我似乎无法弄清楚为什么我的错误百分比很高。

我试图在 X1 和 X2 之间获得一个感知器，它们是具有不同均值和相同协方差的高斯分布数据集。

下面是我的代码：

N=200;
C= [2 1; 1 2]; %Covariance
m1=[0 2];
m2=[1.5 0];%mean
X1 = mvnrnd(m1, C, N/2);
X2 = mvnrnd(m2, C, N/2);

X = [X1; X2];
X = [X ones(N,1)]; %bias
y = [-1*ones(N/2,1); ones(N/2,1)]; %classification

%Split data into training and test 
ii = randperm(N);
Xtr = X(ii(1:N/2),:);
ytr = X(ii(1:N/2),:);
Xts = X(ii(N/2+1:N),:);
yts = y(ii(N/2+1:N),:);
Nts = N/2;

w = randn(3,1);
eta = 0.001;
%learn from training set
for iter=1:500 
j = ceil(rand*N/2);
if( ytr(j)*Xtr(j,:)*w < 0)
w = w + eta*Xtr(j,:)'; 
end
end

%apply what you have learnt to test set
yhts = Xts * w;
disp([yts yhts])
PercentageError = 100*sum(yts .*yhts < 0)/Nts;

我做错了什么，我该如何应对这一挑战？