From 94c7f324e2071e57035b12b1aa6b778c5536b200 Mon Sep 17 00:00:00 2001
From: sanskarmodi8 <sansyprog8@gmail.com>
Date: Wed, 1 Oct 2025 11:17:40 +0530
Subject: [PATCH] added mini batch gradient descent algo in ml dir

---
 .../mini_batch_gradient_descent.py            | 65 +++++++++++++++++++
 1 file changed, 65 insertions(+)
 create mode 100644 machine_learning/mini_batch_gradient_descent.py

diff --git a/machine_learning/mini_batch_gradient_descent.py b/machine_learning/mini_batch_gradient_descent.py
new file mode 100644
index 000000000000..85de096350e4
--- /dev/null
+++ b/machine_learning/mini_batch_gradient_descent.py
@@ -0,0 +1,65 @@
+"""
+Mini-Batch Gradient Descent : https://en.wikipedia.org/wiki/Stochastic_gradient_descent
+Mini-batch gradient descent is an optimization method for training models
+by splitting the data into small batches.
+"""
+
+import numpy as np
+
+
+def mini_batch_gradient_descent(
+    X: np.ndarray, y: np.ndarray, lr: float = 0.01, batch_size: int = 16, n_epochs: int = 50
+):
+    """
+    Mini-Batch Gradient Descent for linear regression.
+
+    Parameters
+    ----------
+    X : np.ndarray
+        Feature matrix.
+    y : np.ndarray
+        Target values.
+    lr : float
+        Learning rate.
+    batch_size : int
+        Size of mini-batches.
+    n_epochs : int
+        Number of training epochs.
+
+    Returns
+    -------
+    weights : np.ndarray
+        Learned weights.
+    bias : float
+        Learned bias.
+
+    Example
+    -------
+    >>> import numpy as np
+    >>> X = np.array([[1],[2],[3],[4]])
+    >>> y = np.array([2,4,6,8])
+    >>> w, b = mini_batch_gradient_descent(X, y, lr=0.1, batch_size=2, n_epochs=100)
+    >>> round(w[0], 1)  # slope close to 2
+    2.0
+    """
+    n_samples, n_features = X.shape
+    weights = np.zeros(n_features)
+    bias = 0
+
+    for _ in range(n_epochs):
+        indices = np.random.permutation(n_samples)
+        X_shuffled, y_shuffled = X[indices], y[indices]
+        for start in range(0, n_samples, batch_size):
+            end = start + batch_size
+            X_batch, y_batch = X_shuffled[start:end], y_shuffled[start:end]
+            y_pred = np.dot(X_batch, weights) + bias
+            error = y_pred - y_batch
+            weights -= lr * (X_batch.T @ error) / len(y_batch)
+            bias -= lr * np.mean(error)
+    return weights, bias
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()