tract-core 0.3.2

Tiny, no-nonsense, self contained, TensorFlow and ONNX inference
Documentation 
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element_map!(Sigmoid, [f32], sigmoid_f32);

const LOW: f32 = -18.0;
const HIGH: f32 = 18.0;
const ALPHA_9: f32 = 4.37031012579801e-11;
const ALPHA_7: f32 = 1.15627324459942e-07;
const ALPHA_5: f32 = 6.08574864600143e-05;
const ALPHA_3: f32 = 8.51377133304701e-03;
const ALPHA_1: f32 = 2.48287947061529e-01;
const BETA_10: f32 = 6.10247389755681e-13;
const BETA_8: f32 = 5.76102136993427e-09;
const BETA_6: f32 = 6.29106785017040e-06;
const BETA_4: f32 = 1.70198817374094e-03;
const BETA_2: f32 = 1.16817656904453e-01;
const BETA_0: f32 = 9.93151921023180e-01;

pub fn sigmoid_f32(x: f32) -> f32 {
    if x <= LOW {
        return 0.0;
    }
    if x >= HIGH {
        return 1.0;
    }
    let x2 = x * x;

    let p = x2 * ALPHA_9 + ALPHA_7;
    let p = x2 * p + ALPHA_5;
    let p = x2 * p + ALPHA_3;
    let p = x2 * p + ALPHA_1;
    let p = p * x;

    let q = x2 * BETA_10 + BETA_8;
    let q = x2 * q + BETA_6;
    let q = x2 * q + BETA_4;
    let q = x2 * q + BETA_2;
    let q = x2 * q + BETA_0;

    p / q + 0.5
}