gagagaga

logic/main.py

@@ -17,40 +17,48 @@ def hypothesis(X, theta):
     return sigmoid(np.dot(X, theta))
 
 
-# Cost function for logistic regression (binary cross-entropy)
-def compute_cost(X, y, theta):
+# Cost function for logistic regression (binary cross-entropy) with L2 regularization
+def compute_cost(X, y, theta, lambda_=0.0):
     """
-    Compute the cost for logistic regression.
+    Compute the cost for logistic regression with L2 regularization.
     """
     m = len(y)
     h = hypothesis(X, theta)
     # Avoid log(0) by clipping values
     h = np.clip(h, 1e-10, 1 - 1e-10)
     cost = -(1 / m) * np.sum(y * np.log(h) + (1 - y) * np.log(1 - h))
-    return cost
+    # L2 regularization (exclude bias term theta[0])
+    reg_term = (lambda_ / (2 * m)) * np.sum(theta[1:] ** 2)
+    return cost + reg_term
 
 
-# Gradient computation for logistic regression
-def compute_gradients(X, y, theta):
+# Gradient computation for logistic regression with L2 regularization
+def compute_gradients(X, y, theta, lambda_=0.0):
     """
-    Compute the gradients for logistic regression.
+    Compute the gradients for logistic regression with L2 regularization.
     """
     m = len(y)
     h = hypothesis(X, theta)
     gradients = (1 / m) * np.dot(X.T, (h - y))
+    # L2 regularization (exclude bias term)
+    reg = (lambda_ / m) * theta
+    reg[0] = 0  # Do not regularize bias
+    gradients += reg
     return gradients
 
 
-# Gradient descent for logistic regression
-def gradient_descent(X, y, theta, learning_rate=0.01, iterations=1000, verbose=True):
+# Gradient descent for logistic regression with L2 regularization
+def gradient_descent(
+    X, y, theta, learning_rate=0.01, iterations=1000, lambda_=0.0, verbose=True
+):
     """
-    Perform gradient descent to optimize theta for logistic regression.
+    Perform gradient descent to optimize theta for logistic regression with L2 regularization.
     """
     cost_history = []
     for i in range(iterations):
-        gradients = compute_gradients(X, y, theta)
+        gradients = compute_gradients(X, y, theta, lambda_)
         theta -= learning_rate * gradients
-        cost = compute_cost(X, y, theta)
+        cost = compute_cost(X, y, theta, lambda_)
         cost_history.append(cost)
         if verbose and (i % 10 == 0 or i == iterations - 1):
             print(
@@ -60,12 +68,13 @@ def gradient_descent(X, y, theta, learning_rate=0.01, iterations=1000, verbose=T
 
 
 # Main function for running logistic regression on custom data
-def main(data, learning_rate=0.01, iterations=100, verbose=True):
+def main(data, learning_rate=0.01, iterations=100, lambda_=0.0, verbose=True):
     """
     Run logistic regression on the provided data.
     data: list of tuples/lists, each [feature1, feature2, label]
     learning_rate: float, step size for gradient descent
     iterations: int, number of iterations
+    lambda_: float, regularization strength
     verbose: bool, print progress
     Returns: optimized theta
     """
@@ -83,7 +92,7 @@ def main(data, learning_rate=0.01, iterations=100, verbose=True):
     print("Input y:\n", y.ravel())
     # Run gradient descent
     theta, cost_history = gradient_descent(
-        X_bias, y, theta, learning_rate, iterations, verbose
+        X_bias, y, theta, learning_rate, iterations, lambda_, verbose
     )
     print("\nFinal theta:", theta.ravel())
     return theta
@@ -98,4 +107,4 @@ if __name__ == "__main__":
         [158, 50, 0],
         [180, 75, 1],
     ]
-    main(data, learning_rate=1, iterations=1, verbose=True)
+    main(data, learning_rate=1, iterations=1, lambda_=0.1, verbose=True)