Wednesday, May 7, 2014

Cost Function Algorithm in Matlab

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