How to fix gradient descent code?

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Jackwhale
Jackwhale 2016-3-30
评论： sivarao K 2021-11-10
I am a novice trying to do a gradient descent with one variable, but cannot figure out how to fix my code (below). Not sure if my for-part is correct. This is the error message: "In an assignment A(:) = B, the number of elements in A and B must be the same." Please help?
data = load('data.txt' );
X = data(:, 1); y = data(:, 2);
m = length(y);
X = [ones(m, 1), data(:,1)]; % Add a column of ones to x
theta = zeros(2, 1); % initialize fitting parameters
num_iters = 1500;
alpha = 0.01;
J = computeCost(X, y, theta)
m = length(y);
J = sum(( X * theta - y ) .^2 )/( 2 * m );
[theta J_history] = gradientDescent(X, y, theta, alpha, num_iters)
J_history = zeros(num_iters, 1);
for iter = 1:num_iters
h=(theta(1)+ theta(2)*X)';
theta(1) = theta(1) - alpha * (1/m) * h * X(:, 1);
theta(2) = theta(2) - alpha * (1/m) * h * X(:, 2);
% Save the cost J in every iteration
J_history(num_iters) = computeCost(X, y, theta);
end
  2 个评论
Walter Roberson
Walter Roberson 2016-3-30
Please show the complete error message, everything in red.
Jackwhale
Jackwhale 2016-3-30
This is the complete error message: "In an assignment A(:) = B, the number of elements in A and B must be the same."

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采纳的回答

Torsten
Torsten 2016-3-30
theta(1) - alpha * (1/m) * h * X(:, 1)
and
theta(2) - alpha * (1/m) * h * X(:, 2)
are 2x1 vectors which are assigned to scalars in the lines
theta(1) = theta(1) - alpha * (1/m) * h * X(:, 1);
theta(2) = theta(2) - alpha * (1/m) * h * X(:, 2);
This is not possible.
Best wishes
Torsten.
  3 个评论
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Torsten
Torsten 2016-3-30
I must admit that I don't understand what your code does.
To answer your question, you had to include comments and explain in more detail the underlying problem and the algorithm to solve it.
Best wishes
Torsten.
Jackwhale
Jackwhale 2016-3-30
To clarify the goal, the objective is to predict the profitability of a food delivery truck when expanding to a new city, based on the city population size. The fi rst data column is the population, the second column is the profi t of a food truck in that city. The chosen approach is the batch gradient descent algorithm, changing the parameters to come closer to the optimal values that will minimise the cost function J(). The idea however is to monitor J(), so as to check the convergence of the gradient descent implementation. The loop structure has been provided, I only need to supply the updates to theta within each iteration - to minimise J(). I have implemented computeCost correctly, but am struggling with implementing the gradient descent correctly.

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更多回答（2 个）

Torsten
Torsten 2016-3-30
编辑：Torsten 2016-3-30
I don't know why you use such a complicated approach.
Just execute
data = load('data.txt' );
A = [ones(length(data(:,1)),1), data(:,1)];
b = data(:,2);
theta = A \ b
to get your optimum theta values.
Best wishes
Torsten.
  14 个评论
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Torsten
Torsten 2016-3-31
You seem to have a strange MATLAB version.
If I set
num_iters=1001,
I get
theta =
5.2147549
- 0.5733459
J_history(1001)
ans =
0.8554026
thus the results expected.
Best wishes
Torsten.
Torsten
Torsten 2016-3-31
I only need to supply the updates to theta within each iteration.
If you can't read from the code I supplied how theta is updated every iteration, then you should really start with MATLAB principles.

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Agbakoba Chukwunoso
Pls help me out.. I'm trying to find gradientdescent with this code but when I run it, it returns gradientdescents to me not the value . 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); end end
  2 个评论
Agbakoba Chukwunoso
Agbakoba Chukwunoso 2020-12-6
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);
end
end
sivarao K
sivarao K 2021-11-10
here 'y' not defined but it excuting how?

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