Add Adagrad optimizer implementation in Pure Numpy

neural_network/optimizers/adagrad.py

@@ -0,0 +1,88 @@
+"""
+Adagrad Optimizer
+
+Implements Adagrad (Adaptive Gradient) for neural network training using NumPy.
+Adagrad adapts the learning rate for each parameter based on historical gradients.
+
+Reference: https://en.wikipedia.org/wiki/Stochastic_gradient_descent#AdaGrad
+Author: Adhithya Laxman Ravi Shankar Geetha
+Date: 2025.10.22
+"""
+
+import numpy as np
+
+
+class Adagrad:
+    """
+    Adagrad optimizer.
+
+    Adapts learning rate individually for each parameter:
+        accumulated_grad += gradient^2
+        param = param - (learning_rate / sqrt(accumulated_grad + epsilon)) * gradient
+    """
+
+    def __init__(self, learning_rate: float = 0.01, epsilon: float = 1e-8) -> None:
+        """
+        Initialize Adagrad optimizer.
+
+        Args:
+            learning_rate (float): Initial learning rate.
+            epsilon (float): Small constant for numerical stability.
+
+        >>> optimizer = Adagrad(learning_rate=0.01, epsilon=1e-8)
+        >>> optimizer.learning_rate
+        0.01
+        """
+        self.learning_rate = learning_rate
+        self.epsilon = epsilon
+        self.accumulated_grad: dict[int, np.ndarray] = {}
+
+    def update(
+        self, param_id: int, params: np.ndarray, gradients: np.ndarray
+    ) -> np.ndarray:
+        """
+        Update parameters using Adagrad.
+
+        Args:
+            param_id (int): Unique identifier for parameter group.
+            params (np.ndarray): Current parameters.
+            gradients (np.ndarray): Gradients of parameters.
+
+        Returns:
+            np.ndarray: Updated parameters.
+
+        >>> optimizer = Adagrad(learning_rate=0.1)
+        >>> params = np.array([1.0, 2.0])
+        >>> grads = np.array([0.1, 0.2])
+        >>> updated = optimizer.update(0, params, grads)
+        >>> updated.shape
+        (2,)
+        """
+        if param_id not in self.accumulated_grad:
+            self.accumulated_grad[param_id] = np.zeros_like(params)
+
+        self.accumulated_grad[param_id] += gradients**2
+        adjusted_lr = self.learning_rate / (
+            np.sqrt(self.accumulated_grad[param_id]) + self.epsilon
+        )
+        return params - adjusted_lr * gradients
+
+
+# Usage example
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+
+    print("Adagrad Example: Minimizing f(x) = x^2")
+
+    optimizer = Adagrad(learning_rate=1.0)
+    x = np.array([5.0])
+
+    for step in range(20):
+        gradient = 2 * x
+        x = optimizer.update(0, x, gradient)
+        if step % 5 == 0:
+            print(f"Step {step}: x = {x[0]:.4f}, f(x) = {x[0] ** 2:.4f}")
+
+    print(f"Final: x = {x[0]:.4f}, f(x) = {x[0] ** 2:.4f}")