[Solved] INT301 Lab 4-Further Practice with Perceptrons

Further Practice with Perceptrons

Demo:

Analyze the given program PerceptronExample, which consists of following parts:

1. (1) Prepare mydata by generating 2 dimensional linearly separable data.

           mydata = rand(500,2);

           % Separate the data into two classes

           acceptindex = abs(mydata(:,1)-mydata(:,2))>0.012;        mydata = mydata(acceptindex,:); % data        myclasses = mydata(:,1)>mydata(:,2); % labels

          [m n]=size(mydata);

   You may check the data distribution using:

             scatter(mydata(:,1),mydata(:,2))

2. (2) Train the perceptron by calling the function PerceptronTrn with the prepared training data (x, y), which will return the connection weights, the bias, and the number of iteration;
3. (3) Test the trained Perceptron model with the testing data (xt, yt), by calling another function PerceptronTst, which will return the testing error;
4. (4) Display the two classes of data points with a separating line.

Exercise:

1. (1) The Perceptron training function uses a learning rate 0.5 and a threshold 0.5. Change these two parameters, e.g. learning rate 0.1 and threshold 0, and observe the differences;
2. (2) Revise the program to calculate the Root-Mean-Square (RMS) error for every input data points and display the error curve, i.e. RMS vs. iteration.