clippy lints

Author: montanalow

algorithms/linfa-ftrl/Cargo.toml

@@ -24,7 +24,7 @@ version = "1.0"
 features = ["derive"]
 
 [dependencies]
-ndarray = { version = "0.15.4", features = ["serde"] }
+ndarray = { version = "0.15", features = ["serde"] }
 ndarray-rand = "0.14.0"
 argmin = { version = "0.9.0", default-features = false }
 argmin-math = { version = "0.3", features = ["ndarray_v0_15-nolinalg"] }

src/correlation.rs

@@ -128,7 +128,7 @@ impl<F: Float> PearsonCorrelation<F> {
     ///
     /// * `dataset`: Data for the correlation analysis
     /// * `num_iter`: optionally number of iterations of the p-value test, if none then no p-value
-    /// are calculate
+    ///   are calculated
     ///
     /// # Example
     ///

src/dataset/impl_dataset.rs

@@ -680,8 +680,8 @@ where
     /// - `k`: the number of folds to apply to the dataset
     /// - `params`: the desired parameters for the fittable algorithm at hand
     /// - `fit_closure`: a closure of the type `(params, training_data) -> fitted_model`
-    /// that will be used to produce the trained model for each fold. The training data given in input
-    /// won't outlive the closure.
+    ///   that will be used to produce the trained model for each fold. The training data given in
+    ///   input won't outlive the closure.
     ///
     /// ## Returns
     ///
@@ -794,9 +794,9 @@ where
     /// - `k`: the number of folds to apply
     /// - `parameters`: a list of models to compare
     /// - `eval`: closure used to evaluate the performance of each trained model. This closure is
-    /// called on the model output and validation targets of each fold and outputs the performance
-    /// score for each target. For single-target dataset the signature is `(Array1, Array1) ->
-    /// Array0`. For multi-target dataset the signature is `(Array2, Array2) -> Array1`.
+    ///   called on the model output and validation targets of each fold and outputs the performance
+    ///   score for each target. For single-target dataset the signature is `(Array1, Array1) ->
+    ///   Array0`. For multi-target dataset the signature is `(Array2, Array2) -> Array1`.
     ///
     /// ### Returns
     ///

src/dataset/mod.rs

@@ -161,7 +161,7 @@ impl Deref for Pr {
 /// # Fields
 ///
 /// * `records`: a two-dimensional matrix with dimensionality (nsamples, nfeatures), in case of
-/// kernel methods a quadratic matrix with dimensionality (nsamples, nsamples), which may be sparse
+///   kernel methods a quadratic matrix with dimensionality (nsamples, nsamples), which may be sparse
 /// * `targets`: a two-/one-dimension matrix with dimensionality (nsamples, ntargets)
 /// * `weights`: optional weights for each sample with dimensionality (nsamples)
 /// * `feature_names`: optional descriptive feature names with dimensionality (nfeatures)
@@ -170,7 +170,7 @@ impl Deref for Pr {
 ///
 /// * `R: Records`: generic over feature matrices or kernel matrices
 /// * `T`: generic over any `ndarray` matrix which can be used as targets. The `AsTargets` trait
-/// bound is omitted here to avoid some repetition in implementation `src/dataset/impl_dataset.rs`
+///   bound is omitted here to avoid some repetition in implementation `src/dataset/impl_dataset.rs`
 #[derive(Debug, Clone, PartialEq)]
 pub struct DatasetBase<R, T>
 where