// Author: Simon-Pierre Boucher — contact@spboucher.ai
//
// Bias-corrected exponential moving average (TensorBoard semantics): with
// smoothing s ∈ [0,1), ema_t = s·ema_{t-1} + (1−s)·x_t, displayed as
// ema_t / (1 − s^(t+1)) so early points aren't dragged toward zero.
import Foundation

enum Smoothing {
    static func ema(_ values: [Double], smoothing: Double) -> [Double] {
        guard smoothing > 0, smoothing < 1, !values.isEmpty else { return values }
        var out: [Double] = []
        out.reserveCapacity(values.count)
        var acc = 0.0
        var correction = 1.0
        for v in values {
            acc = smoothing * acc + (1 - smoothing) * v
            correction *= smoothing
            out.append(acc / (1 - correction))
        }
        return out
    }
}