假设神经网络二分类的过程就是给两个对象A,B称重的过程,比如用天平给A,B称重,至少还得给这个系统提供一个加速度或者引力作为第三个对象C,将A与B的重量分别用C的重量表示才有可能比较A与B的重量。
假设一个场景
如图 按照公式
可以得到
如果ωa,ωb,ωc是已知的情况下ma,mb,mc是可以求出来的,而图C和图D是等价的。
再假设
按照这个模型可以得到
只要ω12, ωx1, ωx2是已知的ωx,ω1,ω2就都可以解出来。
现在假设弹簧并联过程就是分类的过程。
让一个二分类的网络分类mnist的1和2来模拟这个并联分类的过程。
制作一个带有1个3*3卷积核,网络三层节点数分别是49*30*2的网络,分类mnist的1和2,将28*28的图片缩小到9*9,让1向1,0收敛,让2向0,1收敛。将这个网络简写成
d2(mnist 1,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
就相当于
A:m12=m1+m2
B:mx1=mx+m1
C:mx2=mx+m2
收敛条件是
if (Math.abs(f2[0]-y[0])< δ && Math.abs(f2[1]-y[1])< δ )
δ:网络输出值与目标值的差值的绝对值
n:满足收敛条件的网络的迭代次数
前面的大量实验证明在网络的结构不变的情况下,网络的迭代次数只与δ有关,因此可以用n(δ)曲线来评价网络的性能。
再用同样的办法做另外的两个网络
d2(mnistx,1)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
意思是用同样的网络分类mnist的1和一张x图片,让1向1,0收敛,让x向0,1收敛
d2(mnistx,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
意思是用同样的网络分类mnist的2和一张x图片,让x向1,0收敛,让2向0,1收敛
这张图片x就是一个9*9的二维数组是固定不变的
double [][]x=new double[9][9];
for(int n=0 ;n<9;n++){
for(int m=0 ;m<9 ;m++){
x[n][m]=((double)(n+1)*(m+1)/100);
}}
相当于让三个对象两两分类,使其中的任何一个都成为其他两个的参照物。
A,B,C分别对应三个网络
d2(mnist1,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
d2(mnistx,1)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
d2(mnistx,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
ωx1,ωx2都可以用实验的办法测出来。未知数ωx,ω1,ω2可以被解出来。
得到的具体数据是
δ |
实测ω12 |
实测ωx1 |
实测ωx2 |
计算ωx |
计算ω1 |
计算ω2 |
0.1 |
3240.94472 |
3007.42211 |
3048.92965 |
2852.11254 |
3191.22205 |
3293.06646 |
0.01 |
4054.32161 |
3925.70352 |
3923.44221 |
3806.53253 |
4056.81699 |
4051.83082 |
0.001 |
5436.51256 |
5430.06533 |
4862.39698 |
4857.78255 |
6267.98282 |
4867.02459 |
1.00E-04 |
7835.11055 |
8092.86935 |
6943.21106 |
7120.68623 |
9615.98184 |
6778.3771 |
9.00E-05 |
8545.90452 |
8347.01005 |
7159.78392 |
7041.59925 |
10822.3895 |
7284.12635 |
8.00E-05 |
8389.55779 |
8361.38191 |
7269.67337 |
7251.31856 |
10212.0326 |
7288.16827 |
7.00E-05 |
8453.26131 |
8623.83417 |
7217.86432 |
7323.18069 |
11012.4803 |
7116.9648 |
6.00E-05 |
8739.47739 |
8769.86432 |
7562.02513 |
7581.68476 |
10778.5924 |
7542.51764 |
5.00E-05 |
9147.41709 |
9559.63317 |
7825.96482 |
8079.44549 |
12341.1452 |
7594.93654 |
4.00E-05 |
9560.23618 |
9342.8794 |
7985.74372 |
7857.75132 |
12201.9724 |
8120.20152 |
3.00E-05 |
11010.593 |
10700.5879 |
8411.49246 |
8270.82208 |
18737.1058 |
8559.59355 |
2.00E-05 |
10811.6281 |
11149.1508 |
9442.06533 |
9664.38997 |
13629.6778 |
9234.41039 |
1.00E-05 |
14706.6633 |
14293.9347 |
10491.4221 |
10338.443 |
48071.2664 |
10651.3996 |
9.00E-06 |
14214.5327 |
14484.6985 |
11116.6181 |
11244.422 |
24818.2667 |
10993.0752 |
8.00E-06 |
16180.3216 |
15411.4925 |
11266.4673 |
10997.1401 |
81166.5532 |
11556.604 |
7.00E-06 |
14771.7085 |
16059.9347 |
12103.8844 |
12782.7295 |
24736.4317 |
11522.8456 |
6.00E-06 |
17338.0201 |
14472.9598 |
12639.0905 |
11390.6282 |
23308.0547 |
14415.0212 |
5.00E-06 |
16807.0201 |
16330.3769 |
13559.4422 |
13305.4072 |
23243.5347 |
13828.6064 |
4.00E-06 |
22731.2714 |
18221.2613 |
13440.8593 |
12298.0117 |
#NUM! |
14975.8977 |
3.00E-06 |
23345.6884 |
19257.2714 |
14331.804 |
13210.2514 |
#NUM! |
15798.038 |
2.00E-06 |
25483.4673 |
21420.0653 |
15953.392 |
14794.5356 |
#NUM! |
17435.6161 |
1.00E-06 |
41184.3317 |
27909.9045 |
19458.0352 |
17315.1616 |
#NUM! |
22662.8821 |
9.00E-07 |
40069.7286 |
30280.6985 |
18214.7588 |
16947.0862 |
#NUM! |
19817.378 |
8.00E-07 |
45442.1256 |
39508.608 |
20274.0955 |
19652.3264 |
#NUM! |
20958.8786 |
7.00E-07 |
47322.8894 |
33927.6734 |
23308.7286 |
21022.0399 |
#NUM! |
26552.1381 |
6.00E-07 |
49373.3015 |
42641.5628 |
23425.8241 |
22576.1586 |
#NUM! |
24379.2597 |
5.00E-07 |
58231.598 |
42912.2161 |
23533.8945 |
22066.3763 |
#NUM! |
25339.4515 |
4.00E-07 |
65387.7286 |
44772.2563 |
25571.9447 |
23608.2597 |
#NUM! |
28124.3856 |
3.00E-07 |
75583.7889 |
52129.1106 |
27933.2714 |
26041.6474 |
#NUM! |
30307.7503 |
2.00E-07 |
83117.2714 |
65803.6834 |
36739.1608 |
34772.15 |
#NUM! |
39082.8235 |
1.00E-07 |
125417.844 |
80386.4271 |
46477.4824 |
42481.843 |
#NUM! |
51864.8531 |
将计算到的ω1和ω2画成曲线
所以这个网络d2(mnist 1,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)的迭代次数n可以理解成是由参与分类的对象的频率决定的,n的精确的表达式为
因为ω1和ω2是特征的彼此区分的所以可以被分类。因此从数学上证明了神经网络的分类过程是可以用简谐振动来近似的。
再由质量可以得到方程组
所以如果假设mx=1则m1,m2,mx1,mx2,m12都可以解出来得到的数据是
δ |
mx |
m1 |
m2 |
mx1 |
mx2 |
m12 |
0.1 |
1 |
0.798765 |
0.750123 |
1.798765 |
1.750123 |
1.548888 |
0.01 |
1 |
0.880417 |
0.882585 |
1.880417 |
1.882585 |
1.763002 |
0.001 |
1 |
0.600649 |
0.996206 |
1.600649 |
1.996206 |
1.596854 |
1.00E-04 |
1 |
0.548348 |
1.103551 |
1.548348 |
2.103551 |
1.651899 |
9.00E-05 |
1 |
0.423347 |
0.934518 |
1.423347 |
1.934518 |
1.357865 |
8.00E-05 |
1 |
0.504208 |
0.989913 |
1.504208 |
1.989913 |
1.494121 |
7.00E-05 |
1 |
0.442211 |
1.05879 |
1.442211 |
2.05879 |
1.501001 |
6.00E-05 |
1 |
0.494775 |
1.010413 |
1.494775 |
2.010413 |
1.505187 |
5.00E-05 |
1 |
0.4286 |
1.131657 |
1.4286 |
2.131657 |
1.560257 |
4.00E-05 |
1 |
0.414702 |
0.936403 |
1.414702 |
1.936403 |
1.351106 |
3.00E-05 |
1 |
0.194846 |
0.933665 |
1.194846 |
1.933665 |
1.128511 |
2.00E-05 |
1 |
0.50278 |
1.095294 |
1.50278 |
2.095294 |
1.598073 |
1.00E-05 |
1 |
0.046253 |
0.9421 |
1.046253 |
1.9421 |
0.988353 |
9.00E-06 |
1 |
0.205273 |
1.046251 |
1.205273 |
2.046251 |
1.251524 |
8.00E-06 |
1 |
0.018357 |
0.905522 |
1.018357 |
1.905522 |
0.923879 |
7.00E-06 |
1 |
0.267038 |
1.230631 |
1.267038 |
2.230631 |
1.497669 |
6.00E-06 |
1 |
0.238827 |
0.624403 |
1.238827 |
1.624403 |
0.86323 |
5.00E-06 |
1 |
0.327682 |
0.925762 |
1.327682 |
1.925762 |
1.253444 |
4.00E-06 |
1 |
#NUM! |
0.674348 |
#NUM! |
1.674348 |
#NUM! |
3.00E-06 |
1 |
#NUM! |
0.699223 |
#NUM! |
1.699223 |
#NUM! |
2.00E-06 |
1 |
#NUM! |
0.719993 |
#NUM! |
1.719993 |
#NUM! |
1.00E-06 |
1 |
#NUM! |
0.583745 |
#NUM! |
1.583745 |
#NUM! |
9.00E-07 |
1 |
#NUM! |
0.731304 |
#NUM! |
1.731304 |
#NUM! |
8.00E-07 |
1 |
#NUM! |
0.879208 |
#NUM! |
1.879208 |
#NUM! |
7.00E-07 |
1 |
#NUM! |
0.626831 |
#NUM! |
