Solvers no longer use the LevenbergMarquardt, LineSearchType, and NonlEqnAlgorithm options, since these options relate only to the Gauss-Newton algorithm.
Ng机器学习编程作业matlab实现LR时里有这样一段
%% ============= Part 3: Optimizing using fminunc =============
% In this exercise, you will use a built-in function (fminunc) to find the
% optimal parameters theta.
% Set options for fminunc
options = optimset('GradObj', 'on', 'MaxIter', 400);
% Run fminunc to obtain the optimal theta
% This function will return theta and the cost
[theta, cost] = ...
fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
...
================================
不是很能理解optimset和fminunc的用法
[theta, cost] = ...
fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
===============================
我是matlab初学者，这里需要用这个函数来实现梯度下降算法，也就是迭代多次学习theta。请问在fminunc里是怎样实现迭代的呢？那个@t是什么意思？optimset里的‘GradObj’是什么意思？
跪等大神解救！
global xpath
clear FF
x1=(-3:.1:3)'; x2=x1; N=length(x1);
for ii=1:N
for jj=1:N
FF(ii,jj)=rosen([x1(ii) x2(jj)]');
end
end
% quasi-newton
xpath=[];t0=clock;
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','bfgs','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity',...
'MaxFunEvals',150,'OutputFcn',@outftn);
x0=[-1.9 2]';
xout1=fminunc('rosen',x0,opt); % quasi-newton
xbfgs=xpath;
% gradient search
xpath=[];
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','steepdesc','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity','MaxFunEvals',2000,...
'MaxIter',1000,'OutputFcn',@outftn);
xout=fminunc('rosen',x0,opt);
xgs=xpath;
% hybrid GS and BFGS
xpath=[];
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','steepdesc','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity','MaxFunEvals',5,'OutputFcn',@outftn);
xout=fminunc('rosen',x0,opt);
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','bfgs','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity','MaxFunEvals',150,'OutputFcn',@outftn);
xout=fminunc('rosen',xout,opt);
xhyb=xpath;
figure(1);clf
contour(x1,x2,FF',[0:2:10 15:50:1000])
hold on
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with BFGS')
pause(1)
plot(x0(1),x0(2),'ro','Markersize',12); pause(1)
plot(1,1,'rs','Markersize',12); pause(1)
for ii = 1:size(xbfgs,1)
plot(xbfgs(ii,1),xbfgs(ii,2),'bd','Markersize',12); pause(1)
end
hold off
figure(2);clf
contour(x1,x2,FF',[0:2:10 15:50:1000]);
hold on
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with GS')
pause(1)
plot(x0(1),x0(2),'ro','Markersize',12); pause(1)
plot(1,1,'rs','Markersize',12); pause(1)
for ii = 1:size(xgs,1)
plot(xgs(ii,1),xgs(ii,2),'m+','Markersize',12);% pause(1)
end
hold off
figure(3);clf
contour(x1,x2,FF',[0:2:10 15:50:1000]);
hold on
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with GS(5) and BFGS')
pause(1)
plot(x0(1),x0(2),'ro','Markersize',12); pause(1)
plot(1,1,'rs','Markersize',12); pause(1)
for ii = 1:size(xhyb,1)
plot(xhyb(ii,1),xhyb(ii,2),'m+','Markersize',12); pause(1)
end
hold off
figure(4);clf
mesh(x1,x2,FF');
axis([-3 3 -3 3 0 1000])
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with BFGS')
hold on
pause
plot3(x0(1),x0(2),rosen(x0')+5,'ro','Markersize',12,'MarkerFaceColor','r'); pause(1)
plot3(1,1,rosen([1 1]),'ms','Markersize',12,'MarkerFaceColor','m'); pause(1)
for ii = 1:size(xbfgs,1)
plot3(xbfgs(ii,1),xbfgs(ii,2),rosen(xbfgs(ii,:))+5,'gd','MarkerFaceColor','g'); pause(1)
end
%for ii = 1:size(xgs,1)
% plot3(xgs(ii,1),xgs(ii,2),rosen(xgs(ii,:))+5,’m+’); pause(1);
%end
hold off
hh=get(gcf,'children');
set(hh,'View',[-177 89.861],'CameraPosition',[-0.585976 11.1811 5116.63]);
function stop = outftn(x,optimValues,state)
global xpath
stop = 0;
xpath=[xpath;x'];
end
%
function [F,G]=rosen(x)
if size(x,1)==2, x=x'; end
F=100*(x(:,2)-x(:,1).^2).^2+(1-x(:,1)).^2;
G=[100*(4*x(1)^3-4*x(1)*x(2))+2*x(1)-2; 100*(2*x(2)-2*x(1)^2)];
end
global xpath
clear FF
x1=(-3:.1:3)'; x2=x1; N=length(x1);
for ii=1:N
for jj=1:N
FF(ii,jj)=rosen([x1(ii) x2(jj)]');
end
end
% quasi-newton
xpath=[];t0=clock;
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','bfgs','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity',...
