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



▸ Regularized linear regression to study models with different bias-variance properties.

I have recently completed the Machine Learning course from Coursera by Andrew NG.

While doing the course we have to go through various quiz and assignments.

Here, I am sharing my solutions for the weekly assignments throughout the course.

These solutions are for reference only.

It is recommended that you should solve the assignments by yourself honestly then only it makes sense to complete the course.
But, In case you stuck in between, feel free to refer to the solutions provided by me.

NOTE:

Don't just copy paste the code for the sake of completion. 
Even if you copy the code, make sure you understand the code first.

Click here to check out week-5 assignment solutions, Scroll down for the solutions for week-6 assignment.




In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.

It consist of the following files:
  • 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
* indicates files you will need to complete

linearRegCostFunction.m :

function [J, grad] = linearRegCostFunction(X, y, theta, lambda)
  %LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear 
  %regression with multiple variables
  %   [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the 
  %   cost of using theta as the parameter for linear regression to fit the 
  %   data points in X and y. Returns the cost in J and the gradient in grad
  
  % Initialize some useful values
  m = length(y); % number of training examples
  
  % You need to return the following variables correctly 
  J = 0;
  grad = zeros(size(theta));
  
  % ====================== YOUR CODE HERE ======================
  % Instructions: Compute the cost and gradient of regularized linear 
  %               regression for a particular choice of theta.
  %
  %               You should set J to the cost and grad to the gradient.
  %DIMENSIONS:
  %   X = 12x2 = m x 1
  %   y = 12x1 = m x 1
  %   theta = 2x1 = (n+1) x 1
  %   grad = 2x1 = (n+1) x 1
  
  h_x = X * theta; % 12x1
  J = (1/(2*m))*sum((h_x - y).^2) + (lambda/(2*m))*sum(theta(2:end).^2); % scalar
  
  % grad(1) = (1/m)*sum((h_x-y).*X(:,1)); % scalar == 1x1
  grad(1) = (1/m)*(X(:,1)'*(h_x-y)); % scalar == 1x1
  grad(2:end) = (1/m)*(X(:,2:end)'*(h_x-y)) + (lambda/m)*theta(2:end); % n x 1
  % =========================================================================
  
  grad = grad(:);

end




learningCurve.m :

function [error_train, error_val] = ...
      learningCurve(X, y, Xval, yval, lambda)
  %LEARNINGCURVE Generates the train and cross validation set errors needed 
  %to plot a learning curve
  %   [error_train, error_val] = ...
  %       LEARNINGCURVE(X, y, Xval, yval, lambda) returns the train and
  %       cross validation set errors for a learning curve. In particular, 
  %       it returns two vectors of the same length - error_train and 
  %       error_val. Then, error_train(i) contains the training error for
  %       i examples (and similarly for error_val(i)).
  %
  %   In this function, 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.
  %
  
  % Number of training examples
  m = size(X, 1);
  
  % You need to return these values correctly
  error_train = zeros(m, 1);
  error_val   = zeros(m, 1);
  
  % ====================== YOUR CODE HERE ======================
  % Instructions: Fill in this function to return training errors in 
  %               error_train and the cross validation errors in error_val. 
  %               i.e., error_train(i) and 
  %               error_val(i) should give you the errors
  %               obtained after training on i examples.
  %
  % Note: You should evaluate the training error on the first i training
  %       examples (i.e., X(1:i, :) and y(1:i)).
  %
  %       For the cross-validation error, you should instead evaluate on
  %       the _entire_ cross validation set (Xval and yval).
  %
  % Note: If you are using your cost function (linearRegCostFunction)
  %       to compute the training and cross validation error, you should 
  %       call the function with the lambda argument set to 0. 
  %       Do note that you will still need to use lambda when running
  %       the training to obtain the theta parameters.
  %
  % Hint: You can loop over the examples with the following:
  %
  %       for i = 1:m
  %           % Compute train/cross validation errors using training examples 
  %           % X(1:i, :) and y(1:i), storing the result in 
  %           % error_train(i) and error_val(i)
  %           ....
  %           
  %       end
  %
  
  % ---------------------- Sample Solution ----------------------
  
  %DIMENSIONS:
  %   error_train = m x 1
  %   error_val   = m x 1 
  
  for i = 1:m
      Xtrain = X(1:i,:);
      ytrain = y(1:i);
      
      theta = trainLinearReg(Xtrain, ytrain, lambda);
      
      error_train(i) = linearRegCostFunction(Xtrain, ytrain, theta, 0); %for lambda = 0;
      error_val(i)   = linearRegCostFunction(Xval, yval, theta, 0); %for lambda = 0;
  end
  
  % -------------------------------------------------------------
  
  % =========================================================================

end




polyFeatures.m :

function [X_poly] = polyFeatures(X, p)
  %POLYFEATURES Maps X (1D vector) into the p-th power
  %   [X_poly] = POLYFEATURES(X, p) takes a data matrix X (size m x 1) and
  %   maps each example into its polynomial features where
  %   X_poly(i, :) = [X(i) X(i).^2 X(i).^3 ...  X(i).^p];
  %
  
