machine_learning.linear_regression_vectorized
=============================================

.. py:module:: machine_learning.linear_regression_vectorized

.. autoapi-nested-parse::

   Vectorized Linear Regression using Gradient Descent

   Author: Somrita Banerjee (mailto:somritabanerjee126@gmail.com)

   Dataset used: CSGO dataset (ADR vs Rating)

   References:
   https://en.wikipedia.org/wiki/Linear_regression



Functions
---------

.. autoapisummary::

   machine_learning.linear_regression_vectorized.collect_dataset
   machine_learning.linear_regression_vectorized.gradient_descent
   machine_learning.linear_regression_vectorized.main
   machine_learning.linear_regression_vectorized.mean_absolute_error


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

.. py:function:: collect_dataset() -> numpy.ndarray

   Collect dataset of CSGO (ADR vs Rating).

   :return: dataset as a NumPy array

   >>> ds = collect_dataset()
   >>> isinstance(ds, np.ndarray)
   True
   >>> ds.shape[1] >= 2
   True


.. py:function:: gradient_descent(features: numpy.ndarray, labels: numpy.ndarray, alpha: float = 0.000155, iterations: int = 100000) -> numpy.ndarray

   Run gradient descent in a fully vectorized form.

   :param features: dataset features
   :param labels: dataset labels
   :param alpha: learning rate
   :param iterations: number of iterations
   :return: learned feature vector theta

   >>> import numpy as np
   >>> features = np.array([[1, 1], [1, 2], [1, 3]])
   >>> labels = np.array([[1], [2], [3]])
   >>> theta = gradient_descent(
   ...     features, labels, alpha=0.01, iterations=1000  # doctest: +SKIP
   ... )



.. py:function:: main() -> None

.. py:function:: mean_absolute_error(predicted_y: numpy.ndarray, original_y: numpy.ndarray) -> float

   Return mean absolute error.

   >>> pred = np.array([3, -0.5, 2, 7])
   >>> orig = np.array([2.5, 0.0, 2, 8])
   >>> mean_absolute_error(pred, orig)
   0.5