假设我创建了一个分类决策树，如下所示：
HP(1:size(HP), end) = 0; LP(1:size(LP), end) = 1;
% the dt's input & target pop
x = [HP(:,1:end-1); LP(:,1:end-1)]; t = [HP(:,end); LP(:,end)];
dt = fitctree(x,t);
view(dt)
view(dt, 'mode', 'graph');
输出如下：
1 if x2<-21.4866 then node 2 elseif x2>=-21.4866 then node 3 else 1
2 class = 1
3 if x2<20.093 then node 4 elseif x2>=20.093 then node 5 else 0
4 if x1<27.8438 then node 6 elseif x1>=27.8438 then node 7 else 0
5 if x2<39.6866 then node 8 elseif x2>=39.6866 then node 9 else 1
6 if x1<-33.7504 then node 10 elseif x1>=-33.7504 then node 11 else 0
7 class = 1
8 class = 1
9 class = 0
10 class = 1
11 if x2<1.53772 then node 12 elseif x2>=1.53772 then node 13 else 0
12 if x2<-2.50063 then node 14 elseif x2>=-2.50063 then node 15 else 0
13 class = 0
14 if x1<14.2153 then node 16 elseif x1>=14.2153 then node 17 else 0
15 class = 1
16 class = 0
17 class = 1
和
问题：
1）我怎样才能得到所有导致叶子值为'0'的路径？
2）是否有任何基于决策树创建新实例的微妙方法（除了随机生成实例并循环直到所需的输出）？例如，我想创建一个随机实例，上面的树将其分类为“0”