fixed clippy warnings

src/dataloader.rs

@@ -1,6 +1,4 @@
 use std::iter::zip;
-use nalgebra::DMatrix;
-
 use rand::prelude::*;
 use serde::Deserialize;
 
@@ -33,7 +31,6 @@ pub struct DataLine<X, Y> {
     pub label: Y,
 }
 
-pub struct Data<X, Y>(pub Vec<DataLine<X, Y>>);
 
 pub struct OneHotVector{
     pub val: usize
@@ -57,6 +54,8 @@ impl OneHotVector{
 
 }
 
+pub struct Data<X, Y>(pub Vec<DataLine<X, Y>>);
+
 impl<X, Y> Data<X, Y> {
     pub fn shuffle(&mut self) {
         let mut rng = thread_rng();
@@ -67,6 +66,10 @@ impl<X, Y> Data<X, Y> {
         self.0.len()
     }
 
+    pub fn is_empty(&self, ) -> bool{
+        self.0.is_empty()
+    }
+
     pub fn as_batches(&self, batch_size: usize) -> Vec<&[DataLine<X, Y>]> {
         let mut batches = Vec::with_capacity(self.0.len() / batch_size + 1);
         let mut offset = 0;

src/mat.rs

@@ -3,6 +3,10 @@ use std::fmt::Debug;
 use std::ops::AddAssign;
 use nalgebra::DMatrix;
 
+/// matrix add with broadcasting
+/// like the numpy add operation
+/// not sure I even need it anymore, after fixing inconsistencies with the matrix shapes
+/// TODO see if it's still needed, or that standard matrix addition suffices
 pub fn add<T>(v1: DMatrix<T>, v2: DMatrix<T>) -> Result<DMatrix<T>, String>
     where T: PartialEq + Copy + Clone + Debug + Add + Add<Output=T> + AddAssign + 'static
 {

src/net.rs

@@ -6,7 +6,6 @@ use rand::prelude::*;
 use rand_distr::Normal;
 
 use crate::dataloader::{Data, DataLine, OneHotVector};
-use crate::mat;
 use crate::mat::add;
 
 #[derive(Debug)]
@@ -43,7 +42,7 @@ impl Network {
             a = add(b.clone(), w * a).unwrap();
             a.apply(sigmoid_inplace);
         }
-        a.column(1).iter().map(|v| *v).collect()
+        a.column(1).iter().copied().collect()
     }
 
     pub fn sgd(&mut self, mut training_data: Data<f32, OneHotVector>, epochs: usize, minibatch_size: usize, eta: f32, test_data: &Option<Data<f32, OneHotVector>>) {
@@ -127,7 +126,7 @@ impl Network {
         }
         // backward pass
         // delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])
-        let delta: DMatrix<f32> = self.cost_derivative(&activations[activations.len() - 1], y).component_mul((&zs[zs.len() - 1].map(sigmoid_prime)));
+        let delta: DMatrix<f32> = self.cost_derivative(&activations[activations.len() - 1], y).component_mul(&zs[zs.len() - 1].map(sigmoid_prime));
         let index = nabla_b.len() - 1;
         nabla_b[index] = delta.clone();