所以我想确定我的SVM分类器的alpha
,beta
和gamma
值,我训练了我的SVM分类器并确定了alpha和支持向量。我的支持向量每行有3个变量,下面只有我支持向量的10行:
0.0904235536887480 -0.269325475875919 -0.678528701392414
-0.321039098061280 -0.507618180664821 -1.42365662798284
-0.0737761304021185 -0.269641311369441 -0.647521877863172
0.00105779420640393 -0.311226557946309 -0.667506146498475
0.0913098589312967 -0.289462325547514 -0.391261050348894
0.00622693949845773 -0.166248587146820 -0.149546793127464
-0.292302915842567 -0.564676268888150 -1.60153093563523
0.112997393643248 -0.310512134534035 -0.725281274142312
-0.135361511770186 -0.456321702624641 -1.26973221898260
-0.173160731078767 -0.434439033384469 -1.22687774941370
以下同样,我的alpha只有十行:
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
所以我基本上做的是确定如下的系数:
c = (-1)*[0.0904*x1 + (-0.2693)*x2 + (-0.6785)*x3] + (-1)*[(-0.321)*x1 + (-0.5076)*x2 + (-1.4236)*x3].....
等等,直到我的alpha
。
所以我在Matlab中编写了以下代码
syms x1, syms x2, syms x3;
alpha = SVMStruct.Alpha;
svm_vec = SVMStruct.SupportVectors;
for i = 1:size(alpha,1)
c(i) = alpha(i)*(svm_vec(i,1)*x1 + svm_vec(i,2)*x2 + svm_vec(i,3)*x3);
end
sum_it = sum(c);
但这创造了一个非常奇怪的输出:
(107845064549358722206080751595348329973204613074833920445585562521882937008164658045489239834546021458299139*x1)/50534761550197893278639420198779799540396107395587434771118149413836407509953624874438129483687080755200 + (95720990302914087945142311872326568914380675701489099929103269189530321664249312169660240242394455632803627*x2)/134178504805697854567421908803656709124500009291732154392279224305703564767807900528680550698065697177600 + (90626366614084720573448362168042659133754200934323766866906741825007634289583081638045482944881264585156183*x3)/125521827076297992982426946945356276277758073208394596044390242092432367040852552107475353878835652198400
这是预期的产量吗?为什么我把它作为分数?
根据输出,我是否正确实现了我的等式?
答案 0 :(得分:1)
您的计算输入是符号变量,因此使用这些变量的任何计算都将产生 符号变量的精确值。如果您想要结果的近似浮点表示,您需要将总和转换为double
double(sum_it)
为了做到这一点,您需要为x1
,x2
和x3
double(subs(sum(c), [x1, x2, x3], [1, 2, 3]))
话虽如此,我认为你实际上想要的是解决方程组。您可以使用\
运算符执行此操作
variables = svm_vec \ alpha;