Fix rust-2018-idioms warnings

src/cluster/dbscan.rs

@@ -29,8 +29,6 @@
 //! * ["A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", Ester M., Kriegel HP., Sander J., Xu X.](http://faculty.marshall.usc.edu/gareth-james/ISL/)
 //! * ["Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications", Sander J., Ester M., Kriegel HP., Xu X.](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.63.1629&rep=rep1&type=pdf)
 
-extern crate rand;
-
 use std::fmt::Debug;
 use std::iter::Sum;
 

src/cluster/kmeans.rs

@@ -52,8 +52,6 @@
 //! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., 10.3.1 K-Means Clustering](http://faculty.marshall.usc.edu/gareth-james/ISL/)
 //! * ["k-means++: The Advantages of Careful Seeding", Arthur D., Vassilvitskii S.](http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf)
 
-extern crate rand;
-
 use rand::Rng;
 use std::fmt::Debug;
 use std::iter::Sum;

src/ensemble/random_forest_classifier.rs

@@ -45,8 +45,6 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-extern crate rand;
-
 use std::default::Default;
 use std::fmt::Debug;
 

src/ensemble/random_forest_regressor.rs

@@ -42,7 +42,6 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-extern crate rand;
 
 use std::default::Default;
 use std::fmt::Debug;

src/error/mod.rs

@@ -82,7 +82,7 @@ impl PartialEq for Failed {
 }
 
 impl fmt::Display for FailedError {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
         let failed_err_str = match self {
             FailedError::FitFailed => "Fit failed",
             FailedError::PredictFailed => "Predict failed",
@@ -96,7 +96,7 @@ impl fmt::Display for FailedError {
 }
 
 impl fmt::Display for Failed {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
         write!(f, "{}: {}", self.err, self.msg)
     }
 }

src/linalg/mod.rs

@@ -515,7 +515,7 @@ pub trait Matrix<T: RealNumber>:
 {
 }
 
-pub(crate) fn row_iter<F: RealNumber, M: BaseMatrix<F>>(m: &M) -> RowIter<F, M> {
+pub(crate) fn row_iter<F: RealNumber, M: BaseMatrix<F>>(m: &M) -> RowIter<'_, F, M> {
     RowIter {
         m,
         pos: 0,

src/linalg/naive/dense_matrix.rs

@@ -1,4 +1,3 @@
-extern crate num;
 use std::fmt;
 use std::fmt::Debug;
 use std::marker::PhantomData;
@@ -197,7 +196,7 @@ pub struct DenseMatrixIterator<'a, T: RealNumber> {
 }
 
 impl<T: RealNumber> fmt::Display for DenseMatrix<T> {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
         let mut rows: Vec<Vec<f64>> = Vec::new();
         for r in 0..self.nrows {
             rows.push(
@@ -356,7 +355,7 @@ impl<'de, T: RealNumber + fmt::Debug + Deserialize<'de>> Deserialize<'de> for De
         impl<'a, T: RealNumber + fmt::Debug + Deserialize<'a>> Visitor<'a> for DenseMatrixVisitor<T> {
             type Value = DenseMatrix<T>;
 
-            fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
+            fn expecting(&self, formatter: &mut fmt::Formatter<'_>) -> fmt::Result {
                 formatter.write_str("struct DenseMatrix")
             }
 

src/model_selection/mod.rs

@@ -8,7 +8,6 @@
 //! your data.
 //!
 //! In SmartCore you can split your data into training and test datasets using `train_test_split` function.
-extern crate rand;
 
 use crate::linalg::BaseVector;
 use crate::linalg::Matrix;

src/optimization/first_order/gradient_descent.rs

@@ -25,8 +25,8 @@ impl<T: RealNumber> Default for GradientDescent<T> {
 impl<T: RealNumber> FirstOrderOptimizer<T> for GradientDescent<T> {
     fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
         &self,
-        f: &'a F<T, X>,
-        df: &'a DF<X>,
+        f: &'a F<'_, T, X>,
+        df: &'a DF<'_, X>,
         x0: &X,
         ls: &'a LS,
     ) -> OptimizerResult<T, X> {

