ran cargo clippy --fix -- -Wclippy::use_self

src/bin/dmapembed.rs

@@ -73,7 +73,7 @@ pub struct HnswParams {
 
 impl HnswParams {
     pub fn my_default() -> Self {
-        HnswParams {
+        Self {
             max_conn: 64,
             ef_c: 512,
             knbn: 10,
@@ -90,7 +90,7 @@ impl HnswParams {
         distance: String,
         scale_modification: f64,
     ) -> Self {
-        HnswParams {
+        Self {
             max_conn,
             ef_c,
             knbn,
@@ -109,7 +109,7 @@ pub struct QualityParams {
 
 impl Default for QualityParams {
     fn default() -> Self {
-        QualityParams {
+        Self {
             sampling_fraction: 1.0,
         }
     }

src/bin/embed.rs

@@ -64,7 +64,7 @@ pub struct HnswParams {
 
 impl HnswParams {
     pub fn my_default() -> Self {
-        HnswParams {
+        Self {
             max_conn: 64,
             ef_c: 512,
             knbn: 10,
@@ -81,7 +81,7 @@ impl HnswParams {
         distance: String,
         scale_modification: f64,
     ) -> Self {
-        HnswParams {
+        Self {
             max_conn,
             ef_c,
             knbn,
@@ -100,7 +100,7 @@ pub struct QualityParams {
 
 impl Default for QualityParams {
     fn default() -> Self {
-        QualityParams {
+        Self {
             sampling_fraction: 1.0,
         }
     }

src/diffmaps.rs

@@ -62,7 +62,7 @@ impl DiffusionParams {
     /// - optional diffusion time
     /// - optional number of neighbours used in laplacian discretisation
     pub fn new(asked_dim: usize, t_opt: Option<f32>, g_opt: Option<usize>) -> Self {
-        DiffusionParams {
+        Self {
             asked_dim,
             alfa: 1.,
             beta: 0.,
@@ -132,7 +132,7 @@ impl DiffusionParams {
 
 impl Default for DiffusionParams {
     fn default() -> Self {
-        DiffusionParams {
+        Self {
             asked_dim: 2,
             alfa: 1.,
             beta: 0.,
@@ -166,7 +166,7 @@ pub struct DiffusionMaps {
 impl DiffusionMaps {
     /// iitialization from NodeParams
     pub fn new(params: DiffusionParams) -> Self {
-        DiffusionMaps {
+        Self {
             params,
             _node_params: None,
             normed_scales: None,

src/embedder.rs

@@ -512,7 +512,7 @@ where
             Hnsw::<F, DistL2F>::new(max_nb_connection, nb_nodes, nb_layer, ef_c, DistL2F {});
         hnsw.set_keeping_pruned(true);
         // need to store arrayviews to get a sufficient lifetime to call as_slice later
-        let vectors: Vec<ArrayView1<F>> = (0..nb_nodes).map(|i| (embedding.row(i))).collect();
+        let vectors: Vec<ArrayView1<F>> = (0..nb_nodes).map(|i| embedding.row(i)).collect();
         let mut data_with_id = Vec::<(&[F], usize)>::with_capacity(nb_nodes);
         for (i, v) in vectors.iter().enumerate().take(nb_nodes) {
             data_with_id.push((v.as_slice().unwrap(), i));
@@ -1014,7 +1014,9 @@ where
         log::trace!("\n entering EntropyOptim::ce_compute_threaded");
         //
         let b: f64 = self.params.b;
-        let ce_entropy = self
+
+        //
+        self
             .edges
             .par_iter()
             .fold(
@@ -1043,9 +1045,7 @@ where
                     }
                 },
             )
-            .sum::<f64>();
-        //
-        ce_entropy
+            .sum::<f64>()
     } // end of ce_compute_threaded
 
     // TODO : pass functions corresponding to edge_weight and grad_edge_weight as arguments to test others weight function

src/embedparams.rs

@@ -111,7 +111,7 @@ impl EmbedderParams {
         let nb_grad_batch = 20;
         let grad_factor: usize = 4;
         let hierarchy_layer = 0;
-        EmbedderParams {
+        Self {
             asked_dim,
             dmap_init,
             beta,

src/fromhnsw/kgproj.rs

@@ -334,7 +334,7 @@ where
         }
         log::trace!("Projection exiting from new");
         //
-        KGraphProjection {
+        Self {
             layer,
             small_graph: upper_graph,
             proj_data,

src/fromhnsw/kgraph.rs

@@ -129,7 +129,7 @@ where
     /// allocates a graph with expected size nbnodes and nbng neighbours
     pub fn new() -> Self {
         let neighbours_init = Vec::<Vec<OutEdge<F>>>::new();
-        KGraph {
+        Self {
             max_nbng: 0,
             nbnodes: 0,
             neighbours: neighbours_init,

