首先制作一个二分类minst0和2的网络，将minst 28*28的图像缩小到9*9，采用1个3*3的卷积核网络的结构是49*30*2.
画成图
这个网络由两部分组成，左右两边分别向1,0和0,1收敛，左边输入minst的0，右边输入minst的2，让左右两个网络的权重共享，由前面的实验表明这种效果相当于将两个弹簧并联。
| 具体进样顺序 | |||
| x=10 | |||
| δ=0.1 | |||
| 初始化权重 | |||
| 迭代次数 | |||
| minst 0-1 | 1 | 判断是否达到收敛 | |
| minst 2-1 | 2 | 判断是否达到收敛 | |
| 梯度下降 | |||
| minst 0-2 | 3 | 判断是否达到收敛 | |
| minst 2-2 | 4 | 判断是否达到收敛 | |
| 梯度下降 | |||
| …… | |||
| minst 0-4999 | 9997 | 判断是否达到收敛 | |
| minst 2-4999 | 9998 | 判断是否达到收敛 | |
| 梯度下降 | |||
| …… | |||
| 如果4999图片内没有达到收敛标准再次从头循环 | |||
| minst 0-1 | 9999 | 判断是否达到收敛 | |
| minst 2-1 | 10000 | 判断是否达到收敛 | |
| 梯度下降 | |||
| …… | |||
| 每当网路达到收敛标准记录迭代次数和对应的准确率测试结果 | |||
| 将这一过程重复99次 | |||
| x=10 | |||
| δ=0.01 | |||
| … | |||
| x=10 | |||
| δ=1e-6 | |||
| … | |||
| x=500000 | |||
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);
这次实验只改变网络第一层的权重初始化方式，卷积核和第二层的初始化方式不变分别是x=1000，x=200.
第一层的x尝试了从10，100，500，1000，2000，5000，10000，50000，100000，500000。共10个数值。
对应卷积层，第一层，第二层的x分别是 1000-x-200
对应第一层的每个x尝试了δ=0.1到δ=1e-6共22个收敛标准，一共进行了9*22*99次收敛，取平均值。（x=1000的数据前面算过没有统计在内）
收敛条件是
if (Math.abs(f2[0]-y[0])< δ && Math.abs(f2[1]-y[1])< δ )
这个网络简写成
S（minst0）81-（con3*3）49-30-2-（1,0）
S（minst2）81-（con3*3）49-30-2-（0,1）
w=w，w1=w1,w2=w2
进一步简写成
d2（minst0,2）81-con（3*3）49-30-2-（2*k） ,k∈(0,1)
测试集中有980个0和1032个2
首先比较权重变化对网络迭代次数的影响
| n | 1000-50w-200 | 1000-100000-200 | 1000-50000-200 | 1000-10000-200 | 1000-5000-200 | 1000-2000-200 | 1000-1000-200 | 1000-500-200 | 1000-100-200 | 1000-10-200 | |
| 0.1 | 2954.606 | 2925.677 | 2682.434 | 2789.273 | 2851.414 | 2934.99 | 2785.725 | 2469.051 | 1492.364 | 824.5455 | |
| 1.00E-02 | 3905.949 | 3822.828 | 3697.778 | 3839.242 | 3665.414 | 3597.384 | 3620.905 | 3195.273 | 2412.556 | 2290.162 | |
| 1.00E-03 |
| 4922.071 | 5071.838 | 5135.232 | 5039.111 | 5039.828 | 4846.435 | 4716.636 | 4220.495 | 6053.737 | |
| 1.00E-04 | 7745.818 | 7713.869 | 7586.202 | 7566.566 | 7782.485 | 7884.495 | 7709.22 | 7643.263 | 7695.222 | 16028 | |
| 9*1e-5 | 7963.232 | 8079.162 | 7945.636 | 8140.626 | 7738.576 | 7998 | 7951.16 | 7770.828 | 7968.343 | 15690.17 | |
| 8*1e-5 | 8365.172 | 8228.737 | 8023.788 | 8136.414 | 8344.596 | 8009.616 | 8182.51 | 8274.535 | 8373.879 | 13377.99 | |
| 7*1e-5 | 8351.596 | 8663.02 | 8172.141 | 8062.172 | 8338 | 8543.707 | 8193.28 | 8299.495 | 8587.455 | 15126.16 | |
| 6*1e-5 | 8417.556 | 9177.737 | 8753.091 | 8484.495 | 8541.737 | 8616.182 | 8780.59 | 8658 | 8802.596 | 22843.65 | |
| 5*1e-5 | 9031.434 | 8857.374 | 9313.737 | 9411.121 | 8861.606 | 8909.141 | 9227.095 | 8950.667 | 9941.172 | 27780.29 | |
| 4*1e-5 | 9703.253 | 9872.081 | 9288.869 | 9631.778 | 9538.909 | 9668.152 | 9473.415 | 9662.909 | 10444.78 | 27402.92 | |
| 3*1e-5 | 10273.36 | 10113.93 | 10285.41 | 10206.91 | 10629.06 | 10816.66 | 10478 | 10568.34 | 14323.28 | 24808.89 | |
| 2*1e-5 | 11731.25 | 11892.36 | 12266.43 | 11755.62 | 11908.38 | 12202.96 | 12060.84 | 12658.81 | 15518.96 | 41082.65 | |
| 1.00E-05 | 18167.29 | 16326.62 | 17709.88 | 16517.19 | 18050.47 | 17632.76 | 19757.36 | 19631.6 | 27037.91 | 41576.35 | |
| 9*1e-6 | 18092.37 | 17627.63 | 17650.47 | 18858.78 | 18803.26 | 18320.92 | 22245.54 | 22426 | 34867.61 | 55239.19 | |
| 8*1e-6 | 20048.55 | 20191.28 | 20609.12 | 18776.23 | 21052.76 | 20959.47 | 22214.93 | 29472.02 | 32882.16 | 38499.32 | |
| 7*1e-6 | 19530.55 | 23512.86 | 22542.05 | 21879.03 | 23718.92 | 22918.35 | 28045.61 | 27159.87 | 44096.94 | 52918.58 | |
| 6*1e-6 | 24351.96 | 23340.25 | 26207.54 | 24757.15 | 27849.16 | 28211.88 | 28410.34 | 30183.58 | 46843.72 | 46127.79 | |
| 5*1e-6 | 29944.24 | 28645.25 | 28010.44 | 28571.44 | 31057.9 | 30435.4 | 33681.76 | 37208.66 | 53281.34 | 60459.68 | |
| 4*1e-6 | 33255.04 | 35861.74 | 30981.66 | 34554.54 | 35596.87 | 34793.94 | 37281.58 | 40257.97 | 55912.52 | 60573.09 | |
| 3*1e-6 | 42423.89 | 40650.68 | 40574.6 | 43801.63 | 41936.11 | 47156.65 | 45173.59 | 46803.47 | 68222.7 | 57790.01 | |
| 2*1e-6 | 59707.64 | 60227.69 | 60508.31 | 55440.01 | 56348.59 | 58648.75 | 60366.62 | 66742.11 | 79970.38 | 76566.7 | |
| 1.00E-06 | 90144.53 | 82600.19 | 81751.4 | 79639.55 | 85065.39 | 92023.44 | 90392.45 | 93909.44 | 109287.6 | 125939.1 |
1000-50w-200的意思是卷积层，第一层，第二层的权重的初始化方式分别是 r/1000 ，r/500000 ，r/200 ，r是1-99的随机数
规律是非常明显的
| δ | x | n |
| 不变 | 减小 | 增加 |
| 减小 | 不变 | 增加 |
在收敛标准δ不变的情况下x减小n增加，在x不变收敛标准δ减小的情况下n同样增加。
第二步观察权重对网络性能的影响，对应每一组δ和x的组合都分别收敛了99次，
p-ave是99次的平均的准确率，
p-max是99次的最大准确率。
主要通过观察p-ave和p-max的变化来比较权重对分类性能的影响，
首先是p-ave表格
| p-ave | 1000-50w-200 | 1000-100000-200 | 1000-50000-200 | 1000-10000-200 | 1000-5000-200 | 1000-2000-200 | 1000-1000-200 | 1000-500-200 | 1000-100-200 | 1000-10-200 | |
