Easy

Composing Small Functions

~10 mincode completion

Write two functions. The tests call both, so both have to work.

  1. normalise(value, low, high) returns where value sits between low and high, as a number from 0 to 1. The formula is (value - low) / (high - low). With low=0, high=10, the value 2.5 gives 0.25.
  1. normalise_all(values, low, high) returns a list with every value normalised, by calling normalise rather than repeating the formula.

You will use exactly this operation in the ML sections under the name min-max scaling.

Examples

Input
normalise(2.5, 0, 10)
Output
0.25

The low end of the range maps to 0

Input
normalise(5, 5, 15)
Output
0

The high end of the range maps to 1

Input
normalise(15, 5, 15)
Output
1

Hints

Hint 1

A list comprehension expresses this in one line.

Hint 2

A common slip here: duplicated formula instead of calling.

Requirements

  • value: the number to scale

  • low: the value that maps to 0

  • high: the value that maps to 1

  • Return (value - low) / (high - low)

Constraints

  • Standard library only, no imports required

  • Time limit: 200 ms, Memory: 64 MB

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Where this shows up

~10 min

3 employers weight this skill

2 frontier labs, 1 AI product company. Top match scores 80.

Python
def normalise(value, low, high):
    """
    Map a value onto a 0 to 1 scale.

    Args:
        value: the number to scale
        low: the value that maps to 0
        high: the value that maps to 1

    Returns:
        (value - low) / (high - low)
    """
    # YOUR CODE HERE
    pass


def normalise_all(values, low, high):
    """
    Normalise a whole list by calling normalise on each item.

    Args:
        values: a list of numbers
        low, high: the range, as above

    Returns:
        A list of normalised values.
    """
    # YOUR CODE HERE
    pass
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