diff --git a/src/cluster/kmeans.rs b/src/cluster/kmeans.rs
index 3b2d2da..f232631 100644
--- a/src/cluster/kmeans.rs
+++ b/src/cluster/kmeans.rs
@@ -60,8 +60,8 @@ use std::iter::Sum;
 
 use serde::{Deserialize, Serialize};
 
-use crate::error::{FitFailedError, PredictFailedError};
 use crate::algorithm::neighbour::bbd_tree::BBDTree;
+use crate::error::{FitFailedError, PredictFailedError};
 use crate::linalg::Matrix;
 use crate::math::distance::euclidian::*;
 use crate::math::num::RealNumber;
@@ -118,15 +118,23 @@ impl<T: RealNumber + Sum> KMeans<T> {
     /// * `data` - training instances to cluster
     /// * `k` - number of clusters
     /// * `parameters` - cluster parameters
-    pub fn fit<M: Matrix<T>>(data: &M, k: usize, parameters: KMeansParameters) -> Result<KMeans<T>, FitFailedError> {
+    pub fn fit<M: Matrix<T>>(
+        data: &M,
+        k: usize,
+        parameters: KMeansParameters,
+    ) -> Result<KMeans<T>, FitFailedError> {
         let bbd = BBDTree::new(data);
 
         if k < 2 {
-            return Err(FitFailedError::new(&format!("Invalid number of clusters: {}", k)));
+            return Err(FitFailedError::new(&format!(
+                "Invalid number of clusters: {}",
+                k
+            )));
         }
 
         if parameters.max_iter <= 0 {
-            return Err(FitFailedError::new(&format!("Invalid maximum number of iterations: {}",
+            return Err(FitFailedError::new(&format!(
+                "Invalid maximum number of iterations: {}",
                 parameters.max_iter
             )));
         }
@@ -264,16 +272,17 @@ mod tests {
 
     #[test]
     fn invalid_k() {
-        let x = DenseMatrix::from_2d_array(&[
-            &[1., 2., 3.],
-            &[4., 5., 6.],           
-        ]);
+        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", KMeans::fit(&x, 1, Default::default()).unwrap_err().to_string());
-
+        assert!(KMeans::fit(&x, 0, Default::default()).is_err());
+        assert_eq!(
+            "Invalid number of clusters: 1",
+            KMeans::fit(&x, 1, Default::default())
+                .unwrap_err()
+                .to_string()
+        );
     }
 
     #[test]
diff --git a/src/error/mod.rs b/src/error/mod.rs
index 0c6d44f..a644a79 100644
--- a/src/error/mod.rs
+++ b/src/error/mod.rs
@@ -5,26 +5,28 @@ use std::fmt;
 /// Error to be raised when model does not fits data.
 #[derive(Debug)]
 pub struct FitFailedError {
-    details: String
+    details: String,
 }
 
 /// Error to be raised when model prediction cannot be calculated.
 #[derive(Debug)]
 pub struct PredictFailedError {
-    details: String
+    details: String,
 }
 
 impl FitFailedError {
     /// Creates new instance of `FitFailedError`
     /// * `msg` - description of the error
     pub fn new(msg: &str) -> FitFailedError {
-        FitFailedError{details: msg.to_string()}
+        FitFailedError {
+            details: msg.to_string(),
+        }
     }
 }
 
 impl fmt::Display for FitFailedError {
     fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f,"{}",self.details)
+        write!(f, "{}", self.details)
     }
 }
 
@@ -38,13 +40,15 @@ impl PredictFailedError {
     /// Creates new instance of `PredictFailedError`
     /// * `msg` - description of the error
     pub fn new(msg: &str) -> PredictFailedError {
-        PredictFailedError{details: msg.to_string()}
+        PredictFailedError {
+            details: msg.to_string(),
+        }
     }
 }
 
 impl fmt::Display for PredictFailedError {
     fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f,"{}",self.details)
+        write!(f, "{}", self.details)
     }
 }
 
diff --git a/src/lib.rs b/src/lib.rs
index aa2d7c7..5bd22ff 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -75,6 +75,7 @@ pub mod dataset;
 pub mod decomposition;
 /// Ensemble methods, including Random Forest classifier and regressor
 pub mod ensemble;
+pub mod error;
 /// Diverse collection of linear algebra abstractions and methods that power SmartCore algorithms
 pub mod linalg;
 /// Supervised classification and regression models that assume linear relationship between dependent and explanatory variables.
@@ -89,4 +90,3 @@ pub mod neighbors;
 pub(crate) mod optimization;
 /// Supervised tree-based learning methods
 pub mod tree;
-pub mod error;