src/optimization/first_order/lbfgs.rs

@@ -100,8 +100,8 @@ impl<T: RealNumber> LBFGS<T> {
 
     fn update_state<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
         &self,
-        f: &'a F<T, X>,
-        df: &'a DF<X>,
+        f: &'a F<'_, T, X>,
+        df: &'a DF<'_, X>,
         ls: &'a LS,
         state: &mut LBFGSState<T, X>,
     ) {
@@ -162,7 +162,7 @@ impl<T: RealNumber> LBFGS<T> {
         g_converged || x_converged || state.counter_f_tol > self.successive_f_tol
     }
 
-    fn update_hessian<'a, X: Matrix<T>>(&self, _: &'a DF<X>, state: &mut LBFGSState<T, X>) {
+    fn update_hessian<'a, X: Matrix<T>>(&self, _: &'a DF<'_, X>, state: &mut LBFGSState<T, X>) {
         state.dg = state.x_df.sub(&state.x_df_prev);
         let rho_iteration = T::one() / state.dx.dot(&state.dg);
         if !rho_iteration.is_infinite() {
@@ -198,8 +198,8 @@ struct LBFGSState<T: RealNumber, X: Matrix<T>> {
 impl<T: RealNumber> FirstOrderOptimizer<T> for LBFGS<T> {
     fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
         &self,
-        f: &F<T, X>,
-        df: &'a DF<X>,
+        f: &F<'_, T, X>,
+        df: &'a DF<'_, X>,
         x0: &X,
         ls: &'a LS,
     ) -> OptimizerResult<T, X> {

src/optimization/first_order/mod.rs

@@ -12,8 +12,8 @@ use crate::optimization::{DF, F};
 pub trait FirstOrderOptimizer<T: RealNumber> {
     fn optimize<'a, X: Matrix<T>, LS: LineSearchMethod<T>>(
         &self,
-        f: &F<T, X>,
-        df: &'a DF<X>,
+        f: &F<'_, T, X>,
+        df: &'a DF<'_, X>,
         x0: &X,
         ls: &'a LS,
     ) -> OptimizerResult<T, X>;

src/svm/svc.rs

@@ -378,7 +378,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
         (support_vectors, w, b)
     }
 
-    fn initialize(&mut self, cache: &mut Cache<T, M, K>) {
+    fn initialize(&mut self, cache: &mut Cache<'_, T, M, K>) {
         let (n, _) = self.x.shape();
         let few = 5;
         let mut cp = 0;
@@ -402,7 +402,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
         }
     }
 
-    fn process(&mut self, i: usize, x: M::RowVector, y: T, cache: &mut Cache<T, M, K>) -> bool {
+    fn process(&mut self, i: usize, x: M::RowVector, y: T, cache: &mut Cache<'_, T, M, K>) -> bool {
         for j in 0..self.sv.len() {
             if self.sv[j].index == i {
                 return true;
@@ -445,13 +445,13 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
         true
     }
 
-    fn reprocess(&mut self, tol: T, cache: &mut Cache<T, M, K>) -> bool {
+    fn reprocess(&mut self, tol: T, cache: &mut Cache<'_, T, M, K>) -> bool {
         let status = self.smo(None, None, tol, cache);
         self.clean(cache);
         status
     }
 
-    fn finish(&mut self, cache: &mut Cache<T, M, K>) {
+    fn finish(&mut self, cache: &mut Cache<'_, T, M, K>) {
         let mut max_iter = self.sv.len();
 
         while self.smo(None, None, self.parameters.tol, cache) && max_iter > 0 {
@@ -486,7 +486,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
         self.recalculate_minmax_grad = false
     }
 
