From 5766364311053a348ed838cdb57263bbf57c32f4 Mon Sep 17 00:00:00 2001
From: Volodymyr Orlov <vorl@Echo.local>
Date: Tue, 31 Mar 2020 18:24:01 -0700
Subject: [PATCH] fix: minor refactoring

---
 src/algorithm/neighbour/bbd_tree.rs           |  8 +++----
 src/cluster/kmeans.rs                         |  2 +-
 src/decomposition/pca.rs                      |  4 ++--
 src/ensemble/random_forest_classifier.rs      |  2 +-
 src/ensemble/random_forest_regressor.rs       |  2 +-
 src/linalg/evd.rs                             | 22 +++++++++----------
 src/linalg/naive/dense_matrix.rs              | 20 ++++++++---------
 src/linalg/qr.rs                              |  6 ++---
 src/linalg/svd.rs                             |  6 ++---
 src/neighbors/knn.rs                          |  4 +---
 .../first_order/gradient_descent.rs           |  3 +--
 src/optimization/first_order/lbfgs.rs         |  6 ++---
 src/optimization/first_order/mod.rs           |  4 ++--
 src/tree/decision_tree_classifier.rs          |  6 ++---
 src/tree/decision_tree_regressor.rs           |  6 ++---
 15 files changed, 49 insertions(+), 52 deletions(-)

diff --git a/src/algorithm/neighbour/bbd_tree.rs b/src/algorithm/neighbour/bbd_tree.rs
index 39a27e9..d0cea26 100644
--- a/src/algorithm/neighbour/bbd_tree.rs
+++ b/src/algorithm/neighbour/bbd_tree.rs
@@ -5,14 +5,14 @@ use crate::linalg::Matrix;
 use crate::math::distance::euclidian;
 
 #[derive(Debug)]
-pub struct BBDTree<T: FloatExt + Debug> {    
+pub struct BBDTree<T: FloatExt> {    
     nodes: Vec<BBDTreeNode<T>>,
     index: Vec<usize>,
     root: usize
 }
 
 #[derive(Debug)]
-struct BBDTreeNode<T: FloatExt + Debug> {    
+struct BBDTreeNode<T: FloatExt> {    
     count: usize,
     index: usize,
     center: Vec<T>,
@@ -23,7 +23,7 @@ struct BBDTreeNode<T: FloatExt + Debug> {
     upper: Option<usize>
 }
 
-impl<T: FloatExt + Debug> BBDTreeNode<T> {
+impl<T: FloatExt> BBDTreeNode<T> {
     fn new(d: usize) -> BBDTreeNode<T> {
         BBDTreeNode {
             count: 0,
@@ -38,7 +38,7 @@ impl<T: FloatExt + Debug> BBDTreeNode<T> {
     }
 }
 
-impl<T: FloatExt + Debug> BBDTree<T> {
+impl<T: FloatExt> BBDTree<T> {
     pub fn new<M: Matrix<T>>(data: &M) -> BBDTree<T> {
         let nodes = Vec::new();
 
diff --git a/src/cluster/kmeans.rs b/src/cluster/kmeans.rs
index 89ad5ef..7000405 100644
--- a/src/cluster/kmeans.rs
+++ b/src/cluster/kmeans.rs
@@ -56,7 +56,7 @@ impl Default for KMeansParameters {
      }
 }
 
-impl<T: FloatExt + Debug + Sum> KMeans<T>{
+impl<T: FloatExt + Sum> KMeans<T>{
     pub fn new<M: Matrix<T>>(data: &M, k: usize, parameters: KMeansParameters) -> KMeans<T> {
 
         let bbd = BBDTree::new(data);
diff --git a/src/decomposition/pca.rs b/src/decomposition/pca.rs
index e8238f1..68d884c 100644
--- a/src/decomposition/pca.rs
+++ b/src/decomposition/pca.rs
@@ -3,7 +3,7 @@ use crate::math::num::FloatExt;
 use crate::linalg::{Matrix};
 