src/graphlaplace.rs

@@ -26,7 +26,7 @@ pub(crate) struct GraphLaplacian {
 
 impl GraphLaplacian {
     pub fn new(sym_laplacian: MatRepr<f32>, degrees: Array1<f32>) -> Self {
-        GraphLaplacian {
+        Self {
             sym_laplacian,
             degrees,
             svd_res: None,

src/hdbscan/kruskal.rs

@@ -28,7 +28,7 @@ where
     fn new(parent: Vec<Ix>) -> Self {
         let rank = vec![1; parent.len()];
         //
-        UnionFind { parent, rank }
+        Self { parent, rank }
     } // end of new
 
     fn find(&mut self, mut node: Ix) -> Ix {

src/hdbscan/sl.rs

@@ -60,7 +60,7 @@ pub struct Dendrogram<NodeIdx: PrimInt, F: Float> {
 
 impl<NodeIdx: PrimInt, F: Float> Dendrogram<NodeIdx, F> {
     pub fn new(nbstep: usize) -> Self {
-        Dendrogram {
+        Self {
             steps: Vec::<UnionStep<NodeIdx, F>>::with_capacity(nbstep),
         }
     }
@@ -138,7 +138,7 @@ where
         let kgraph = kgraph_from_hnsw_all(hnsw, nbng).unwrap();
         //
         let nbstep = kgraph.get_nb_nodes() - nbcluster;
-        SLclustering {
+        Self {
             kgraph,
             dendrogram: Dendrogram::<NodeIdx, F>::new(nbstep),
             nbcluster,

src/tools/entropy.rs

@@ -30,7 +30,7 @@ where
     F: Float + AddAssign + SubAssign + MulAssign + DivAssign + std::iter::Sum,
 {
     pub fn new(q: F, value: F) -> Self {
-        RenyiEntropy {
+        Self {
             kind: EntropyKind::Renyi,
             q,
             value,
@@ -68,7 +68,7 @@ where
             }
         }
         let np = p.iter().map(|&x| x / sum).collect();
-        DiscreteProba {
+        Self {
             p: np,
             entropy: None,
         }
@@ -111,7 +111,7 @@ where
     } // end of entropy_renyi
 
     // compute relative entropy at q=1
-    fn relative_renyi_entropy_1(&self, other: &DiscreteProba<F>) -> F {
+    fn relative_renyi_entropy_1(&self, other: &Self) -> F {
         let zero = F::zero();
         self.p
             .iter()
@@ -127,9 +127,10 @@ where
     }
 
     //
-    fn relative_renyi_entropy_q(&self, other: &DiscreteProba<F>, q: F) -> F {
+    fn relative_renyi_entropy_q(&self, other: &Self, q: F) -> F {
         let zero = F::zero();
-        let entropy = self
+
+        self
             .p
             .iter()
             .zip(other.p.iter())
@@ -140,8 +141,7 @@ where
                     zero
                 }
             })
-            .sum();
-        entropy
+            .sum()
     }
 
     /// computes mean diversity of other with respect to self.
@@ -150,7 +150,7 @@ where
     /// $$ \sum_{self.p_{i} != 0} self.p_{i} * \phi(\frac{other.p_{i}}{self.p_{i}})$$
     ///
     ///
-    pub fn relative_renyi_entropy(&self, other: &DiscreteProba<F>, q: F) -> F {
+    pub fn relative_renyi_entropy(&self, other: &Self, q: F) -> F {
         if near_to_1(q) {
             self.relative_renyi_entropy_1(other)
         } else {

src/tools/nodeparam.rs

@@ -26,23 +26,23 @@ pub struct OutEdge<F> {
 
 impl<F> OutEdge<F> {
     pub fn new(node: NodeIdx, weight: F) -> Self {
-        OutEdge { node, weight }
+        Self { node, weight }
     }
 }
 
 impl<F> PartialEq for OutEdge<F>
 where
     F: Float,
 {
-    fn eq(&self, other: &OutEdge<F>) -> bool {
+    fn eq(&self, other: &Self) -> bool {
         self.weight == other.weight
     } // end eq
 }
 
 // CAVEAT coud use the PointWithOrder<T> implementation for Ord which panic on Nan.
 /// order points by distance to self.
 impl<F: Float> PartialOrd for OutEdge<F> {
-    fn partial_cmp(&self, other: &OutEdge<F>) -> Option<Ordering> {
+    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
         self.weight.partial_cmp(&other.weight)
     } // end cmp
 } // end impl PartialOrd
@@ -53,8 +53,8 @@ where
     F: Float + FromPrimitive,
 {
     //
-    fn from(neighbour: Neighbour) -> OutEdge<F> {
-        OutEdge {
+    fn from(neighbour: Neighbour) -> Self {
+        Self {
             node: neighbour.d_id,
             weight: F::from_f32(neighbour.distance).unwrap(),
         }
@@ -76,7 +76,7 @@ pub struct NodeParam {
 
 impl NodeParam {
     pub fn new(scale: f32, edges: Vec<OutEdge<f32>>) -> Self {
-        NodeParam { scale, edges }
+        Self { scale, edges }
     }
 
