Created folder for losses in Machine_Learning (#9969)

* Created folder for losses in Machine_Learning * Update binary_cross_entropy.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update mean_squared_error.py * Update binary_cross_entropy.py * Update mean_squared_error.py * Update binary_cross_entropy.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update mean_squared_error.py * Update binary_cross_entropy.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update mean_squared_error.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update binary_cross_entropy.py * Update mean_squared_error.py * Update binary_cross_entropy.py * Update mean_squared_error.py * Update machine_learning/losses/binary_cross_entropy.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update machine_learning/losses/mean_squared_error.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update machine_learning/losses/binary_cross_entropy.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update mean_squared_error.py * Update machine_learning/losses/mean_squared_error.py Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> * Update binary_cross_entropy.py * Update mean_squared_error.py * Update binary_cross_entropy.py * Update mean_squared_error.py * Update mean_squared_error.py * Update binary_cross_entropy.py * renamed: losses -> loss_functions * updated 2 files * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update mean_squared_error.py * Update mean_squared_error.py * Update binary_cross_entropy.py * Update mean_squared_error.py --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
2025-07-20 19:42:45 +08:00 · 2023-10-08 21:34:43 +05:30
parent 6860daea60
commit 81b29066d2
2 changed files with 110 additions and 0 deletions
--- a/machine_learning/loss_functions/mean_squared_error.py
+++ b/machine_learning/loss_functions/mean_squared_error.py
@ -0,0 +1,51 @@
+"""
+Mean Squared Error (MSE) Loss Function
+
+Description:
+MSE measures the mean squared difference between true values and predicted values.
+It serves as a measure of the model's accuracy in regression tasks.
+
+Formula:
+MSE = (1/n) * Σ(y_true - y_pred)^2
+
+Source:
+[Wikipedia - Mean squared error](https://en.wikipedia.org/wiki/Mean_squared_error)
+"""
+
+import numpy as np
+
+
+def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """
+    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])
+    >>> mean_squared_error(true_values, predicted_values)
+    0.028000000000000032
+    >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
+    >>> mean_squared_error(true_labels, predicted_probs)
+    Traceback (most recent call last):
+        ...
+    ValueError: Input arrays must have the same length.
+    """
+    if len(y_true) != len(y_pred):
+        raise ValueError("Input arrays must have the same length.")
+
+    squared_errors = (y_true - y_pred) ** 2
+    return np.mean(squared_errors)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()