 #[derive(Debug)]
-pub struct PCA<T: FloatExt + Debug, M: Matrix<T>> {    
+pub struct PCA<T: FloatExt, M: Matrix<T>> {    
     eigenvectors: M,
     eigenvalues: Vec<T>,
     projection: M,
@@ -24,7 +24,7 @@ impl Default for PCAParameters {
      }
 }
 
-impl<T: FloatExt + Debug, M: Matrix<T>> PCA<T, M> {
+impl<T: FloatExt, M: Matrix<T>> PCA<T, M> {
 
     pub fn new(data: &M, n_components: usize, parameters: PCAParameters) -> PCA<T, M> {
 
diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index e33741e..4a0835c 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -39,7 +39,7 @@ impl Default for RandomForestClassifierParameters {
      }
 }
 
-impl<T: FloatExt + Debug> RandomForestClassifier<T> {
+impl<T: FloatExt> RandomForestClassifier<T> {
 
     pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: RandomForestClassifierParameters) -> RandomForestClassifier<T> {        
         let (_, num_attributes) = x.shape();
diff --git a/src/ensemble/random_forest_regressor.rs b/src/ensemble/random_forest_regressor.rs
index db16935..1d4dec6 100644
--- a/src/ensemble/random_forest_regressor.rs
+++ b/src/ensemble/random_forest_regressor.rs
@@ -36,7 +36,7 @@ impl Default for RandomForestRegressorParameters {
      }
 }
 
-impl<T: FloatExt + Debug> RandomForestRegressor<T> {
+impl<T: FloatExt> RandomForestRegressor<T> {
 
     pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: RandomForestRegressorParameters) -> RandomForestRegressor<T> {        
         let (n_rows, num_attributes) = x.shape();                
diff --git a/src/linalg/evd.rs b/src/linalg/evd.rs
index adbad8a..a74e186 100644
--- a/src/linalg/evd.rs
+++ b/src/linalg/evd.rs
@@ -6,13 +6,13 @@ use crate::math::num::FloatExt;
 use std::fmt::Debug;
 
 #[derive(Debug, Clone)]
-pub struct EVD<T: FloatExt + Debug, M: BaseMatrix<T>> {        
+pub struct EVD<T: FloatExt, M: BaseMatrix<T>> {        
     pub d: Vec<T>,
     pub e: Vec<T>,
     pub V: M
 }
 
-impl<T: FloatExt + Debug, M: BaseMatrix<T>> EVD<T, M> {
+impl<T: FloatExt, M: BaseMatrix<T>> EVD<T, M> {
     pub fn new(V: M, d: Vec<T>, e: Vec<T>) -> EVD<T, M> {
         EVD {
             d: d,
@@ -22,7 +22,7 @@ impl<T: FloatExt + Debug, M: BaseMatrix<T>> EVD<T, M> {
     }
 }
 
-pub trait EVDDecomposableMatrix<T: FloatExt + Debug>: BaseMatrix<T> {    
+pub trait EVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {    
     
     fn evd(&self, symmetric: bool) -> EVD<T, Self>{
         self.clone().evd_mut(symmetric)
@@ -68,7 +68,7 @@ pub trait EVDDecomposableMatrix<T: FloatExt + Debug>: BaseMatrix<T> {
     }
 }
 
-fn tred2<T: FloatExt + Debug, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<T>) {
+fn tred2<T: FloatExt, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<T>) {
     
     let (n, _) = V.shape();
     for i in 0..n {
@@ -172,7 +172,7 @@ fn tred2<T: FloatExt + Debug, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &m
     e[0] = T::zero();
 }
 
-fn tql2<T: FloatExt + Debug, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<T>) {
+fn tql2<T: FloatExt, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mut Vec<T>) {
     let (n, _) = V.shape();
     for i in 1..n {
         e[i - 1] = e[i];
@@ -288,7 +288,7 @@ fn tql2<T: FloatExt + Debug, M: BaseMatrix<T>>(V: &mut M, d: &mut Vec<T>, e: &mu
     }
 }
 
-fn balance<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &mut M) -> Vec<T> {
+fn balance<T: FloatExt, M: BaseMatrix<T>>(A: &mut M) -> Vec<T> {
     let radix = T::two();
     let sqrdx = radix * radix;
 