1.626831 |
#NUM! |
6.00E-07 |
1 |
#NUM! |
0.857549 |
#NUM! |
1.857549 |
#NUM! |
5.00E-07 |
1 |
#NUM! |
0.758346 |
#NUM! |
1.758346 |
#NUM! |
4.00E-07 |
1 |
#NUM! |
0.704631 |
#NUM! |
1.704631 |
#NUM! |
3.00E-07 |
1 |
#NUM! |
0.738294 |
#NUM! |
1.738294 |
#NUM! |
2.00E-07 |
1 |
#NUM! |
0.791573 |
#NUM! |
1.791573 |
#NUM! |
1.00E-07 |
1 |
#NUM! |
0.670904 |
#NUM! |
1.670904 |
#NUM! |
比较计算出来的m1和m2的曲线
这个结果表明被分类对象的质量也是特征分开的。
所以现在已经从数学上证明了将神经网络理解成是振子系统是可能的。
关于调参
δ收敛标准
r学习率
x权重分母,(1-99的随机数)/x
num隐藏层节点数量
n迭代次数
p-ave 网络收敛199次的准确率的平均值
p-max网络收敛199次的准确率的最大值
t收敛时间
δ |
r |
x |
num |
n |
p-ave |
p-max |
t |
减小 |
不变 |
不变 |
不变 |
增加 |
增加 |
增加 |
增加 |
减小 |
减小 |
不变 |
不变 |
增加 |
增加 |
增加 |
增加 |
减小 |
不变 |
减小 |
不变 |
增加 |
增加 |
增加 |
增加 |
减小 |
不变 |
不变 |
减小 |
增加 |
增加 |
增加 |
可能有极小值 |
减小 |
减小 |
减小 |
减小 |
增加 |
增加 |
增加 |
可能有极小值 |
不变 |
减小 |
不变 |
不变 |
增加 |
增加 |
小幅增加,几乎是定值 |
增加 |
不变 |
不变 |
减小 |
不变 |
增加 |
增加 |
几乎是定值 |
增加 |
不变 |
不变 |
不变 |
减小 |
增加 |
增加 |
几乎是定值 |
开口向上有极小值 |
不变 |
减小 |
减小 |
减小 |
增加 |
增加 |
几乎是定值 |
可能有极小值 |
比如这个网络
d2(minst0,3)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
先让学习率r=1e-3,再次让学习率r=0.1,会导致网络看起来收敛速度快好多,但性能时好时坏。
因为r增大会导致迭代次数n减小,使得网络在更短的时间里可能达到更小的δ,而δ减小导致准确率增加;
r增大也会导致平均性能下降,使网络性能不稳定。
实验数据 |
学习率 0.1 |
权重初始化方式 |
Random rand1 =new Random(); |
int ti1=rand1.nextInt(98)+1; |
int xx=1; |
if(ti1%2==0) |
{ xx=-1;} |
tw[a][b]=xx*((double)ti1/x); |
第一层第二层和卷积核的权重的初始化的x分别为1000,1000,200 |
d2(mnist x,1)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
的数据在《神经网络与并联的弹簧》2019-2-2
https://blog.csdn.net/georgesale/article/details/86749333
d2(mnist x,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
的数据在《神经网络与振子动力系统---验算实例二分类0,2》2019-2-9
https://blog.csdn.net/georgesale/article/details/86802727
d2(mnist1,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)的数据
12 |
||||||||
f2[0] |
f2[1] |
迭代次数n |
平均准确率p-ave |
δ |
耗时ms/次 |
耗时ms/199次 |
耗时min/199次 |
最大准确率p-max |
0.5023827 |
0.4974736 |
16.437186 |
0.5073869 |
0.5 |
810.02513 |
161210 |
2.6868333 |
0.7816251 |
0.397708 |
0.6016682 |
2102.5729 |
0.8864776 |
0.4 |
1670.3166 |
332393 |
5.5398833 |
0.9584488 |
0.2889459 |
0.7108977 |
2517.3015 |
0.8866934 |
0.3 |
1837.8693 |
365751 |
6.09585 |
0.9626039 |
0.208201 |
0.7921997 |
2821.0603 |
0.8819095 |
0.2 |
1964.3266 |
390901 |
6.5150167 |
0.9561404 |
0.1100802 |
0.8899132 |
3240.9447 |
0.8815685 |
0.1 |
2135.7136 |
425007 |
7.08345 |
0.9644506 |
0.0272711 |
0.972727 |
4054.3216 |
0.9054042 |
0.01 |
2482.2462 |
493968 |
8.2328 |
0.9718375 |
0.0809869 |
0.9190128 |
5436.5126 |
0.9073669 |
0.001 |
3083.6884 |
613654 |
10.227567 |
0.9759926 |
0.0854835 |
0.9145163 |
7835.1106 |
0.9183173 |
1.00E-04 |
4005.5678 |
797111 |
13.285183 |
0.9787627 |
0.1156265 |
0.8843735 |
8545.9045 |
0.9129164 |
9.00E-05 |
4547.0603 |
904866 |
15.0811 |
0.9787627 |
0.0804468 |
0.9195531 |
8389.5578 |
0.9215074 |
8.00E-05 |
4416.1508 |
878814 |
14.6469 |
0.9819945 |
0.1658606 |
0.8341393 |
8453.2613 |
0.9066686 |
7.00E-05 |
3618.7286 |
720127 |
12.002117 |
0.9801477 |
0.080436 |
0.919564 |
8739.4774 |
0.9232938 |
6.00E-05 |
4779.9749 |
951215 |
15.853583 |
0.9806094 |
0.1105784 |
0.8894216 |
9147.4171 |
0.9212545 |
5.00E-05 |
5030.1156 |
1000993 |
16.683217 |
0.9833795 |
0.155798 |
0.844202 |
9560.2362 |
0.9127493 |
4.00E-05 |
5161.4171 |
1027122 |
17.1187 |
0.9815328 |
0.1909676 |
0.8090324 |
11010.593 |
0.9041305 |
3.00E-05 |
5377.6784 |
1070158 |
17.835967 |
0.9819945 |
0.1608132 |
0.8391868 |
10811.628 |
0.9150786 |
2.00E-05 |
5808.1256 |
1155818 |
19.263633 |
0.9759926 |
0.2914602 |
0.7085397 |
14706.663 |
0.8996947 |
1.00E-05 |
7410.8191 |
1474753 |
24.579217 |
0.9815328 |
0.2311591 |
0.7688409 |
14214.533 |
0.9082161 |
9.00E-06 |
6693.7739 |
1332061 |
22.201017 |
0.9815328 |
0.3216101 |
0.6783899 |
16180.322 |
0.8999383 |
8.00E-06 |
8096.8844 |
1611295 |
26.854917 |
0.9810711 |
0.2613087 |
0.7386913 |
14771.709 |
0.9083413 |
7.00E-06 |
6779.5427 |
1349145 |
22.48575 |
0.9824561 |
0.2814088 |
0.7185912 |
17338.02 |
0.9131693 |
6.00E-06 |
8348.6884 |
1661389 |
27.689817 |
0.9810711 |
0.3065339 |
0.6934661 |
16807.02 |
0.8994488 |
5.00E-06 |
7783.799 |
1548977 |
25.816283 |
0.9815328 |
0.3668348 |
0.6331652 |
22731.271 |
0.9040563 |
4.00E-06 |
10680.613 |
2125442 |
35.424033 |
0.9819945 |
0.3417091 |
0.6582909 |
23345.688 |
0.9076639 |
3.00E-06 |
10480.337 |
2085587 |
34.759783 |
0.9801477 |
0.3316587 |
0.6683413 |
25483.467 |
0.9111161 |
2.00E-06 |
11123.176 |
2213513 |
36.891883 |
0.9829178 |
0.4321608 |
0.5678392 |
41184.332 |
0.9101881 |
1.00E-06 |
16846.216 |
3352421 |
55.873683 |
0.9833795 |
0.4120604 |
0.5879396 |
40069.729 |
0.9071048 |
9.00E-07 |
16596.849 |
3302773 |
55.046217 |
0.9810711 |
0.4271357 |
0.5728643 |
45442.126 |
0.9149 |
8.00E-07 |
18435.915 |
3668748 |
61.1458 |
0.9833795 |
0.437186 |
0.562814 |
47322.889 |
0.9163361 |
7.00E-07 |
18987.095 |
3778436 |
62.973933 |
0.9856879 |
0.4472362 |
0.5527638 |
49373.302 |
0.9099537 |
6.00E-07 |
16642.618 |
3311885 |
55.198083 |
0.9824561 |
0.4321608 |
0.5678392 |
58231.598 |
0.9154568 |
5.00E-07 |
19481.638 |
3876877 |
64.614617 |
0.988458 |
0.5527638 |
0.4472362 |
65387.729 |
0.9140485 |
4.00E-07 |
21621.925 |
4302766 |
71.712767 |
0.9843029 |
0.5125628 |
0.4874372 |
75583.789 |
0.9151529 |
3.00E-07 |
27926.186 |
5557311 |
92.62185 |
0.9861496 |
0.4170854 |
0.5829146 |
83117.271 |
0.9344692 |
2.00E-07 |
34695.98 |
6904500 |
115.075 |
0.9866113 |
0.5477387 |
0.4522613 |
125417.84 |
0.9359934 |
1.00E-07 |
50101.719 |
9970242 |
166.1707 |
0.9875346 |
f2[0] f2[1] 迭代次数n 平均准确率p-ave δ 耗时ms/次
0.532407423 0.473921242 1 0.523545706 0.5 1410
0.498737088 0.50020728 24 0.781625115 0.5 971
0.49721811 0.500578771 32 0.476454294 0.5 940
0.500582555 0.499922061 47 0.523545706 0.5 843
0.499860097 0.50018386 38 0.554478301 0.5 923
0.459137274 0.503046 2 0.476454294 0.5 798
0.530324093 0.485589644 1 0.523545706 0.5 814
0.520870239 0.454683221 1 0.523545706 0.5 814
0.54418103 0.489618106 1 0.523545706 0.5 830
