Fix clippy issues.

Author: ralfbiedert

Cargo.toml

@@ -14,18 +14,10 @@ exclude = [
     "docs/*",
 ]
 
-[lib]
-name = "ffsvm"
-path = "src/lib.rs"
-crate-type = ["rlib"]
-
 [dependencies]
 simd_aligned = "0.6.1"
 #simd_aligned = { path = "../simd_aligned" }
 
 [dev-dependencies]
 rand = "0.8.5"
 
-[profile.release]
-opt-level = 3
-lto = true

benches/svm_dense.rs

@@ -24,7 +24,7 @@ mod svm_dense {
             problem_mut[i as usize] = i as f32;
         }
 
-        move || (&svm).predict_value(&mut problem).expect("This should work")
+        move || svm.predict_value(&mut problem).expect("This should work")
     }
 
     // RBF

benches/svm_sparse.rs

@@ -24,7 +24,7 @@ mod svm_sparse {
             problem_mut[i as usize] = i as f32;
         }
 
-        move || (&svm).predict_value(&mut problem).expect("This should work")
+        move || svm.predict_value(&mut problem).expect("This should work")
     }
 
     // RBF

src/errors.rs

@@ -51,9 +51,9 @@ pub enum Error {
 // }
 
 impl From<ParseFloatError> for Error {
-    fn from(_e: ParseFloatError) -> Self { Error::Parsing("ParseFloatError".to_owned()) }
+    fn from(_e: ParseFloatError) -> Self { Self::Parsing("ParseFloatError".to_owned()) }
 }
 
 impl From<ParseIntError> for Error {
-    fn from(_: ParseIntError) -> Self { Error::Parsing("ParseIntError".to_owned()) }
+    fn from(_: ParseIntError) -> Self { Self::Parsing("ParseIntError".to_owned()) }
 }

src/parser.rs

@@ -127,7 +127,7 @@ impl<'a> TryFrom<&'a str> for ModelFile<'a> {
         for line in input.lines() {
             let tokens = line.split_whitespace().collect::<Vec<_>>();
 
-            match tokens.get(0) {
+            match tokens.first() {
                 // Single value headers
                 //
                 // svm_type c_svc
@@ -190,7 +190,7 @@ impl<'a> TryFrom<&'a str> for ModelFile<'a> {
                             let split = x.split(':').collect::<Vec<&str>>();
 
                             Some(Attribute {
-                                index: split.get(0)?.parse::<u32>().ok()?,
+                                index: split.first()?.parse::<u32>().ok()?,
                                 value: split.get(1)?.parse::<f32>().ok()?,
                             })
                         })

src/sparse.rs

@@ -21,11 +21,11 @@ impl<T> SparseVector<T>
 where
     T: Clone + Copy + Default,
 {
-    pub fn new() -> Self { Self { entries: Vec::new() } }
+    pub const fn new() -> Self { Self { entries: Vec::new() } }
 
     pub fn clear(&mut self) { self.entries.clear(); }
 
-    pub fn iter(&self) -> SparseVectorIter<'_, T> { SparseVectorIter { vector: self, index: 0 } }
+    pub const fn iter(&self) -> SparseVectorIter<'_, T> { SparseVectorIter { vector: self, index: 0 } }
 }
 
 /// Basic iterator struct to go over matrix
@@ -41,7 +41,7 @@ where
     pub(crate) index: usize,
 }
 
-impl<'a, T> Iterator for SparseVectorIter<'a, T>
+impl<T> Iterator for SparseVectorIter<'_, T>
 where
     T: Clone + Copy + Default,
 {
@@ -86,10 +86,7 @@ where
     fn index_mut(&mut self, index: usize) -> &mut T {
         // TODO: Beautify me
 
-        let highest_so_far: i32 = match self.entries.last() {
-            None => -1,
-            Some(x) => x.index as i32,
-        };
+        let highest_so_far: i32 = self.entries.last().map_or(-1, |x| x.index as i32);
 
         if index as i32 <= highest_so_far {
             unimplemented!("We still need to implement unsorted insertion. As of today, you need to insert element in strictly ascending order.");
@@ -126,7 +123,7 @@ where
     pub fn row(&self, row: usize) -> &SparseVector<T> { &self.vectors[row] }
 