@@ -341,7 +341,7 @@ fn balance<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &mut M) -> Vec<T> {
     return scale;
 }
 
-fn elmhes<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &mut M) -> Vec<usize> {
+fn elmhes<T: FloatExt, M: BaseMatrix<T>>(A: &mut M) -> Vec<usize> {
     let (n, _) = A.shape();
     let mut perm = vec![0; n];
 
@@ -387,7 +387,7 @@ fn elmhes<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &mut M) -> Vec<usize> {
     return perm;
 }   
 
-fn eltran<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &M, V: &mut M, perm: &Vec<usize>) {
+fn eltran<T: FloatExt, M: BaseMatrix<T>>(A: &M, V: &mut M, perm: &Vec<usize>) {
     let (n, _) = A.shape();
     for mp in (1..n - 1).rev() {
         for k in mp + 1..n {
@@ -404,7 +404,7 @@ fn eltran<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &M, V: &mut M, perm: &Vec<us
     }
 }
 
-fn hqr2<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &mut M, V: &mut M, d: &mut Vec<T>, e: &mut Vec<T>) {
+fn hqr2<T: FloatExt, M: BaseMatrix<T>>(A: &mut M, V: &mut M, d: &mut Vec<T>, e: &mut Vec<T>) {
     let (n, _) = A.shape();                                                           
     let mut z = T::zero();        
     let mut s = T::zero();
@@ -742,7 +742,7 @@ fn hqr2<T: FloatExt + Debug, M: BaseMatrix<T>>(A: &mut M, V: &mut M, d: &mut Vec
     }        
 }
 
-fn balbak<T: FloatExt + Debug, M: BaseMatrix<T>>(V: &mut M, scale: &Vec<T>) {
+fn balbak<T: FloatExt, M: BaseMatrix<T>>(V: &mut M, scale: &Vec<T>) {
     let (n, _) = V.shape();
     for i in 0..n {
         for j in 0..n {
@@ -751,7 +751,7 @@ fn balbak<T: FloatExt + Debug, M: BaseMatrix<T>>(V: &mut M, scale: &Vec<T>) {
     }
 }
 
-fn sort<T: FloatExt + Debug, M: BaseMatrix<T>>(d: &mut Vec<T>, e: &mut Vec<T>, V: &mut M) {         
+fn sort<T: FloatExt, M: BaseMatrix<T>>(d: &mut Vec<T>, e: &mut Vec<T>, V: &mut M) {         
     let n = d.len();
     let mut temp = vec![T::zero(); n];
     for j in 1..n {
diff --git a/src/linalg/naive/dense_matrix.rs b/src/linalg/naive/dense_matrix.rs
index 3be158e..dd38361 100644
--- a/src/linalg/naive/dense_matrix.rs
+++ b/src/linalg/naive/dense_matrix.rs
@@ -16,7 +16,7 @@ use crate::linalg::qr::QRDecomposableMatrix;
 use crate::math::num::FloatExt;
 
 #[derive(Debug, Clone)]
-pub struct DenseMatrix<T: FloatExt + Debug> {
+pub struct DenseMatrix<T: FloatExt> {
 
     ncols: usize,
     nrows: usize,
@@ -24,7 +24,7 @@ pub struct DenseMatrix<T: FloatExt + Debug> {
 
 }
 
-impl<T: FloatExt + Debug> fmt::Display for DenseMatrix<T> {
+impl<T: FloatExt> fmt::Display for DenseMatrix<T> {
     fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
         let mut rows: Vec<Vec<f64>> = Vec::new();
         for r in 0..self.nrows {
@@ -34,7 +34,7 @@ impl<T: FloatExt + Debug> fmt::Display for DenseMatrix<T> {
     }
 }
 
-impl<T: FloatExt + Debug> DenseMatrix<T> {  
+impl<T: FloatExt> DenseMatrix<T> {  
     
     fn new(nrows: usize, ncols: usize, values: Vec<T>) -> Self {
         DenseMatrix {
@@ -182,15 +182,15 @@ impl<T: FloatExt + fmt::Debug + Serialize> Serialize for DenseMatrix<T> {
     }
 }
 