0.486092097 0.500146762 20 0.476454294 0.5 844
0.491390152 0.549917687 2 0.476454294 0.5 813
0.500258618 0.499702299 51 0.523545706 0.5 814
0.517941548 0.438668847 1 0.523545706 0.5 799
0.463990915 0.523073752 2 0.476454294 0.5 782
0.5001082 0.494276601 21 0.523545706 0.5 814
0.454700001 0.598822182 2 0.476454294 0.5 781
0.463720049 0.530765101 2 0.476454294 0.5 784
0.500414745 0.499690426 31 0.523545706 0.5 798
0.499076 0.503368132 22 0.476454294 0.5 845
0.503701945 0.499639195 23 0.523545706 0.5 845
0.499699254 0.502482247 34 0.476454294 0.5 860
0.500231795 0.499230296 47 0.523545706 0.5 861
0.500219043 0.496375905 29 0.523545706 0.5 823
0.531951326 0.46774272 1 0.523545706 0.5 782
0.499939847 0.500005226 42 0.415974146 0.5 830
0.498059164 0.561305932 2 0.476454294 0.5 797
0.514143153 0.451222746 1 0.523545706 0.5 784
0.500962546 0.499969143 39 0.523545706 0.5 798
0.499982128 0.513501669 18 0.476454294 0.5 799
0.461722547 0.53613066 2 0.476454294 0.5 782
0.501369234 0.499799163 39 0.523545706 0.5 829
0.533596207 0.484441002 1 0.523545706 0.5 782
0.542252406 0.491891689 1 0.523545706 0.5 784
0.499878313 0.500389257 44 0.728070175 0.5 813
0.484226614 0.514171087 2 0.476454294 0.5 783
0.500482181 0.499978134 47 0.523545706 0.5 813
0.498122568 0.500054585 24 0.458910434 0.5 814
0.526398335 0.489601668 1 0.523545706 0.5 783
0.511284534 0.486708858 1 0.523545706 0.5 783
0.477105969 0.547300588 2 0.476454294 0.5 782
0.541844163 0.466557998 1 0.523545706 0.5 782
0.500339488 0.499143948 39 0.523545706 0.5 829
0.523070532 0.41397974 1 0.523545706 0.5 799
0.498972702 0.500849852 46 0.577100646 0.5 813
0.47120585 0.501343351 2 0.476454294 0.5 782
0.524262275 0.447876236 1 0.523545706 0.5 807
0.499765654 0.500070913 44 0.493074792 0.5 839
0.50494237 0.499595663 15 0.523545706 0.5 800
0.464952785 0.518003625 2 0.476454294 0.5 833
0.460482697 0.50102793 2 0.476454294 0.5 813
0.499601668 0.500462503 50 0.439519852 0.5 798
0.517370287 0.482577374 1 0.523545706 0.5 798
0.507137615 0.475038997 1 0.523545706 0.5 841
0.486407113 0.509079783 2 0.476454294 0.5 813
0.520088723 0.45605303 1 0.523545706 0.5 815
0.500063761 0.499943625 25 0.523545706 0.5 797
0.499523494 0.500901211 36 0.503693444 0.5 830
0.500133973 0.499012754 35 0.523545706 0.5 813
0.500147353 0.499344187 47 0.523545706 0.5 830
0.499481832 0.502254337 36 0.532317636 0.5 798
0.478631473 0.515580508 2 0.476454294 0.5 782
0.499475497 0.501916815 38 0.476454294 0.5 814
0.533302365 0.460443941 1 0.523545706 0.5 814
0.470339205 0.50633883 2 0.476454294 0.5 782
0.501661863 0.499862009 25 0.523545706 0.5 815
0.539194412 0.453423823 1 0.523545706 0.5 781
0.498783415 0.500643878 42 0.471837488 0.5 799
0.523076825 0.444501883 1 0.523545706 0.5 798
0.500583377 0.49996831 45 0.523545706 0.5 830
0.480115185 0.535922213 2 0.476454294 0.5 782
0.547246023 0.492820118 1 0.523545706 0.5 798
0.462404519 0.553240331 2 0.476454294 0.5 782
0.499189522 0.500592319 44 0.479686057 0.5 830
0.498081933 0.500231638 30 0.476454294 0.5 798
0.576370767 0.478660363 1 0.523545706 0.5 799
0.463433555 0.522385523 2 0.476454294 0.5 797
0.49944076 0.50160457 42 0.665281625 0.5 814
0.51370431 0.49317857 1 0.523545706 0.5 814
0.49868665 0.500119749 46 0.421975993 0.5 799
0.489279055 0.500447158 2 0.476454294 0.5 797
0.499599297 0.500921439 42 0.510156971 0.5 814
0.500800068 0.498562012 27 0.523545706 0.5 798
0.523292112 0.471132527 1 0.523545706 0.5 799
0.50235894 0.499969315 35 0.523545706 0.5 797
0.469680084 0.516622758 2 0.476454294 0.5 799
0.499692552 0.502095095 32 0.466759003 0.5 797
0.500146291 0.498164219 37 0.523545706 0.5 830
0.528651225 0.426843403 1 0.523545706 0.5 798
0.499698836 0.504393095 28 0.476454294 0.5 783
0.53909057 0.467192874 1 0.523545706 0.5 798
0.507804082 0.452849088 1 0.523545706 0.5 798
0.573352127 0.451030081 1 0.523545706 0.5 813
0.504591165 0.457678925 1 0.523545706 0.5 832
0.504741432 0.492169437 1 0.523545706 0.5 797
0.530655368 0.443808459 1 0.523545706 0.5 799
0.525555976 0.488890825 1 0.523545706 0.5 797
0.499982729 0.502355104 36 0.463527239 0.5 861
0.436503373 0.521498552 2 0.476454294 0.5 798
0.539741217 0.483837393 1 0.523545706 0.5 830
0.500139993 0.499311933 41 0.523545706 0.5 814
0.500404207 0.498254323 27 0.523545706 0.5 799
0.426524964 0.527264752 2 0.476454294 0.5 813
0.464005124 0.571822495 2 0.476454294 0.5 806
0.499902046 0.501046682 44 0.458910434 0.5 813
0.496559535 0.500508241 32 0.476454294 0.5 799
0.437729608 0.509771427 2 0.476454294 0.5 781
0.557748025 0.490157441 1 0.523545706 0.5 799
0.528400124 0.419957471 1 0.523545706 0.5 799
0.499111431 0.503185393 32 0.552169898 0.5 830
0.49563541 0.500348026 28 0.476454294 0.5 782
0.442331612 0.545843902 2 0.476454294 0.5 798
0.494863532 0.518770644 2 0.476454294 0.5 782
0.500808916 0.49953831 43 0.523545706 0.5 799
0.498753974 0.50038106 34 0.443674977 0.5 814
0.469107552 0.553860733 2 0.476454294 0.5 799
0.490987233 0.502416576 2 0.476454294 0.5 796
0.494465637 0.506060801 2 0.476454294 0.5 784
0.471484086 0.548373506 2 0.476454294 0.5 782
0.536750204 0.496327128 1 0.523545706 0.5 814
0.500052127 0.497664621 39 0.523545706 0.5 798
0.540650997 0.473762841 1 0.523545706 0.5 814
0.528928983 0.445963312 1 0.523545706 0.5 797
0.502139699 0.499504314 39 0.523545706 0.5 799
0.522706032 0.458203606 1 0.523545706 0.5 798
0.499191241 0.50016376 36 0.510156971 0.5 815
0.499544928 0.501107329 46 0.504616805 0.5 812
0.522268096 0.443553265 1 0.523545706 0.5 783
0.498764211 0.500508081 44 0.457987073 0.5 845
0.498762826 0.500160261 44 0.440443213 0.5 814
0.536176745 0.457761333 1 0.523545706 0.5 798
0.507453293 0.480881881 1 0.523545706 0.5 798
0.499193118 0.500586464 46 0.728070175 0.5 798
0.516352709 0.444566181 1 0.523545706 0.5 814
0.499644939 0.531165423 4 0.476454294 0.5 798
0.513242607 0.484491558 1 0.523545706 0.5 815
0.523171031 0.445694487 1 0.523545706 0.5 781
0.549294374 0.475853261 1 0.523545706 0.5 799
