Added tests to constant kernel

Cargo.toml

@@ -8,7 +8,7 @@ license = "MIT OR Apache-2.0"
 [dependencies]
 cblas = "0.2"
 lapacke = "0.2"
-ndarray = "0.14"
+ndarray = { version = "0.14", features = ["approx"] }
 ordered-float = "1.0"
 rand = "0.7"
 rand_xoshiro = "0.4"

src/expm.rs

@@ -88,7 +88,7 @@ const PADE_COEFF_13: [f64; 14] = [
 fn pade_error_coefficient(m: u64) -> f64 {
     use statrs::function::factorial::{binomial, factorial};
 
-    return 1.0 / (binomial(2 * m, m) * factorial(2 * m + 1));
+    1.0 / (binomial(2 * m, m) * factorial(2 * m + 1))
 }
 
 #[allow(non_camel_case_types)]

src/fitter/recursive.rs

@@ -227,15 +227,12 @@ impl Fitter for RecursiveFitter {
         let mut ms = Vec::new();
         let mut vs = Vec::new();
 
-        let locations = ts
-            .iter()
-            .map(|t| {
-                self.ts
-                    .iter()
-                    .position(|tc| t <= tc)
-                    .unwrap_or_else(|| self.ts.len())
-            })
-            .collect::<Vec<_>>();
+        let locations = ts.iter().map(|t| {
+            self.ts
+                .iter()
+                .position(|tc| t <= tc)
+                .unwrap_or_else(|| self.ts.len())
+        });
 
         for (i, nxt) in locations.into_iter().enumerate() {
             if nxt == self.ts.len() {

src/kernel.rs

@@ -11,6 +11,7 @@ pub use matern32::Matern32;
 pub use matern52::Matern52;
 
 pub trait Kernel {
+    fn k_mat(&self, ts1: &[f64], ts2: Option<&[f64]>) -> ArrayD<f64>;
     fn k_diag(&self, ts: &[f64]) -> Array1<f64>;
     fn order(&self) -> usize;
     fn state_mean(&self, t: f64) -> Array1<f64>;
@@ -18,6 +19,14 @@ pub trait Kernel {
     fn measurement_vector(&self) -> Array1<f64>;
     fn feedback(&self) -> Array2<f64>;
 
+    fn noise_effect(&self) -> Array2<f64> {
+        unimplemented!();
+    }
+
+    fn noise_density(&self) -> Array2<f64> {
+        unimplemented!();
+    }
+
     fn transition(&self, t0: f64, t1: f64) -> Array2<f64> {
         let f = self.feedback();
 
@@ -51,6 +60,10 @@ pub trait Kernel {
 }
 
 impl Kernel for Vec<Box<dyn Kernel>> {
+    fn k_mat(&self, _ts1: &[f64], _ts2: Option<&[f64]>) -> ArrayD<f64> {
+        unimplemented!();
+    }
+
     fn k_diag(&self, ts: &[f64]) -> Array1<f64> {
         self.iter()
             .fold(Array1::zeros(ts.len()), |k_diag: Array1<f64>, kernel| {

src/kernel/constant.rs

@@ -1,4 +1,5 @@
 use ndarray::prelude::*;
+use ndarray::IxDyn;
 
 use super::Kernel;
 
@@ -13,6 +14,13 @@ impl Constant {
 }
 
 impl Kernel for Constant {
+    fn k_mat(&self, ts1: &[f64], ts2: Option<&[f64]>) -> ArrayD<f64> {
+        let n = ts1.len();
+        let m = ts2.map_or(n, |ts| ts.len());
+
+        ArrayD::ones(IxDyn(&[n, m])) * self.var
+    }
+
     fn k_diag(&self, ts: &[f64]) -> Array1<f64> {
         Array1::ones(ts.len()) * self.var
     }
@@ -37,6 +45,10 @@ impl Kernel for Constant {
         array![[0.0]]
     }
 
