Update ex2.m

ex2.m

@@ -1,21 +1,3 @@
-%% Machine Learning Online Class - Exercise 2: Logistic Regression
-%
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the logistic
-%  regression exercise. You will need to complete the following functions 
-%  in this exericse:
-%
-%     sigmoid.m
-%     costFunction.m
-%     predict.m
-%     costFunctionReg.m
-%
-%  For this exercise, you will not need to change any code in this file,
-%  or any other files other than those mentioned above.
-%
-
 %% Initialization
 clear ; close all; clc
 
@@ -27,7 +9,7 @@
 X = data(:, [1, 2]); y = data(:, 3);
 
 %% ==================== Part 1: Plotting ====================
-%  We start the exercise by first plotting the data to understand the 
+%  We start by first plotting the data to understand the 
 %  the problem we are working with.
 
 fprintf(['Plotting data with + indicating (y = 1) examples and o ' ...
@@ -50,17 +32,16 @@
 
 
 %% ============ Part 2: Compute Cost and Gradient ============
-%  In this part of the exercise, you will implement the cost and gradient
-%  for logistic regression. You neeed to complete the code in 
-%  costFunction.m
+%  In this part, I have implemented the cost and gradient
+%  for logistic regression.
 
-%  Setup the data matrix appropriately, and add ones for the intercept term
+%  Setting up the data matrix appropriately, and adding ones for the intercept term
 [m, n] = size(X);
 
 % Add intercept term to x and X_test
 X = [ones(m, 1) X];
 
-% Initialize fitting parameters
+% Initializing fitting parameters
 initial_theta = zeros(n + 1, 1);
 
 % Compute and display initial cost and gradient
@@ -72,7 +53,7 @@
 fprintf(' %f \n', grad);
 fprintf('Expected gradients (approx):\n -0.1000\n -12.0092\n -11.2628\n');
 
-% Compute and display cost and gradient with non-zero theta
+% Computing and displaying cost and gradient with non-zero theta
 test_theta = [-24; 0.2; 0.2];
 [cost, grad] = costFunction(test_theta, X, y);
 
@@ -87,29 +68,29 @@
 
 
 %% ============= Part 3: Optimizing using fminunc  =============
-%  In this exercise, you will use a built-in function (fminunc) to find the
+%  I have used a built-in function (fminunc) to find the
 %  optimal parameters theta.
 
-%  Set options for fminunc
+%  Setting options for fminunc
 options = optimset('GradObj', 'on', 'MaxIter', 400);
 
-%  Run fminunc to obtain the optimal theta
+%  Running fminunc to obtain the optimal theta
 %  This function will return theta and the cost 
 [theta, cost] = ...
 	fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
 
-% Print theta to screen
+% Printing theta to screen
 fprintf('Cost at theta found by fminunc: %f\n', cost);
 fprintf('Expected cost (approx): 0.203\n');
 fprintf('theta: \n');
 fprintf(' %f \n', theta);
 fprintf('Expected theta (approx):\n');
 fprintf(' -25.161\n 0.206\n 0.201\n');
 
-% Plot Boundary
+% Ploting Boundary
 plotDecisionBoundary(theta, X, y);
 
-% Put some labels 
+% Puting some labels 
 hold on;
 % Labels and Legend
 xlabel('Exam 1 score')
@@ -123,17 +104,15 @@
 pause;
 
 %% ============== Part 4: Predict and Accuracies ==============
-%  After learning the parameters, you'll like to use it to predict the outcomes
-%  on unseen data. In this part, you will use the logistic regression model
+%  After learning the parameters, I have used it to predict the outcomes
+%  on unseen data. In this part, I have used the logistic regression model
 %  to predict the probability that a student with score 45 on exam 1 and 
 %  score 85 on exam 2 will be admitted.
 %
-%  Furthermore, you will compute the training and test set accuracies of 
-%  our model.
-%
-%  Your task is to complete the code in predict.m
+%  Furthermore, I have computed the training and test set accuracies of 
+%  the model.
 
-%  Predict probability for a student with score 45 on exam 1 
+%  Predicting probability for a student with score 45 on exam 1 
 %  and score 85 on exam 2 
 
 prob = sigmoid([1 45 85] * theta);