0.517954941 0.495352118 1 0.523545706 0.5 798
0.504528734 0.47110955 1 0.523545706 0.5 789
0.496457709 0.500021153 26 0.499076639 0.5 798
0.501011797 0.499797489 37 0.523545706 0.5 829
0.500352052 0.499694343 41 0.523545706 0.5 798
0.50943532 0.46653427 1 0.523545706 0.5 798
0.489217862 0.514088293 2 0.476454294 0.5 783
0.499101841 0.500068407 38 0.379963066 0.5 798
0.499743722 0.502354551 20 0.525392428 0.5 797
0.552294642 0.490630555 1 0.523545706 0.5 830
0.499692745 0.500142581 42 0.675900277 0.5 814
0.501270897 0.499805901 39 0.523545706 0.5 799
0.502474717 0.452964912 1 0.523545706 0.5 782
0.495433468 0.500058043 26 0.476454294 0.5 829
0.501025807 0.499730124 33 0.523545706 0.5 814
0.497327783 0.544936833 2 0.476454294 0.5 797
0.500236778 0.499944628 43 0.523545706 0.5 822
0.50012001 0.499024298 39 0.523545706 0.5 799
0.495243839 0.504969788 2 0.476454294 0.5 797
0.547594221 0.442367809 1 0.523545706 0.5 799
0.508366572 0.493348442 1 0.523545706 0.5 781
0.540756948 0.491111823 1 0.523545706 0.5 815
0.563347475 0.463597808 1 0.523545706 0.5 782
0.491817119 0.533001288 2 0.476454294 0.5 799
0.499591084 0.50247397 8 0.475992613 0.5 782
0.499611219 0.50329609 30 0.609879963 0.5 814
0.499982938 0.501646767 38 0.444598338 0.5 813
0.496392835 0.500299744 16 0.476454294 0.5 783
0.501790596 0.499935855 31 0.523545706 0.5 814
0.462722987 0.50838485 2 0.476454294 0.5 783
0.499392339 0.501158318 40 0.486149584 0.5 797
0.498266578 0.50009054 34 0.475069252 0.5 861
0.500700609 0.494988912 27 0.523545706 0.5 798
0.524791907 0.462081301 1 0.523545706 0.5 783
0.586680018 0.498324524 1 0.523545706 0.5 829
0.492041308 0.533131773 2 0.476454294 0.5 814
0.501580073 0.499853639 31 0.523545706 0.5 798
0.500047666 0.498547505 33 0.523545706 0.5 799
0.451134809 0.580527551 2 0.476454294 0.5 782
0.490068115 0.529734791 2 0.476454294 0.5 799
0.499766525 0.502046384 30 0.599261311 0.5 796
0.499733723 0.501679545 42 0.476915974 0.5 831
0.500374466 0.494713751 21 0.523545706 0.5 797
0.503579619 0.498903886 25 0.523545706 0.5 815
0.522870354 0.464678809 1 0.523545706 0.5 782
0.493996754 0.525227019 2 0.476454294 0.5 814
0.499923848 0.500914575 38 0.450138504 0.5 813
0.459593911 0.609734843 2 0.476454294 0.5 799
0.548507557 0.439585904 1 0.523545706 0.5 798
0.437771918 0.569441732 2 0.476454294 0.5 814
0.46010404 0.513000304 2 0.476454294 0.5 781
0.487009336 0.510012034 2 0.476454294 0.5 815
0.471196165 0.50349474 2 0.476454294 0.5 782
0.469467795 0.534031407 2 0.476454294 0.5 799
0.493057068 0.538347435 2 0.476454294 0.5 829
0.456831421 0.523857366 2 0.476454294 0.5 782
0.499799825 0.501782423 38 0.472760849 0.5 814
0.501031641 0.499679682 39 0.523545706 0.5 814
0.519742107 0.477113067 1 0.523545706 0.5 814
0.477023042 0.516500585 2 0.476454294 0.5 783
0.566589372 0.455289068 1 0.523545706 0.5 781
0.498264338 0.500146511 36 0.444136657 0.5 830
161210
0.39645888 0.605026077 2136 0.913204063 0.4 2254
0.379618016 0.615639445 1410 0.941828255 0.4 1378
0.392712177 0.604953734 1410 0.923822715 0.4 1361
0.399972166 0.602487164 2006 0.888273315 0.4 1676
0.39613979 0.602272484 1732 0.90166205 0.4 1518
0.36673453 0.631756453 2104 0.868421053 0.4 1686
0.383627602 0.622595224 2468 0.89242844 0.4 1942
0.390673528 0.607564711 1792 0.885041551 0.4 1564
0.384680542 0.611154372 2120 0.878578024 0.4 1643
0.376245251 0.631049468 1482 0.852262235 0.4 1409
0.375510146 0.623807298 1410 0.936288089 0.4 1361
0.381248217 0.615284351 3020 0.916435826 0.4 2192
0.37937074 0.615521011 1410 0.926131117 0.4 1392
0.3958204 0.600386055 2104 0.86611265 0.4 1628
0.603548705 0.391368587 2243 0.601569714 0.4 1722
0.3993342 0.606192695 2060 0.934903047 0.4 1611
0.398385753 0.601408192 2006 0.840258541 0.4 1634
0.394481303 0.608077422 2076 0.920590951 0.4 1628
0.396483319 0.604899305 2218 0.882271468 0.4 1705
0.397351786 0.606002973 2486 0.915050785 0.4 1801
0.394099805 0.606075436 2104 0.887349954 0.4 1692
0.385239555 0.615035023 2438 0.872576177 0.4 1832
0.393917951 0.61046593 1792 0.870729455 0.4 1534
0.387192911 0.604844417 1248 0.922899354 0.4 1299
0.394034241 0.605545078 4094 0.872576177 0.4 2739
0.603764044 0.398466532 1081 0.704524469 0.4 1252
0.385529419 0.609537963 2488 0.883194829 0.4 1832
0.395976737 0.60318702 1912 0.933056325 0.4 1580
0.394065339 0.60734881 2488 0.87488458 0.4 1800
0.398642597 0.60578725 1368 0.888734995 0.4 1347
0.386547319 0.613742723 1792 0.855032318 0.4 1533
0.383021611 0.618460552 2070 0.879039705 0.4 1644
0.396563129 0.600780165 2104 0.922899354 0.4 1665
0.384744079 0.614452312 2006 0.820406279 0.4 1643
0.392860955 0.607071683 3032 0.866574331 0.4 2035
0.393409859 0.606054771 2358 0.901200369 0.4 1802
0.377437073 0.618654779 2104 0.895660203 0.4 1675
0.395210712 0.603091253 2226 0.862880886 0.4 1722
0.392958734 0.60934827 1482 0.861957525 0.4 1409
0.382339377 0.611731979 2226 0.855493998 0.4 1689
0.386551026 0.610844905 4158 0.858264081 0.4 2525
0.391340616 0.611592056 2468 0.896583564 0.4 1847
0.394564289 0.608238187 1684 0.869344414 0.4 1511
0.394222912 0.610407027 1912 0.903047091 0.4 1567
0.39793954 0.607344055 2200 0.832409972 0.4 1674
0.369544437 0.627134262 2104 0.886888273 0.4 1676
0.382296446 0.623006842 2448 0.944136657 0.4 1784
0.3900506 0.605121002 2438 0.957063712 0.4 1817
0.391877253 0.603067773 1116 0.891966759 0.4 1267
0.392248965 0.602084605 1912 0.944136657 0.4 1580
0.395193636 0.6042013 2442 0.855032318 0.4 1769
0.393656293 0.604602084 1962 0.909048938 0.4 1612
0.363913657 0.62728126 1638 0.939058172 0.4 1518
0.380885948 0.618565858 3020 0.925207756 0.4 2052
0.383837652 0.618927442 2104 0.886426593 0.4 1675
0.605629727 0.392703532 1747 0.575253924 0.4 1502
0.377833833 0.616969815 3020 0.891505078 0.4 2004
0.3864433 0.613023447 2688 0.88411819 0.4 1902
0.390253834 0.6055922 2104 0.908587258 0.4 1674
0.388360945 0.612365395 1638 0.93767313 0.4 1471
0.390002816 0.604606523 1248 0.927054478 0.4 1315