| 0.1 | 0.877754 | 0.871779 | 0.882568 | 0.874716 | 0.870429 | 0.878808 | 0.879297 | 0.864153 | 0.879084 | 0.811736 | |
| 1.00E-02 | 0.854585 | 0.882674 | 0.880103 | 0.867502 | 0.888362 | 0.880264 | 0.879878 | 0.861417 | 0.847933 | 0.865574 | |
| 1.00E-03 | 0.834925 | 0.859399 | 0.856362 | 0.831903 | 0.82885 | 0.858184 | 0.847488 | 0.845006 | 0.85705 | 0.902539 | |
| 1.00E-04 | 0.866282 |
| 0.856613 | 0.852079 | 0.879862 | 0.854514 | 0.854036 | 0.848781 | 0.865594 | 0.929333 | |
| 9*1e-5 | 0.882021 | 0.876243 | 0.868551 | 0.865434 | 0.872543 | 0.885229 | 0.858675 | 0.871403 | 0.875103 | 0.924202 | |
| 8*1e-5 | 0.881137 | 0.859961 | 0.841145 | 0.852587 | 0.836125 | 0.861734 | 0.865089 | 0.85838 | 0.884727 | 0.928108 | |
| 7*1e-5 | 0.858094 | 0.853977 | 0.883944 | 0.870248 | 0.856939 | 0.857752 | 0.872112 | 0.862226 | 0.877904 | 0.919117 | |
| 6*1e-5 | 0.870278 | 0.870148 | 0.858646 | 0.861036 | 0.887207 | 0.866774 | 0.870482 | 0.867532 | 0.874982 | 0.93098 | |
| 5*1e-5 | 0.858787 | 0.867161 | 0.860986 | 0.836878 | 0.865815 | 0.863195 | 0.861961 | 0.872171 | 0.880766 | 0.933445 | |
| 4*1e-5 | 0.872789 | 0.82137 | 0.869475 | 0.860047 | 0.850654 | 0.84359 | 0.875582 | 0.867311 | 0.889858 | 0.929941 | |
| 3*1e-5 | 0.8473 | 0.846858 | 0.850448 | 0.853691 | 0.835166 | 0.842837 | 0.858576 | 0.848088 | 0.845744 | 0.934163 | |
| 2*1e-5 | 0.828142 | 0.83746 | 0.849353 | 0.831652 | 0.847124 | 0.826415 | 0.831161 | 0.849675 | 0.846688 | 0.944309 | |
| 1.00E-05 | 0.84594 | 0.841155 | 0.867497 | 0.854715 | 0.86461 | 0.849238 | 0.852058 | 0.871147 | 0.903568 | 0.938621 | |
| 9*1e-6 | 0.87321 | 0.866046 | 0.841903 | 0.865735 | 0.879817 | 0.867487 | 0.880181 | 0.873301 | 0.909904 | 0.945951 | |
| 8*1e-6 | 0.871227 | 0.877377 | 0.882543 | 0.858244 | 0.879928 | 0.877392 | 0.885067 | 0.905376 | 0.909026 | 0.941869 | |
| 7*1e-6 | 0.857983 | 0.894969 | 0.869475 | 0.867286 | 0.879636 | 0.896078 | 0.882391 | 0.907464 | 0.904638 | 0.953627 | |
| 6*1e-6 | 0.881529 | 0.886052 | 0.902158 | 0.87072 | 0.891771 | 0.893784 | 0.896943 | 0.896686 | 0.90886 | 0.947592 | |
| 5*1e-6 | 0.879024 | 0.90639 | 0.888045 | 0.880435 | 0.89038 | 0.898889 | 0.912898 | 0.907931 | 0.92729 | 0.942738 | |
| 4*1e-6 | 0.885003 | 0.909201 | 0.897188 | 0.891123 | 0.918348 | 0.903187 | 0.908745 | 0.903679 | 0.928575 | 0.945956 | |
| 3*1e-6 | 0.923128 | 0.925588 | 0.907946 | 0.907841 | 0.92365 | 0.927204 | 0.921827 | 0.923098 | 0.936015 | 0.952598 | |
| 2*1e-6 | 0.93102 | 0.917907 | 0.942492 | 0.921185 | 0.92488 | 0.924463 | 0.93123 | 0.942803 | 0.950871 | 0.952276 | |
| 1.00E-06 | 0.946849 | 0.942938 | 0.946493 | 0.954751 | 0.950901 | 0.947652 | 0.951384 | 0.954224 | 0.953551 | 0.961117 |
| δ | x | p-ave |
| 不变 | 减小 | 增加 |
| 减小 | 不变 | 增加 |
在收敛标准δ不变的情况下x减小p-ave增加，在x不变收敛标准δ减小的情况下p-ave同样增加。也就是无论减小δ或者x都有助于改善网络的平均性能。
与之对应的如果希望网络每次启动都能有一个很好的准确率，一个选择就是将x设的小一点，让收敛标准也尽量的小一点。代价是迭代次数也会成倍的增加。
再观察权重对网络最大性能p-max的影响
| p-max | 1000-50w-200 | 1000-100000-200 | 1000-50000-200 | 1000-10000-200 | 1000-5000-200 | 1000-2000-200 | 1000-1000-200 | 1000-500-200 | 1000-100-200 | 1000-10-200 | |
| 0.1 | 0.969682 | 0.97167 | 0.970676 | 0.970179 | 0.968191 | 0.970676 | 0.968191 | 0.972664 | 0.967197 | 0.922465 | |
| 1.00E-02 | 0.97167 |
| 0.967197 | 0.972664 | 0.972167 | 0.969185 | 0.972664 | 0.972664 | 0.97167 | 0.957256 | |
| 1.00E-03 | 0.974155 | 0.975646 | 0.97664 | 0.972664 | 0.973658 | 0.97664 | 0.980119 | 0.974155 | 0.977137 | 0.958748 | |
| 1.00E-04 | 0.975149 | 0.978628 | 0.981113 | 0.979125 | 0.980616 | 0.974652 | 0.982107 | 0.979622 | 0.98161 | 0.97167 | |
| 9*1e-5 | 0.980119 | 0.982107 | 0.98161 | 0.975646 | 0.981113 | 0.978131 | 0.978628 | 0.97664 | 0.982604 | 0.979622 | |
| 8*1e-5 | 0.980616 | 0.983101 | 0.977137 | 0.977634 | 0.980119 | 0.980119 | 0.983598 | 0.980616 | 0.983598 | 0.973161 | |
| 7*1e-5 | 0.982107 | 0.980119 | 0.979125 | 0.979125 | 0.979125 | 0.978628 | 0.982107 | 0.980119 | 0.982107 | 0.972664 | |
| 6*1e-5 | 0.980119 | 0.981113 | 0.973658 | 0.979125 | 0.980616 | 0.980119 | 0.98161 | 0.979622 | 0.978628 | 0.984095 | |
| 5*1e-5 | 0.978628 | 0.98161 | 0.980119 | 0.980616 | 0.974155 | 0.980119 | 0.980616 | 0.98161 | 0.982107 | 0.978628 | |
| 4*1e-5 | 0.978131 | 0.982604 | 0.983101 | 0.978628 | 0.979125 | 0.980119 | 0.984592 | 0.979125 | 0.983598 | 0.98161 | |
| 3*1e-5 | 0.978628 | 0.982107 | 0.983101 | 0.982107 | 0.982107 | 0.977137 | 0.98161 | 0.977137 | 0.979622 | 0.979622 | |
| 2*1e-5 | 0.978131 | 0.977137 | 0.978628 | 0.978628 | 0.982604 | 0.978131 | 0.978131 | 0.98161 | 0.979125 | 0.982604 | |
