Don't let Network consume training nor test data

examples/example_sine.rs

@@ -66,10 +66,10 @@ fn main() -> Result<(), Box<dyn Error>> {
     )));
 
     // train network on training data
-    network.fit(x_train, y_train, 100, 0.05, true);
+    network.fit(&x_train, &y_train, 100, 0.05, true);
 
     // predict test dataset
-    let y_test_pred = network.predict(x_test.clone());
+    let y_test_pred = network.predict(&x_test);
 
     // create the chart
     let buf = BitMapBackend::new("./examples/sine.png", (800, 600)).into_drawing_area();

examples/example_xor.rs

@@ -45,10 +45,10 @@ fn main() {
     )));
 
     // train network on training data
-    network.fit(x_train, y_train, 1000, 0.1, false);
+    network.fit(&x_train, &y_train, 1000, 0.1, false);
 
     // print predictions
-    let y_test = network.predict(x_test.clone());
+    let y_test = network.predict(&x_test);
     println!("{}", x_test.get(0).unwrap());
     for i in 0..y_test.len() {
         print!("input: {}\t\t", x_test.get(i).unwrap());

src/lib.rs

@@ -25,7 +25,7 @@ impl Network {
         self.layers.push(layer);
     }
 
-    pub fn predict(&mut self, inputs: Vec<Array1<f64>>) -> Vec<Array1<f64>> {
+    pub fn predict(&mut self, inputs: &[Array1<f64>]) -> Vec<Array1<f64>> {
         assert!(!inputs.is_empty());
         let mut result = vec![];
 
@@ -35,7 +35,7 @@ impl Network {
             for layer in &mut self.layers {
                 output = layer.forward_pass(output.view());
             }
-            result.push(output.to_owned());
+            result.push(output);
         }
 
         result
@@ -43,8 +43,8 @@ impl Network {
 
     pub fn fit(
         &mut self,
-        x_train: Vec<Array1<f64>>,
+        x_train: &[Array1<f64>],
-        y_train: Vec<Array1<f64>>,
+        y_train: &[Array1<f64>],
         epochs: usize,
         learning_rate: f64,
         trivial_optimize: bool,