0.39956677 0.600983204 2076 0.903970452 0.4 1650
0.603701116 0.397923605 2215 0.552169898 0.4 1690
0.392168098 0.610457136 1630 0.956602031 0.4 1455
0.371755252 0.619925 2774 0.861034164 0.4 1941
0.394973621 0.600230658 1410 0.933979686 0.4 1369
0.370063067 0.625865759 2104 0.900277008 0.4 1658
0.392741286 0.607412899 1612 0.850877193 0.4 1472
0.392164675 0.607446031 1298 0.942751616 0.4 1330
0.390857171 0.610555287 2784 0.913204063 0.4 1944
0.393476361 0.609646418 2612 0.944136657 0.4 1830
0.383566315 0.623277559 1816 0.901200369 0.4 1566
0.397055133 0.600332203 2400 0.852262235 0.4 1800
0.389389015 0.605285532 2218 0.889658356 0.4 1736
0.398586868 0.604844895 1896 0.922899354 0.4 1597
0.389689522 0.60581578 1762 0.878116343 0.4 1553
0.397070313 0.607545529 1624 0.923361034 0.4 1471
0.382995854 0.608255268 1898 0.923361034 0.4 1581
0.386946313 0.613946977 1912 0.931209603 0.4 1580
0.397124934 0.604715743 1482 0.871652816 0.4 1417
0.388503439 0.607548264 2104 0.905355494 0.4 1643
0.377469597 0.616006125 2448 0.921514312 0.4 1800
0.383134772 0.61511854 2774 0.870267775 0.4 1931
0.398111731 0.601400359 1784 0.899815328 0.4 1535
0.384075969 0.62005321 2986 0.924746076 0.4 1989
0.388795989 0.606662341 2494 0.915512465 0.4 1815
0.377061963 0.624180225 1722 0.899353647 0.4 1503
0.383833319 0.609680902 3032 0.867036011 0.4 2004
0.398328101 0.604779314 3338 0.858725762 0.4 2167
0.398746987 0.602480485 2486 0.915512465 0.4 1832
0.395298564 0.600301261 2274 0.91920591 0.4 1741
0.396894059 0.601101542 1732 0.859649123 0.4 1503
0.395596617 0.604012992 2968 0.903047091 0.4 2028
0.399114321 0.604419066 2666 0.906740536 0.4 1933
0.3962615 0.610447486 2168 0.890581717 0.4 1705
0.393369781 0.609662143 1708 0.851338873 0.4 1533
0.393392784 0.606409921 1364 0.864265928 0.4 1347
0.392379812 0.608308241 3444 0.879501385 0.4 2195
0.389421208 0.60767273 1662 0.915512465 0.4 1455
0.392092416 0.606149478 2438 0.890581717 0.4 1817
0.39864329 0.603606873 2776 0.832409972 0.4 1925
0.391971564 0.609865832 2448 0.929362881 0.4 1833
0.396611923 0.604074816 2488 0.893813481 0.4 1834
0.359774456 0.629793092 2006 0.87488458 0.4 1659
0.388636542 0.614377535 2014 0.900277008 0.4 1643
0.393995336 0.604362207 1410 0.931209603 0.4 1377
0.394246809 0.608988609 3498 0.86565097 0.4 2239
0.386009231 0.61183857 1922 0.89289012 0.4 1581
0.381673254 0.615839344 2574 0.889658356 0.4 1867
0.391634263 0.605892589 2164 0.861495845 0.4 1706
0.395636873 0.600126006 2732 0.881809788 0.4 1926
0.393696515 0.610639576 2104 0.929362881 0.4 1675
0.363739643 0.644409838 1482 0.902585411 0.4 1441
0.393750164 0.603882599 1982 0.945060018 0.4 1611
0.392655128 0.606341481 1804 0.926131117 0.4 1519
0.396406988 0.610730175 2394 0.863342567 0.4 1847
0.388485571 0.611955734 2104 0.878116343 0.4 1648
0.395792971 0.607199685 1804 0.921975993 0.4 1565
0.39156268 0.604514544 1804 0.924284395 0.4 1549
0.39776956 0.604924006 2448 0.891505078 0.4 1799
0.396874829 0.602956832 2290 0.898430286 0.4 1768
0.38564149 0.614862531 2438 0.940904894 0.4 1801
0.60315827 0.394842593 1747 0.557248384 0.4 1533
0.377980241 0.627191549 2104 0.903047091 0.4 1662
0.393705708 0.603054957 1770 0.889196676 0.4 1503
0.396113614 0.605999166 1566 0.918744229 0.4 1439
0.378702177 0.615922416 1922 0.89242844 0.4 1581
0.390325267 0.604355617 1788 0.90212373 0.4 1550
0.376201262 0.614455605 2688 0.907202216 0.4 1909
0.382925777 0.615802209 2488 0.900738689 0.4 1831
0.397089199 0.610192766 2574 0.909048938 0.4 1853
0.389391347 0.603692653 3962 0.873499538 0.4 2419
0.388004747 0.612742346 2290 0.891966759 0.4 1753
0.396799695 0.60088646 1290 0.907202216 0.4 1347
0.392350752 0.604090845 3240 0.90166205 0.4 2129
0.393109592 0.612946362 1756 0.923822715 0.4 1518
0.395503449 0.602155425 1566 0.92843952 0.4 1456
0.379387411 0.615100574 2394 0.906740536 0.4 1769
0.383868538 0.620111903 2574 0.87488458 0.4 1879
0.379103731 0.619208669 3020 0.916435826 0.4 2051
0.38572137 0.61182501 2326 0.871652816 0.4 1753
0.389946487 0.607078329 1792 0.869344414 0.4 1518
0.378811804 0.616596915 1912 0.903508772 0.4 1565
0.388280028 0.605134195 1898 0.915050785 0.4 1558
0.383007246 0.614316858 1898 0.916435826 0.4 1599
0.399170996 0.600375849 1684 0.908125577 0.4 1488
0.389327522 0.613889316 2006 0.894275162 0.4 1612
0.397687197 0.600202548 1482 0.958448753 0.4 1393
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0.393741719 0.604423073 2104 0.896121884 0.4 1643
0.392246689 0.61144544 1922 0.906278855 0.4 1596
0.394061915 0.605063525 2732 0.879039705 0.4 1927
0.377520927 0.627836521 1922 0.877192982 0.4 1580
0.379339234 0.621005765 1630 0.952446907 0.4 1443
0.393442902 0.605572293 3020 0.902585411 0.4 2082
0.385035844 0.613346721 1792 0.851800554 0.4 1548
0.386650727 0.613981242 2290 0.878116343 0.4 1800
0.376849511 0.619955178 1482 0.956602031 0.4 1416
0.390937542 0.608155631 2354 0.886888273 0.4 1786
0.393647317 0.60343575 1410 0.887811634 0.4 1393
0.396111137 0.603702737 3186 0.916435826 0.4 2084
0.39920804 0.601136845 1418 0.881809788 0.4 1377
0.395644956 0.603786391 1410 0.912280702 0.4 1377
0.603231828 0.398054413 1775 0.623268698 0.4 1553
0.387350099 0.615713572 2986 0.920129271 0.4 2036
0.394396954 0.604618454 2426 0.904893813 0.4 1800
0.391319401 0.609034449 2358 0.913665743 0.4 1752
0.390809162 0.606808733 1316 0.921514312 0.4 1363
0.39096356 0.601072373 1540 0.944136657 0.4 1472
0.390394978 0.608808999 1990 0.912280702 0.4 1644
0.393661891 0.608503782 1116 0.908125577 0.4 1252
0.395917538 0.602883292 1922 0.853185596 0.4 1583
0.385234289 0.612342713 1792 0.883194829 0.4 1517
0.398593439 0.603459946 2048 0.921514312 0.4 1647
0.399353826 0.601249523 1482 0.945521699 0.4 1408
0.37377954 0.630960536 2006 0.834718375 0.4 1597
0.39946406 0.606952688 2048 0.942751616 0.4 1627
0.399266333 0.602777439 1912 0.904893813 0.4 1566
0.391264032 0.605931678 1992 0.933518006 0.4 1612
0.393343097 0.604977316 2048 0.909510619 0.4 1627