| 1.00E-05 | 0.979125 | 0.979622 | 0.982604 | 0.980616 | 0.981113 | 0.981113 | 0.982604 | 0.980119 | 0.982604 | 0.983101 | |
| 9*1e-6 | 0.98161 | 0.983598 | 0.985586 | 0.982107 | 0.982107 | 0.981113 | 0.983598 | 0.98161 | 0.984095 | 0.987078 | |
| 8*1e-6 | 0.981113 | 0.982604 | 0.982604 | 0.981113 | 0.983598 | 0.983101 | 0.982604 | 0.983598 | 0.985089 | 0.983101 | |
| 7*1e-6 | 0.978628 | 0.982107 | 0.982107 | 0.985586 | 0.984095 | 0.983101 | 0.982107 | 0.983598 | 0.985586 | 0.985089 | |
| 6*1e-6 | 0.982604 | 0.984592 | 0.985586 | 0.983101 | 0.98161 | 0.982107 | 0.984095 | 0.983598 | 0.985586 | 0.982604 | |
| 5*1e-6 | 0.984592 | 0.980616 | 0.985089 | 0.983101 | 0.982604 | 0.985089 | 0.987575 | 0.980616 | 0.985089 | 0.985089 | |
| 4*1e-6 | 0.985586 | 0.984095 | 0.982604 | 0.983598 | 0.985089 | 0.983101 | 0.986083 | 0.984592 | 0.986581 | 0.982604 | |
| 3*1e-6 | 0.983598 | 0.982604 | 0.983598 | 0.985586 | 0.984095 | 0.982604 | 0.986083 | 0.984095 | 0.985586 | 0.980616 | |
| 2*1e-6 | 0.986084 | 0.985586 | 0.985089 | 0.984095 | 0.982107 | 0.984095 | 0.985586 | 0.985089 | 0.984095 | 0.987078 | |
| 1.00E-06 | 0.986084 | 0.987078 | 0.985586 | 0.985586 | 0.986581 | 0.985089 | 0.985586 | 0.987078 | 0.987078 | 0.99006 |
| δ | x | p-max |
| 不变 | 减小 | 小幅增加 |
| 减小 | 不变 | 增加 |
在收敛标准δ不变的情况下x减小p-max的变化不明显但是按照前面的大量实验p-ave和p-max的变化趋势是一样的，这个p-max应该是小幅增加的。在x不变收敛标准δ减小的情况下p-max增加。
比较明显的对比x=50万和x=10的数据
| p-max | 1000-50w--200 | 1000-10-200 |
| 0.1 | 0.969682 | 0.922465 |
| 1.00E-02 | 0.97167 | 0.957256 |
| 1.00E-03 | 0.974155 | 0.958748 |
| 1.00E-04 | 0.975149 | 0.97167 |
| 9*1e-5 | 0.980119 | 0.979622 |
| 8*1e-5 | 0.980616 | 0.973161 |
| 7*1e-5 | 0.982107 | 0.972664 |
| 6*1e-5 | 0.980119 | 0.984095 |
| 5*1e-5 | 0.978628 | 0.978628 |
| 4*1e-5 | 0.978131 | 0.98161 |
| 3*1e-5 | 0.978628 | 0.979622 |
| 2*1e-5 | 0.978131 | 0.982604 |
| 1.00E-05 | 0.979125 | 0.983101 |
| 9*1e-6 | 0.98161 | 0.987078 |
| 8*1e-6 | 0.981113 | 0.983101 |
| 7*1e-6 | 0.978628 | 0.985089 |
| 6*1e-6 | 0.982604 | 0.982604 |
| 5*1e-6 | 0.984592 | 0.985089 |
| 4*1e-6 | 0.985586 | 0.982604 |
| 3*1e-6 | 0.983598 | 0.980616 |
| 2*1e-6 | 0.986083 | 0.987078 |
| 1.00E-06 | 0.986083 | 0.99006 |
X=50w的网络在δ比较大的区间的性能要好于x=10，但是大部分区间二者是相差不多的。所以可以大致的得出结论对于一定的δ的网络的x越小p-max越大，但是增幅相当小可以近似的认为对于一定的δ网络的p-max与权重初始化方式几乎无关是一个定值。
对比《学习率对神经网络迭代次数和准确率的影响以及近似的数学表达式》2019-1-25的结论（r是学习率）
| δ | r | x | n | p-ave | p-max |
| 减小 | 不变 | 不变 | 增加 | 增加 | 增加 |
| 减小 | 不变 | 减小 | 增加 | 增加 | 增加 |
| 减小 | 减小 | 不变 | 增加 | 增加 | 增加 |
| 不变 | 减小 | 减小 | 增加 | 增加 | 小幅增加，几乎是定值 |
| 不变 | 不变 | 减小 | 增加 | 增加 | 几乎是定值 |
| 不变 | 减小 | 不变 | 增加 | 增加 | 几乎是定值 |
| 减小 | 减小 | 减小 | 增加 | 增加 | 增加 |
从表格对比可以看出当δ减小时学习率r和权重初始化方式x的减小会使网络的平均性能和最大性能都增加。
但在δ不变的情况下减小r或者减小x会有助于提升平均性能，但对最大性能仅有很小幅提升甚至可以认为是定值。
甚至为简便起见可以近似的认为网路的最大性能p-max只与收敛标准δ有关在δ不变的情况下p-max几乎就是定值，也就是在δ不变的情况下学习率r或者权重初始化方式x对最大性能的影响都非常的小近似可以忽略。
如果需要调整学习率和权重他们应该是同向变化，否则如果r减小x增大性能会相互抵消。
实验数据
学习率r=0.1
当x=1000的数据已经在《神经网络收敛标准与准确率之间的数学关系》中给出了
| 1000-10-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.492183564 | 0.500506592 | 4.96969697 | 0.524143021 | 0.5 | 772.4848485 | 76508 | 1.275133333 | 0.843936382 |
| 0.488191584 | 0.512761199 | 149.9292929 | 0.680005824 | 0.4 | 815.3636364 | 80722 | 1.345366667 | 0.905069583 |
| 0.428191453 | 0.570718588 | 322.6060606 | 0.75905677 | 0.3 | 876.3434343 | 86758 | 1.445966667 | 0.935387674 |
| 0.451571838 | 0.548452236 | 460.4444444 | 0.791834849 | 0.2 | 916.2929293 | 90713 | 1.511883333 | 0.918489066 |
| 0.436345525 | 0.56320101 | 824.5454545 | 0.811735647 | 0.1 | 1028.858586 | 101857 | 1.697616667 | 0.922465209 |
| 0.365656302 | 0.63432816 | 2290.161616 | 0.865574231 | 0.01 | 704.3434343 | 69738 | 1.1623 | 0.957256461 |
| 0.464681982 | 0.535316431 | 6053.737374 | 0.90253931 | 0.001 | 2677.545455 | 265093 | 4.418216667 | 0.958747515 |
| 0.464650941 | 0.535348926 | 16028 | 0.929333092 | 1.00E-04 | 5823.181818 | 576495 | 9.60825 | 0.97166998 |
| 0.414154605 | 0.585845394 | 15690.17172 | 0.924202261 | 9.00E-05 | 5710.989899 | 565388 | 9.423133333 | 0.979622266 |
| 0.414151453 | 0.585848434 | 13377.9899 | 0.928108119 | 8.00E-05 | 4987.828283 | 493795 | 8.229916667 | 0.973161034 |
| 0.404049828 | 0.595950335 | 15126.16162 | 0.919116613 | 7.00E-05 | 5576.989899 | 552122 | 9.202033333 | 0.972664016 |
| 0.414150038 | 0.585849844 | 22843.64646 | 0.930979778 | 6.00E-05 | 8003.777778 | 792374 | 13.20623333 | 0.984095427 |
| 0.555549876 | 0.444450097 | 27780.29293 | 0.933444786 | 5.00E-05 | 8707.555556 | 862048 | 14.36746667 | 0.978628231 |
| 0.474749269 | 0.525250751 | 27402.91919 | 0.929940559 | 4.00E-05 | 9354.979798 | 926146 | 15.43576667 | 0.981610338 |
