Please I need helpe to implement this codes:

import numpy as np

import math

import sys

sys.path.append("..")

from misc.utils import MyUtils

class LinearRegression:

def __init__(self):

self.w = None # The (d+1) x 1 numpy array weight matrix

self.degree = 1

def fit(self, X, y, CF = True, lam = 0, eta = 0.01, epochs = 1000, degree = 1):

''' Find the fitting weight vector and save it in self.w.

parameters:

X: n x d matrix of samples, n samples, each has d features, excluding the bias feature

y: n x 1 matrix of lables

CF: True - use the closed-form method. False - use the gradient descent based method

lam: the ridge regression parameter for regularization

eta: the learning rate used in gradient descent

epochs: the maximum epochs used in gradient descent

degree: the degree of the Z-space

'''

self.degree = degree

X = MyUtils.z_transform(X, degree = self.degree)

if CF:

self._fit_cf(X, y, lam)

else:

self._fit_gd(X, y, lam, eta, epochs)

def _fit_cf(self, X, y, lam = 0):

''' Compute the weight vector using the clsoed-form method.

Save the result in self.w

X: n x d matrix, n samples, each has d features, excluding the bias feature

y: n x 1 matrix of labels. Each element is the label of each sample.

''

## Enter your code here that implements the closed-form method for

## linear regression

def _fit_gd(self, X, y, lam = 0, eta = 0.01, epochs = 1000):

''' Compute the weight vector using the gradient desecent based method.

Save the result in self.w

X: n x d matrix, n samples, each has d features, excluding the bias feature

y: n x 1 matrix of labels. Each element is the label of each sample.

'''

## Enter your code here that implements the gradient descent based method

## for linear regression

def predict(self, X):

''' parameter:

X: n x d matrix, the n samples, each has d features, excluding the bias feature

return:

n x 1 matrix, each matrix element is the regression value of each sample

'''

## Enter your code here that produces the label vector for the given samples saved

## in the matrix X. Make sure your predication is calculated at the same Z

## space where you trained your model. Make sure X has been normalized via the same

## normalization used by the training process.

def error(self, X, y):

''' parameters:

X: n x d matrix of future samples

y: n x 1 matrix of labels

return:

the MSE for this test set (X,y) using the trained model

'''

## Enter your code here that calculates the MSE between your predicted

## label vector and the given label vector y, for the sample set saved in matraix X

## Make sure your predication is calculated at the same Z space where you trained your model.

## Make sure X has been normalized via the same normalization used by the training process.