Question

为了识别3个班级，我采用了3个单层感知器，使得，

如果数据属于第1类，则perceptron1 = 1，perceptron2 = 0，perceptron3 = 0

如果数据属于第2类，则perceptron1 = 0，perceptron2 = 1，perceptron3 = 0

如果数据属于第3类，则perceptron1 = 0，perceptron2 = 0，perceptron3 = 1

我确实编写了代码，现在很奇怪我可以看到识别1级和3级r的感知器工作得很好而不是2级。现在我找不到错误的位置。所以，请你帮我吗？

主程序代码：

clc;
% INPUT
% amount of values
points = 300;
% stepsize
s = 1.0;
% INITIALIZE
% Booleans
TRUE  = 1;
FALSE = 0;
feature = 3;
% generate data
%D = generateRandomData(points);

%keeps tracks for hits for each classes in each iteration
p1=0;
p2=0;
p3=0;
ax = 10;
bx = 70;
d=zeros(3,points);
xx1 = 2 + 1.*randn(points/3,1);
xx2 = 10 + 1.*randn(points/3,1);
xx3 = 20 + 1.*randn(points/3,1);
D=(bx-ax).*rand(points,feature) + ax;
for k = 1:points/3
   D(k,1) = xx1(k);
   D(k+points/3,1) = xx2(k);
   D(k+2*points/3,1) = xx3(k);
end
D =  D(randperm(end),:);

for n = drange(1:points)
if D(n,1) <=5.0000   
  d(1,n)=1;
  d(2,n)=0;
  d(3,n)=0;

elseif D(n,1) >=7.0000 && D(n,1) <= 13.0000
d(1,n)=0;
d(2,n)=1;
d(3,n)=0;

elseif D(n,1) >= 17.0000   
  d(1,n)=0;
  d(2,n)=0;
  d(3,n)=1;
end
end

% x-values
% training set
%d = D(:,feature+1);
% weights
w1 = zeros(feature+1,1);
w2 = zeros(feature+1,1);
w3 = zeros(feature+1,1);
% 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

    %CLASS 1
    sumx=0;sum1=0;
    for n =1 : feature
    sumx=D(i,n) * w1(n+1);
    sum1=sum1+sumx;
    end
    bias1=b*w1(1);
    m1=bias1+sum1;
    c1 = activate( m1 );
    a1 = errorFunction(c1,d(1,i)); 
    if a1 ~= 0
        % ajust weights
        for n1 = 1 : feature
            h=n1+1;
        w1(h) = w1(h)+a1*s*D(i,n1);
        end
        w1(1)=w1(1)+a1*s*b;
        else
        % increase correctness counter
        p1 = p1 + 1;
    end


    %CLASS 2
    sumy=0;sum2=0;
    for n =1 : feature
    sumy=D(i,n) * w2(n+1);
    sum2=sumy+sum2;
    end
    bias2=b*w2(1);
    m2=bias2+sum2;
    c2 = activate( m2 );
    disp('-------------');
    disp(c2);
    disp(d(2,i));
    disp('-------------');
    a2 = errorFunction(c2,d(2,i)); 
    if a2 ~= 0
        % ajust weights
        for n1 = 1 : feature
            h=n1+1;
        w2(h) = w2(h)+a2*s*D(i,n1);
        end
        w2(1)=w2(1)+a2*s*b;
        else
        % increase correctness counter
        p2 = p2 + 1;

    end


    %CLASS 3
    sumz=0;sum3=0;
    for n =1 : feature
    sumz=D(i,n) * w3(n+1);
    sum3=sumz+sum3;
    end
    bias3=b*w3(1);
    m3=bias3+sum3;
    c3 = activate( m3 );
    a3 = errorFunction(c3,d(3,i)); 
    if a3 ~= 0
        % ajust weights
        for n1 = 1 : feature
            h=n1+1;
        w3(h) = w3(h)+a3*s*D(i,n1);
        end
        w3(1)=w3(1)+a3*s*b;
    else
        % increase correctness counter
        p3 = p3 + 1;
    end

end
 %  p/3 >= points/1.4 
 %p2 == p1 && p1 == p
if (p1+p2+p3)/2>=points
    %disp(p);
   isCorrect = TRUE;
else
    %p33=p22;
    %p22=p11;
    %p11=p;
    %p=0;
    p1=0;
    p2=0;
    p3=0;
end

end
disp(p1);
disp(p2);
disp(p3);

test=15;
ax = 10;
bx = 70;
D1=(bx-ax).*rand(test,feature) + ax;

xy1 = 2 + 1.*randn(test/3,1);
xy2 = 10 + 1.*randn(test/3,1);
xy3 = 20 + 1.*randn(test/3,1);

for k = 1:test/3
D1(k,1) = xy1(k);
D1(k+test/3,1) = xy2(k);
D1(k+2*test/3,1) = xy3(k);
end
D1 =  D1(randperm(end),:);

test1=zeros(3,test);

for n = drange(1:test)
if D1(n,1) <= 5.0000   
  test1(1,n)=1;
  test1(2,n)=0;
  test1(3,n)=0;
end
if D1(n,1) >=7.0000 && D1(n,1) <= 13.0000
  test1(1,n)=0;
  test1(2,n)=1;
  test1(3,n)=0;
end
if D1(n,1) >= 17.0000   
  test1(1,n)=0;
  test1(2,n)=0;
  test1(3,n)=1;
end
end

for i=1 : test

  sumx=0;sum1=0;sum2=0;sum3=0;
    for n =1 : feature
    sumx=D1(i,n) * w1(n+1);
    sum1=sumx+sum1;
    end
    bias=b*w1(1);
    m=bias+sum1;
    c = activate( m );
    a1 = errorFunction(c,test1(1,i)); 


    %CLASS 2
    for n =1 : feature
    sumx=D1(i,n) * w2(n+1);
    sum2=sumx+sum2;
    end
    bias=b*w2(1);
    m=bias+sum2;
    c = activate( m );
    a2 = errorFunction(c,test1(2,i)); 

    %CLASS 3
    for n =1 : feature
    sumx=D1(i,n) * w3(n+1);
    sum3=sumx+sum3;
    end
    bias=b*w3(1);
    m=bias+sum3;
    c = activate( m );
    a3 = errorFunction(c,test1(3,i)); 

    % if outcome was wrong
    if a1 == 0.0 && a2 == 0.0 && a3 == 0.0
        disp('correct');
   else disp('incorrect');
    end
end

激活功能代码

function f = activate(x)
f = (1/2)*(sign(x)+1);
%f = 1 / (1 + exp(-x));
%f = tanh(x);
end

错误功能检查的代码

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 = 0.0;
end

端