【matlab】機器學習線性分類和隨機梯度下降

讓我們直接從代碼開始，下面開始分佈講解
【注】想要mnist請回復博主


% z=+1,-1;
% result=-0.3;負類 -1  分對了   +1 分錯了 y*result>0
%        =0.4;正類 +1  分對了   -1 分錯了

% z*(w'*x)=z*w'*x=w'*z*x=w'*y
    
%w1*x1+w2*x2+w3=0;
%w'*x+b=0;
%w'x=0;
% (x1,x2,1)
% (w1,w2,w3)

data=randn(2,40);
data(:,21:40)=data(:,21:40)+5;

 scatter(data(1,1:20),data(2,1:20),'*');
    hold on;
scatter(data(1,21:40),data(2,21:40),'+');
    hold on;
 

w=randn(3,1);
data(3,:)=ones(1,40);
z=ones(1,40);
z(21:40)=-1;
for i=1:40
    data(:,i)=data(:,i).*z(i);
end 

for i=1:40
    result1(:,i)=w'*data(:,i);
end 

for s=1:1000
for i=1:40
    result1(:,i)=w'*data(:,i);
end 
dydx=zeros(3,1);

for i=1:40
    if(result1(:,i)<=0)
        dydx=dydx+data(:,i);
    end
end
dydx=-1.*dydx;

amda=1;
w=w-amda.*dydx;
end



%x2=-(w1*x1+w3)/w2;
%w1=randn(1,1);
%w2=randn(1,1);
%w3=randn(1,1);

%w(1)*x(1)+w(2)*x(2)+b=0
%w'x+b=0;
w=randn(2,1);
b=randn(1,1);
%w=randn(784,1);

b=randn(10,1);
w=randn(784,10); 
for i=1:60000
    results(:,i)=w'.*trdata(:,i)+b;
end

[maxv,indexv]=max(results);


%w1*x1+w2*x2+...+w784*x784+w785=0;

%data(1,1)*w1+data(2,1)*w2+w3

for i=1:40
   result(i)=w'*data(:,i)+b;
end
%x2=-(w1*x1+w3)/w2;
x1=[-4,8];
x2=-1.*(w(1).*x1+w(3))./w(2);
plot(x1,x2);

%data(2,1)


for i=21:40
    scatter(data(1,i),data(2,i),'+');
    hold on;
end