  
  % You need to return the following variables correctly.
  X_poly = zeros(numel(X), p); % m x p
  
  % ====================== YOUR CODE HERE ======================
  % Instructions: Given a vector X, return a matrix X_poly where the p-th
  %               column of X contains the values of X to the p-th power.
  %
  %
  % Here, X does not include X0 == 1 column
  
  %%%% WORKING: Using for loop %%%%%%
  % for i = 1:p
  %     X_poly(:,i) = X(:,1).^i;
  % end
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  
  X_poly(:,1:p) = X(:,1).^(1:p); % w/o for loop
  
  % =========================================================================
end




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validationCurve.m :

function [lambda_vec, error_train, error_val] = ...
      validationCurve(X, y, Xval, yval)
  %VALIDATIONCURVE Generate the train and validation errors needed to
  %plot a validation curve that we can use to select lambda
  %   [lambda_vec, error_train, error_val] = ...
  %       VALIDATIONCURVE(X, y, Xval, yval) returns the train
  %       and validation errors (in error_train, error_val)
  %       for different values of lambda. You are given the training set (X,
  %       y) and validation set (Xval, yval).
  %
  
  % Selected values of lambda (you should not change this)
  lambda_vec = [0 0.001 0.003 0.01 0.03 0.1 0.3 1 3 10]';
  
  % You need to return these variables correctly.
  error_train = zeros(length(lambda_vec), 1);
  error_val = zeros(length(lambda_vec), 1);
  
  % ====================== YOUR CODE HERE ======================
  % Instructions: Fill in this function to return training errors in
  %               error_train and the validation errors in error_val. The
  %               vector lambda_vec contains the different lambda parameters
  %               to use for each calculation of the errors, i.e,
  %               error_train(i), and error_val(i) should give
  %               you the errors obtained after training with
  %               lambda = lambda_vec(i)
  %
  % Note: You can loop over lambda_vec with the following:
  %
  %       for i = 1:length(lambda_vec)
  %           lambda = lambda_vec(i);
  %           % Compute train / val errors when training linear
  %           % regression with regularization parameter lambda
  %           % You should store the result in error_train(i)
  %           % and error_val(i)
  %           ....
  %
  %       end
  %
  %
  
  % Here, X & Xval are already including x0 i.e 1's column in it
  
  m = size(X, 1);
  
  %% %%%%% WORKING: BUT UNNECESSARY for loop for i is inovolved %%%%%%%%%%%
  % for i = 1:m
  %     for j = 1:length(lambda_vec);
  %         lambda = lambda_vec(j);
  %         Xtrain = X(1:i,:);
  %         ytrain = y(1:i);
  %
  %         theta = trainLinearReg(Xtrain, ytrain, lambda);
  %
  %         error_train(j) = linearRegCostFunction(Xtrain, ytrain, theta, 0); % lambda = 0;
  %         error_val(j)   = linearRegCostFunction(Xval, yval, theta, 0); % lambda = 0;
  %     end
  % end
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  
  %% %%%%%%% WORKING: BUT UNNECESSARY for loop for i is inovolved %%%%%%%%%%%
  % for j = 1:length(lambda_vec)
  %     lambda = lambda_vec(j);
  %     for i = 1:m
  %         Xtrain = X(1:i,:);
  %         ytrain = y(1:i);
  %
  %         theta = trainLinearReg(Xtrain, ytrain, lambda);
  %
  %         error_train(j) = linearRegCostFunction(Xtrain, ytrain, theta, 0); % lambda = 0;
  %         error_val(j)   = linearRegCostFunction(Xval, yval, theta, 0); % lambda = 0;
  %     end
  % end
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  
  %% %%% NOT WORKING: BUT UNNECESSARY for loop inside learningCurve function is inovolved %%%%%%
  % for j = 1:length(lambda_vec)
  %     lambda = lambda_vec(j);
  %
  %     [error_train_temp, error_val_temp] = ...
  %     learningCurve(X, y, ...
  %                   Xval, yval, ...
  %                   lambda);
  %
  %     error_train(j) = error_train_temp(end);
  %     error_val(j) = error_val_temp(end);
  % end
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  
  
  %% %%%%% WORKING: OPTIMISED (Only 1 for loop) %%%%%%%%%%%
  for j = 1:length(lambda_vec)
      lambda = lambda_vec(j);
      
      theta = trainLinearReg(X, y, lambda);
      error_train(j) = linearRegCostFunction(X, y, theta, 0); % lambda = 0;
      error_val(j)   = linearRegCostFunction(Xval, yval, theta, 0); % lambda = 0
  end
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  
  % =========================================================================
  
end

I tried to provide optimized solutions like vectorized implementation for each assignment. If you think that more optimization can be done, then put suggest the corrections / improvements.

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Click here to see solutions for all Machine Learning Coursera Assignments.
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Feel free to ask doubts in the comment section. I will try my best to solve it.
If you find this helpful by any mean like, comment and share the post.
This is the simplest way to encourage me to keep doing such work.

Thanks and Regards,
-Akshay P. Daga





1 comment:

  1. 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 ??

    ReplyDelete