gugugaga

logic/main.py

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+import numpy as np
+
+
+# Logistic (sigmoid) function
+def sigmoid(z):
+    """
+    Compute the sigmoid of z.
+    """
+    return 1 / (1 + np.exp(-z))
+
+
+# Logistic regression hypothesis
+def hypothesis(X, theta):
+    """
+    Compute the hypothesis for logistic regression.
+    """
+    return sigmoid(np.dot(X, theta))
+
+
+# Cost function for logistic regression (binary cross-entropy)
+def compute_cost(X, y, theta):
+    """
+    Compute the cost for logistic regression.
+    """
+    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
+
+
+# Gradient computation for logistic regression
+def compute_gradients(X, y, theta):
+    """
+    Compute the gradients for logistic regression.
+    """
+    m = len(y)
+    h = hypothesis(X, theta)
+    gradients = (1 / m) * np.dot(X.T, (h - y))
+    return gradients
+
+
+# Gradient descent for logistic regression
+def gradient_descent(X, y, theta, learning_rate=0.01, iterations=1000, verbose=True):
+    """
+    Perform gradient descent to optimize theta for logistic regression.
+    """
+    cost_history = []
+    for i in range(iterations):
+        gradients = compute_gradients(X, y, theta)
+        theta -= learning_rate * gradients
+        cost = compute_cost(X, y, theta)
+        cost_history.append(cost)
+        if verbose and (i % 10 == 0 or i == iterations - 1):
+            print(
+                f"Iteration {i+1:4d}: Cost = {cost:.6f}, Gradients = {gradients.ravel()}, Theta = {theta.ravel()}"
+            )
+    return theta, cost_history
+
+
+# Main function for running logistic regression on custom data
+def main(data, learning_rate=0.01, iterations=100, 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
+    verbose: bool, print progress
+    Returns: optimized theta
+    """
+    # Prepare data
+    data = np.array(data)
+    X = data[:, :2].astype(float)  # Features
+    y = data[:, 2].astype(float).reshape(-1, 1)  # Labels
+    m = X.shape[0]
+    # Add bias term (column of 1s)
+    X_bias = np.c_[np.ones((m, 1)), X]
+    # Initialize theta
+    theta = np.zeros((X_bias.shape[1], 1))
+    print("Initial theta:", theta.ravel())
+    print("Input X (with bias):\n", X_bias)
+    print("Input y:\n", y.ravel())
+    # Run gradient descent
+    theta, cost_history = gradient_descent(
+        X_bias, y, theta, learning_rate, iterations, verbose
+    )
+    print("\nFinal theta:", theta.ravel())
+    return theta
+
+
+# Example usage with your data
+if __name__ == "__main__":
+    # Each row: [t1, t2, y]
+    data = [
+        [172, 68, 1],
+        [163, 52, 0],
+        [158, 50, 0],
+        [180, 75, 1],
+    ]
+    main(data, learning_rate=1, iterations=1, verbose=True)