fix: fixes suggested by Clippy

src/linear/logistic_regression.rs

@@ -52,6 +52,7 @@
 //!
 //! <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>
+use std::cmp::Ordering;
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
@@ -232,12 +233,12 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
             yi[i] = classes.iter().position(|c| yc == *c).unwrap();
         }
 
-        if k < 2 {
-            Err(Failed::fit(&format!(
+        match k.cmp(&2) {
+            Ordering::Less => Err(Failed::fit(&format!(
                 "incorrect number of classes: {}. Should be >= 2.",
                 k
-            )))
-        } else if k == 2 {
+            ))),
+            Ordering::Equal => {
                 let x0 = M::zeros(1, num_attributes + 1);
 
                 let objective = BinaryObjectiveFunction {
@@ -256,7 +257,8 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
                     num_attributes: num_attributes,
                     num_classes: k,
                 })
-        } else {
+            }
+            Ordering::Greater => {
                 let x0 = M::zeros(1, (num_attributes + 1) * k);
 
                 let objective = MultiClassObjectiveFunction {
@@ -279,6 +281,7 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
                 })
             }
         }
+    }
 
     /// Predict class labels for samples in `x`.
     /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
@@ -286,7 +289,6 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
         let n = x.shape().0;
         let mut result = M::zeros(1, n);
         if self.num_classes == 2 {
-            let (nrows, _) = x.shape();
             let y_hat: Vec<T> = x.matmul(&self.coefficients.transpose()).get_col_as_vec(0);
             let intercept = self.intercept.get(0, 0);
             for i in 0..n {