0.361359549 0.635807877 1482 0.950600185 0.4 1394
0.386333871 0.612657659 2358 0.890581717 0.4 1772
0.374764984 0.620080958 1248 0.930747922 0.4 1322
0.369297401 0.628484828 3020 0.90212373 0.4 2035
0.378332047 0.621400996 3338 0.861957525 0.4 2192
0.603111171 0.39666392 1747 0.534626039 0.4 1547
0.396902636 0.608268818 2358 0.90166205 0.4 1753
0.394983712 0.602068616 2104 0.938134811 0.4 1684
0.397877428 0.607846002 2732 0.873499538 0.4 1910
0.379824266 0.613912318 2060 0.905817175 0.4 1631
0.383843661 0.611267394 1946 0.90166205 0.4 1565
0.390977341 0.609650539 1482 0.916435826 0.4 1426
0.379321388 0.627992696 1324 0.911819021 0.4 1346
0.369385063 0.628301416 1518 0.873961219 0.4 1409
0.395495638 0.602834417 1116 0.858264081 0.4 1252
0.3919073 0.605901251 1364 0.943674977 0.4 1345
0.398482164 0.603194181 2580 0.841181902 0.4 1870
0.39271266 0.60527707 2354 0.89242844 0.4 1784
332393
0.293429302 0.703272286 2688 0.891043398 0.3 1906
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0.267495235 0.731160505 1638 0.913204063 0.3 1473
0.297693946 0.702311117 2200 0.833795014 0.3 1721
0.297051988 0.702035461 1784 0.890581717 0.3 1503
0.293577744 0.703505649 2666 0.909510619 0.3 1863
0.289229954 0.703488723 1922 0.908125577 0.3 1580
0.274453912 0.718784624 2984 0.905355494 0.3 2020
0.27795066 0.72164579 2664 0.861957525 0.3 1884
0.25861096 0.739949632 3338 0.878578024 0.3 2144
0.277799356 0.716888905 3338 0.870267775 0.3 2129
0.288321247 0.711790083 4736 0.877192982 0.3 2943
0.259273864 0.742401506 2442 0.876269621 0.3 1785
0.292897233 0.707723433 1898 0.911357341 0.3 1565
0.288847807 0.709201264 1662 0.887349954 0.3 1471
0.293934601 0.707908393 2038 0.855032318 0.3 1631
0.29554162 0.705580292 2494 0.91966759 0.3 1852
0.29964065 0.705165587 2486 0.873961219 0.3 1830
0.282770893 0.717664227 3032 0.84810711 0.3 2051
0.280946056 0.71681902 2104 0.894736842 0.3 1659
0.277838123 0.719936045 4158 0.835180055 0.3 2473
0.278913733 0.717817655 1896 0.927977839 0.3 1558
0.278656522 0.723636567 2060 0.909972299 0.3 1644
0.287362855 0.70903484 2580 0.893351801 0.3 1894
0.257972854 0.740387394 3498 0.859187442 0.3 2225
0.273214694 0.727151275 2486 0.959833795 0.3 1814
0.284162575 0.707889564 2060 0.908125577 0.3 1645
0.299469296 0.702550749 2716 0.887349954 0.3 1959
0.290770604 0.707386382 5058 0.871191136 0.3 2974
0.250241963 0.742050251 3742 0.838411819 0.3 2333
0.278948579 0.72495586 3032 0.867036011 0.3 2020
0.29569863 0.71681491 2104 0.883194829 0.3 1647
0.282365042 0.717319384 2310 0.913204063 0.3 1752
0.295226735 0.702402549 2872 0.92843952 0.3 1989
0.281761693 0.722080824 2200 0.855032318 0.3 1690
0.29286166 0.705190345 1888 0.917359187 0.3 1550
0.291034768 0.714656226 2104 0.922437673 0.3 1642
0.270951472 0.728598512 1912 0.934903047 0.3 1604
0.296036164 0.706549318 2358 0.828254848 0.3 1737
0.27477626 0.71770879 3350 0.870729455 0.3 2161
0.280269488 0.722669019 2218 0.906740536 0.3 1674
0.277016257 0.72202036 1482 0.871652816 0.3 1425
0.277397625 0.716693072 3962 0.861957525 0.3 2379
0.270616965 0.729058329 1756 0.877654663 0.3 1519
0.293925297 0.711199557 1762 0.876269621 0.3 1533
0.297025759 0.706234952 4158 0.767313019 0.3 2534
0.286015031 0.712619016 2358 0.890120037 0.3 1767
0.297161739 0.700363067 3688 0.864727608 0.3 2333
0.29964059 0.708662621 2438 0.846260388 0.3 1769
0.289512276 0.709592808 3338 0.904893813 0.3 2145
0.28978375 0.70670553 2684 0.897506925 0.3 1941
0.291141697 0.710430792 2986 0.8388735 0.3 2031
0.289820578 0.708963129 2354 0.908587258 0.3 1800
0.288438878 0.716530744 2218 0.906278855 0.3 1753
0.294768693 0.710801259 1922 0.863342567 0.3 1596
0.277619722 0.722726338 4548 0.908125577 0.3 2676
0.296041445 0.708131744 2218 0.908587258 0.3 1753
0.283480178 0.718490661 2354 0.917359187 0.3 1753
0.268615064 0.724582953 2358 0.904432133 0.3 1768
0.278959871 0.722371508 2784 0.886888273 0.3 1976
0.28450712 0.725262658 1662 0.888273315 0.3 1472
0.288668078 0.710210131 2488 0.882271468 0.3 1801
0.299430887 0.704579801 1662 0.88365651 0.3 1455
0.289067281 0.714223838 2684 0.935364728 0.3 1927
0.298386018 0.70698401 3498 0.816712835 0.3 2244
0.257155857 0.732264269 1482 0.849953832 0.3 1425
0.70530171 0.293315586 2215 0.681440443 0.3 1720
0.299759954 0.706077282 1912 0.90212373 0.3 1550
0.274580856 0.729773688 2104 0.889196676 0.3 1658
0.285886466 0.705912201 1992 0.925669437 0.3 1616
0.28970658 0.716779399 3032 0.849492151 0.3 2051
0.261752473 0.735544796 2076 0.936749769 0.3 1644
0.279852259 0.730413859 2060 0.90212373 0.3 1627
0.293950013 0.706122169 2716 0.859649123 0.3 1941
0.282676948 0.721418481 2022 0.938596491 0.3 1613
0.288359553 0.706158589 1792 0.861957525 0.3 1534
0.276327834 0.722814134 2486 0.951985226 0.3 1879
0.299970511 0.70469446 2820 0.858725762 0.3 1972
0.268713372 0.731886235 2070 0.912280702 0.3 1659
0.290495362 0.717683102 3032 0.88411819 0.3 2037
0.291046761 0.700771863 1638 0.87534626 0.3 1456
0.256951323 0.728207546 2060 0.906740536 0.3 1627
0.298067714 0.702919136 2060 0.895198523 0.3 1628
0.296676165 0.704656816 1482 0.950600185 0.3 1392
0.261464603 0.740379104 6362 0.835180055 0.3 3741
0.723220852 0.281415143 2215 0.685133887 0.3 1706
0.29339404 0.70957884 2438 0.88411819 0.3 1801
0.289518252 0.714946841 1912 0.923361034 0.3 1612
0.287620989 0.709029981 2442 0.781163435 0.3 1833
0.286017902 0.710271824 5078 0.856417359 0.3 2911
0.286481124 0.712842038 1934 0.884579871 0.3 1580
0.2925448 0.707357218 2136 0.898891967 0.3 1660
0.287620026 0.713375588 2684 0.891043398 0.3 1879
0.290658162 0.711197374 3020 0.900738689 0.3 2040
0.269721574 0.729674663 2006 0.855493998 0.3 1634
0.281811455 0.714896984 4544 0.867959372 0.3 2660
0.275297479 0.725321407 2820 0.909510619 0.3 1926
0.244889365 0.747014281 1364 0.860572484 0.3 1345
0.285968388 0.705325431 1792 0.825484765 0.3 1519
0.292441728 0.712015555 2332 0.896583564 0.3 1768
0.264210122 0.734395283 1804 0.91920591 0.3 1578
