或者怎么运用确定的权重和阈值评价一条新数据得到类似结果？？？？？感恩感恩

  p=[ ... 1.0000 0.2360 0.5815 0.4000 0.6684 0.2374 0.3908 0.9672 0.1003 0.7150 0.1000 0.1000 0.6737 0.1000 0.5367 0.6911 0.4818 0.8912 0.8291 0.4375 0.8280 0.6000 0.5500 0.5385 0.4938 0.3077;...
    0.5000 0.1460 0.7801 1.0000 1.0000 0.2507 0.6765 0.9891 1.0000 0.2688 1.0000 0.2845 0.9744 0.6566 0.5328 0.7801 0.7300 0.9802 0.1209 0.1000 0.3160 0.7000 0.3000 0.6077 0.1141 0.4462;...
    0.5000 0.8812 0.3857 0.4000 0.7632 0.1000 0.4989 0.9790 0.8616 1.0000 0.4600 0.5325 0.9238 0.7485 0.6485 0.3857 0.7170 0.6934 0.7835 0.8875 0.4960 0.4000 1.0000 0.3308 0.3531 0.8615;...
    0.5000 0.8506 0.6730 1.0000 0.8579 0.5481 0.4699 0.9857 0.6003 0.3250 0.5200 0.9982 0.8037 0.9987 0.1819 0.6730 0.1164 0.5945 0.6981 0.5500 0.3080 0.4000 0.5500 0.6077 0.5219 0.5638];
t=[0.5256 0.5145 0.5771 0.5808 0.5661 0.5512 0.5380 0.6586 0.5417 0.4808 0.4897 0.5560 0.6355 0.5859];
net=newff([0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1],[10 1],{'logsig','purelin'},'traingdx');
rand('state',0);
net.trainparam.epochs=16000;
net.trainparam.goal=0.000001;
net.trainparam.lr=0.05;
p=p';
net=train(net,p,t);
a=sim(net,p)
q=[0.5000 0.8506 0.8062 1.0000 0.6211 0.6370 0.5833 0.9664 0.2039 0.1688 0.7400 0.1912 0.9454 0.9732 0.6458 0.9062 0.3491 0.7923 0.4532 0.1000 0.3440 0.1000 0.5500 0.1692 0.4094 0.6538];
q=q'
b=net.iw{1,1}
c=net.lw{2,1}
b1=net.b{1}
b2=net.b{2}

怎么评价q