"""Polynomial Regression implementation from scratch.

This module contains the PolynomialRegression class that implements
polynomial regression by extending linear regression with polynomial features.
"""


class PolynomialRegression:
    """Polynomial Regression implementation from scratch.

    This class implements polynomial regression by transforming features
    into polynomial features and then applying linear regression.

    Attributes:
        degree (int): Degree of polynomial features
        linear_model (LinearRegression): Underlying linear regression model

    Example:
        >>> from linear_regression.models.polynomial_regression import PolynomialRegression
        >>> import numpy as np
        >>>
        >>> # Create sample data
        >>> X = np.array([[1], [2], [3], [4], [5]])
        >>> y = np.array([1, 4, 9, 16, 25])  # y = x^2
        >>>
        >>> # Create and train model
        >>> model = PolynomialRegression(degree=2)
        >>> model.fit(X, y)
        >>>
        >>> # Make predictions
        >>> predictions = model.predict(X)
    """

    def __init__(self, degree=2, learning_rate=0.01, n_iterations=1000, fit_intercept=True):  # pragma: no cover
        """Initialize PolynomialRegression model.

        Args:
            degree (int): Degree of polynomial features
            learning_rate (float): Learning rate for gradient descent
            n_iterations (int): Number of iterations for gradient descent
            fit_intercept (bool): Whether to fit intercept term
        """
        # TODO: Implement initialization
        pass

    def fit(self, X, y, method="gradient_descent"):  # pragma: no cover
        """Fit the polynomial regression model to training data.

        Args:
            X (np.ndarray): Training features of shape (n_samples, n_features)
            y (np.ndarray): Training targets of shape (n_samples,)
            method (str): Method to use ('gradient_descent' or 'normal_equation')

        Returns:
            self: Returns self for method chaining
        """
        # TODO: Implement fitting logic with polynomial features
        pass

    def predict(self, X):  # pragma: no cover
        """Make predictions using the trained model.

        Args:
            X (np.ndarray): Features to predict on of shape (n_samples, n_features)

        Returns:
            np.ndarray: Predictions of shape (n_samples,)
        """
        # TODO: Implement prediction logic
        pass

    def score(self, X, y):  # pragma: no cover
        """Calculate R² score (coefficient of determination).

        Args:
            X (np.ndarray): Features of shape (n_samples, n_features)
            y (np.ndarray): True targets of shape (n_samples,)

        Returns:
            float: R² score
        """
        # TODO: Implement R² calculation
        pass

    def _create_polynomial_features(self, X):  # pragma: no cover
        """Transform features into polynomial features.

        Args:
            X (np.ndarray): Input features of shape (n_samples, n_features)

        Returns:
            np.ndarray: Polynomial features of shape (n_samples, n_poly_features)
        """
        # TODO: Implement polynomial feature transformation
        pass
