Rolling and exponentially weighted values

Coral.Window works on a one-dimensional tensor[n, f32] and returns a tensor with the same length. Its operations are rolling_sum, rolling_mean, rolling_std, rolling_min, rolling_max, and ewm. Use a positive rolling window; the positions before a full window is available contain NaN. rolling_std uses sample standard deviation (ddof=1). ewm(values, alpha) starts at the first input value and then uses alpha * value + (1 - alpha) * previous (adjust=False); supply an alpha between 0 and 1.

module Coral.BookWindow
import Coral.Window (rolling_mean)
export (main)
def main() -> f32 = {
  values = to_tensor([1.0f32, 2.0f32, 3.0f32, 4.0f32, 5.0f32])
  means = rolling_mean(values, 3i64)
  index(to_list(means), 4i64)
}

main returns 4.0, the mean of 3.0, 4.0, and 5.0.