     /// for a given node index return corresponding edge if it is in neighbours, None else
@@ -99,7 +99,7 @@ impl NodeParam {
 
 impl Default for NodeParam {
     fn default() -> Self {
-        NodeParam {
+        Self {
             scale: 0f32,
             edges: Vec::<OutEdge<f32>>::new(),
         }
@@ -115,7 +115,7 @@ pub struct NodeParams {
 
 impl NodeParams {
     pub fn new(params: Vec<NodeParam>, max_nbng: usize) -> Self {
-        NodeParams { params, max_nbng }
+        Self { params, max_nbng }
     }
     //
     pub fn get_node_param(&self, node: NodeIdx) -> &NodeParam {

src/tools/svdapprox.rs

@@ -70,7 +70,7 @@ where
         let mat: Array2<F> =
             ArrayBase::from_shape_fn(dims, |_| F::from_f64(stdnormal.sample(&mut rng)).unwrap());
         //
-        RandomGaussianMatrix { mat }
+        Self { mat }
     }
 } // end of impl block for RandomGaussianMatrix
 
@@ -82,7 +82,7 @@ struct RandomGaussianGenerator<F> {
 impl<F: Float + FromPrimitive> RandomGaussianGenerator<F> {
     pub fn new() -> Self {
         let rng = Xoshiro256PlusPlus::seed_from_u64(4664397);
-        RandomGaussianGenerator::<F> {
+        Self {
             rng,
             _ty: PhantomData,
         }
@@ -136,23 +136,23 @@ where
 {
     /// initialize a MatRepr from an Array2
     #[inline]
-    pub fn from_array2(mat: Array2<F>) -> MatRepr<F> {
-        MatRepr {
+    pub fn from_array2(mat: Array2<F>) -> Self {
+        Self {
             data: MatMode::FULL(mat),
         }
     }
 
-    pub fn from_trimat(trimat: TriMat<F>) -> MatRepr<F> {
-        MatRepr {
+    pub fn from_trimat(trimat: TriMat<F>) -> Self {
+        Self {
             data: MatMode::CSR(trimat.to_csr()),
         }
     }
 
     /// initialize a MatRepr from a CsMat
     #[inline]
-    pub fn from_csrmat(mat: CsMat<F>) -> MatRepr<F> {
+    pub fn from_csrmat(mat: CsMat<F>) -> Self {
         assert!(mat.is_csr());
-        MatRepr {
+        Self {
             data: MatMode::CSR(mat),
         }
     }
@@ -227,9 +227,9 @@ where
     /// return a transposed copy
     pub fn transpose_owned(&self) -> Self {
         match &self.data {
-            MatMode::FULL(mat) => MatRepr::<F>::from_array2(mat.t().to_owned()),
+            MatMode::FULL(mat) => Self::from_array2(mat.t().to_owned()),
             // in CSR mode we must reconvert to csr beccause the transposed view is csc
-            MatMode::CSR(csmat) => MatRepr::<F>::from_csrmat(csmat.transpose_view().to_csr()),
+            MatMode::CSR(csmat) => Self::from_csrmat(csmat.transpose_view().to_csr()),
         }
     } // end of transpose_owned
 
@@ -307,7 +307,7 @@ impl RangePrecision {
         } else {
             step_arg
         };
-        RangePrecision {
+        Self {
             epsil,
             step,
             max_rank,
@@ -329,7 +329,7 @@ pub struct RangeRank {
 impl RangeRank {
     /// initializes a RangeRank structure with asked rank and maximum QR decompositions
     pub fn new(rank: usize, nbiter: usize) -> Self {
-        RangeRank { rank, nbiter }
+        Self { rank, nbiter }
     }
 } // end of RangeRank
 

src/utils/mnistio.rs

@@ -21,7 +21,7 @@ pub struct MnistData {
 }
 
 impl MnistData {
-    pub fn new(image_filename: String, label_filename: String) -> std::io::Result<MnistData> {
+    pub fn new(image_filename: String, label_filename: String) -> std::io::Result<Self> {
         let image_path = PathBuf::from(image_filename.clone());
         let image_file = OpenOptions::new().read(true).open(image_path)?;
         let mut image_io = BufReader::new(image_file);
@@ -31,7 +31,7 @@ impl MnistData {
         let labels_file = OpenOptions::new().read(true).open(label_path)?;
         let mut labels_io = BufReader::new(labels_file);
         let labels = read_label_file(&mut labels_io);
-        Ok(MnistData {
+        Ok(Self {
             _image_filename: image_filename,
             _label_filename: label_filename,
             images,