| 0.363642645 | 0.636357381 | 24808.88889 | 0.934162701 | 3.00E-05 | 8523.464646 | 843827 | 14.06378333 | 0.979622266 |
| 0.424244851 | 0.575755135 | 41082.64646 | 0.944308894 | 2.00E-05 | 13600.79798 | 1346482 | 22.44136667 | 0.982604374 |
| 0.45454638 | 0.545453619 | 41576.35354 | 0.9386208 | 1.00E-05 | 13681.69697 | 1354491 | 22.57485 | 0.983101392 |
| 0.303033035 | 0.696966968 | 55239.19192 | 0.945950559 | 9.00E-06 | 17978.40404 | 1779866 | 29.66443333 | 0.987077535 |
| 0.494949432 | 0.505050568 | 38499.32323 | 0.941868988 | 8.00E-06 | 12840.41414 | 1271202 | 21.1867 | 0.983101392 |
| 0.393940742 | 0.606059259 | 52918.57576 | 0.953626724 | 7.00E-06 | 17495 | 1732009 | 28.86681667 | 0.985089463 |
| 0.414142323 | 0.585857673 | 46127.78788 | 0.947592224 | 6.00E-06 | 14310.53535 | 1416775 | 23.61291667 | 0.982604374 |
| 0.424242953 | 0.575757044 | 60459.67677 | 0.942737514 | 5.00E-06 | 19158.92929 | 1896735 | 31.61225 | 0.985089463 |
| 0.484848651 | 0.515151355 | 60573.09091 | 0.94595558 | 4.00E-06 | 18257.86869 | 1807530 | 30.1255 | 0.982604374 |
| 0.393939853 | 0.606060143 | 57790.0101 | 0.952597546 | 3.00E-06 | 18113.93939 | 1793295 | 29.88825 | 0.980616302 |
| 0.474747554 | 0.525252445 | 76566.69697 | 0.952276242 | 2.00E-06 | 22773.30303 | 2254576 | 37.57626667 | 0.987077535 |
| 0.43434353 | 0.56565647 | 125939.0808 | 0.961117136 | 1.00E-06 | 38580.63636 | 3819484 | 63.65806667 | 0.990059642 |
| 1000-100-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.5051684 | 0.498971998 | 9 | 0.512992751 | 0.5 | 766.4444444 | 75878 | 1.264633333 | 0.803677932 |
| 0.515090671 | 0.485910498 | 683.9393939 | 0.742845955 | 0.4 | 964.6262626 | 95498 | 1.591633333 | 0.950795229 |
| 0.480013958 | 0.519975488 | 888.2121212 | 0.862998775 | 0.3 | 1030.828283 | 102068 | 1.701133333 | 0.964214712 |
| 0.451197579 | 0.548980475 | 1193.212121 | 0.88286945 | 0.2 | 1124.030303 | 111279 | 1.85465 | 0.968190855 |
| 0.537943461 | 0.461889392 | 1492.363636 | 0.879084081 | 0.1 | 1215.656566 | 120350 | 2.005833333 | 0.967196819 |
| 0.614178895 | 0.385837814 | 2412.555556 | 0.847932606 | 0.01 | 1499.191919 | 148420 | 2.473666667 | 0.97166998 |
| 0.494968205 | 0.505032715 | 4220.494949 | 0.857049621 | 0.001 | 2065.010101 | 204436 | 3.407266667 | 0.977137177 |
| 0.474751594 | 0.525248294 | 7695.222222 | 0.865594313 | 1.00E-04 | 3144.20202 | 311276 | 5.187933333 | 0.981610338 |
| 0.525248242 | 0.474751521 | 7968.343434 | 0.875102918 | 9.00E-05 | 3243.444444 | 321101 | 5.351683333 | 0.982604374 |
| 0.464651186 | 0.535348908 | 8373.878788 | 0.884726992 | 8.00E-05 | 3355.20202 | 332165 | 5.536083333 | 0.98359841 |
| 0.42424983 | 0.575750222 | 8587.454545 | 0.877904291 | 7.00E-05 | 3442.010101 | 340759 | 5.679316667 | 0.982107356 |
| 0.434348344 | 0.565651582 | 8802.59596 | 0.874982429 | 6.00E-05 | 2832.747475 | 280442 | 4.674033333 | 0.978628231 |
| 0.404047849 | 0.595952169 | 9941.171717 | 0.88076591 | 5.00E-05 | 3862.919192 | 382432 | 6.373866667 | 0.982107356 |
| 0.393945498 | 0.606054496 | 10444.77778 | 0.889857823 | 4.00E-05 | 4064.040404 | 402344 | 6.705733333 | 0.98359841 |
| 0.636358422 | 0.363641561 | 14323.28283 | 0.845743719 | 3.00E-05 | 5293.59596 | 524069 | 8.734483333 | 0.979622266 |
| 0.575754864 | 0.424245184 | 15518.9596 | 0.846687551 | 2.00E-05 | 5682.838384 | 562603 | 9.376716667 | 0.979125249 |
| 0.535352864 | 0.464647124 | 27037.90909 | 0.903568488 | 1.00E-05 | 9460.979798 | 936637 | 15.61061667 | 0.982604374 |
| 0.535353158 | 0.464646838 | 34867.60606 | 0.909904211 | 9.00E-06 | 10823.48485 | 1071541 | 17.85901667 | 0.984095427 |
| 0.525252248 | 0.474747763 | 32882.16162 | 0.909025644 | 8.00E-06 | 11375.07071 | 1126132 | 18.76886667 | 0.985089463 |
| 0.494949485 | 0.505050536 | 44096.93939 | 0.90463783 | 7.00E-06 | 15160.90909 | 1500930 | 25.0155 | 0.985586481 |
| 0.606059568 | 0.393940439 | 46843.71717 | 0.908859971 | 6.00E-06 | 13974.52525 | 1383495 | 23.05825 | 0.985586481 |
| 0.575756918 | 0.424243079 | 53281.34343 | 0.927289797 | 5.00E-06 | 18190.40404 | 1800852 | 30.0142 | 0.985089463 |
| 0.494949575 | 0.505050433 | 55912.51515 | 0.928575015 | 4.00E-06 | 17098.82828 | 1692787 | 28.21311667 | 0.986580517 |
| 0.636362889 | 0.363637113 | 68222.69697 | 0.936015222 | 3.00E-06 | 22019.66667 | 2179957 | 36.33261667 | 0.985586481 |
| 0.686868079 | 0.313131921 | 79970.38384 | 0.950870534 | 2.00E-06 | 26184.58586 | 2592274 | 43.20456667 | 0.984095427 |
| 0.565656446 | 0.434343554 | 109287.596 | 0.953551419 | 1.00E-06 | 33779.70707 | 3344192 | 55.73653333 | 0.987077535 |
