OMG, I'm close now!

src/fitter/recursive.rs

@@ -1,3 +1,4 @@
+use derivative::Derivative;
 use ndarray::prelude::*;
 use ndarray::stack;
 use ndarray_linalg::Inverse;
@@ -6,8 +7,11 @@ use crate::kernel::Kernel;
 
 use super::Fitter;
 
+#[derive(Derivative)]
+#[derivative(Debug)]
 pub struct RecursiveFitter {
     ts_new: Vec<f64>,
+    #[derivative(Debug = "ignore")]
     kernel: Box<dyn Kernel>,
     ts: Vec<f64>,
     ms: ArrayD<f64>,
@@ -122,7 +126,7 @@ impl Fitter for RecursiveFitter {
 
     fn fit(&mut self) {
         if !self.is_allocated() {
-            // raise RuntimeError("new data since last call to `allocate()`")
+            panic!("new data since last call to `allocate()`");
         }
 
         if self.ts.is_empty() {
@@ -146,7 +150,7 @@ impl Fitter for RecursiveFitter {
             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]));
+                &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)