Problem while implementing "Gradient Descent Algorithm" in Matlab
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I'm solving a programming assignment in machine learning course. In which I've to implement "Gradient Descent Algorithm" like below
I'm using the following code
data = load('ex1data1.txt');
% text file conatins 2 values in each row separated by commas
X = [ones(m, 1), data(:,1)];
theta = zeros(2, 1);
iterations = 1500;
alpha = 0.01;
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
for iter = 1:num_iters
k=1:m;
j1=(1/m)*sum((theta(1)+theta(2).*X(k,2))-y(k))
j2=((1/m)*sum((theta(1)+theta(2).*X(k,2))-y(k)))*(X(k,2))
theta(1)=theta(1)-alpha*(j1);
theta(2)=theta(2)-alpha*(j2);
J_history(iter) = computeCost(X, y, theta);
end
end
theta = gradientDescent(X, y, theta, alpha, iterations);
On running the above code I'm getting this error message
3 Comments
Accepted Answer
More Answers (11)
Jayan Joshi
on 15 Oct 2019
Edited: Jayan Joshi
on 15 Oct 2019
predictions =X*theta;
theta=theta-(alpha/m*sum((predictions-y).*X))';
0 Comments
Margo Khokhlova
on 19 Oct 2015
Edited: Walter Roberson
on 19 Oct 2015
Well, sort of super late, but you just made it wrong with the brackets... This one works for me:
k=1:m;
j1=(1/m)*sum((theta(1)+theta(2).*X(k,2))-y(k))
j2=(1/m)*sum(((theta(1)+theta(2).*X(k,2))-y(k)).*X(k,2))
theta(1)=theta(1)-alpha*(j1);
theta(2)=theta(2)-alpha*(j2);
1 Comment
Shekhar Raj
on 19 Sep 2019
Below Code works for me -
Prediction = X * theta;
temp1 = alpha/m * sum((Prediction - y));
temp2 = alpha/m * sum((Prediction - y) .* X(:,2));
theta(1) = theta(1) - temp1;
theta(2) = theta(2) - temp2;
2 Comments
Jayan Joshi
on 15 Oct 2019
Thank you this really helped. I tried more vectorized form of this and it worked.
predictions =X*theta;
theta=theta-(alpha/m*sum((predictions-y).*X))';
Lomg Ma
on 24 Jan 2021
How did you manage to vectorize it that much? I don't understand how to translate the formula to code, seems confusing
Sesha Sai Anudeep Karnam
on 7 Aug 2019
Edited: Sesha Sai Anudeep Karnam
on 7 Aug 2019
temp0 = theta(1)-alpha*((1/m)*(theta(1)+theta(2).*X(k,2)-y(k)));
temp1 = theta(2)- alpha*((1/m)*(theta(1)+theta(2).*X(k,2)-y(k)).*X(k,2));
theta(1) = temp0;
theta(2) = temp1;
% this code gives approximate values but while submitting I'm getting 0points for this
% Theta found by gradient descent:
% -3.588389
% 1.123667
% Expected theta values (approx)
% -3.6303
% 1.1664
% How to overcome this??
2 Comments
Shekhar Raj
on 19 Sep 2019
Below code gave the exact value -
for iter = 1:num_iters
% ====================== YOUR CODE HERE ======================
% Instructions: Perform a single gradient step on the parameter vector
% theta.
%
% Hint: While debugging, it can be useful to print out the values
% of the cost function (computeCost) and gradient here.
%
Prediction = X * theta;
temp1 = alpha/m * sum((Prediction - y));
temp2 = alpha/m * sum((Prediction - y) .* X(:,2));
theta(1) = theta(1) - temp1;
theta(2) = theta(2) - temp2;
% ============================================================
Amber Hall
on 15 Aug 2021
i've tried this code but still get error due to not enough input arguments for m = length(y) ? do you know what may be the cause as it appears i have coded correctly
ICHEN WU
on 8 Nov 2015
Can you tell me why my answer is not correct? I felt they are the same.
theta(1)=theta(1)-(alpha/m)*sum( (X*theta)-y);
theta(2)=theta(2)-(alpha/m)*sum( ((X*theta)-y)'*X(:,2));
5 Comments
pavan B
on 20 Feb 2017
above one works perfect .try below code of mine too
earlier i used h = X * theta; a0 = (1/m)*sum((h-y)); a1 = (1/m)*sum((h-y)'*x1); surprisingly it didn't work
working code: x1 = X(:,2); a0 = (1/m)*sum((X * theta-y)); a1 = (1/m)*sum((X * theta-y)'*x1); a = [a0;a1]; theta = theta- (alpha*a);
if anyone find out whats wrong with my earlier code it would be appreciated.
Leon Cai
on 6 Apr 2017
yea I tried h = X*theta and it didn't work too, I'm thinking that when we use the variable h, as we update theta, the value of h will remain unchanged.
Ali Dezfooli
on 17 Jun 2016
In this line
X = [ones(m, 1), data(:,1)];
You add bias to your X, but in the formula of your picture (Ng's slides) when you want to compute theta(2) you should remove it.
0 Comments
Utkarsh Anand
on 17 Mar 2018
Looking at the problem, I also think that you cannot initiate Theta as Zero.
0 Comments
Rajeswari G
on 2 Jan 2021
error = (X * theta) - y;
theta = theta - ((alpha/m) * X'*error);
In this equation why we take x'?
1 Comment
Bee Ling TAN
on 15 Aug 2021
This is because X is a 97x2 matrix. To perform dot products, only X' (2x97)will make the answer valid to be 2x1 vectors, entrys are theta(1)&theta(2) respectively.
Wamin Thammanusati
on 21 Feb 2021
Edited: Wamin Thammanusati
on 21 Feb 2021
The code below works for this case (one variable) and also multiple variables -
for iter = 1:num_iters
Hypothesis = X * theta;
for i=1:size(X,2)
theta(i) = theta(i) - alpha/m * sum((Hypothesis-y) .* X(:,i));
end
end
1 Comment
Amber Hall
on 15 Aug 2021
having tried the same code i am struggling to understand what i am doing wrong - i receive error due to not enough jnput arguments for m = length(y) line. do you have any ideas?
Chong Lu
on 16 Nov 2021
Edited: Walter Roberson
on 27 Nov 2021
temp1 = theta(1) - alpha*(sum(X*theta - y)/m);
temp2 = theta(2) - alpha*(sum((X*theta - y).*X(:,2))/m);
theta(1) = temp1;
theta(2) = temp2;
0 Comments
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