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Even closer to get example "basic" up and running!

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This commit is contained in:
Anders Olsson
2020-02-20 12:57:51 +01:00
parent 7528b3b67b
commit 6840699144
4 changed files with 103 additions and 76 deletions
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2
Cargo.toml
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@@ -10,7 +10,7 @@ ndarray = "0.13"
ndarray-linalg = { version = "0.12" }
cblas = "0.2"
lapacke = "0.2"
statrs = "0.10"
statrs = "0.12"
ordered-float = "1.0"
rand = "0.6"
rand_xoshiro = "0.1"

3
src/fitter.rs
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@@ -4,7 +4,10 @@ pub use recursive::RecursiveFitter;
pub trait Fitter {
fn add_sample(&mut self, t: f64) -> usize;
fn allocate(&mut self);
fn is_allocated(&self) -> bool;
fn fit(&mut self);
fn vs(&self, idx: usize) -> f64;

147
src/fitter/recursive.rs
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@@ -1,5 +1,6 @@
use ndarray::prelude::*;
use ndarray::stack;
use ndarray_linalg::Inverse;
use crate::kernel::Kernel;
@@ -15,15 +16,15 @@ pub struct RecursiveFitter {
xs: ArrayD<f64>,
is_fitted: bool,
h: Array1<f64>,
i: ArrayD<f64>,
i: Array2<f64>,
a: Vec<Array2<f64>>,
q: Vec<Array2<f64>>,
m_p: ArrayD<f64>,
p_p: ArrayD<f64>,
m_f: ArrayD<f64>,
p_f: ArrayD<f64>,
m_s: ArrayD<f64>,
p_s: ArrayD<f64>,
m_p: Vec<Array1<f64>>,
p_p: Vec<Array2<f64>>,
m_f: Vec<Array1<f64>>,
p_f: Vec<Array2<f64>>,
m_s: Vec<Array1<f64>>,
p_s: Vec<Array2<f64>>,
}
impl RecursiveFitter {
@@ -41,15 +42,15 @@ impl RecursiveFitter {
xs: Array::zeros(0).into_dyn(),
is_fitted: true,
h,
i: Array::eye(m).into_dyn(),
i: Array::eye(m),
a: Vec::new(),
q: Vec::new(),
m_p: Array::zeros((0, m)).into_dyn(),
p_p: Array::zeros((0, m, m)).into_dyn(),
m_f: Array::zeros((0, m)).into_dyn(),
p_f: Array::zeros((0, m, m)).into_dyn(),
m_s: Array::zeros((0, m)).into_dyn(),
p_s: Array::zeros((0, m, m)).into_dyn(),
m_p: Vec::new(),
p_p: Vec::new(),
m_f: Vec::new(),
p_f: Vec::new(),
m_s: Vec::new(),
p_s: Vec::new(),
}
}
}
@@ -81,43 +82,19 @@ impl Fitter for RecursiveFitter {
self.xs = stack![Axis(0), self.xs, zeros];
// Initialize the predictive, filtering and smoothing distributions.
let mean = self
.ts_new
.iter()
.flat_map(|t| self.kernel.state_mean(*t).to_vec().into_iter())
.collect::<Vec<f64>>();
for t in &self.ts_new {
let mean = self.kernel.state_mean(*t);
let mean = Array::from_shape_vec((self.ts_new.len(), self.kernel.order()), mean)
.expect("failed to create mean matrix")
.into_dyn();
self.m_p.push(mean.clone());
self.m_f.push(mean.clone());
self.m_s.push(mean);
let cov = self
.ts_new
.iter()
.flat_map(|t| {
self.kernel
.state_cov(*t)
.iter()
.cloned()
.collect::<Vec<f64>>()
})
.collect::<Vec<f64>>();
let cov = self.kernel.state_cov(*t);
let cov = Array3::from_shape_vec(
(self.ts_new.len(), self.kernel.order(), self.kernel.order()),
cov,
)
.expect("failed to create cov matrix")
.into_dyn();
self.m_p = stack![Axis(0), self.m_p, mean];
self.p_p = stack![Axis(0), self.p_p, cov];
self.m_f = stack![Axis(0), self.m_f, mean];
self.p_f = stack![Axis(0), self.p_f, cov];
