Initial Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

LLM Generation

Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

Output

import numpy as np def update(high, highest_number, delta): mask = np.arange(len(high)) != highest_number high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask]) return high # Call the function high = np.array([1, 2, 3, 4, 5]) highest_number = 2 delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) updated_high = update(high, highest_number, delta) print(updated_high)

Extract Code

I extracted the following code from that output: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.arange(len(high)) != highest_number
    high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask])
    return high

# Call the function
high = np.array([1, 2, 3, 4, 5])
highest_number = 2
delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5])

updated_high = update(high, highest_number, delta)
print(updated_high)

Check if all of the following conditions are true:

LLM Generation

Query

Below is a student's answer to a question about how to vectorize this loop: def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high Here is their code: import numpy as np def update(high, highest_number, delta): mask = np.arange(len(high)) != highest_number high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask]) return high # Call the function high = np.array([1, 2, 3, 4, 5]) highest_number = 2 delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) updated_high = update(high, highest_number, delta) print(updated_high) Did the student try to vectorize the loop? Don't tell me if they are correct or not. (I have tests for that separately which will catch any bugs.) Just tell me if they tried to vectorize the loop. End your answer with either the string "The student passes" if they tried to do a vectorized loop, or "The student fails" if they just wrote a for loop again.

Output

The student passes

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.arange(len(high)) != highest_number
    high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask])
    return high

# Call the function
high = np.array([1, 2, 3, 4, 5])
highest_number = 2
delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5])

updated_high = update(high, highest_number, delta)
print(updated_high)


import numpy as np

answer = list(update(np.array([2, 5, 9, 1, 2, 0, 3, 8]), 4, np.array([1, 2, 1, 4, 3, 2, 1, 2])))
expected = [2, 3, 4, 1, 2, 0, 3, 3]
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: [1 2 3 2 2] All tests passed

Substring Evaluation

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

Final Answer: True

Initial Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

LLM Generation

Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

Output

import numpy as np def update(high, highest_number, delta): mask = np.ones(len(high), dtype=bool) mask[highest_number] = False high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask]) return high # Example usage high = np.array([1, 2, 3, 4, 5]) delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) highest_number = 2 updated_high = update(high, highest_number, delta) print(updated_high)

Extract Code

I extracted the following code from that output: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.ones(len(high), dtype=bool)
    mask[highest_number] = False
    high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask])
    
    return high

Check if all of the following conditions are true:

LLM Generation

Query

Below is a student's answer to a question about how to vectorize this loop: def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high Here is their code: import numpy as np def update(high, highest_number, delta): mask = np.ones(len(high), dtype=bool) mask[highest_number] = False high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask]) return high Did the student try to vectorize the loop? Don't tell me if they are correct or not. (I have tests for that separately which will catch any bugs.) Just tell me if they tried to vectorize the loop. End your answer with either the string "The student passes" if they tried to do a vectorized loop, or "The student fails" if they just wrote a for loop again.

Output

The student passes

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.ones(len(high), dtype=bool)
    mask[highest_number] = False
    high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask])
    
    return high


import numpy as np

answer = list(update(np.array([2, 5, 9, 1, 2, 0, 3, 8]), 4, np.array([1, 2, 1, 4, 3, 2, 1, 2])))
expected = [2, 3, 4, 1, 2, 0, 3, 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

Final Answer: True

Initial Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

LLM Generation

Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

Output

import numpy as np def update(high, highest_number, delta): mask = np.arange(len(high)) != highest_number high = np.minimum(high, high[highest_number] + delta[highest_number] - delta) return high # Example usage: high = np.array([1, 2, 3, 4, 5]) highest_number = 2 delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) updated_high = update(high, highest_number, delta) print(updated_high)

Extract Code

I extracted the following code from that output: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.arange(len(high)) != highest_number
    high = np.minimum(high, high[highest_number] + delta[highest_number] - delta)
    return high

Check if all of the following conditions are true:

LLM Generation

Query

Below is a student's answer to a question about how to vectorize this loop: def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high Here is their code: import numpy as np def update(high, highest_number, delta): mask = np.arange(len(high)) != highest_number high = np.minimum(high, high[highest_number] + delta[highest_number] - delta) return high Did the student try to vectorize the loop? Don't tell me if they are correct or not. (I have tests for that separately which will catch any bugs.) Just tell me if they tried to vectorize the loop. End your answer with either the string "The student passes" if they tried to do a vectorized loop, or "The student fails" if they just wrote a for loop again.

