diff --git a/src/lib.rs b/src/lib.rs index 6e6205f..c7c99c8 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,7 +1,8 @@ #![allow( clippy::type_complexity, clippy::too_many_arguments, - clippy::many_single_char_names + clippy::many_single_char_names, + clippy::unnecessary_wraps )] #![warn(missing_docs)] #![warn(missing_doc_code_examples)] diff --git a/src/linalg/mod.rs b/src/linalg/mod.rs index 264815b..cadbc3a 100644 --- a/src/linalg/mod.rs +++ b/src/linalg/mod.rs @@ -1,3 +1,4 @@ +#![allow(clippy::wrong_self_convention)] //! # Linear Algebra and Matrix Decomposition //! //! Most machine learning algorithms in SmartCore depend on linear algebra and matrix decomposition methods from this module. @@ -265,7 +266,7 @@ pub trait BaseVector: Clone + Debug { sum += xi * xi; } mu /= div; - sum / div - mu * mu + sum / div - mu.powi(2) } /// Computes the standard deviation. fn std(&self) -> T { diff --git a/src/linalg/stats.rs b/src/linalg/stats.rs index 45a17af..5a1dd38 100644 --- a/src/linalg/stats.rs +++ b/src/linalg/stats.rs @@ -61,7 +61,7 @@ pub trait MatrixStats: BaseMatrix { sum += a * a; } mu /= div; - *x_i = sum / div - mu * mu; + *x_i = sum / div - mu.powi(2); } x diff --git a/src/linear/lasso_optimizer.rs b/src/linear/lasso_optimizer.rs index 4f5011f..c4340fc 100644 --- a/src/linear/lasso_optimizer.rs +++ b/src/linear/lasso_optimizer.rs @@ -138,7 +138,7 @@ impl> InteriorPointOptimizer { for i in 0..p { self.prb[i] = T::two() + self.d1[i]; - self.prs[i] = self.prb[i] * self.d1[i] - self.d2[i] * self.d2[i]; + self.prs[i] = self.prb[i] * self.d1[i] - self.d2[i].powi(2); } let normg = grad.norm2(); diff --git a/src/optimization/first_order/lbfgs.rs b/src/optimization/first_order/lbfgs.rs index 5dedfe6..322df03 100644 --- a/src/optimization/first_order/lbfgs.rs +++ b/src/optimization/first_order/lbfgs.rs @@ -1,3 +1,4 @@ +#![allow(clippy::suspicious_operation_groupings)] use std::default::Default; use std::fmt::Debug;