Please help me solve this problem.
Linear Algebra using Octave with some more knowledge of machine learning
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Here is the assignment:
You will investigate linear regression using gradient descent and the normal equations
The training set of housing prices in Portland, Oregon is in files ex3x and ex3y, where
$y are the prices and the inputs $x are the living area and the number of bedrooms. We will use only living area in this assignment
First part of the assignment is to calculate linear estimator using normal equation, and then use the parameters to calculate price of the house with 1650sf,
File to modify: gradinet3student_neq
Second part of the assignment is to calculate linear estimator using gradient descent, and then use the parameters to calculate price of the house with 1650sf,
File to modify: gradinet3student_GD
Note: Recall when you are applying GD, it is good practice to normalize the input data (subtract the average and scale by standard deviation), to bring all data in the similar range. During prediction you need to do the same.
Send me house price estimate and weights for part one and two, in following format:
In this exercise, you will investigate linear regression using gradient descent and the normal equations
The training set of housing prices in Portland, Oregon is in files ex3x and ex3y, where
$y are the prices and the inputs $x are the living area and the number of bedrooms. We will use only living area in this assignment
First part of the assignment is to calculate linear estimator using normal equation, and then use the parameters to calculate price of the house with 1650sf,
File to modify: gradinet3student_neq
Second part of the assignment is to calculate linear estimator using gradient descent, and then use the parameters to calculate price of the house with 1650sf,
File to modify: gradinet3student_GD
Note: Recall when you are applying GD, it is good practice to normalize the input data (subtract the average and scale by standard deviation), to bring all data in the similar range. During prediction you need to do the same.
Send me house price estimate and weights for part one and two, in following format:
Normal eq model
GD model
Price of 1650sf
W0
W1
%in this exercise, you will investigate linear regression using gradient descent
% and the normal equations
%This is a training set of housing prices in Portland, Oregon, where
%y are the prices and the inputs $x are the living area and the number of bedrooms.
%we will use only living area in this assigment
% initialize samples of x and y training dataset
%
x2 = load(‘ex3x.dat’);
y = load(‘ex3y.dat’); %house cost
x=x2(:,1); %only living area
m = length(y); % store the number of training examples
x = [ones(m, 1), x]; % Add a column of ones to x
%the area values from your training data are actually in the second column of x
#Gradient descent
xorg=x(:,2);
#normilze the data
sigma = std(x);
mu = mean(x);
x(:,2) = (x(:,2) – mu(2))./ sigma(2);
mse = zeros(100, 100); % initialize mse to 100×100 matrix of 0’s
w0 = linspace(100000,500000,100);
w1 = linspace(1000,300000,100);
for i = 1:length(w0)
for j = 1:length(w1)
t = [w0(i); w1(j)];
mse(i,j) =1/m* (y-x*t)’*(y-x*t);
end
end
% Plot the surface plot
% Because of the way meshgrids work in the surf command, we need to
% transpose mse before calling surf, or else the axes will be flipped
mse = mse’;
figure;
surf(w0, w1, mse);
xlabel(‘\w0’); ylabel(‘\w1’);
w = zeros(2, 1);
iterations = 100
alpha = 0.07
cost=zeros(iterations,1);
for iter = 1:iterations
%insert your code here
% cost(iter)=…
% this is the code to calculate weights
% w=w – your code * alpha;
%end of your code
wdraw0(iter)=w(1);
wdraw1(iter)=w(2);
end
disp(“w0g w1g”),disp(w);
% now plot Cost
% technically, the first cost at the zero-eth iteration
% but Matlab/Octave doesn’t have a zero index
figure;
plot(0:iterations-1, cost(1:iterations), ‘-‘)
xlabel(‘Number of iterations’)
ylabel(‘Cost – mean square error ‘)
#normilize 1650sf
%caling 1650sf your code here:
%newxs= your code
newxs=
disp(“house prediction for 1650s, gradient descent:”),disp([1,newxs]*w);
#plot data and regression line
figure % open a new figure window
# plot your training set (and label the axes):
plot(xorg,y,’o’);
ylabel(‘house cost’)
xlabel(‘house sf’)
hold
yp=x*w;
plot(xorg,yp,’-‘);
%in this exercise, you will investigate linear regression using gradient descent
% and the normal equations
%This is a training set of housing prices in Portland, Oregon, where
%y are the prices and the inputs $x are the living area and the number of bedrooms.
%we will use only living area in this assigment
% initialize samples of x and y training dataset
%
x2 = load(‘ex3x.dat’);
y = load(‘ex3y.dat’); %house cost
x=x2(:,1); %only living area
m = length(y); % store the number of training examples
x = [ones(m, 1), x]; % Add a column of ones to x
%the area values from your training data are actually in the second column of x
%calculate parametar w using normal equation
%your code here
%
%w=
%code end
disp(“w0 w1”),disp(wn);
#predict the house price with 1650sf
disp(“house prediction for 1650s, normal eq:”),disp([1,1650]*wn);
yp=x*wn;
#plot data and regression line
figure % open a new figure window
# plot your training set (and label the axes):
plot(x(:,2),y,’o’);
ylabel(‘house cost’)
xlabel(‘house sf’)
hold
plot(x(:,2),yp,’-‘);
2.1040000e+03 3.0000000e+00
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2.5670000e+03 4.0000000e+00
1.2000000e+03 3.0000000e+00
8.5200000e+02 2.0000000e+00
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1.2030000e+03 3.0000000e+00
3.9990000e+05
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