-impl<T: FloatExt + Debug> SVDDecomposableMatrix<T> for DenseMatrix<T> {}
+impl<T: FloatExt> SVDDecomposableMatrix<T> for DenseMatrix<T> {}
 
-impl<T: FloatExt + Debug> EVDDecomposableMatrix<T> for DenseMatrix<T> {}
+impl<T: FloatExt> EVDDecomposableMatrix<T> for DenseMatrix<T> {}
 
-impl<T: FloatExt + Debug> QRDecomposableMatrix<T> for DenseMatrix<T> {}
+impl<T: FloatExt> QRDecomposableMatrix<T> for DenseMatrix<T> {}
 
-impl<T: FloatExt + Debug> Matrix<T> for DenseMatrix<T> {}
+impl<T: FloatExt> Matrix<T> for DenseMatrix<T> {}
 
-impl<T: FloatExt + Debug> PartialEq for DenseMatrix<T> {
+impl<T: FloatExt> PartialEq for DenseMatrix<T> {
     fn eq(&self, other: &Self) -> bool {
         if self.ncols != other.ncols || self.nrows != other.nrows {
             return false
@@ -213,13 +213,13 @@ impl<T: FloatExt + Debug> PartialEq for DenseMatrix<T> {
     }
 }
 
-impl<T: FloatExt + Debug> Into<Vec<T>> for DenseMatrix<T> {
+impl<T: FloatExt> Into<Vec<T>> for DenseMatrix<T> {
     fn into(self) -> Vec<T> {
         self.values
     }
 }
 
-impl<T: FloatExt + Debug> BaseMatrix<T> for DenseMatrix<T> {   
+impl<T: FloatExt> BaseMatrix<T> for DenseMatrix<T> {   
     
     type RowVector = Vec<T>;
 
diff --git a/src/linalg/qr.rs b/src/linalg/qr.rs
index bdc1f1d..853e1a3 100644
--- a/src/linalg/qr.rs
+++ b/src/linalg/qr.rs
@@ -6,13 +6,13 @@ use crate::math::num::FloatExt;
 use crate::linalg::BaseMatrix;
 
 #[derive(Debug, Clone)]
-pub struct QR<T: FloatExt + Debug, M: BaseMatrix<T>> {            
+pub struct QR<T: FloatExt, M: BaseMatrix<T>> {            
     QR: M,
     tau: Vec<T>,
     singular: bool
 }
 
-impl<T: FloatExt + Debug, M: BaseMatrix<T>> QR<T, M> {
+impl<T: FloatExt, M: BaseMatrix<T>> QR<T, M> {
     pub fn new(QR: M, tau: Vec<T>) -> QR<T, M> {
 
         let mut singular = false;
@@ -112,7 +112,7 @@ impl<T: FloatExt + Debug, M: BaseMatrix<T>> QR<T, M> {
     }
 }
 
-pub trait QRDecomposableMatrix<T: FloatExt + Debug>: BaseMatrix<T> { 
+pub trait QRDecomposableMatrix<T: FloatExt>: BaseMatrix<T> { 
 
     fn qr(&self) -> QR<T, Self> {
         self.clone().qr_mut()
diff --git a/src/linalg/svd.rs b/src/linalg/svd.rs
index a975960..67298a5 100644
--- a/src/linalg/svd.rs
+++ b/src/linalg/svd.rs
@@ -5,7 +5,7 @@ use crate::math::num::FloatExt;
 use std::fmt::Debug;
 
 #[derive(Debug, Clone)]
-pub struct SVD<T: FloatExt + Debug, M: SVDDecomposableMatrix<T>> {
+pub struct SVD<T: FloatExt, M: SVDDecomposableMatrix<T>> {
     pub U: M,
     pub V: M,
     pub s: Vec<T>,
@@ -15,7 +15,7 @@ pub struct SVD<T: FloatExt + Debug, M: SVDDecomposableMatrix<T>> {
     tol: T
 }
 
