feat: refactoring, adds Result to most public API

src/cluster/kmeans.rs

@@ -61,7 +61,7 @@ use std::iter::Sum;
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::bbd_tree::BBDTree;
-use crate::error::{FitFailedError, PredictFailedError};
+use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::distance::euclidian::*;
 use crate::math::num::RealNumber;
@@ -122,19 +122,16 @@ impl<T: RealNumber + Sum> KMeans<T> {
         data: &M,
         k: usize,
         parameters: KMeansParameters,
-    ) -> Result<KMeans<T>, FitFailedError> {
+    ) -> Result<KMeans<T>, Failed> {
         let bbd = BBDTree::new(data);
 
         if k < 2 {
-            return Err(FitFailedError::new(&format!(
-                "Invalid number of clusters: {}",
-                k
-            )));
+            return Err(Failed::fit(&format!("invalid number of clusters: {}", k)));
         }
 
         if parameters.max_iter <= 0 {
-            return Err(FitFailedError::new(&format!(
-                "Invalid maximum number of iterations: {}",
+            return Err(Failed::fit(&format!(
+                "invalid maximum number of iterations: {}",
                 parameters.max_iter
             )));
         }
@@ -191,7 +188,7 @@ impl<T: RealNumber + Sum> KMeans<T> {
 
     /// Predict clusters for `x`
     /// * `x` - matrix with new data to transform of size _KxM_ , where _K_ is number of new samples and _M_ is number of features.
-    pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, PredictFailedError> {
+    pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
         let (n, _) = x.shape();
         let mut result = M::zeros(1, n);
 
@@ -274,11 +271,9 @@ mod tests {
     fn invalid_k() {
         let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.]]);
 
-        println!("{:?}", KMeans::fit(&x, 0, Default::default()));
-
         assert!(KMeans::fit(&x, 0, Default::default()).is_err());
         assert_eq!(
-            "Invalid number of clusters: 1",
+            "Fit failed: invalid number of clusters: 1",
             KMeans::fit(&x, 1, Default::default())
                 .unwrap_err()
                 .to_string()