Output

The student passes

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.arange(len(high)) != highest_number
    high = np.minimum(high, high[highest_number] + delta[highest_number] - delta)
    return high


import numpy as np

answer = list(update(np.array([2, 5, 9, 1, 2, 0, 3, 8]), 4, np.array([1, 2, 1, 4, 3, 2, 1, 2])))
expected = [2, 3, 4, 1, 2, 0, 3, 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

Final Answer: True

Initial Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

LLM Generation

Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

Output

import numpy as np def update(high, highest_number, delta): comparisons = np.tile(delta[highest_number], (len(high), 1)) - delta updates = np.minimum(high, high[highest_number] + comparisons) updates[highest_number] = high[highest_number] return updates

Extract Code

I extracted the following code from that output: 

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import numpy as np

def update(high, highest_number, delta):
    comparisons = np.tile(delta[highest_number], (len(high), 1)) - delta
    updates = np.minimum(high, high[highest_number] + comparisons)
    
    updates[highest_number] = high[highest_number]
    
    return updates

Check if all of the following conditions are true:

LLM Generation

Query

Below is a student's answer to a question about how to vectorize this loop: def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high Here is their code: import numpy as np def update(high, highest_number, delta): comparisons = np.tile(delta[highest_number], (len(high), 1)) - delta updates = np.minimum(high, high[highest_number] + comparisons) updates[highest_number] = high[highest_number] return updates Did the student try to vectorize the loop? Don't tell me if they are correct or not. (I have tests for that separately which will catch any bugs.) Just tell me if they tried to vectorize the loop. End your answer with either the string "The student passes" if they tried to do a vectorized loop, or "The student fails" if they just wrote a for loop again.

Output

The student tries to vectorize the loop. The student passes.

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def update(high, highest_number, delta):
    comparisons = np.tile(delta[highest_number], (len(high), 1)) - delta
    updates = np.minimum(high, high[highest_number] + comparisons)
    
    updates[highest_number] = high[highest_number]
    
    return updates


import numpy as np

answer = list(update(np.array([2, 5, 9, 1, 2, 0, 3, 8]), 4, np.array([1, 2, 1, 4, 3, 2, 1, 2])))
expected = [2, 3, 4, 1, 2, 0, 3, 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 16, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Substring Evaluation

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

Final Answer: False

Initial Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

LLM Generation

Query

Vectorize this update loop. Call the function `update`: import numpy as np def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high

Output

import numpy as np def update(high, highest_number, delta): mask = np.arange(len(high)) != highest_number high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask]) return high # Example usage high = np.array([1, 2, 3, 4, 5]) highest_number = 2 delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) updated_high = update(high, highest_number, delta) print(updated_high)

Extract Code

I extracted the following code from that output: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.arange(len(high)) != highest_number
    high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask])
    return high

high = np.array([1, 2, 3, 4, 5])
highest_number = 2
delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
updated_high = update(high, highest_number, delta)

Check if all of the following conditions are true:

LLM Generation

Query

Below is a student's answer to a question about how to vectorize this loop: def update(high, highest_number, delta): for i in range(len(high)): if i != highest_number: high[i] = min(high[i], high[highest_number] + delta[highest_number] - delta[i]) return high Here is their code: import numpy as np def update(high, highest_number, delta): mask = np.arange(len(high)) != highest_number high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask]) return high high = np.array([1, 2, 3, 4, 5]) highest_number = 2 delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) updated_high = update(high, highest_number, delta) Did the student try to vectorize the loop? Don't tell me if they are correct or not. (I have tests for that separately which will catch any bugs.) Just tell me if they tried to vectorize the loop. End your answer with either the string "The student passes" if they tried to do a vectorized loop, or "The student fails" if they just wrote a for loop again.

Output

The student tried to vectorize the loop. The student passes.

Substring Evaluation

Testing if the previous output contains the string student passes: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def update(high, highest_number, delta):
    mask = np.arange(len(high)) != highest_number
    high[mask] = np.minimum(high[mask], high[highest_number] + delta[highest_number] - delta[mask])
    return high

high = np.array([1, 2, 3, 4, 5])
highest_number = 2
delta = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
updated_high = update(high, highest_number, delta)


import numpy as np

answer = list(update(np.array([2, 5, 9, 1, 2, 0, 3, 8]), 4, np.array([1, 2, 1, 4, 3, 2, 1, 2])))
expected = [2, 3, 4, 1, 2, 0, 3, 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

Final Answer: True