0.269021157 0.733239076 2688 0.903508772 0.3 1955
0.274419283 0.727633159 5232 0.807940905 0.3 3039
0.292879302 0.711427072 3324 0.898891967 0.3 2297
0.287543703 0.717752298 2060 0.916435826 0.3 1693
0.294254809 0.708647235 2394 0.861495845 0.3 1857
0.296770425 0.700699165 3018 0.885503232 0.3 2092
0.278843964 0.718813655 2688 0.902585411 0.3 1924
0.290598582 0.714240763 2448 0.942289935 0.3 1832
0.265630584 0.730778793 2448 0.944598338 0.3 1819
0.285038199 0.713745959 2984 0.898430286 0.3 2035
0.292320017 0.70906736 1892 0.921514312 0.3 1566
0.289700329 0.709808354 2448 0.927516159 0.3 1817
0.291464971 0.711988603 2986 0.859187442 0.3 2035
0.276056525 0.729150193 2104 0.877192982 0.3 1660
0.2843993 0.714388086 1926 0.927977839 0.3 1581
0.292350581 0.712742138 1722 0.881809788 0.3 1517
0.297480681 0.702320576 3350 0.852262235 0.3 2223
0.271929547 0.730620161 2070 0.920590951 0.3 1675
0.287766428 0.70351513 1732 0.860110803 0.3 1534
0.276480722 0.729925735 2060 0.898430286 0.3 1658
0.264000247 0.741160281 1482 0.91089566 0.3 1378
0.241646168 0.745409773 1410 0.905817175 0.3 1396
0.263365694 0.738274018 2960 0.916897507 0.3 2021
0.280811021 0.711820552 1934 0.934441367 0.3 1612
0.297752995 0.70350502 1784 0.85687904 0.3 1518
0.294516573 0.702965178 1912 0.927516159 0.3 1581
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0.287937434 0.706122396 3330 0.86565097 0.3 2177
0.298468004 0.708026477 1934 0.90166205 0.3 1565
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0.298521527 0.703625468 2442 0.917359187 0.3 1769
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0.299469615 0.701587364 2944 0.906278855 0.3 1988
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0.286340402 0.712061991 1762 0.90212373 0.3 1565
0.287645276 0.717301776 1896 0.926592798 0.3 1611
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0.283106027 0.709065465 1364 0.962603878 0.3 1377
0.710963767 0.288947829 1709 0.743305633 0.3 1502
0.266127506 0.736726962 4158 0.836103416 0.3 2480
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0.270803577 0.729563187 4158 0.809325946 0.3 2505
0.277856727 0.730772947 2060 0.885964912 0.3 1658
0.261118029 0.727081274 2666 0.901200369 0.3 1869
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0.267858471 0.731251124 3338 0.903970452 0.3 2208
0.283157963 0.724940351 2784 0.827331487 0.3 1926
0.281708476 0.71542274 2060 0.915974146 0.3 1643
0.281168952 0.715852828 3020 0.894736842 0.3 2040
0.281276917 0.720478646 1482 0.889658356 0.3 1425
0.28600329 0.71275415 2820 0.90212373 0.3 1942
0.286143254 0.701593513 1410 0.914127424 0.3 1362
0.243080415 0.755556311 6040 0.879039705 0.3 3256
0.28750258 0.716873747 2688 0.907202216 0.3 1879
0.267587361 0.734591023 2104 0.885041551 0.3 1642
0.296634824 0.706283803 3204 0.861957525 0.3 2114
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0.264955148 0.731106786 1410 0.912742382 0.3 1384
0.270325432 0.729115246 1792 0.858725762 0.3 1518
0.284504663 0.716051775 2666 0.916435826 0.3 1894
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0.25830303 0.746898398 2442 0.861957525 0.3 1817
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0.287265712 0.712272141 4626 0.883194829 0.3 2664
0.277667269 0.717928523 2986 0.872576177 0.3 2020
0.287466558 0.713141156 1804 0.915050785 0.3 1549
0.290324806 0.710955155 3266 0.924746076 0.3 2175
0.239043278 0.755146379 2104 0.91089566 0.3 1659
0.279755104 0.715909303 2438 0.949215143 0.3 1864
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0.292265053 0.716092957 2986 0.84810711 0.3 2019
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0.292492597 0.711025369 3372 0.809787627 0.3 2145
0.294376624 0.706405045 2426 0.90212373 0.3 1800
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0.296312034 0.703816262 2290 0.900277008 0.3 1738
365751
0.13217985 0.871043778 2684 0.902585411 0.2 1926
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0.182735545 0.817775181 2104 0.880424746 0.2 1642
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0.146907592 0.844771624 2332 0.899815328 0.2 1800
0.180345838 0.822381276 2784 0.871191136 0.2 2021
0.193313854 0.806868151 3576 0.889658356 0.2 2253
0.800746034 0.197788194 3773 0.78578024 0.2 2317
0.182100496 0.823142087 3338 0.888273315 0.2 2176
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0.154772664 0.848168769 2688 0.918744229 0.2 1894
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0.810078575 0.186996824 3783 0.684672207 0.2 2379
0.183152805 0.813704554 1922 0.867959372 0.2 1581
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0.183926145 0.815283834 5462 0.881809788 0.2 3053
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0.197340737 0.803967216 2688 0.920129271 0.2 1926
0.192462516 0.808575895 2684 0.934903047 0.2 1898
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0.198349512 0.802628152 2048 0.924284395 0.2 1627
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0.177460024 0.82252625 1892 0.921514312 0.2 1568
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0.179456937 0.817658957 2688 0.933056325 0.2 1898
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0.191628382 0.818706449 2684 0.926592798 0.2 1934
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0.197616344 0.804265443 5680 0.868882733 0.2 3100
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0.178457367 0.81871938 1912 0.93721145 0.2 1596
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0.1579359 0.848422971 2060 0.90166205 0.2 1644
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0.805726291 0.19700385 3005 0.710987996 0.2 2020
0.194394834 0.806165858 3588 0.84810711 0.2 2254
0.194512826 0.804986255 4742 0.875807941 0.2 2723
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0.189708557 0.811577909 3032 0.715604801 0.2 2020
0.194550163 0.809505329 3032 0.834718375 0.2 2035
