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Unify limiters

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This commit is contained in:
numzero 2024-06-29 00:10:17 +03:00
parent 75a6da9cae
commit 88bfae9608
2 changed files with 30 additions and 64 deletions
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118
src/bin/flat/fns.rs
View File

@ -1,13 +1,18 @@
use crate::float_fun::FloatExt2;
pub trait Limiter {
fn value(&self, x: f32) -> f32;
fn derivative(&self, x: f32) -> f32;
}
pub struct LinearLimiter {
pub min: f32,
pub max: f32,
}
impl LinearLimiter {
pub fn value(&self, x: f32) -> f32 { (self.min, self.max).inverse_lerp(x.abs()).clamp(0.0, 1.0) }
pub fn derivative(&self, x: f32) -> f32 { if x.abs() > self.min && x.abs() < self.max { x.signum() / (self.max - self.min) } else { 0.0 } }
impl Limiter for LinearLimiter {
fn value(&self, x: f32) -> f32 { (self.min, self.max).inverse_lerp(x.abs()).clamp(0.0, 1.0) }
fn derivative(&self, x: f32) -> f32 { if x.abs() > self.min && x.abs() < self.max { x.signum() / (self.max - self.min) } else { 0.0 } }
}
pub struct SmoothstepLimiter {
@ -15,12 +20,12 @@ pub struct SmoothstepLimiter {
pub max: f32,
}
impl SmoothstepLimiter {
pub fn value(&self, x: f32) -> f32 {
impl Limiter for SmoothstepLimiter {
fn value(&self, x: f32) -> f32 {
let y = (self.min, self.max).inverse_lerp(x.abs()).clamp(0.0, 1.0);
3.0 * y * y - 2.0 * y * y * y
}
pub fn derivative(&self, x: f32) -> f32 {
fn derivative(&self, x: f32) -> f32 {
if x.abs() > self.min && x.abs() < self.max {
let t = (self.min, self.max).inverse_lerp(x.abs());
6.0 * x.signum() * t * (1.0 - t) / (self.max - self.min)
@ -35,12 +40,12 @@ pub struct SmootherstepLimiter {
pub max: f32,
}
impl SmootherstepLimiter {
pub fn value(&self, x: f32) -> f32 {
impl Limiter for SmootherstepLimiter {
fn value(&self, x: f32) -> f32 {
let y = (self.min, self.max).inverse_lerp(x.abs()).clamp(0.0, 1.0);
6.0 * y.powi(5) - 15.0 * y.powi(4) + 10.0 * y.powi(3)
}
pub fn derivative(&self, x: f32) -> f32 {
fn derivative(&self, x: f32) -> f32 {
if x.abs() > self.min && x.abs() < self.max {
let t = (self.min, self.max).inverse_lerp(x.abs());
30.0 * (t * (1.0 - t)).powi(2) * x.signum() / (self.max - self.min)
@ -79,84 +84,45 @@ impl QuadraticAccelerator {
#[cfg(test)]
mod test {
use super::*;
use approx::{abs_diff_eq, AbsDiffEq, assert_abs_diff_eq};
fn test_limiter(testee: impl Limiter, min: f32, max: f32, δ: f32) {
let ε = 1.0e-4f32;
let margin = 1.0 / 16.0;
let mul = 1.0 + margin;
for x in itertools_num::linspace(0., min, 10) {
assert_abs_diff_eq!(testee.value(x), 0., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 0., epsilon = ε);
}
for x in itertools_num::linspace(max, mul * max, 10) {
assert_abs_diff_eq!(testee.value(x), 1., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 1., epsilon = ε);
}
for x in itertools_num::linspace(-mul * max, mul * max, 100) {
// Currently, the derivative is discontinuous at ±min and ±max... let’s just skip these for now.
if x.abs().abs_diff_eq(&min, δ) || x.abs().abs_diff_eq(&max, δ) {
continue;
}
let df_num = (testee.value(x + δ) - testee.value(x - δ)) / (2. * δ);
let df_expl = testee.derivative(x);
assert!(abs_diff_eq!(df_expl, df_num, epsilon = ε), "At x={x}, df/dx:\nnumerical: {df_num}\nexplicit: {df_expl}\n");
}
}
#[test]
fn test_linear_limiter() {
let testee = super::LinearLimiter { min: 20.0, max: 30.0 };
let ε = 1.0e-4f32;
let δ = 1.0 / 8.0; // Mathematically, you want this to be small. Computationally, you don’t.
let margin = 1.0 / 16.0;
let mul = 1.0 + margin;
for x in itertools_num::linspace(0., testee.min, 10) {
assert_abs_diff_eq!(testee.value(x), 0., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 0., epsilon = ε);
}
for x in itertools_num::linspace(testee.max, mul * testee.max, 10) {
