added code for ploting input training set

Author: Quinn Liu

supervisedLearning/logisticRegression/README.md

@@ -3,7 +3,7 @@ Using Logistic Regression For Prediction
 
 This specific folder contains an example of using logistic regression to predict
 whether a student gets admitted into a university or not. But understand that by 
-just giving a different ```inputTrainingSet.txt``` file you can easily use logistic
+just giving a different ```ex2data1.txt``` file you can easily use logistic
 regression to predict something you want!
 
 <h2>How to use this code:</h2>

supervisedLearning/logisticRegression/ex2.m

@@ -5,28 +5,26 @@
 
 %% Load Data
 %  The first two columns contains the exam scores and the third column
-%  contains the label.
+%  contains the label
 
 data = load('ex2data1.txt');
-X = data(:, [1, 2]); y = data(:, 3);
-
-%% ==================== Part 1: Plotting ====================
-%  We start the exercise by first plotting the data to understand the 
-%  the problem we are working with.
+X = data(:, [1, 2]); % X is a #OfExamScores x 2 matrix
+y = data(:, 3);      % y is a #OfExamScores x 1 matrix
 
+%  It's always a good idea to first plot the problem to solve
 fprintf(['Plotting data with + indicating (y = 1) examples and o ' ...
          'indicating (y = 0) examples.\n']);
-
 plotData(X, y);
 
 % Put some labels 
 hold on;
 % Labels and Legend
+title('Acceptance of Previous Students Based on Scores on 2 Exams')
 xlabel('Exam 1 score')
 ylabel('Exam 2 score')
 
 % Specified in plot order
-legend('Admitted', 'Not admitted')
+legend('Admitted Students', 'Not admitted Students')
 hold off;
 
 fprintf('\nProgram paused. Press enter to continue.\n');

supervisedLearning/logisticRegression/plotData.m

@@ -12,17 +12,14 @@ function plotData(X, y)
 %               examples and 'ko' for the negative examples.
 %
 
-
-
-
-
-
-
-
-
-% =========================================================================
-
-
+% Find indices of positive and negative examples
+indicesOfPositiveLabels = find(y == 1);
+indicedOfNegativeLabels = find(y == 0);
+
+plot(X(indicesOfPositiveLabels, 1), X(indicesOfPositiveLabels, 2),
+	'k+', 'LineWidth', 2, 'MarkerSize', 7);
+plot(X(indicedOfNegativeLabels, 1), X(indicedOfNegativeLabels, 2),
+	'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);
 
 hold off;