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0.80715784 0.193783477 1767 0.753924284 0.2 1519
0.19528614 0.80389139 5450 0.841181902 0.2 3081
390901
0.088721664 0.909652641 2060 0.894275162 0.1 1627
0.047210525 0.951927406 3928 0.924284395 0.1 2396
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0.070198175 0.929084622 2688 0.948753463 0.1 1894
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0.086466024 0.915301006 3730 0.893351801 0.1 2348
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0.098961888 0.903105375 2104 0.922899354 0.1 1644
0.093463818 0.906721948 2060 0.912742382 0.1 1642
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0.081207526 0.918777986 4992 0.907202216 0.1 2864
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0.088019548 0.911425939 2070 0.930747922 0.1 1658
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0.084314249 0.918492886 3020 0.897968606 0.1 2067
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0.080593665 0.919974095 2486 0.960295476 0.1 1832
0.091195677 0.907313288 3032 0.707294552 0.1 2053
0.067175341 0.931141335 2688 0.915050785 0.1 1924
0.096010319 0.902441551 3526 0.873961219 0.1 2270
0.092475537 0.908326824 5462 0.86611265 0.1 3038
0.094555294 0.907787921 3692 0.923822715 0.1 2338
0.096106408 0.9008919 4598 0.813481071 0.1 2649
0.091511578 0.90612355 2688 0.90166205 0.1 1911
0.090437634 0.90859683 2486 0.9644506 0.1 1830
0.091908338 0.905482749 3020 0.888273315 0.1 2060
0.06348597 0.936635782 2688 0.924746076 0.1 1946
0.080403302 0.9171443 3338 0.880424746 0.1 2160
0.097664108 0.903439234 3588 0.90166205 0.1 2288
0.089048579 0.910272728 3266 0.911819021 0.1 2176
0.074581293 0.925842847 2438 0.927516159 0.1 1800
0.094791063 0.905991607 2438 0.915512465 0.1 1800
0.071568533 0.928380624 4158 0.813942752 0.1 2521
0.079750789 0.921181347 2688 0.946906741 0.1 1925
0.087563361 0.909381282 2486 0.903047091 0.1 1831
0.065342602 0.933408443 3928 0.922437673 0.1 2379
0.060000327 0.941136073 3338 0.877192982 0.1 2194
0.094858781 0.907894721 4368 0.834718375 0.1 2567
0.08457759 0.916836442 5122 0.843951985 0.1 2928
0.07188431 0.925011503 2574 0.90212373 0.1 1862
0.08758629 0.912231873 2688 0.884579871 0.1 1909
0.098014735 0.900638131 2358 0.914589104 0.1 1769
0.093541802 0.907108342 4158 0.746999077 0.1 2460
0.09730085 0.902178033 3742 0.898430286 0.1 2331
0.088441055 0.912794363 2580 0.945060018 0.1 1836
0.085270208 0.91709609 2580 0.865189289 0.1 1831
0.078257421 0.922130322 1926 0.942289935 0.1 1598
0.077301806 0.921756101 2684 0.944136657 0.1 1894
0.071385303 0.929971746 1324 0.899815328 0.1 1316
0.087311471 0.912869435 3390 0.916435826 0.1 2207
0.098081875 0.902314058 2666 0.918744229 0.1 1896
0.911432865 0.090681221 3783 0.730378578 0.1 2365
0.09443872 0.901179072 2688 0.921975993 0.1 1918
0.093485242 0.905252756 3742 0.855032318 0.1 2327
0.087524547 0.914325688 3272 0.924284395 0.1 2129
0.085828944 0.912140579 2688 0.940443213 0.1 1959
0.091710796 0.906545258 2688 0.917359187 0.1 1942
0.092048028 0.908504645 2784 0.863342567 0.1 1956
0.075856274 0.925100111 2688 0.879501385 0.1 1941
0.080188634 0.918967036 2104 0.930286242 0.1 1628
0.095476784 0.905236639 3258 0.907663897 0.1 2145
0.041660148 0.958141615 3944 0.891966759 0.1 2411
0.088305643 0.910777025 2486 0.918744229 0.1 1816
0.093814934 0.903805767 3266 0.869806094 0.1 2192
0.097062875 0.904245232 3114 0.918744229 0.1 2082
0.088501514 0.910620212 1922 0.902585411 0.1 1596
0.078455662 0.923207846 3632 0.891505078 0.1 2317
0.907737367 0.094481128 3255 0.748384118 0.1 2175
0.085755852 0.912277459 4442 0.881348107 0.1 2664
0.096773733 0.90255362 2218 0.926592798 0.1 1737
0.092186287 0.906844718 2104 0.901200369 0.1 1658
0.068993412 0.930390637 3928 0.925207756 0.1 2428
0.072310012 0.92748423 3350 0.837950139 0.1 2193
0.074328458 0.924692782 4706 0.884579871 0.1 2696
0.096467765 0.904503694 4736 0.817174515 0.1 2731
0.097264334 0.902515263 3252 0.838411819 0.1 2166
0.069232286 0.930406877 3742 0.853185596 0.1 2333
0.073929054 0.926205575 2442 0.911357341 0.1 1815
0.07534387 0.927587335 3020 0.831486611 0.1 2099
0.075507642 0.921154871 3100 0.849492151 0.1 2082
0.072622399 0.927172172 3542 0.915974146 0.1 2270
0.094682239 0.907177636 5282 0.80378578 0.1 3010
0.090430571 0.910020286 3020 0.907202216 0.1 2051
0.083208326 0.913819076 3338 0.903047091 0.1 2176
0.096494159 0.90366544 4406 0.854108957 0.1 2631
0.092604101 0.903903729 4368 0.890581717 0.1 2618
0.065888318 0.9369283 3032 0.826408126 0.1 2021
0.05839434 0.943124888 2448 0.959833795 0.1 1801
0.08881549 0.91219454 3798 0.889196676 0.1 2317
0.078236557 0.922994424 2006 0.849492151 0.1 1627
0.091404067 0.907115354 3100 0.888273315 0.1 2051
0.089035544 0.912281957 3928 0.898430286 0.1 2395
0.068943373 0.93414644 3928 0.842566944 0.1 2443
0.087809312 0.912557126 2438 0.900738689 0.1 1801
0.098464797 0.902993263 4208 0.861034164 0.1 2488
0.09134977 0.910415736 2104 0.932132964 0.1 1664
0.097973404 0.902000719 3908 0.840720222 0.1 2379
0.090845231 0.909619039 2494 0.900277008 0.1 1847
0.097658065 0.903869714 3576 0.890581717 0.1 2287
0.092305556 0.90663361 2218 0.898430286 0.1 1721
0.092852091 0.901755727 2774 0.901200369 0.1 2004
0.081202386 0.920570666 2486 0.961680517 0.1 1815
0.082913458 0.911264572 4766 0.859187442 0.1 2755
0.094661501 0.902118999 3602 0.929824561 0.1 2269
0.09676632 0.901765291 3434 0.842105263 0.1 2207
0.067872345 0.932127386 3742 0.860572484 0.1 2318
0.075892736 0.926164144 2358 0.882271468 0.1 1736
0.077494369 0.921809678 3742 0.860572484 0.1 2364
0.076138922 0.92637082 3032 0.761311173 0.1 2051
0.099041539 0.900595004 2688 0.906740536 0.1 1926
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493968
8.78E-04 0.999117286 6070 0.898430286 0.001 3339
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2213513
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6904500
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9970242