Created folder for losses in Machine_Learning

machine_learning/losses/binary_cross_entropy.py

@@ -0,0 +1,31 @@
+import numpy as np
+
+
+def binary_cross_entropy(y_true, y_pred, epsilon=1e-15):
+    """
+    Calculate the BCE Loss between true labels and predicted probabilities.
+
+    Parameters:
+    - y_true: True binary labels (0 or 1).
+    - y_pred: Predicted probabilities for class 1.
+    - epsilon: Small constant to avoid numerical instability.
+
+    Returns:
+    - bce_loss: Binary Cross-Entropy Loss.
+
+    Example Usage:
+    true_labels = np.array([0, 1, 1, 0, 1])
+    predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
+    bce_loss = binary_cross_entropy(true_labels, predicted_probs)
+    print(f"Binary Cross-Entropy Loss: {bce_loss}")
+    """
+    # Clip predicted probabilities to avoid log(0) and log(1)
+    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)
+
+    # Calculate binary cross-entropy loss
+    bce_loss = -(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))
+
+    # Take the mean over all samples
+    bce_loss = np.mean(bce_loss)
+
+    return bce_loss

machine_learning/losses/mean_squared_error.py

@@ -0,0 +1,27 @@
+import numpy as np
+
+
+def mean_squared_error(y_true, y_pred):
+    """
+    Calculate the Mean Squared Error (MSE) between two arrays.
+
+    Parameters:
+    - y_true: The true values (ground truth).
+    - y_pred: The predicted values.
+
+    Returns:
+    - mse: The Mean Squared Error between y_true and y_pred.
+
+    Example usage:
+    true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+    predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
+    mse = mean_squared_error(true_values, predicted_values)
+    print(f"Mean Squared Error: {mse}")
+    """
+    if len(y_true) != len(y_pred):
+        raise ValueError("Input arrays must have the same length.")
+
+    squared_errors = np.square(np.subtract(y_true, y_pred))
+    mse = np.mean(squared_errors)
+
+    return mse