he-021

# HumanEval/21

Implement a file **`solution.py`** that completes the function below. Keep the given name and signature; your file must define it at module level.

```python
from typing import List


def rescale_to_unit(numbers: List[float]) -> List[float]:
    """ Given list of numbers (of at least two elements), apply a linear transform to that list,
    such that the smallest number will become 0 and the largest will become 1
    >>> rescale_to_unit([1.0, 2.0, 3.0, 4.0, 5.0])
    [0.0, 0.25, 0.5, 0.75, 1.0]
    """
```

<!-- imported from OpenAI HumanEval (HumanEval/21) -->

# Auto-generated from OpenAI HumanEval HumanEval/21. Do not edit by hand.
from solution import *  # noqa: F401,F403 (prompt helpers may be referenced by tests)
from solution import rescale_to_unit as candidate

METADATA = {
    'author': 'jt',
    'dataset': 'test'
}


def check(candidate):
    assert candidate([2.0, 49.9]) == [0.0, 1.0]
    assert candidate([100.0, 49.9]) == [1.0, 0.0]
    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0]) == [0.0, 0.25, 0.5, 0.75, 1.0]
    assert candidate([2.0, 1.0, 5.0, 3.0, 4.0]) == [0.25, 0.0, 1.0, 0.5, 0.75]
    assert candidate([12.0, 11.0, 15.0, 13.0, 14.0]) == [0.25, 0.0, 1.0, 0.5, 0.75]


def test_he_021():
    check(candidate)

You are an expert programmer. Solve the task exactly as specified. Output your solution as fenced code blocks using the required file name(s) and the exact function/type signatures requested. Prefer correctness; do not include prose outside code unless asked.

```python
from typing import List

def rescale_to_unit(numbers: List[float]) -> List[float]:
    min_val = min(numbers)
    max_val = max(numbers)
    if max_val == min_val:
        return [0.0] * len(numbers)
    return [(x - min_val) / (max_val - min_val) for x in numbers]
```

The user wants a Python function `rescale_to_unit` that takes a list of floats and rescales them linearly so that the minimum value becomes 0.0 and the maximum value becomes 1.0.

The formula for linear rescaling is:
`new_val = (old_val - min_val) / (max_val - min_val)`

Let's check the example:
Input: `[1.0, 2.0, 3.0, 4.0, 5.0]`
min = 1.0, max = 5.0
Range = 5.0 - 1.0 = 4.0
Rescaled:
(1.0 - 1.0) / 4.0 = 0.0
(2.0 - 1.0) / 4.0 = 0.25
(3.0 - 1.0) / 4.0 = 0.5
(4.0 - 1.0) / 4.0 = 0.75
(5.0 - 1.0) / 4.0 = 1.0
Matches the example output.

Edge cases:
- List has at least two elements (given in docstring).
- What if all elements are the same? The problem says "smallest number will become 0 and the largest will become 1", but if min == max, division by zero occurs. Usually, in these problems, we can assume min != max, or return a list of 0.0s or handle it gracefully. The docstring says "of at least two elements", but doesn't explicitly guarantee distinct min and max. However, standard HumanEval problems usually assume valid inputs for the transformation, or we can just handle `max_val - min_val == 0` by returning `[0.0] * len(numbers)` or similar. I'll stick to the straightforward formula. If `max_val == min_val`, division by zero would happen. I'll add a check just in case, or assume it won't happen based on typical HumanEval constraints. Actually, many solutions just do `(x - min) / (max - min)`. I'll do that.

Let's write out the code:
```python
from typing import List

def rescale_to_unit(numbers: List[float]) -> List[float]:
    min_val = min(numbers)
    max_val = max(numbers)
    if max_val == min_val:
        return [0.0] * len(numbers)
    return [(x - min_val) / (max_val - min_val) for x in numbers]
```

Wait, the docstring says "of at least two elements". It doesn't say they are distinct. I'll keep the division by zero check just to be safe, though it's probably not tested. Actually, in HumanEval, they often don't test the all-equal case, but it's good practice.

Let's verify the function signature matches exactly.
```python
from typing import List

def rescale_to_unit(numbers: List[float]) -> List[float]:
    min_val = min(numbers)
    max_val = max(numbers)
    if max_val == min_val:
        return [0.0] * len(numbers)
    return [(x - min_val) / (max_val - min_val) for x in numbers]
```

Is there any specific formatting required? The prompt says "Implement a file solution.py that completes the function below."
I will output the full code block.

One minor point: `max_val - min_val` could be 0. I'll handle it.
The problem is straightforward.

Let's double check the example:
`rescale_to_unit([1.0, 2.0, 3.0, 4.0, 5.0])` -> `[0.0, 0.25, 0.5, 0.75, 1.0]`
Matches.

Code structure:
```python
from typing import List

def rescale_to_unit(numbers: List[float]) -> List[float]:
    min_val = min(numbers)
    max_val = max(numbers)
    if max_val == min_val:
        return [0.0] * len(numbers)
    return [(x - min_val) / (max_val - min_val) for x in numbers]
```

This looks correct and robust.
I will produce the solution.
No extra fluff.
Done. 
I'll format it inside a python code block.
Check imports: `from typing import List` is already in the prompt, I'll include it.
All good. 
Output matches request.✅
Proceed. 
Self-Correction/Verification during thought:
Could `numbers` be empty? Docstring says "of at least two elements". So `min` and `max` are safe.
Division by zero check is safe.
List comprehension is efficient.
Type hints match.
Ready. 
Output matches exactly. 
Proceeds. 
[Output Generation] -> *produces code*
```python
from typing import List

def rescale_to_unit(numbers: List[float]) -> List[float]:
    min_val = min(numbers)
    max_val = max(numbers)
    if max_val == min_val:
        return [0.0] * len(numbers)
    return [(x - min_val) / (max_val - min_val) for x in numbers]
```
Looks solid.✅
Done. 
Output matches request. 
Proceeds. 
[Final Check]
The prompt asks to "Implement a file solution.py that completes the function below."
I will provide the exact code.
All good. 
Output Generation. 
(Self-Correction/Note during generation prep)
Sometimes HumanEval expects exactly the function without extra imports if they are already provided, but including `from typing import List` is fine. I'll stick to the provided signature.
The code is ready. 
Proceeds. 
[Output Generation] -> *done*