp[i - 1, j] + 1, dp[i, j - 1] + 1, dp[i - 1, j - 1] + cost)

            # Transposition cost if the characters match and the previous ones don't.
            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] + cost)

    return dp[m, n]





answer = levenshtein(np.array([3, 5, 2, 4, 8, 7]), np.array([3, 4, 2, 4, 8, 7, 9]))
expected = 3
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: 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 with character swaps having a cost of 2. This implementation takes two NumPy int arrays as input and runs in nopython mode. 

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import numpy as np
import numba

@numba.njit(cache=True)
def levenshtein(arr1, arr2):
    m, n = arr1.shape[0], arr2.shape[0]

    # Initialize the distance matrix
    distance_matrix = np.zeros((m + 1, n + 1), dtype=np.int32)

    for i in range(m + 1):
        distance_matrix[i, 0] = i
    for j in range(n + 1):
        distance_matrix[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if arr1[i - 1] == arr2[j - 1]:
                cost = 0
            elif arr1[i - 1] != arr2[j - 1] and (i > 1 and j > 1 and arr1[i - 1] == arr2[j - 2] and arr1[i - 2] == arr2[j - 1]):
                cost = 2
            else:
                cost = 1

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

    return distance_matrix[m, n]

# Example usage:
arr1 = np.array([1, 2, 3])
arr2 = np.array([3, 2, 1])
distance = levenshtein(arr1, arr2)
print(distance)

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba

@numba.njit(cache=True)
def levenshtein(arr1, arr2):
    m, n = arr1.shape[0], arr2.shape[0]

    # Initialize the distance matrix
    distance_matrix = np.zeros((m + 1, n + 1), dtype=np.int32)

    for i in range(m + 1):
        distance_matrix[i, 0] = i
    for j in range(n + 1):
        distance_matrix[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if arr1[i - 1] == arr2[j - 1]:
                cost = 0
            elif arr1[i - 1] != arr2[j - 1] and (i > 1 and j > 1 and arr1[i - 1] == arr2[j - 2] and arr1[i - 2] == arr2[j - 1]):
                cost = 2
            else:
                cost = 1

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

    return distance_matrix[m, n]

# Example usage:
arr1 = np.array([1, 2, 3])
arr2 = np.array([3, 2, 1])
distance = levenshtein(arr1, arr2)
print(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
import numba

@numba.njit(cache=True)
def levenshtein(arr1, arr2):
    m, n = arr1.shape[0], arr2.shape[0]

    # Initialize the distance matrix
    distance_matrix = np.zeros((m + 1, n + 1), dtype=np.int32)

    for i in range(m + 1):
        distance_matrix[i, 0] = i
    for j in range(n + 1):
        distance_matrix[0, j] = j

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if arr1[i - 1] == arr2[j - 1]:
                cost = 0
            elif arr1[i - 1] != arr2[j - 1] and (i > 1 and j > 1 and arr1[i - 1] == arr2[j - 2] and arr1[i - 2] == arr2[j - 1]):
                cost = 2
            else:
                cost = 1

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

    return distance_matrix[m, n]

# Example usage:
arr1 = np.array([1, 2, 3])
arr2 = np.array([3, 2, 1])
distance = levenshtein(arr1, arr2)
print(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: 2 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: 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

Sure, here is a Numba implementation of the Levenshtein distance algorithm with the specified conditions: 

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import numpy as np
import numba

@numba.njit(fastmath=True)
def levenshtein(a, b):
    a = np.ascontiguousarray(a)
    b = np.ascontiguousarray(b)

    m = a.size
    n = b.size

    matrix = np.zeros((m + 1, n + 1), dtype=np.int64)

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

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                substitution_cost = 0
            elif a[i - 1] != b[j - 1]:
                substitution_cost = 2
            deletion_cost = matrix[i - 1, j] + 1
            insertion_cost = matrix[i, j - 1] + 1
            substitution_cost = matrix[i - 1, j - 1] + substitution_cost

            matrix[i, j] = min(insertion_cost, deletion_cost, substitution_cost)

    return matrix[m, n]
This function takes two numpy int arrays `a` and `b` as input, and returns the Levenshtein distance between them. Thecost of substitution is 2, while the cost of insertion and deletion is 1. The function is decorated with `@numba.njit(fastmath=True)` to ensure it runs in Numba's nopython mode for best performance.

Extract Code

I extracted the following code from that output: 

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import numpy as np
import numba

@numba.njit(fastmath=True)
def levenshtein(a, b):
    a = np.ascontiguousarray(a)
    b = np.ascontiguousarray(b)

    m = a.size
    n = b.size

    matrix = np.zeros((m + 1, n + 1), dtype=np.int64)

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

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                substitution_cost = 0
            elif a[i - 1] != b[j - 1]:
                substitution_cost = 2
            deletion_cost = matrix[i - 1, j] + 1
            insertion_cost = matrix[i, j - 1] + 1
            substitution_cost = matrix[i - 1, j - 1] + substitution_cost

            matrix[i, j] = min(insertion_cost, deletion_cost, substitution_cost)

    return matrix[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

@numba.njit(fastmath=True)
def levenshtein(a, b):
    a = np.ascontiguousarray(a)
    b = np.ascontiguousarray(b)

    m = a.size
    n = b.size

    matrix = np.zeros((m + 1, n + 1), dtype=np.int64)

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

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if a[i - 1] == b[j - 1]:
                substitution_cost = 0
            elif a[i - 1] != b[j - 1]:
                substitution_cost = 2
            deletion_cost = matrix[i - 1, j] + 1
            insertion_cost = matrix[i, j - 1] + 1
            substitution_cost = matrix[i - 1, j - 1] + substitution_cost

            matrix[i, j] = min(insertion_cost, deletion_cost, substitution_cost)

    return matrix[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: 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: True

Substring Evaluation

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

Final Answer: True

Final Answer: True