Add Hinge Loss (TheAlgorithms#648)

Author: mobley-trent

DIRECTORY.md

@@ -144,6 +144,7 @@
     * [K Means](https://github.com/TheAlgorithms/Rust/blob/master/src/machine_learning/k_means.rs)
     * [Linear Regression](https://github.com/TheAlgorithms/Rust/blob/master/src/machine_learning/linear_regression.rs)
     * Loss Function
+      * [Hinge Loss](https://github.com/TheAlgorithms/Rust/blob/master/src/machine_learning/loss_function/hinge_loss.rs)
       * [Kl Divergence Loss](https://github.com/TheAlgorithms/Rust/blob/master/src/machine_learning/loss_function/kl_divergence_loss.rs)
       * [Mean Absolute Error Loss](https://github.com/TheAlgorithms/Rust/blob/master/src/machine_learning/loss_function/mean_absolute_error_loss.rs)
       * [Mean Squared Error Loss](https://github.com/TheAlgorithms/Rust/blob/master/src/machine_learning/loss_function/mean_squared_error_loss.rs)

src/machine_learning/loss_function/hinge_loss.rs

@@ -0,0 +1,38 @@
+//! # Hinge Loss
+//!
+//! The `hng_loss` function calculates the Hinge loss, which is a
+//! loss function used for classification problems in machine learning.
+//!
+//! ## Formula
+//!
+//! For a pair of actual and predicted values, represented as vectors `y_true` and
+//! `y_pred`, the Hinge loss is calculated as:
+//!
+//! - loss = `max(0, 1 - y_true * y_pred)`.
+//!
+//! It returns the average loss by dividing the `total_loss` by total no. of
+//! elements.
+//!
+pub fn hng_loss(y_true: &[f64], y_pred: &[f64]) -> f64 {
+    let mut total_loss: f64 = 0.0;
+    for (p, a) in y_pred.iter().zip(y_true.iter()) {
+        let loss: f64 = (1.0 - a * p).max(0.0);
+        total_loss += loss;
+    }
+    total_loss / (y_pred.len() as f64)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_hinge_loss() {
+        let predicted_values: Vec<f64> = vec![-1.0, 1.0, 1.0];
+        let actual_values: Vec<f64> = vec![-1.0, -1.0, 1.0];
+        assert_eq!(
+            hng_loss(&predicted_values, &actual_values),
+            0.6666666666666666
+        );
+    }
+}

src/machine_learning/loss_function/mod.rs

@@ -1,7 +1,9 @@
+mod hinge_loss;
 mod kl_divergence_loss;
 mod mean_absolute_error_loss;
 mod mean_squared_error_loss;
 
+pub use self::hinge_loss::hng_loss;
 pub use self::kl_divergence_loss::kld_loss;
 pub use self::mean_absolute_error_loss::mae_loss;
 pub use self::mean_squared_error_loss::mse_loss;

src/machine_learning/mod.rs

@@ -7,6 +7,7 @@ mod optimization;
 pub use self::cholesky::cholesky;
 pub use self::k_means::k_means;
 pub use self::linear_regression::linear_regression;
+pub use self::loss_function::hng_loss;
 pub use self::loss_function::kld_loss;
 pub use self::loss_function::mae_loss;
 pub use self::loss_function::mse_loss;