assert_abs_diff_eq!(testee.value(x), 1., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 1., epsilon = ε);
}
for x in itertools_num::linspace(-mul * testee.max, mul * testee.max, 100) {
// Currently, the derivative is discontinuous at ±min and ±max... let’s just skip these for now.
if x.abs().abs_diff_eq(&testee.min, δ) || x.abs().abs_diff_eq(&testee.max, δ) {
continue;
}
let df_num = (testee.value(x + δ) - testee.value(x - δ)) / (2. * δ);
let df_expl = testee.derivative(x);
assert!(abs_diff_eq!(df_expl, df_num, epsilon = ε), "At x={x}, df/dx:\nnumerical: {df_num}\nexplicit: {df_expl}\n");
}
test_limiter(LinearLimiter { min: 20.0, max: 30.0 }, 20.0, 30.0, 1.0 / 8.0);
}
#[test]
fn test_smoothsteo_limiter() {
let testee = super::SmoothstepLimiter { min: 20.0, max: 30.0 };
let ε = 1.0e-4f32;
let δ = 1.0 / 8.0; // Mathematically, you want this to be small. Computationally, you don’t.
let margin = 1.0 / 16.0;
let mul = 1.0 + margin;
for x in itertools_num::linspace(0., testee.min, 10) {
assert_abs_diff_eq!(testee.value(x), 0., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 0., epsilon = ε);
}
for x in itertools_num::linspace(testee.max, mul * testee.max, 10) {
assert_abs_diff_eq!(testee.value(x), 1., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 1., epsilon = ε);
}
for x in itertools_num::linspace(-mul * testee.max, mul * testee.max, 100) {
// Currently, the derivative is discontinuous at ±min and ±max... let’s just skip these for now.
if x.abs().abs_diff_eq(&testee.min, δ) || x.abs().abs_diff_eq(&testee.max, δ) {
continue;
}
let df_num = (testee.value(x + δ) - testee.value(x - δ)) / (2. * δ);
let df_expl = testee.derivative(x);
assert!(abs_diff_eq!(df_expl, df_num, epsilon = ε), "At x={x}, df/dx:\nnumerical: {df_num}\nexplicit: {df_expl}\n");
}
fn test_smoothstep_limiter() {
test_limiter(SmoothstepLimiter { min: 20.0, max: 30.0 }, 20.0, 30.0, 1.0 / 32.0);
}
#[test]
fn test_smootherstep_limiter() {
let testee = super::SmootherstepLimiter { min: 20.0, max: 30.0 };
let ε = 1.0e-4f32;
let δ = 1.0 / 32.0; // Mathematically, you want this to be small. Computationally, you don’t.
let margin = 1.0 / 16.0;
let mul = 1.0 + margin;
for x in itertools_num::linspace(0., testee.min, 10) {
assert_abs_diff_eq!(testee.value(x), 0., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 0., epsilon = ε);
}
for x in itertools_num::linspace(testee.max, mul * testee.max, 10) {
assert_abs_diff_eq!(testee.value(x), 1., epsilon = ε);
assert_abs_diff_eq!(testee.value(-x), 1., epsilon = ε);
}
for x in itertools_num::linspace(-mul * testee.max, mul * testee.max, 100) {
// Currently, the derivative is discontinuous at ±min and ±max... let’s just skip these for now.
if x.abs().abs_diff_eq(&testee.min, δ) || x.abs().abs_diff_eq(&testee.max, δ) {
continue;
}
let df_num = (testee.value(x + δ) - testee.value(x - δ)) / (2. * δ);
let df_expl = testee.derivative(x);
assert!(abs_diff_eq!(df_expl, df_num, epsilon = ε), "At x={x}, df/dx:\nnumerical: {df_num}\nexplicit: {df_expl}\n");
}
test_limiter(SmootherstepLimiter { min: 20.0, max: 30.0 }, 20.0, 30.0, 1.0 / 32.0);
}
#[test]

4
src/bin/flat/tube/metric.rs
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@ -1,5 +1,5 @@
use glam::{f32, Mat2, Vec2, vec2};
use crate::fns;
use crate::fns::{self, Limiter};
use crate::riemann::{Decomp2, Metric, Tens2};
#[derive(Copy, Clone, Debug)]
@ -11,7 +11,7 @@ pub struct Tube {
}
impl Tube {
fn fx(&self) -> fns::SmootherstepLimiter { fns::SmootherstepLimiter { min: self.inner_radius, max: self.outer_radius } }
fn fx(&self) -> impl Limiter { fns::SmootherstepLimiter { min: self.inner_radius, max: self.outer_radius } }
fn fy(&self) -> fns::QuadraticAccelerator { fns::QuadraticAccelerator { internal: self.internal_halflength, external: self.external_halflength } }
pub fn y(&self, v: f32) -> f32 { self.fy().x(v) }