add: cargo format

examples/perceptron_OR.rs

@@ -3,40 +3,27 @@ use rustic_ml::perceptron::Perceptron;
 
 fn main() {
     //Initialize the perceptron with a learning rate of 0.1
-    let mut perceptron = Perceptron::init()
-                                                .learning_rate(0.1)
-                                                .bias(-1.0);
+    let mut perceptron = Perceptron::init().learning_rate(0.1).bias(-1.0);
 
     // OR training data set
     // This training dataset includes all possible versions of the OR
-    let x_train: Vec<(f64, f64)> = vec![
-        (0.0, 0.0),
-        (0.0, 1.0),
-        (1.0, 0.0),
-        (1.0, 1.0),
-    ];
+    let x_train: Vec<(f64, f64)> = vec![(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0)];
 
     // Corresponding labels (binary classification: 0 or 1)
     let y_train = vec![0.0, 1.0, 1.0, 1.0];
 
-
     // Train the perceptron using the fit method
     // Alternatively use the fit() method to not log the accuracy over time
-    // Here with 10 epochs 
+    // Here with 10 epochs
     perceptron.fit_with_logging(&x_train, &y_train, 10);
-
-    // Test data 
-    let test_data = vec![
-        (0.0, 0.0),  
-        (1.0, 0.0),  
-        (0.0, 1.0),  
-        (1.0, 1.0),  
-    ];
+
+    // Test data
+    let test_data = vec![(0.0, 0.0), (1.0, 0.0), (0.0, 1.0), (1.0, 1.0)];
 
     // Calculate the accuracy for the model
     let accuracy = perceptron.calculate_accuracy(&test_data, &y_train);
 
-    // Print the accuracy 
+    // Print the accuracy
     println!("Model accuracy: {}%", accuracy);
 
     // Print model details

examples/perceptron_movie_classification.rs

@@ -2,7 +2,7 @@
 use rustic_ml::perceptron::Perceptron;
 
 fn main() {
-    // Lets imagine we have the following data: 
+    // Lets imagine we have the following data:
     //
     // ________________________________________________
     // | Movie # | Alice    | Bob       | Profitable? |
@@ -21,34 +21,22 @@ fn main() {
     // Our goal is to classify a profitable movie, based on two critics score.
     // The score goes from 1-6.
 
-
     //Initialize the perceptron with a learning rate of 0.1
-    let mut perceptron = Perceptron::init()
-                                                .learning_rate(1.0)
-                                                .bias(-1.0);
-
+    let mut perceptron = Perceptron::init().learning_rate(1.0).bias(-1.0);
 
-    // Setting up training data 
-    let x_train: Vec<(f64, f64)> = vec![
-        (1.0, 1.0),
-        (4.0, 3.0),
-        (3.0, 5.0),
-        (5.0, 6.0),
-        (2.0, 3.0),
-    ];
+    // Setting up training data
+    let x_train: Vec<(f64, f64)> = vec![(1.0, 1.0), (4.0, 3.0), (3.0, 5.0), (5.0, 6.0), (2.0, 3.0)];
     let y_train = vec![0.0, 1.0, 1.0, 1.0, 0.0];
 
-
     // Training until it learns the system.
-    // This is something we can do since we know the data is linearly separable. (Plot out the data and see for yourself). 
+    // This is something we can do since we know the data is linearly separable. (Plot out the data and see for yourself).
     // We can also use `fit_until_halt` if we don't want to log each epoch
     perceptron.fit_until_halt_with_logging(&x_train, &y_train);
-
 
     // Calculate the accuracy for the model
     let accuracy = perceptron.calculate_accuracy(&x_train, &y_train);
 
-    // Print the accuracy 
+    // Print the accuracy
     println!("Model accuracy: {}%", accuracy);
 
     // Print model details