+    fn noise_effect(&self) -> Array2<f64> {
+        array![[1.0]]
+    }
+
     fn transition(&self, _t0: f64, _t1: f64) -> Array2<f64> {
         array![[1.0]]
     }
@@ -45,3 +57,71 @@ impl Kernel for Constant {
         array![[0.0]]
     }
 }
+
+#[cfg(test)]
+mod tests {
+    use approx::assert_abs_diff_eq;
+    use rand::{distributions::Standard, thread_rng, Rng};
+
+    use super::*;
+
+    #[test]
+    fn test_kernel_matrix() {
+        let kernel = Constant::new(2.5);
+
+        let ts = [1.26, 1.46, 2.67];
+
+        assert_abs_diff_eq!(
+            kernel.k_mat(&ts, None),
+            array![[2.5, 2.5, 2.5], [2.5, 2.5, 2.5], [2.5, 2.5, 2.5]].into_dyn()
+        );
+    }
+
+    #[test]
+    fn test_kernel_diag() {
+        let kernel = Constant::new(2.5);
+
+        let ts: Vec<_> = thread_rng()
+            .sample_iter::<f64, _>(Standard)
+            .take(10)
+            .map(|x| x * 10.0)
+            .collect();
+
+        assert_eq!(kernel.k_mat(&ts, None).diag(), kernel.k_diag(&ts));
+    }
+
+    #[test]
+    fn test_kernel_order() {
+        let kernel = Constant::new(2.5);
+
+        let m = kernel.order();
+
+        assert_eq!(kernel.state_mean(0.0).shape(), &[m]);
+        assert_eq!(kernel.state_cov(0.0).shape(), &[m, m]);
+        assert_eq!(kernel.measurement_vector().shape(), &[m]);
+        assert_eq!(kernel.feedback().shape(), &[m, m]);
+        assert_eq!(kernel.noise_effect().shape()[0], m);
+        assert_eq!(kernel.transition(0.0, 1.0).shape(), &[m, m]);
+        assert_eq!(kernel.noise_cov(0.0, 1.0).shape(), &[m, m]);
+    }
+
+    #[test]
+    fn test_ssm_variance() {
+        let kernel = Constant::new(2.5);
+
+        let ts: Vec<_> = thread_rng()
+            .sample_iter::<f64, _>(Standard)
+            .take(10)
+            .map(|x| x * 10.0)
+            .collect();
+
+        let h = kernel.measurement_vector();
+
+        let vars = ts
+            .iter()
+            .map(|t| h.dot(&kernel.state_cov(*t)).dot(&h))
+            .collect::<Vec<_>>();
+
+        assert_abs_diff_eq!(Array::from(vars), kernel.k_diag(&ts));
+    }
+}

src/kernel/exponential.rs

@@ -14,6 +14,10 @@ impl Exponential {
 }
 
 impl Kernel for Exponential {
+    fn k_mat(&self, _ts1: &[f64], _ts2: Option<&[f64]>) -> ArrayD<f64> {
+        unimplemented!();
+    }
+
     fn k_diag(&self, ts: &[f64]) -> Array1<f64> {
         Array1::ones(ts.len()) * self.var
     }

src/kernel/matern32.rs

@@ -20,6 +20,10 @@ impl Matern32 {
 }
 
 impl Kernel for Matern32 {
+    fn k_mat(&self, _ts1: &[f64], _ts2: Option<&[f64]>) -> ArrayD<f64> {
+        unimplemented!();
+    }
+
     fn k_diag(&self, ts: &[f64]) -> Array1<f64> {
         Array1::ones(ts.len()) * self.var
     }

src/kernel/matern52.rs

@@ -19,6 +19,10 @@ impl Matern52 {
 }
 
 impl Kernel for Matern52 {
+    fn k_mat(&self, _ts1: &[f64], _ts2: Option<&[f64]>) -> ArrayD<f64> {
+        unimplemented!();
+    }
+
     fn k_diag(&self, ts: &[f64]) -> Array1<f64> {
         Array1::ones(ts.len()) * self.var
     }