-    fn clean(&mut self, cache: &mut Cache<T, M, K>) {
+    fn clean(&mut self, cache: &mut Cache<'_, T, M, K>) {
         self.find_min_max_gradient();
 
         let gmax = self.gmax;
@@ -520,7 +520,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
         &mut self,
         idx_1: Option<usize>,
         idx_2: Option<usize>,
-        cache: &mut Cache<T, M, K>,
+        cache: &mut Cache<'_, T, M, K>,
     ) -> Option<(usize, usize, T)> {
         match (idx_1, idx_2) {
             (None, None) => {
@@ -614,7 +614,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
         idx_1: Option<usize>,
         idx_2: Option<usize>,
         tol: T,
-        cache: &mut Cache<T, M, K>,
+        cache: &mut Cache<'_, T, M, K>,
     ) -> bool {
         match self.select_pair(idx_1, idx_2, cache) {
             Some((idx_1, idx_2, k_v_12)) => {
@@ -653,7 +653,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
         }
     }
 
-    fn update(&mut self, v1: usize, v2: usize, step: T, cache: &mut Cache<T, M, K>) {
+    fn update(&mut self, v1: usize, v2: usize, step: T, cache: &mut Cache<'_, T, M, K>) {
         self.sv[v1].alpha -= step;
         self.sv[v2].alpha += step;
 

src/svm/svr.rs

@@ -469,7 +469,7 @@ impl<T: Clone> Cache<T> {
         }
     }
 
-    fn get<F: Fn() -> Vec<T>>(&self, i: usize, or: F) -> Ref<Vec<T>> {
+    fn get<F: Fn() -> Vec<T>>(&self, i: usize, or: F) -> Ref<'_, Vec<T>> {
         if self.data[i].borrow().is_none() {
             self.data[i].replace(Some(or()));
         }

src/tree/decision_tree_classifier.rs

@@ -334,7 +334,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
 
         let mut visitor = NodeVisitor::<T, M>::new(0, samples, &order, &x, &yi, 1);
 
-        let mut visitor_queue: LinkedList<NodeVisitor<T, M>> = LinkedList::new();
+        let mut visitor_queue: LinkedList<NodeVisitor<'_, T, M>> = LinkedList::new();
 
         if tree.find_best_cutoff(&mut visitor, mtry) {
             visitor_queue.push_back(visitor);
@@ -392,7 +392,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
 
     fn find_best_cutoff<M: Matrix<T>>(
         &mut self,
-        visitor: &mut NodeVisitor<T, M>,
+        visitor: &mut NodeVisitor<'_, T, M>,
         mtry: usize,
     ) -> bool {
         let (n_rows, n_attr) = visitor.x.shape();
@@ -455,7 +455,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
 
     fn find_best_split<M: Matrix<T>>(
         &mut self,
-        visitor: &mut NodeVisitor<T, M>,
+        visitor: &mut NodeVisitor<'_, T, M>,
         n: usize,
         count: &Vec<usize>,
         false_count: &mut Vec<usize>,

src/tree/decision_tree_regressor.rs

@@ -240,7 +240,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
 
         let mut visitor = NodeVisitor::<T, M>::new(0, samples, &order, &x, &y_m, 1);
 
-        let mut visitor_queue: LinkedList<NodeVisitor<T, M>> = LinkedList::new();
+        let mut visitor_queue: LinkedList<NodeVisitor<'_, T, M>> = LinkedList::new();
 
         if tree.find_best_cutoff(&mut visitor, mtry) {
             visitor_queue.push_back(visitor);
@@ -298,7 +298,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
 
     fn find_best_cutoff<M: Matrix<T>>(
         &mut self,
-        visitor: &mut NodeVisitor<T, M>,
+        visitor: &mut NodeVisitor<'_, T, M>,
         mtry: usize,
     ) -> bool {
         let (_, n_attr) = visitor.x.shape();
@@ -332,7 +332,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
 
     fn find_best_split<M: Matrix<T>>(
         &mut self,
-        visitor: &mut NodeVisitor<T, M>,
+        visitor: &mut NodeVisitor<'_, T, M>,
         n: usize,
         sum: T,
         parent_gain: T,