| 1000-500-200 | ||||||||
| 02 | ||||||||
| 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 | 最大准确率p-max |
| 0.503567045 | 0.497136506 | 16.19191919 | 0.514930618 | 0.5 | 683.8383838 | 67700 | 1.128333333 | 0.792743539 |
| 0.512980987 | 0.486414056 | 1580.343434 | 0.74396048 | 0.4 | 1128.383838 | 111710 | 1.861833333 | 0.97166998 |
| 0.519521155 | 0.480258831 | 1833.878788 | 0.833845412 | 0.3 | 1191.767677 | 118033 | 1.967216667 | 0.973161034 |
| 0.574819038 | 0.424728918 | 2203.161616 | 0.842540715 | 0.2 | 1298.515152 | 128553 | 2.14255 | 0.970675944 |
| 0.671588942 | 0.3282027 | 2469.050505 | 0.864153463 | 0.1 | 1388.525253 | 137464 | 2.291066667 | 0.972664016 |
| 0.643952394 | 0.356042257 | 3195.272727 | 0.861417354 | 0.01 | 1664.808081 | 164817 | 2.74695 | 0.972664016 |
| 0.454624968 | 0.545376418 | 4716.636364 | 0.845005723 | 0.001 | 2088.676768 | 206779 | 3.446316667 | 0.97415507 |
| 0.535345181 | 0.46465493 | 7643.262626 | 0.848781051 | 1.00E-04 | 2960.575758 | 293097 | 4.88495 | 0.979622266 |
| 0.52524831 | 0.474751665 | 7770.828283 | 0.871402896 | 9.00E-05 | 3000.141414 | 297015 | 4.95025 | 0.976640159 |
| 0.454548392 | 0.545451573 | 8274.535354 | 0.858380023 | 8.00E-05 | 3265.959596 | 323330 | 5.388833333 | 0.980616302 |
| 0.444448216 | 0.555551887 | 8299.494949 | 0.862225636 | 7.00E-05 | 3467.030303 | 343237 | 5.720616667 | 0.980119284 |
| 0.363649866 | 0.636350164 | 8658 | 0.867532181 | 6.00E-05 | 3586.747475 | 355088 | 5.918133333 | 0.979622266 |
| 0.464648439 | 0.535351574 | 8950.666667 | 0.872171014 | 5.00E-05 | 3666.474747 | 362982 | 6.0497 | 0.981610338 |
| 0.424246723 | 0.575753241 | 9662.909091 | 0.867311284 | 4.00E-05 | 1942.505051 | 192308 | 3.205133333 | 0.979125249 |
| 0.484848725 | 0.515151279 | 10568.34343 | 0.848088238 | 3.00E-05 | 3812.525253 | 377440 | 6.290666667 | 0.977137177 |
| 0.525251966 | 0.474748057 | 12658.80808 | 0.849674679 | 2.00E-05 | 4428.10101 | 438382 | 7.306366667 | 0.981610338 |
| 0.545453659 | 0.454546331 | 19631.59596 | 0.871146856 | 1.00E-05 | 6480.717172 | 641607 | 10.69345 | 0.980119284 |
| 0.464646817 | 0.535353209 | 22426 | 0.8733006 | 9.00E-06 | 7311.171717 | 723806 | 12.06343333 | 0.981610338 |
| 0.545453987 | 0.454546017 | 29472.0202 | 0.905375826 | 8.00E-06 | 9406.979798 | 931291 | 15.52151667 | 0.98359841 |
| 0.414142229 | 0.585857771 | 27159.86869 | 0.907464305 | 7.00E-06 | 8793.646465 | 870571 | 14.50951667 | 0.98359841 |
| 0.505050371 | 0.494949621 | 30183.57576 | 0.896685543 | 6.00E-06 | 9690.060606 | 959316 | 15.9886 | 0.98359841 |
| 0.575756845 | 0.424243153 | 37208.65657 | 0.907931201 | 5.00E-06 | 11876.20202 | 1175744 | 19.59573333 | 0.980616302 |
| 0.616160752 | 0.383839248 | 40257.9697 | 0.903678937 | 4.00E-06 | 12129.39394 | 1200810 | 20.0135 | 0.984592445 |
| 0.606060077 | 0.39393992 | 46803.47475 | 0.923097777 | 3.00E-06 | 14782.81818 | 1463501 | 24.39168333 | 0.984095427 |
| 0.636363119 | 0.363636878 | 66742.11111 | 0.942802779 | 2.00E-06 | 20689.76768 | 2048288 | 34.13813333 | 0.985089463 |
| 0.535353482 | 0.46464652 | 93909.44444 | 0.95422415 | 1.00E-06 | 28744.9798 | 2845753 | 47.42921667 | 0.987077535 |
| 1000-2000-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.501306151 | 0.499333729 | 16.28282828 | 0.501536237 | 0.5 | 782.3939394 | 77476 | 1.291266667 | 0.627733598 |
| 0.506777775 | 0.493484654 | 1984.717172 | 0.758338856 | 0.4 | 1451.040404 | 143657 | 2.394283333 | 0.973161034 |
| 0.563290119 | 0.436976425 | 2279.474747 | 0.842781694 | 0.3 | 1557.989899 | 154259 | 2.570983333 | 0.97166998 |
| 0.560602138 | 0.439715098 | 2461.565657 | 0.878361146 | 0.2 | 1617.10101 | 160096 | 2.668266667 | 0.971172962 |
| 0.621564225 | 0.378658472 | 2934.989899 | 0.87880796 | 0.1 | 1782.686869 | 176488 | 2.941466667 | 0.970675944 |
| 0.494693453 | 0.505317126 | 3597.383838 | 0.880263871 | 0.01 | 2020.151515 | 199997 | 3.333283333 | 0.969184891 |
| 0.454603808 | 0.545395728 | 5039.828283 | 0.858184228 | 0.001 | 2502.636364 | 247763 | 4.129383333 | 0.976640159 |
| 0.515147093 | 0.484852838 | 7884.494949 | 0.854514328 | 1.00E-04 | 3503.131313 | 346813 | 5.780216667 | 0.974652087 |
| 0.464647501 | 0.535352579 | 7998 | 0.88522903 | 9.00E-05 | 3530.454545 | 349518 | 5.8253 | 0.978131213 |
| 0.48484979 | 0.515150303 | 8009.616162 | 0.861733639 | 8.00E-05 | 3547.080808 | 351161 | 5.852683333 | 0.980119284 |
| 0.535350129 | 0.464649977 | 8543.707071 | 0.857752475 | 7.00E-05 | 3715.79798 | 367865 | 6.131083333 | 0.978628231 |
