feat: add SGD optimizer for neural networks

machine_learning/neural_network/optimizers/sgd.py

@@ -0,0 +1,24 @@
+"""
+Stochastic Gradient Descent (SGD) optimizer.
+"""
+
+from typing import List
+
+
+def sgd_update(weights: List[float], grads: List[float], lr: float) -> List[float]:
+    """
+    Update weights using SGD.
+
+    Args:
+        weights (List[float]): Current weights
+        grads (List[float]): Gradients
+        lr (float): Learning rate
+
+    Returns:
+        List[float]: Updated weights
+
+    Example:
+        >>> sgd_update([0.5, -0.2], [0.1, -0.1], 0.01)
+        [0.499, -0.199]
+    """
+    return [w - lr * g for w, g in zip(weights, grads)]

machine_learning/neural_network/optimizers/test_sgd.py

@@ -0,0 +1,13 @@
+from sgd import sgd_update
+
+
+def test_sgd():
+    weights = [0.5, -0.2]
+    grads = [0.1, -0.1]
+    updated = sgd_update(weights, grads, lr=0.01)
+    assert updated == [0.499, -0.199], f"Expected [0.499, -0.199], got {updated}"
+
+
+if __name__ == "__main__":
+    test_sgd()
+    print("SGD test passed!")

test_sgd.py

@@ -0,0 +1,13 @@
+from neural_network.optimizers.sgd import sgd_update
+
+
+def test_sgd():
+    weights = [0.5, -0.2]
+    grads = [0.1, -0.1]
+    updated = sgd_update(weights, grads, lr=0.01)
+    assert updated == [0.499, -0.199], f"Expected [0.499, -0.199], got {updated}"
+
+
+if __name__ == "__main__":
+    test_sgd()
+    print("SGD test passed!")