Adds hinge loss function algorithm (#10628)

* Adds exponential moving average algorithm

* code clean up

* spell correction

* Modifies I/O types of function

* Replaces generator function

* Resolved mypy type error

* readibility of code and documentation

* Update exponential_moving_average.py

* Adds hinge loss function

* suggested doc and refactoring changes

* refactoring

---------

Co-authored-by: Christian Clauss <[email protected]>

Author: PoojanSmart

Diff for: machine_learning/loss_functions/hinge_loss.py

@@ -0,0 +1,64 @@
+"""
+Hinge Loss
+
+Description:
+Compute the Hinge loss used for training SVM (Support Vector Machine).
+
+Formula:
+loss = max(0, 1 - true * pred)
+
+Reference: https://en.wikipedia.org/wiki/Hinge_loss
+
+Author: Poojan Smart
+Email: [email protected]
+"""
+
+import numpy as np
+
+
+def hinge_loss(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """
+    Calculate the mean hinge loss for y_true and y_pred for binary classification.
+
+    Args:
+        y_true: Array of actual values (ground truth) encoded as -1 and 1.
+        y_pred: Array of predicted values.
+
+    Returns:
+        The hinge loss between y_true and y_pred.
+
+    Examples:
+    >>> y_true = np.array([-1, 1, 1, -1, 1])
+    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
+    >>> hinge_loss(y_true, pred)
+    1.52
+    >>> y_true = np.array([-1, 1, 1, -1, 1, 1])
+    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
+    >>> hinge_loss(y_true, pred)
+    Traceback (most recent call last):
+    ...
+    ValueError: Length of predicted and actual array must be same.
+    >>> y_true = np.array([-1, 1, 10, -1, 1])
+    >>> pred = np.array([-4, -0.3, 0.7, 5, 10])
+    >>> hinge_loss(y_true, pred)
+    Traceback (most recent call last):
+    ...
+    ValueError: y_true can have values -1 or 1 only.
+    """
+
+    if len(y_true) != len(y_pred):
+        raise ValueError("Length of predicted and actual array must be same.")
+
+    # Raise value error when y_true (encoded labels) have any other values
+    # than -1 and 1
+    if np.any((y_true != -1) & (y_true != 1)):
+        raise ValueError("y_true can have values -1 or 1 only.")
+
+    hinge_losses = np.maximum(0, 1.0 - (y_true * y_pred))
+    return np.mean(hinge_losses)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()