| 0.484849621 | 0.515150499 | 8616.181818 | 0.866774103 | 6.00E-05 | 3711.141414 | 367403 | 6.123383333 | 0.980119284 |
| 0.434347888 | 0.565652072 | 8909.141414 | 0.86319457 | 5.00E-05 | 2825.676768 | 279742 | 4.662366667 | 0.980119284 |
| 0.515152165 | 0.484847911 | 9668.151515 | 0.843589975 | 4.00E-05 | 3953.828283 | 391429 | 6.523816667 | 0.980119284 |
| 0.494950238 | 0.505049704 | 10816.65657 | 0.842836918 | 3.00E-05 | 4334.616162 | 429130 | 7.152166667 | 0.977137177 |
| 0.595955978 | 0.404044 | 12202.9596 | 0.826415246 | 2.00E-05 | 4804.878788 | 475684 | 7.928066667 | 0.978131213 |
| 0.4949494 | 0.505050589 | 17632.75758 | 0.849237906 | 1.00E-05 | 6583.989899 | 651818 | 10.86363333 | 0.98111332 |
| 0.555554534 | 0.444445453 | 18320.91919 | 0.867486997 | 9.00E-06 | 6815.212121 | 674711 | 11.24518333 | 0.98111332 |
| 0.444445075 | 0.555554925 | 20959.47475 | 0.877392212 | 8.00E-06 | 7756.606061 | 767906 | 12.79843333 | 0.983101392 |
| 0.454545794 | 0.545454203 | 22918.35354 | 0.896078077 | 7.00E-06 | 8406.808081 | 832280 | 13.87133333 | 0.983101392 |
| 0.464646755 | 0.535353258 | 28211.87879 | 0.893783762 | 6.00E-06 | 10338.11111 | 1023474 | 17.0579 | 0.982107356 |
| 0.535353173 | 0.464646831 | 30435.40404 | 0.898889491 | 5.00E-06 | 10874.72727 | 1076600 | 17.94333333 | 0.985089463 |
| 0.565656132 | 0.434343863 | 34793.93939 | 0.903186939 | 4.00E-06 | 12302.56566 | 1217957 | 20.29928333 | 0.983101392 |
| 0.606060063 | 0.393939936 | 47156.64646 | 0.92720445 | 3.00E-06 | 16668.35354 | 1650173 | 27.50288333 | 0.982604374 |
| 0.666666084 | 0.333333916 | 58648.74747 | 0.924463321 | 2.00E-06 | 19635.34343 | 1943903 | 32.39838333 | 0.984095427 |
| 0.636363397 | 0.363636604 | 92023.44444 | 0.947652469 | 1.00E-06 | 30968.23232 | 3065858 | 51.09763333 | 0.985089463 |
| 1000-5000-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.502352011 | 0.499979164 | 24.2020202 | 0.514840251 | 0.5 | 745.3131313 | 73790 | 1.229833333 | 0.925447316 |
| 0.494048215 | 0.506032759 | 1994.818182 | 0.772305561 | 0.4 | 1378.89899 | 136513 | 2.275216667 | 0.969681909 |
| 0.558002277 | 0.44190028 | 2418.89899 | 0.841883045 | 0.3 | 1517.20202 | 150207 | 2.50345 | 0.972664016 |
| 0.482527504 | 0.517551318 | 2525.424242 | 0.875379039 | 0.2 | 1552.838384 | 153732 | 2.5622 | 0.978628231 |
| 0.553491759 | 0.446527203 | 2851.414141 | 0.870428941 | 0.1 | 1658.171717 | 164165 | 2.736083333 | 0.968190855 |
| 0.504829238 | 0.49517787 | 3665.414141 | 0.888361749 | 0.01 | 1925.222222 | 190597 | 3.176616667 | 0.972166998 |
| 0.565561403 | 0.434438239 | 5039.111111 | 0.828850132 | 0.001 | 2375.69697 | 235198 | 3.919966667 | 0.973658052 |
| 0.46464929 | 0.535350659 | 7782.484848 | 0.879862241 | 1.00E-04 | 3270.080808 | 323740 | 5.395666667 | 0.980616302 |
| 0.414150747 | 0.585849349 | 7738.575758 | 0.872542523 | 9.00E-05 | 3249.343434 | 321687 | 5.36145 | 0.98111332 |
| 0.55554536 | 0.444454786 | 8344.59596 | 0.836124666 | 8.00E-05 | 3491.313131 | 345642 | 5.7607 | 0.980119284 |
| 0.484846548 | 0.515153476 | 8338 | 0.856939173 | 7.00E-05 | 3478.949495 | 344417 | 5.740283333 | 0.979125249 |
| 0.36364892 | 0.636351157 | 8541.737374 | 0.887207061 | 6.00E-05 | 3578.232323 | 354248 | 5.904133333 | 0.980616302 |
| 0.515150134 | 0.484849843 | 8861.606061 | 0.86581521 | 5.00E-05 | 3630.353535 | 359407 | 5.990116667 | 0.97415507 |
| 0.444446563 | 0.55555348 | 9538.909091 | 0.850653654 | 4.00E-05 | 3852.111111 | 381366 | 6.3561 | 0.979125249 |
| 0.555554022 | 0.444445982 | 10629.06061 | 0.835165773 | 3.00E-05 | 4207.373737 | 416531 | 6.942183333 | 0.982107356 |
| 0.525251632 | 0.474748367 | 11908.38384 | 0.847124325 | 2.00E-05 | 4622.939394 | 457672 | 7.627866667 | 0.982604374 |
| 0.474747663 | 0.525252335 | 18050.47475 | 0.864610318 | 1.00E-05 | 6660.030303 | 659352 | 10.9892 | 0.98111332 |
| 0.454545959 | 0.545454044 | 18803.26263 | 0.879817057 | 9.00E-06 | 6898.313131 | 682937 | 11.38228333 | 0.982107356 |
| 0.515151137 | 0.48484886 | 21052.75758 | 0.879927506 | 8.00E-06 | 7598.20202 | 752224 | 12.53706667 | 0.98359841 |
| 0.494949649 | 0.505050346 | 23718.91919 | 0.879636323 | 7.00E-06 | 8452.949495 | 836844 | 13.9474 | 0.984095427 |
| 0.474747524 | 0.525252482 | 27849.16162 | 0.891770589 | 6.00E-06 | 9847.282828 | 974886 | 16.2481 | 0.981610338 |
| 0.585857945 | 0.414142053 | 31057.89899 | 0.890379943 | 5.00E-06 | 10952.88889 | 1084339 | 18.07231667 | 0.982604374 |
| 0.525252249 | 0.474747751 | 35596.86869 | 0.918348495 | 4.00E-06 | 11845.11111 | 1172670 | 19.5445 | 0.985089463 |
| 0.515151388 | 0.484848608 | 41936.11111 | 0.923650019 | 3.00E-06 | 13401.87879 | 1326802 | 22.11336667 | 0.984095427 |