-pub trait SVDDecomposableMatrix<T: FloatExt + Debug>: BaseMatrix<T> {
+pub trait SVDDecomposableMatrix<T: FloatExt>: BaseMatrix<T> {
 
     fn svd_solve_mut(self, b: Self) -> Self {
         self.svd_mut().solve(b)
@@ -373,7 +373,7 @@ pub trait SVDDecomposableMatrix<T: FloatExt + Debug>: BaseMatrix<T> {
     }
 }
 
-impl<T: FloatExt + Debug, M: SVDDecomposableMatrix<T>> SVD<T, M> {
+impl<T: FloatExt, M: SVDDecomposableMatrix<T>> SVD<T, M> {
     pub fn new(U: M, V: M, s: Vec<T>) -> SVD<T, M> {
         let m = U.shape().0;
         let n = V.shape().0;     
diff --git a/src/neighbors/knn.rs b/src/neighbors/knn.rs
index 070373b..3cb96eb 100644
--- a/src/neighbors/knn.rs
+++ b/src/neighbors/knn.rs
@@ -1,5 +1,3 @@
-use std::fmt::Debug;
-
 use crate::math::num::FloatExt;
 use crate::linalg::{Matrix, row_iter};
 use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
@@ -13,7 +11,7 @@ pub struct KNNClassifier<'a, T: FloatExt> {
     k: usize,        
 }
 
-impl<'a, T: FloatExt + Debug> KNNClassifier<'a, T> {
+impl<'a, T: FloatExt> KNNClassifier<'a, T> {
 
     pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, k: usize, distance: &'a dyn Fn(&Vec<T>, &Vec<T>) -> T, algorithm: KNNAlgorithmName) -> KNNClassifier<'a, T> {
 
diff --git a/src/optimization/first_order/gradient_descent.rs b/src/optimization/first_order/gradient_descent.rs
index bf54cc5..b642534 100644
--- a/src/optimization/first_order/gradient_descent.rs
+++ b/src/optimization/first_order/gradient_descent.rs
@@ -1,5 +1,4 @@
 use std::default::Default;
-use std::fmt::Debug;
 
 use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
@@ -23,7 +22,7 @@ impl<T: FloatExt> Default for GradientDescent<T> {
      }
 }
 
-impl<T: FloatExt + Debug> FirstOrderOptimizer<T> for GradientDescent<T>
+impl<T: FloatExt> FirstOrderOptimizer<T> for GradientDescent<T>
 {
 
     fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(&self, f: &'a F<T, X>, df: &'a DF<X>, x0: &X, ls: &'a LS) -> OptimizerResult<T, X> {        
diff --git a/src/optimization/first_order/lbfgs.rs b/src/optimization/first_order/lbfgs.rs
index d2edc53..9b1605e 100644
--- a/src/optimization/first_order/lbfgs.rs
+++ b/src/optimization/first_order/lbfgs.rs
@@ -35,7 +35,7 @@ impl<T: FloatExt> Default for LBFGS<T> {
      }
 }
 
-impl<T: FloatExt + Debug> LBFGS<T> {
+impl<T: FloatExt> LBFGS<T> {
 
     fn two_loops<X: Matrix<T>>(&self, state: &mut LBFGSState<T, X>) {        
 
@@ -169,7 +169,7 @@ impl<T: FloatExt + Debug> LBFGS<T> {
 }
 
 #[derive(Debug)]
-struct LBFGSState<T: FloatExt + Debug, X: Matrix<T>> {
+struct LBFGSState<T: FloatExt, X: Matrix<T>> {
     x: X,
     x_prev: X,
     x_f: T,
@@ -189,7 +189,7 @@ struct LBFGSState<T: FloatExt + Debug, X: Matrix<T>> {
     alpha: T
 }
 
-impl<T: FloatExt + Debug> FirstOrderOptimizer<T> for LBFGS<T> {
+impl<T: FloatExt> FirstOrderOptimizer<T> for LBFGS<T> {
 
     fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(&self, f: &F<T, X>, df: &'a DF<X>, x0: &X, ls: &'a LS) -> OptimizerResult<T, X> {     
         
diff --git a/src/optimization/first_order/mod.rs b/src/optimization/first_order/mod.rs
index 499db21..fae47ea 100644
--- a/src/optimization/first_order/mod.rs
+++ b/src/optimization/first_order/mod.rs
@@ -9,12 +9,12 @@ use crate::linalg::Matrix;
 use crate::optimization::line_search::LineSearchMethod;
 use crate::optimization::{F, DF};
 