self.m_s = stack![Axis(0), self.m_s, mean];
self.p_s = stack![Axis(0), self.p_s, cov];
self.p_p.push(cov.clone());
self.p_f.push(cov.clone());
self.p_s.push(cov);
}
// Compute the new transition and noise covariance matrices.
for i in (self.ts.len() - n_new)..self.ts.len() {
@@ -139,8 +116,80 @@ impl Fitter for RecursiveFitter {
self.ts_new.clear();
}
fn is_allocated(&self) -> bool {
self.ts_new.is_empty()
}
fn fit(&mut self) {
todo!();
if !self.is_allocated() {
// raise RuntimeError("new data since last call to `allocate()`")
}
if self.ts.is_empty() {
self.is_fitted = true;
return;
}
// Forward pass (Kalman filter).
for i in 0..self.ts.len() {
if i > 0 {
self.m_p[i] = self.a[i - 1].dot(&self.m_f[i - 1]);
self.p_p[i] =
self.a[i - 1].dot(&self.p_f[i - 1]).dot(&self.a[i - 1].t()) + &self.q[i - 1];
}
// These are slightly modified equations to work with tau and nu.
let k = self.p_p[i].dot(&self.h)
/ (1.0 + self.xs[i] * self.h.dot(&self.p_p[i]).dot(&self.h));
let k = Array1::from(k);
self.m_f[i] =
(&self.m_p[i] + &k) * (&self.ns[i] - &self.xs[i] * &self.h.dot(&self.m_p[i]));
// Covariance matrix is computed using the Joseph form.
let outer = (self.xs[i] * &k)
.iter()
.flat_map(|a| self.h.iter().map(move |b| a * b))
.collect::<Vec<f64>>();
let outer = Array::from_shape_vec((self.h.len(), self.h.len()), outer)
.expect("failed to create outer matrix");
let z = &self.i - &outer;
let outer = k
.iter()
.flat_map(|a| k.iter().map(move |b| a * b))
.collect::<Vec<f64>>();
let outer = Array::from_shape_vec((self.h.len(), self.h.len()), outer)
.expect("failed to create outer matrix");
self.p_f[i] = z.dot(&self.p_p[i]).dot(&z.t()) + self.xs[i] * outer;
}
// Backward pass (RTS smoother).
for i in (0..self.ts.len()).rev() {
if i == self.ts.len() - 1 {
self.m_s[i] = self.m_f[i].clone();
self.p_s[i] = self.p_f[i].clone();
} else {
let g = self.a[i]
.dot(&self.p_f[i])
.dot(&self.p_p[i + 1].inv().expect("failed to inverse matrix"));
self.m_s[i] = &self.m_f[i] + &g.dot(&(&self.m_s[i + 1] - &self.m_p[i + 1]));
self.p_s[i] =
&self.p_f[i] + &g.dot(&(&self.p_s[i + 1] - &self.p_p[i + 1])).dot(&g.t());
}
self.ms[i] = self.h.dot(&self.m_s[i]);
self.vs[i] = self.h.dot(&self.p_s[i]).dot(&self.h);
}
self.is_fitted = true;
}
fn vs(&self, idx: usize) -> f64 {

27
src/utils.rs
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@@ -38,7 +38,7 @@ const QS: [f64; 6] = [
fn normcdf(x: f64) -> f64 {
// If X ~ N(0,1), returns P(X < x).
// erfc(-x / SQRT2) / 2.0
// https://docs.rs/statrs/0.12.0/statrs/function/erf/fn.erfc.html
todo!();
}
@@ -85,29 +85,4 @@ pub fn logphi(z: f64) -> (f64, f64) {
(res, dres)
}
/*
if z * z < 0.0492:
# First case: z close to zero.
coef = -z / SQRT2PI
val = 0
for c in CS:
val = coef * (c + val)
res = -2 * val - log(2)
dres = exp(-(z * z) / 2 - res) / SQRT2PI
elif z < -11.3137:
# Second case: z very small.
num = 0.5641895835477550741
for r in RS:
num = -z * num / SQRT2 + r
den = 1.0
for q in QS:
den = -z * den / SQRT2 + q
res = log(num / (2 * den)) - (z * z) / 2
dres = abs(den / num) * sqrt(2.0 / pi)
else:
res = log(normcdf(z))
dres = exp(-(z * z) / 2 - res) / SQRT2PI
return res, dres
*/
}