| 0.686868021 | 0.313131976 | 56348.58586 | 0.924880013 | 2.00E-06 | 16819.27273 | 1665108 | 27.7518 | 0.982107356 |
| 0.707070354 | 0.292929647 | 85065.39394 | 0.950900657 | 1.00E-06 | 26576.43434 | 2631067 | 43.85111667 | 0.986580517 |
| 1000-10000-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.49795382 | 0.507409447 | 22.49494949 | 0.552754182 | 0.5 | 686.3535354 | 67949 | 1.132483333 | 0.947316103 |
| 0.533016389 | 0.466343544 | 2116.535354 | 0.706498383 | 0.4 | 1305.737374 | 129268 | 2.154466667 | 0.968687873 |
| 0.525457677 | 0.47411552 | 2248.161616 | 0.848645501 | 0.3 | 1347.878788 | 133440 | 2.224 | 0.968687873 |
| 0.547388994 | 0.452419504 | 2482.050505 | 0.873667088 | 0.2 | 1417.636364 | 140348 | 2.339133333 | 0.966699801 |
| 0.503307367 | 0.496728716 | 2789.272727 | 0.874716348 | 0.1 | 1508.20202 | 149312 | 2.488533333 | 0.970178926 |
| 0.574227364 | 0.425763605 | 3839.242424 | 0.867502058 | 0.01 | 1841.818182 | 182340 | 3.039 | 0.972664016 |
| 0.626081315 | 0.373918392 | 5135.232323 | 0.831902524 | 0.001 | 2249.818182 | 222747 | 3.71245 | 0.972664016 |
| 0.464645446 | 0.535354341 | 7566.565657 | 0.852079443 | 1.00E-04 | 2988.050505 | 295817 | 4.930283333 | 0.979125249 |
| 0.565644624 | 0.434355353 | 8140.626263 | 0.865433661 | 9.00E-05 | 3170.191919 | 313881 | 5.23135 | 0.975646123 |
| 0.55554613 | 0.444453953 | 8136.414141 | 0.852586501 | 8.00E-05 | 3145.989899 | 311453 | 5.190883333 | 0.977634195 |
| 0.414150921 | 0.585849112 | 8062.171717 | 0.870248208 | 7.00E-05 | 3135.10101 | 310375 | 5.172916667 | 0.979125249 |
| 0.454548657 | 0.545451432 | 8484.494949 | 0.861035805 | 6.00E-05 | 3246.959596 | 321450 | 5.3575 | 0.979125249 |
| 0.474749161 | 0.525250867 | 9411.121212 | 0.836877724 | 5.00E-05 | 3403.454545 | 336943 | 5.615716667 | 0.980616302 |
| 0.484847799 | 0.51515223 | 9631.777778 | 0.86004679 | 4.00E-05 | 2282.323232 | 225953 | 3.765883333 | 0.978628231 |
| 0.404045597 | 0.595954385 | 10206.90909 | 0.853690985 | 3.00E-05 | 4033.414141 | 399310 | 6.655166667 | 0.982107356 |
| 0.565655238 | 0.434344757 | 11755.61616 | 0.831651505 | 2.00E-05 | 4079.636364 | 403886 | 6.731433333 | 0.978628231 |
| 0.52525204 | 0.474747957 | 16517.19192 | 0.854715143 | 1.00E-05 | 6781.969697 | 671417 | 11.19028333 | 0.980616302 |
| 0.474747783 | 0.525252228 | 18858.77778 | 0.865734884 | 9.00E-06 | 7862.444444 | 778386 | 12.9731 | 0.982107356 |
| 0.5959583 | 0.404041714 | 18776.23232 | 0.858244473 | 8.00E-06 | 7867.929293 | 778927 | 12.98211667 | 0.98111332 |
| 0.484848713 | 0.515151296 | 21879.0303 | 0.867286182 | 7.00E-06 | 8397.686869 | 831373 | 13.85621667 | 0.985586481 |
| 0.565655869 | 0.43434413 | 24757.15152 | 0.870720124 | 6.00E-06 | 9305.747475 | 921271 | 15.35451667 | 0.983101392 |
| 0.535353262 | 0.464646733 | 28571.44444 | 0.880434564 | 5.00E-06 | 10615.08081 | 1050895 | 17.51491667 | 0.983101392 |
| 0.595958982 | 0.404041023 | 34554.53535 | 0.891122959 | 4.00E-06 | 12687.26263 | 1256040 | 20.934 | 0.98359841 |
| 0.575757165 | 0.424242829 | 43801.62626 | 0.907840834 | 3.00E-06 | 16182.11111 | 1602029 | 26.70048333 | 0.985586481 |
| 0.636363146 | 0.363636856 | 55440.0101 | 0.921185011 | 2.00E-06 | 19426.31313 | 1923220 | 32.05366667 | 0.984095427 |
| 0.57575744 | 0.424242559 | 79639.54545 | 0.95475129 | 1.00E-06 | 29653.9596 | 2935742 | 48.92903333 | 0.985586481 |
| 1000-50000-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.49210002 | 0.504571131 | 21.6969697 | 0.527511698 | 0.5 | 910.4242424 | 90148 | 1.502466667 | 0.956262425 |
| 0.512070717 | 0.487871085 | 1965.505051 | 0.767139587 | 0.4 | 260.3535354 | 25792 | 0.429866667 | 0.967196819 |
| 0.529361969 | 0.470551496 | 2392.030303 | 0.833619495 | 0.3 | 1705.444444 | 168839 | 2.813983333 | 0.972664016 |
| 0.56610623 | 0.434323107 | 2586.949495 | 0.85860594 | 0.2 | 1777.545455 | 175977 | 2.93295 | 0.968687873 |
| 0.526774701 | 0.47318005 | 2682.434343 | 0.882568227 | 0.1 | 1853.212121 | 183469 | 3.057816667 | 0.970675944 |
| 0.484946006 | 0.515038122 | 3697.777778 | 0.880103219 | 0.01 | 2211.424242 | 218962 | 3.649366667 | 0.967196819 |
| 0.50500782 | 0.49499262 | 5071.838384 | 0.856361829 | 0.001 | 2698.212121 | 267123 | 4.45205 | 0.976640159 |
| 0.505045906 | 0.494954184 | 7586.20202 | 0.856612848 | 1.00E-04 | 3275.020202 | 324227 | 5.403783333 | 0.98111332 |
| 0.404049054 | 0.595951053 | 7945.636364 | 0.868551318 | 9.00E-05 | 3294.828283 | 326188 | 5.436466667 | 0.981610338 |
| 0.535345109 | 0.464654914 | 8023.787879 | 0.841145049 | 8.00E-05 | 3323.181818 | 328995 | 5.48325 | 0.977137177 |
