train.m

%Create training set
p1 = [1, 4];
t1 = 0;

p2 = [1, 5];
t2 = 0;

p3 = [2, 4];
t3 = 0;

p4 = [2, 5];
t4 = 0;

p5 = [3, 1];
t5 = 1;

p6 = [3, 2];
t6 = 1;

p7 = [4, 1];
t7 = 1;

p8 = [4, 2];
t8 = 1;

patterns = [p1]';
targets = [t1];

%Initialize a PerceptronLayer neural network 
perceptron = PerceptronLayer(2, 1, "hardlim");

%print perceptron weight and bias

%print patterns and training set
disp("Patterns")
disp(patterns)
disp("Targets")
disp(targets)
perceptron = trainPerceptron(perceptron, patterns, targets);
testPerceptron(perceptron, patterns, targets);


%this function initializes and trains a PerceptronLayer neural network to classify two-dimensional vectors according to the training set provided
function perceptron = trainPerceptron(perceptron, patterns, targets)
    %initialize the neural network
    arguments
        perceptron PerceptronLayer
        patterns double
        targets double
    end

    %train the neural network
    perceptron = perceptron.input_setter(patterns);
    perceptron = perceptron.output_setter(targets);
    perceptron = perceptron.learn();
end

function testPerceptron(perceptron, patterns, targets)
    %initialize the neural network
    arguments
        perceptron PerceptronLayer
        patterns double
        targets double
    end

    %test the neural network by calling forward and comparing the output (a) to the target
    for i = 1:size(patterns, 2)
        a = perceptron.forward(patterns(:, i));
        %assert that the output is equal to the target
        disp("a " + a + "targets " + targets(1));
        assert(a == targets(i), "Test failed!");
    end
    fprintf("Successful!");
    
end