## computeCost(X, y , theta)

`````` function J = computeCost(X, y, theta)
%COMPUTECOST Compute cost for linear regression
%  J = COMPUTECOST(X, y, theta) computes the cost of using theta as the
%  parameter for linear regression to fit the data points in X and y
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta
%        You should set J to the cost.
prediction = X * theta;
square = (prediction - y).^2;
J = 1/(2*m)*sum(square);
% =========================================================================
end
``````

#### 1 comment:

Tasdeeq Sofi said...

I have two data sets ans I have fitted lines through to these data sets through least square fitting. Now i need to match the slopes of these two fitted lines.By varying a parameter say alpha the slopes of these two lines change, I have to find optimum alpha such that the slopes of these two fitted lines are equal or the difference is small.

Can you please help me?
Thanks