TODO: Lienar Regression with least Mean Squares (LMS) Optimize the model through gradient descent. *Please complete the TODOs. * !pip install wget import os import random import traceback from pdb import set_trace import sys import numpy as np from abc import ABC, abstractmethod import traceback from util.timer import Timer from util.data import split_data, feature_label_split, Standardization from util.metrics import mse from datasets.HousingDataset import HousingDataset class BaseModel(ABC):     """ Super class for ITCS Machine Learning Class"""     @abstractmethod     def fit(self, X, y):         pass     @abstractmethod     def predict(self, X):         pass class LinearModel(BaseModel):     """         Abstract class for a linear model         Attributes         ==========         w       ndarray                 weight vector/matrix     """     def __init__(self):         """             weight vector w is initialized as None         """         self.w = None     # check if the matrix is 2-dimensional. if not, raise an exception     def _check_matrix(self, mat, name):         if len(mat.shape) != 2:             raise ValueError(f"Your matrix {name} shape is not 2D! Matrix {name} has the shape {mat.shape}")     # add a biases     def add_ones(self, X):         """             add a column basis to X input matrix         """         self._check_matrix(X, 'X')         return np.hstack((np.ones((X.shape[0], 1)), X))     ####################################################     #### abstract funcitons ############################     @abstractmethod     def fit(self, X: np.ndarray, y: np.ndarray):         """             train linear model             Args:                 X:  Input data                 y:  targets/labels         """         pass     @abstractmethod     def predict(self, X: np.ndarray):         """             apply the learned model to input X             parameters             ----------             X     2d array                   input data         """         pass class LeastMeanSquares(LinearModel):     """         Performs regression using least mean squares (gradient descent)         attributes:             w (np.ndarray): weight matrix             alpha (float): learning rate or step size             epochs (int): Number of epochs to run for mini-batch                 gradient descent             seed (int): Seed to be used for NumPy's RandomState class                 or universal seed np.random.seed() function.     """     def __init__(self, alpha: float, epochs: int, seed: int = None):         super().__init__()         self.w = None         self.alpha = alpha         self.epochs = epochs         self.seed = seed     def fit(self, X: np.ndarray, y: np.ndarray) -> None:         """ Used to train our model to learn optimal weights.             TODO:                 Finish this method by adding code to perform LMS in order to learn the                 weights `self.w`.         """         pass  # TODO replace this line with your code     def predict(self, X: np.ndarray) -> np.ndarray:         """ Used to make a prediction using the learned weights.             TODO:                 Finish this method by adding code to make a prediction given the learned                 weights `self.w`.         """         # TODO (REQUIRED) Add code below         # TODO (REQUIRED) Store predictions below by replacing np.ones()         y_hat = np.ones([len(X), 1])         return y_hat