- some clippy fixes

src/metric.rs

@@ -442,7 +442,7 @@ where
 
             self.metric.replace(IntMut {
                 mu: mu.to_vec(),
-                metric: metric,
+                metric,
                 derivative: dmetric.clone(),
             });
             // self.metric.replace(IntMut {

src/optimize.rs

@@ -379,6 +379,20 @@ mod tests {
 
     #[test]
     fn test_lbfgs_quadratic_with_minimum() {
+        struct QuadraticWithMinimum;
+
+        impl ObjectiveFunction<f64> for QuadraticWithMinimum {
+            fn evaluate(&self, point: &[f64]) -> f64 {
+                let x = point[0];
+                (x - 2.0).powi(2)
+            }
+
+            fn gradient(&self, point: &[f64]) -> Option<Vec<f64>> {
+                let x = point[0];
+                Some(vec![2.0 * (x - 2.0)])
+            }
+        }
+
         let f = QuadraticWithMinimum;
         let initial_point = vec![0.0];
         let config = OptimizationConfig {
@@ -412,17 +426,3 @@ mod tests {
         }
     }
 }
-
-struct QuadraticWithMinimum;
-
-impl ObjectiveFunction<f64> for QuadraticWithMinimum {
-    fn evaluate(&self, point: &[f64]) -> f64 {
-        let x = point[0];
-        (x - 2.0).powi(2)
-    }
-
-    fn gradient(&self, point: &[f64]) -> Option<Vec<f64>> {
-        let x = point[0];
-        Some(vec![2.0 * (x - 2.0)])
-    }
-}

src/register.rs

@@ -10,18 +10,15 @@ use std::marker::PhantomData;
 
 /// Optimizer type for registration
 #[derive(Clone, Debug, PartialEq)]
+#[derive(Default)]
 pub enum Optimizer {
     /// L-BFGS optimizer (fast, requires consistent gradients)
+    #[default]
     LBFGS,
     /// Adaptive Stochastic Gradient Descent (robust, handles noisy gradients)
     ASGD,
 }
 
-impl Default for Optimizer {
-    fn default() -> Self {
-        Self::LBFGS
-    }
-}
 
 #[derive(Clone, Debug)]
 pub struct RegistrationStep {
@@ -358,12 +355,11 @@ impl<D: Dimension> Registration<D> {
                 &scales,
             );
 
-            if let Some(result) = optimization_result {
-                if result.optimal_point.iter().all(|i| i.is_finite()) {
+            if let Some(result) = optimization_result
+                && result.optimal_point.iter().all(|i| i.is_finite()) {
                     p = self.fixed_mu.combine(&result.optimal_point);
                 }
         }
-        }
         Ok(Transform::<D>::new(p, fixed.shape().to_vec()))
     }