function Yd = Nu_SVR_Sim(svm,Xt)
% 輸入?yún)?shù):
% svm 支持向量機(結構體變量)
% the following fields:
% ker - 核參數(shù)
% type - linear : k(x,y) = x'*y
% poly : k(x,y) = (x'*y+c)^d
% gauss : k(x,y) = exp(-0.5*(norm(x-y)/s)^2)
% tanh : k(x,y) = tanh(g*x'*y+c)
% degree - Degree d of polynomial kernel (positive scalar).
% offset - Offset c of polynomial and tanh kernel (scalar, negative for tanh).
% width - Width s of Gauss kernel (positive scalar).
% gamma - Slope g of the tanh kernel (positive scalar).
% x - 訓練樣本
% y - 訓練目標;
% a - 拉格朗日乘子
%
% Xt 測試樣本,n×d的矩陣,n為樣本個數(shù),d為樣本維數(shù)

% 輸出參數(shù):
% Yd 測試輸出,n×1的矩陣,n為樣本個數(shù),值為+1或-1

% ------------------------------------------------------------%

ker = svm.ker;
X = svm.x;
Y = svm.y;
a = svm.a; % 這里實際值為 a(1:n)-a(n+1:end),見文件"Nu_SVR_Train.m"第56行

% ------------------------------------------------------------%
% 求 b

epsilon = 1e-8; % 如果"絕對值"小于此值則認為是0
i_sv = find(abs(a)>epsilon); % 支持向量下標,這里對abs(a)進行判定

tmp = a'*Calckernel(ker,X,X(i_sv,:)); % 行向量
b = Y(i_sv)-tmp'; % 符號不一樣,決策函數(shù)就不一樣,實際上是一回事!見文件"Nu_SVR_Train.m"第33行
%b = Y(i_sv)+tmp';
b = mean(b);

% ------------------------------------------------------------%
% 測試輸出

nt = size(Xt,1); % 測試樣本數(shù)
tmp = a'*Calckernel(ker,X,Xt); % 符號不一樣,決策函數(shù)就不一樣,實際上是一回事!見文件"Nu_SVR_Train.m"第33行
%tmp = -a'*Calckernel(ker,X,Xt);
Yd = (tmp+b)';