'MaxFunEvals',150,'OutputFcn',@outftn);
x0=[-1.9 2]';
xout1=fminunc('rosen',x0,opt); % quasi-newton
xbfgs=xpath;
% gradient search
xpath=[];
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','steepdesc','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity','MaxFunEvals',2000,...
'MaxIter',1000,'OutputFcn',@outftn);
xout=fminunc('rosen',x0,opt);
xgs=xpath;
% hybrid GS and BFGS
xpath=[];
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','steepdesc','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity','MaxFunEvals',5,'OutputFcn',@outftn);
xout=fminunc('rosen',x0,opt);
opt=optimset('fminunc');
opt=optimset(opt,'Hessupdate','bfgs','gradobj','on','Display','Iter',...
'LargeScale','off','InitialHessType','identity','MaxFunEvals',150,'OutputFcn',@outftn);
xout=fminunc('rosen',xout,opt);
xhyb=xpath;
figure(1);clf
contour(x1,x2,FF',[0:2:10 15:50:1000])
hold on
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with BFGS')
pause(1)
plot(x0(1),x0(2),'ro','Markersize',12); pause(1)
plot(1,1,'rs','Markersize',12); pause(1)
for ii = 1:size(xbfgs,1)
plot(xbfgs(ii,1),xbfgs(ii,2),'bd','Markersize',12); pause(1)
end
hold off
figure(2);clf
contour(x1,x2,FF',[0:2:10 15:50:1000]);
hold on
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with GS')
pause(1)
plot(x0(1),x0(2),'ro','Markersize',12); pause(1)
plot(1,1,'rs','Markersize',12); pause(1)
for ii = 1:size(xgs,1)
plot(xgs(ii,1),xgs(ii,2),'m+','Markersize',12);% pause(1)
end
hold off
figure(3);clf
contour(x1,x2,FF',[0:2:10 15:50:1000]);
hold on
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with GS(5) and BFGS')
pause(1)
plot(x0(1),x0(2),'ro','Markersize',12); pause(1)
plot(1,1,'rs','Markersize',12); pause(1)
for ii = 1:size(xhyb,1)
plot(xhyb(ii,1),xhyb(ii,2),'m+','Markersize',12); pause(1)
end
hold off
figure(4);clf
mesh(x1,x2,FF');
axis([-3 3 -3 3 0 1000])
xlabel('x_1')
ylabel('x_2')
title('Rosenbrock with BFGS')
hold on
pause
plot3(x0(1),x0(2),rosen(x0')+5,'ro','Markersize',12,'MarkerFaceColor','r'); pause(1)
plot3(1,1,rosen([1 1]),'ms','Markersize',12,'MarkerFaceColor','m'); pause(1)
for ii = 1:size(xbfgs,1)
plot3(xbfgs(ii,1),xbfgs(ii,2),rosen(xbfgs(ii,:))+5,'gd','MarkerFaceColor','g'); pause(1)
end
%for ii = 1:size(xgs,1)
% plot3(xgs(ii,1),xgs(ii,2),rosen(xgs(ii,:))+5,’m+’); pause(1);
%end
hold off
hh=get(gcf,'children');
set(hh,'View',[-177 89.861],'CameraPosition',[-0.585976 11.1811 5116.63]);
function stop = outftn(x,optimValues,state)
global xpath
stop = 0;
xpath=[xpath;x'];
end
%
function [F,G]=rosen(x)
if size(x,1)==2, x=x'; end
F=100*(x(:,2)-x(:,1).^2).^2+(1-x(:,1)).^2;
G=[100*(4*x(1)^3-4*x(1)*x(2))+2*x(1)-2; 100*(2*x(2)-2*x(1)^2)];
end
近日闲来无事，总有一种无形的力量萦绕在朕身边，让朕精神涣散，昏昏欲睡。
可是，像朕这么有职业操守的社畜怎么能在上班期间睡瞌睡呢，我不禁陷入了沉思。。。。
突然旁边的IOS同事问：‘嘿，兄弟，我发现一个网站的图片很有意思啊，能不能帮我保存下来提升我的开发灵感？’
作为一个坚强的社畜怎么能说自己不行呢，当时朕就不假思索的答应：‘oh， It’s simple. Wait for me for a ...