-pub trait FirstOrderOptimizer<T: FloatExt + Debug> {
+pub trait FirstOrderOptimizer<T: FloatExt> {
     fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(&self, f: &F<T, X>, df: &'a DF<X>, x0: &X, ls: &'a LS) -> OptimizerResult<T, X>;    
 }
 
 #[derive(Debug, Clone)]
-pub struct OptimizerResult<T: FloatExt + Debug, X: Matrix<T>> 
+pub struct OptimizerResult<T: FloatExt, X: Matrix<T>> 
 {
     pub x: X,
     pub f_x: T,
diff --git a/src/tree/decision_tree_classifier.rs b/src/tree/decision_tree_classifier.rs
index 985196b..a5ff657 100644
--- a/src/tree/decision_tree_classifier.rs
+++ b/src/tree/decision_tree_classifier.rs
@@ -68,7 +68,7 @@ impl<T: FloatExt> Node<T> {
      }
 }
 
-struct NodeVisitor<'a, T: FloatExt + Debug, M: Matrix<T>> {
+struct NodeVisitor<'a, T: FloatExt, M: Matrix<T>> {
     x: &'a M,
     y: &'a Vec<usize>,
     node: usize,
@@ -115,7 +115,7 @@ fn impurity<T: FloatExt>(criterion: &SplitCriterion, count: &Vec<usize>, n: usiz
     return impurity;
 }
 
-impl<'a, T: FloatExt + Debug, M: Matrix<T>> NodeVisitor<'a, T, M> {    
+impl<'a, T: FloatExt, M: Matrix<T>> NodeVisitor<'a, T, M> {    
 
     fn new(node_id: usize, samples: Vec<usize>, order: &'a Vec<Vec<usize>>, x: &'a M, y: &'a Vec<usize>, level: u16) -> Self {
         NodeVisitor {
@@ -147,7 +147,7 @@ pub(in crate) fn which_max(x: &Vec<usize>) -> usize {
     return which;
 }
 
-impl<T: FloatExt + Debug> DecisionTreeClassifier<T> {
+impl<T: FloatExt> DecisionTreeClassifier<T> {
 
     pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: DecisionTreeClassifierParameters) -> DecisionTreeClassifier<T> {
         let (x_nrows, num_attributes) = x.shape();
diff --git a/src/tree/decision_tree_regressor.rs b/src/tree/decision_tree_regressor.rs
index c347c93..a0c574a 100644
--- a/src/tree/decision_tree_regressor.rs
+++ b/src/tree/decision_tree_regressor.rs
@@ -56,7 +56,7 @@ impl<T: FloatExt> Node<T> {
      }
 }
 
-struct NodeVisitor<'a, T: FloatExt + Debug, M: Matrix<T>> {
+struct NodeVisitor<'a, T: FloatExt, M: Matrix<T>> {
     x: &'a M,
     y: &'a M,
     node: usize,
@@ -67,7 +67,7 @@ struct NodeVisitor<'a, T: FloatExt + Debug, M: Matrix<T>> {
     level: u16
 }
 
-impl<'a, T: FloatExt + Debug, M: Matrix<T>> NodeVisitor<'a, T, M> {    
+impl<'a, T: FloatExt, M: Matrix<T>> NodeVisitor<'a, T, M> {    
 
     fn new(node_id: usize, samples: Vec<usize>, order: &'a Vec<Vec<usize>>, x: &'a M, y: &'a M, level: u16) -> Self {
         NodeVisitor {
@@ -84,7 +84,7 @@ impl<'a, T: FloatExt + Debug, M: Matrix<T>> NodeVisitor<'a, T, M> {
 
 }
 
-impl<T: FloatExt + Debug> DecisionTreeRegressor<T> {
+impl<T: FloatExt> DecisionTreeRegressor<T> {
 
     pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: DecisionTreeRegressorParameters) -> DecisionTreeRegressor<T> {
         let (x_nrows, num_attributes) = x.shape();