machine_learning.ridge_regression
=================================

.. py:module:: machine_learning.ridge_regression


Attributes
----------

.. autoapisummary::

   machine_learning.ridge_regression.data


Classes
-------

.. autoapisummary::

   machine_learning.ridge_regression.RidgeRegression


Module Contents
---------------

.. py:class:: RidgeRegression(alpha: float = 0.001, lambda_: float = 0.1, iterations: int = 1000)

   .. py:method:: compute_cost(features: numpy.ndarray, target: numpy.ndarray) -> float

      Compute the cost function with regularization.

      :param features: Input features, shape (m, n)
      :param target: Target values, shape (m,)
      :return: Computed cost

      Example:
      >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10)
      >>> features = np.array([[1, 2], [2, 3], [4, 6]])
      >>> target = np.array([1, 2, 3])
      >>> rr.fit(features, target)
      >>> cost = rr.compute_cost(features, target)
      >>> isinstance(cost, float)
      True



   .. py:method:: feature_scaling(features: numpy.ndarray) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

      Normalize features to have mean 0 and standard deviation 1.

      :param features: Input features, shape (m, n)
      :return: Tuple containing:
          - Scaled features
          - Mean of each feature
          - Standard deviation of each feature

      Example:
      >>> rr = RidgeRegression()
      >>> features = np.array([[1, 2], [2, 3], [4, 6]])
      >>> scaled_features, mean, std = rr.feature_scaling(features)
      >>> np.allclose(scaled_features.mean(axis=0), 0)
      True
      >>> np.allclose(scaled_features.std(axis=0), 1)
      True



   .. py:method:: fit(features: numpy.ndarray, target: numpy.ndarray) -> None

      Fit the Ridge Regression model to the training data.

      :param features: Input features, shape (m, n)
      :param target: Target values, shape (m,)

      Example:
      >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10)
      >>> features = np.array([[1, 2], [2, 3], [4, 6]])
      >>> target = np.array([1, 2, 3])
      >>> rr.fit(features, target)
      >>> rr.theta is not None
      True



   .. py:method:: mean_absolute_error(y_true: numpy.ndarray, y_pred: numpy.ndarray) -> float

      Compute Mean Absolute Error (MAE) between true and predicted values.

      :param y_true: Actual target values, shape (m,)
      :param y_pred: Predicted target values, shape (m,)
      :return: MAE

      Example:
      >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10)
      >>> y_true = np.array([1, 2, 3])
      >>> y_pred = np.array([1.1, 2.1, 2.9])
      >>> mae = rr.mean_absolute_error(y_true, y_pred)
      >>> isinstance(mae, float)
      True



   .. py:method:: predict(features: numpy.ndarray) -> numpy.ndarray

      Predict values using the trained model.

      :param features: Input features, shape (m, n)
      :return: Predicted values, shape (m,)

      Example:
      >>> rr = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=10)
      >>> features = np.array([[1, 2], [2, 3], [4, 6]])
      >>> target = np.array([1, 2, 3])
      >>> rr.fit(features, target)
      >>> predictions = rr.predict(features)
      >>> predictions.shape == target.shape
      True



   .. py:attribute:: alpha
      :value: 0.001



   .. py:attribute:: iterations
      :value: 1000



   .. py:attribute:: lambda_
      :value: 0.1



   .. py:attribute:: theta
      :type:  numpy.ndarray | None
      :value: None



.. py:data:: data
   :value: None