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use core;
use core::cmp::min;
use conv::{ApproxFrom, ConvAsUtil, ConvUtil, ValueFrom};
use float_ord::sort as sort_floats;
use super::Estimate;
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct Quantile {
q: [f64; 5],
n: [i64; 5],
m: [f64; 5],
dm: [f64; 5],
}
impl Quantile {
#[inline]
pub fn new(p: f64) -> Quantile {
assert!(0. <= p && p <= 1.);
Quantile {
q: [0.; 5],
n: [1, 2, 3, 4, 0],
m: [1., 1. + 2.*p, 1. + 4.*p, 3. + 2.*p, 5.],
dm: [0., p/2., p, (1. + p)/2., 1.],
}
}
#[inline]
pub fn p(&self) -> f64 {
self.dm[2]
}
#[inline]
fn parabolic(&self, i: usize, d: f64) -> f64 {
debug_assert_eq!(d.abs(), 1.);
let s: i64 = d.approx().unwrap();
self.q[i] + d / f64::approx_from(self.n[i + 1] - self.n[i - 1]).unwrap()
* (f64::approx_from(self.n[i] - self.n[i - 1] + s).unwrap()
* (self.q[i + 1] - self.q[i])
/ f64::approx_from(self.n[i + 1] - self.n[i]).unwrap()
+ f64::approx_from(self.n[i + 1] - self.n[i] - s).unwrap()
* (self.q[i] - self.q[i - 1])
/ f64::approx_from(self.n[i] - self.n[i - 1]).unwrap())
}
#[inline]
fn linear(&self, i: usize, d: f64) -> f64 {
debug_assert_eq!(d.abs(), 1.);
let sum = if d < 0. { i - 1 } else { i + 1 };
self.q[i] + d * (self.q[sum] - self.q[i])
/ f64::approx_from(self.n[sum] - self.n[i]).unwrap()
}
#[inline]
pub fn quantile(&self) -> f64 {
if self.len() >= 5 {
return self.q[2];
}
if self.is_empty() {
return 0.;
}
let mut heights: [f64; 4] = [
self.q[0], self.q[1], self.q[2], self.q[3]
];
let len = usize::value_from(self.len()).unwrap();
sort_floats(&mut heights[..len]);
let desired_index = ConvUtil::approx_as::<f64>(len).unwrap() * self.p() - 1.;
let mut index = desired_index.ceil();
if desired_index == index && index >= 0. {
let index: usize = index.approx().unwrap();
if index < len - 1 {
return 0.5*self.q[index] + 0.5*self.q[index + 1];
}
}
index = index.max(0.);
let mut index: usize = index.approx().unwrap();
index = min(index, len - 1);
self.q[index]
}
#[inline]
pub fn len(&self) -> u64 {
u64::value_from(self.n[4]).unwrap()
}
#[inline]
pub fn is_empty(&self) -> bool {
self.len() == 0
}
}
impl core::default::Default for Quantile {
fn default() -> Quantile {
Quantile::new(0.5)
}
}
impl Estimate for Quantile {
#[inline]
fn add(&mut self, x: f64) {
if self.n[4] < 5 {
self.q[usize::value_from(self.n[4]).unwrap()] = x;
self.n[4] += 1;
if self.n[4] == 5 {
sort_floats(&mut self.q);
}
return;
}
let mut k: usize;
if x < self.q[0] {
self.q[0] = x;
k = 0;
} else {
k = 4;
for i in 1..5 {
if x < self.q[i] {
k = i;
break;
}
}
if self.q[4] < x {
self.q[4] = x;
}
};
for i in k..5 {
self.n[i] += 1;
}
for i in 0..5 {
self.m[i] += self.dm[i];
}
for i in 1..4 {
let d: f64 = self.m[i] - f64::approx_from(self.n[i]).unwrap();
if d >= 1. && self.n[i + 1] - self.n[i] > 1 ||
d <= -1. && self.n[i - 1] - self.n[i] < -1 {
let d = d.signum();
let q_new = self.parabolic(i, d);
if self.q[i - 1] < q_new && q_new < self.q[i + 1] {
self.q[i] = q_new;
} else {
self.q[i] = self.linear(i, d);
}
let delta: i64 = d.approx().unwrap();
self.n[i] += delta;
}
}
}
fn estimate(&self) -> f64 {
self.quantile()
}
}
#[test]
fn reference() {
let observations = [
0.02, 0.5, 0.74, 3.39, 0.83,
22.37, 10.15, 15.43, 38.62, 15.92,
34.60, 10.28, 1.47, 0.40, 0.05,
11.39, 0.27, 0.42, 0.09, 11.37,
];
let mut q = Quantile::new(0.5);
for &o in observations.iter() {
q.add(o);
}
assert_eq!(q.n, [1, 6, 10, 16, 20]);
assert_eq!(q.m, [1., 5.75, 10.50, 15.25, 20.0]);
assert_eq!(q.len(), 20);
assert_eq!(q.quantile(), 4.2462394088036435);
}