entropy

Computes the Shannon entropy of all grouped values.

entropy(xs:list, [normalize=bool]) -> float

Description

The entropy function calculates the Shannon entropy of the values in xs, which measures the amount of uncertainty or randomness in the data. Higher entropy values indicate more randomness, while lower values indicate more predictability.

The entropy is calculated as: H(x) = -sum(p(x[i]) * log(p(x[i]))), where p(x[i]) is the probability of each unique value.

`xs: list`

The values to evaluate.

`normalize: bool (optional)`

Optional parameter to normalize the entropy between 0 and 1. When true, the entropy is divided by log(number of unique values). Defaults to false.

Examples

Compute the entropy of values

from {x: 1}, {x: 1}, {x: 2}, {x: 3}
summarize entropy_value=entropy(x)

{
  entropy_value: 1.0397207708399179,
}

Compute the normalized entropy

from {x: 1}, {x: 1}, {x: 2}, {x: 3}
summarize normalized_entropy=entropy(x, normalize=true)

{
  normalized_entropy: 0.946394630357186,
}