     #[inline]
-    pub fn row_iter(&self) -> SparseMatrixIter<'_, T> { SparseMatrixIter { matrix: self, index: 0 } }
+    pub const fn row_iter(&self) -> SparseMatrixIter<'_, T> { SparseMatrixIter { matrix: self, index: 0 } }
 }
 
 impl<T> Index<(usize, usize)> for SparseMatrix<T>

src/svm/class.rs

@@ -7,7 +7,7 @@ use simd_aligned::{
 /// Represents one class of the SVM model.
 #[derive(Clone, Debug)]
 #[doc(hidden)]
-pub(crate) struct Class<M32> {
+pub struct Class<M32> {
     /// The label of this class
     pub(crate) label: i32,
 

src/svm/core/dense.rs

@@ -60,7 +60,7 @@ impl DenseSVM {
     ///
     /// If the label was found its index returned in the [`Option`], otherwise `None`
     /// is returned.
-    pub fn class_index_for_label(&self, label: i32) -> Option<usize> {
+    #[must_use] pub fn class_index_for_label(&self, label: i32) -> Option<usize> {
         for (i, class) in self.classes.iter().enumerate() {
             if class.label != label {
                 continue;
@@ -84,7 +84,7 @@ impl DenseSVM {
     ///
     /// If the index was found it is returned in the [`Option`], otherwise `None`
     /// is returned.
-    pub fn class_label_for_index(&self, index: usize) -> Option<i32> {
+    #[must_use] pub fn class_label_for_index(&self, index: usize) -> Option<i32> {
         if index >= self.classes.len() {
             None
         } else {
@@ -132,10 +132,10 @@ impl DenseSVM {
     }
 
     /// Returns number of attributes, reflecting the libSVM model.
-    pub const fn attributes(&self) -> usize { self.num_attributes }
+    #[must_use] pub const fn attributes(&self) -> usize { self.num_attributes }
 
     /// Returns number of classes, reflecting the libSVM model.
-    pub fn classes(&self) -> usize { self.classes.len() }
+    #[must_use] pub fn classes(&self) -> usize { self.classes.len() }
 }
 
 impl Predict<VecSimd<f32x8>> for DenseSVM {
@@ -173,7 +173,7 @@ impl<'a> TryFrom<&'a str> for DenseSVM {
     }
 }
 
-impl<'a, 'b> TryFrom<&'a ModelFile<'b>> for DenseSVM {
+impl<'a> TryFrom<&'a ModelFile<'_>> for DenseSVM {
     type Error = Error;
 
     fn try_from(raw_model: &'a ModelFile<'_>) -> Result<Self, Error> {

src/svm/core/mod.rs

@@ -154,10 +154,10 @@ macro_rules! compute_multiclass_probabilities_impl {
                 let diff = (-qp[t] + pqp) / q[(t, t)];
 
                 probabilities[t] += diff;
-                pqp = (pqp + diff * (diff * q[(t, t)] + 2.0 * qp[t])) / (1.0 + diff) / (1.0 + diff);
+                pqp = diff.mul_add(diff.mul_add(q[(t, t)], 2.0 * qp[t]), pqp) / (1.0 + diff) / (1.0 + diff);
 
                 for j in 0 .. num_classes {
-                    qp[j] = (qp[j] + diff * q[(t, j)]) / (1.0 + diff);
+                    qp[j] = diff.mul_add(q[(t, j)], qp[j]) / (1.0 + diff);
                     probabilities[j] /= 1.0 + diff;
                 }
             }

src/svm/core/sparse.rs

@@ -171,7 +171,7 @@ impl Predict<SparseVector<f32>> for SparseSVM {
     fn predict_probability(&self, problem: &mut FeatureVector<SparseVector<f32>>) -> Result<(), Error> { predict_probability_impl!(self, problem) }
 }
 
-impl<'a, 'b> TryFrom<&'a str> for SparseSVM {
+impl<'a> TryFrom<&'a str> for SparseSVM {
     type Error = Error;
 
     fn try_from(input: &'a str) -> Result<Self, Error> {