coursera linear regression programming assignment

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Coursera: Machine Learning (Week 6) [Assignment Solution] - Andrew NG

Recommended Machine Learning Courses: Coursera: Machine Learning    Coursera: Deep Learning Specialization Coursera: Machine Learning with Python Coursera: Advanced Machine Learning Specialization Udemy: Machine Learning LinkedIn: Machine Learning Eduonix: Machine Learning edX: Machine Learning Fast.ai: Introduction to Machine Learning for Coders

• ex5.m - Octave/MATLAB script that steps you through the exercise
• ex5data1.mat - Dataset
• submit.m - Submission script that sends your solutions to our servers
• featureNormalize.m - Feature normalization function
• fmincg.m - Function minimization routine (similar to fminunc)
• plotFit.m - Plot a polynomial fit
• trainLinearReg.m - Trains linear regression using your cost function
• [*] linearRegCostFunction.m - Regularized linear regression cost function
• [*] learningCurve.m - Generates a learning curve
• [*] polyFeatures.m - Maps data into polynomial feature space
• [*] validationCurve.m - Generates a cross validation curve
• Video - YouTube videos featuring Free IOT/ML tutorials

linearRegCostFunction.m :

Learningcurve.m :, polyfeatures.m :, check-out our free tutorials on iot (internet of things):.

validationCurve.m :

29 comments.

Thankyou for your solutions :) I have 2 questions : 1) I see that the sizes of test set and validation set are 21X1 each while that of training set is only 12X1, why is the training set's size smaller that the test and validation set? 2) Why do we put lamda =0 while finding the error_train and error_val in both the functions learningCurve.m and validationCurve.m ??

These codes are not working for me. they're running but not giving any marks.

Is there any other code (from other website) which is working for you? If that's the case, Please let me know I will recheck my codes. Otherwise, You must be doing small mistake from your end. Either way please let me know.

Your code has been working for me. its not just good to copy and paste only. its good to understand the code. Thanks for your help.

My code is working and show correct result but while submit the code grader not give marks.

It's difficult for me to tell you what's wrong with your code without checking it. I doubt, You have to debug your code on your own. Sorry.

Please help.

In learningCurve.m when i write the same code as given above. it is showing me this division by zero.944305e-31 warning: called from fmincg at line 102 column 12 can someone help me to fix it

Hi Akshay, In the polyFeatures.m file, when the X is raised to the power of 1,2,3,4 and so on, the X value is divided by thousand before the power calculation. why is that. -15.9368 -29.1530 36.1895 37.4922 -48.0588 -8.9415 15.3078 -34.7063 1.3892 -44.3838 7.0135 22.7627 The above dataset is divided by 1000 in the second iteration. please help to clarify.

Hello, thank you for your effort, please I have a question regarding your solution for the linearRegCostFunction. I didn t understand when we need to add the Sum function and when we re not suppose to add it. could you please explain that. Thank you in advance

If you look at cost function equations, you have to calculate (elementwise) square of the difference then summation of that. In that case you have to use "sum" function. In general, if you do matrix multiplication then it already consist "sum of the product" so separate "sum" function is not required. But, if you do element wise multiplication of square operation on matrices (indicated by .* or .^ respectively) then you have to do "sum" operation separately for the summation purpose.

Ah I get it, thank you so much for this explination

Hi - For validation curve, dont think you need the 1:m loop....the way it is implemented , only the last iteration of the loop matters. Below code is working with only one loop: len = length(lambda_vec) ; for i = 1:len lambda = lambda_vec(i) ; [theta] = trainLinearReg(X, y,lambda ); error_train(i) = linearRegCostFunction(X,y,theta,0) ; error_val(i) = linearRegCostFunction(Xval,yval,theta,0) ; end

I am not sure if you can help but i cant find where is the problem. I am getting those answers for learning curves and my learning curve is identical to the one in ex5. It shows 0 points tho. Can someone help me. # Training Examples Train Error Cross Validation Error 1 0.000000 205.121096 2 0.000000 110.300366 3 3.286595 45.010231 4 2.842678 48.368911 5 13.154049 35.865165 6 19.443963 33.829962 7 20.098522 31.970986 8 18.172859 30.862446 9 22.609405 31.135998 10 23.261462 28.936207 11 24.317250 29.551432 12 22.373906 29.433818

For linearRegCostFunction, Don't understand why there's two grads. Grad(1) and Grad(2:end)?

and why remove horizontal bias unit from X? X(: 2:end)

As per the theory taught in lecture, We don't apply regularization on the first term, regularization is only applied from 2nd to end term. that's why we break the grads into 2 parts Grad(1) and Grad(2:m) do the processing separately and then and combine them.

thanks man. and thank you for sharing your work. really helps people who are stuck.

reg_gradient = lambda/m*(theta(2:end))' grad = ((1/m)*(h-y)'*X) + reg_gradient; Here is what i did by the way. Didn't break the grads into 2 parts but still works. Do you really need to break it into two parts? Am i applying regularization on the first term using this method?

Can you explain this part of the for loop? Xtrain = X(1:i,:); ytrain = y(1:i); how are you getting the x and y values in a for loop for the training curve? I know the cross validation x and y values are already given but what's your method of getting values for the training function?

Here, we are increasing the size of training set in each iteration from 1 upto m and plotting the graph of train and test error. Please check the comments given in each function. you will find it helpful. Eg. you will compute the train and test errors for % dataset sizes from 1 up to m. In practice, when working with larger % datasets, you might want to do this in larger intervals.

hi.. can you please help.. In validation curve.. why have you put lambda = 0 for error_train(j) and error_val(j). why are we not using different values of lamba here are different values of lambda only needed for theta?

In validation curve, we are calculating error_train(j) and error_val(j) without regularization. So, to remove the regularization term we have set lambda = 0.

Thankyou man!!. Your solutions are a saviour

I keep getting non conformant arguments linearRegCostFunction: operator *: nonconformant arguments (op1 is 12x1, op2 is 9x1) I have even copy and paste your code for it. What could be the reason?

When you start attempting the work, only run the section you are working on (click on section, then run section). Clicking on "run" activates other sections and may alter the saved variables in your workspace.

even i am facing the same problem sir,did you get how to solve If so please explain me

have you tried optional part?

whenever i try to run the code with (ex5) on MATLAB. It shows "unrecognized function or variable "ex5". please help how to resolve this.

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This repository contains my code implementation of Linear Regression, as programming assignment of week 2 of Coursera's Machine Learning course by Andrew Ng

jaylamberte99/linear-regression-assignment-week-2-coursera

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