在MATLAB中使用感知器对数据进行分类

我正在生成可以线性分离的随机数据。我想编写自己的感知器版本以将它们分开。我知道有些帖子也有类似的问题-但我找不到我的错误。我真的被困住了。该算法有效，但似乎无法收敛。如果您能帮助我，我将不胜感激。

我的代码：

single_layer_perceptron.m

% INPUT
% amount of values
points = 20;
% stepsize
s = 1.0;

% INITIALIZE
% Booleans
TRUE  = 1;
FALSE = 0;

% generate data
D = generateRandomData(points);
% x-values
x = D(:,1);
% y-values
y = D(:,2);
% training set
d = D(:,3);
% weights
w = zeros(3,1);
% bias
b = 1;
% sucsess flag
isCorrect = FALSE;
% correctly predicted values counter
p = 0;
% COMPUTE 

% while at east one point is not correctly classified
while isCorrect == FALSE
    % for every point in the dataset
    for i=1 : points
        % calculate outcome with current weight
        c = heaviside(b * w(1) + x(i) * w(2) + y(i) * w(3));
        % compare output with training set
        a = errorFunction(c,d(i)); 
        % if outcome was wrong
        if a ~= 0
            % ajust weights
            w(1) = w(1) + a*s*b;
            w(2) = w(2) + a*s*x(i);
            w(3) = w(3) + a*s*y(i);
        else
            % increase correctness counter
            p = p + 1;
        end
    end

    %disp(w);
    disp(p);

    if p >= points
       isCorrect = TRUE;
    end

    p = 0;

end

generateRandomData.m

function f = generateRandomData(points)
% generates random data that can be lineary seperated (silent)
% generate random function y = mx + n
m = 2  * rand * sign(randn);   % in (-2,2)/0
n = 10  * rand + 5;            % in (5,15)

% generate random points
x = 20 * rand(points,2);        % in ((0,20), (0,20))

% labeling
f = [x, zeros(points,1)];
for i=1:length(x(:,1))
    y = m*x(i,1) + n;
    if x(i,2) > y
        f(i,3) = 1;
    end    
end

end

activationFunctionHeaviside.m

function f = activationFunctionHeaviside(x)
f = (1/2)*(sign(x)+1);
end

errorFunction.m

function f = errorFunction(c,d)
% w has been classified as c - w should be d

if c < d 
    % reaction too small 
    f = -1;
elseif c > d
    % reaction too large
    f = 1;
else
    % reaction correct
    f = 0;
end

end

非常感谢！