| 0.343448699 | 0.656551377 | 8172.141414 | 0.883943812 | 7.00E-05 | 3386.454545 | 335259 | 5.58765 | 0.979125249 |
| 0.42425087 | 0.575749137 | 8753.090909 | 0.858646103 | 6.00E-05 | 3592.535354 | 355662 | 5.9277 | 0.973658052 |
| 0.444449146 | 0.555550796 | 9313.737374 | 0.860985602 | 5.00E-05 | 3776.070707 | 373831 | 6.230516667 | 0.980119284 |
| 0.464647315 | 0.535352702 | 9288.868687 | 0.869475069 | 4.00E-05 | 3722.89899 | 368583 | 6.14305 | 0.983101392 |
| 0.525251745 | 0.47474826 | 10285.41414 | 0.850447818 | 3.00E-05 | 4028.141414 | 398793 | 6.64655 | 0.983101392 |
| 0.515150956 | 0.484849019 | 12266.43434 | 0.849353375 | 2.00E-05 | 3907.474747 | 386845 | 6.447416667 | 0.978628231 |
| 0.484848948 | 0.515151058 | 17709.87879 | 0.867497038 | 1.00E-05 | 6392.292929 | 632838 | 10.5473 | 0.982604374 |
| 0.595958086 | 0.404041906 | 17650.47475 | 0.841903127 | 9.00E-06 | 6375.969697 | 631222 | 10.52036667 | 0.985586481 |
| 0.434344433 | 0.565655582 | 20609.12121 | 0.882543125 | 8.00E-06 | 7323.747475 | 725054 | 12.08423333 | 0.982604374 |
| 0.53535304 | 0.46464696 | 22542.05051 | 0.869475069 | 7.00E-06 | 7958.555556 | 787900 | 13.13166667 | 0.982107356 |
| 0.48484846 | 0.515151549 | 26207.53535 | 0.902157761 | 6.00E-06 | 9195.525253 | 910358 | 15.17263333 | 0.985586481 |
| 0.525252269 | 0.474747733 | 28010.44444 | 0.888045465 | 5.00E-06 | 9637.919192 | 954160 | 15.90266667 | 0.985089463 |
| 0.565656042 | 0.434343951 | 30981.65657 | 0.897187582 | 4.00E-06 | 10584.81818 | 1047900 | 17.465 | 0.982604374 |
| 0.555555311 | 0.444444688 | 40574.59596 | 0.907946262 | 3.00E-06 | 13636.69697 | 1350035 | 22.50058333 | 0.98359841 |
| 0.59595926 | 0.404040742 | 60508.31313 | 0.942491516 | 2.00E-06 | 19987.63636 | 1978777 | 32.97961667 | 0.985089463 |
| 0.59595942 | 0.404040581 | 81751.40404 | 0.946492761 | 1.00E-06 | 26766.68687 | 2649905 | 44.16508333 | 0.985586481 |
| 1000-10w-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.498562972 | 0.498547334 | 25.83838384 | 0.527481575 | 0.5 | 738.2626263 | 73093 | 1.218216667 | 0.958747515 |
| 0.509150989 | 0.490787486 | 1992.020202 | 0.774474366 | 0.4 | 1355.676768 | 134214 | 2.2369 | 0.967693837 |
| 0.533293858 | 0.467441132 | 2253.969697 | 0.840336767 | 0.3 | 1432.272727 | 141799 | 2.363316667 | 0.970178926 |
| 0.519913857 | 0.480264975 | 2630.79798 | 0.873847822 | 0.2 | 1552.20202 | 153670 | 2.561166667 | 0.969184891 |
| 0.570186527 | 0.429585518 | 2925.676768 | 0.871779425 | 0.1 | 1654.474747 | 163797 | 2.72995 | 0.97166998 |
| 0.464668041 | 0.535315421 | 3822.828283 | 0.882673655 | 0.01 | 1965.676768 | 194605 | 3.243416667 | 0.975646123 |
| 0.454568082 | 0.545430493 | 4922.070707 | 0.859399161 | 0.001 | 2335.363636 | 231204 | 3.8534 | 0.975646123 |
| 0.474745571 | 0.525254479 | 7713.868687 | 0.874746471 | 1.00E-04 | 3247.10101 | 321464 | 5.357733333 | 0.978628231 |
| 0.373752011 | 0.626247982 | 8079.161616 | 0.876242545 | 9.00E-05 | 3355.242424 | 332170 | 5.536166667 | 0.982107356 |
| 0.454550521 | 0.545449628 | 8228.737374 | 0.859961443 | 8.00E-05 | 3404.040404 | 337002 | 5.6167 | 0.983101392 |
| 0.474750939 | 0.525249138 | 8663.020202 | 0.853977147 | 7.00E-05 | 3545.858586 | 351045 | 5.85075 | 0.980119284 |
| 0.484848662 | 0.515151359 | 9177.737374 | 0.8701478 | 6.00E-05 | 3725.090909 | 368785 | 6.146416667 | 0.98111332 |
| 0.525249719 | 0.474750296 | 8857.373737 | 0.867160672 | 5.00E-05 | 3651.353535 | 361485 | 6.02475 | 0.981610338 |
| 0.545451349 | 0.454548678 | 9872.080808 | 0.821369761 | 4.00E-05 | 3981.363636 | 394159 | 6.569316667 | 0.982604374 |
| 0.474749184 | 0.525250805 | 10113.92929 | 0.846858244 | 3.00E-05 | 4059.818182 | 401924 | 6.698733333 | 0.982107356 |
| 0.545452915 | 0.45454711 | 11892.36364 | 0.837460088 | 2.00E-05 | 4679.464646 | 463268 | 7.721133333 | 0.977137177 |
| 0.4949496 | 0.505050407 | 16326.61616 | 0.84115509 | 1.00E-05 | 6089.888889 | 602900 | 10.04833333 | 0.979622266 |
| 0.535352953 | 0.464647063 | 17627.62626 | 0.866046147 | 9.00E-06 | 6534.222222 | 646891 | 10.78151667 | 0.98359841 |
| 0.474747664 | 0.525252322 | 20191.28283 | 0.877377151 | 8.00E-06 | 7396.656566 | 732274 | 12.20456667 | 0.982604374 |
| 0.49494972 | 0.505050268 | 23512.85859 | 0.894968572 | 7.00E-06 | 8465.060606 | 838043 | 13.96738333 | 0.982107356 |
| 0.515151257 | 0.484848752 | 23340.25253 | 0.886052373 | 6.00E-06 | 8419.616162 | 833544 | 13.8924 | 0.984592445 |
| 0.424242961 | 0.57575704 | 28645.25253 | 0.906389943 | 5.00E-06 | 10152.19192 | 1005068 | 16.75113333 | 0.980616302 |
| 0.505050406 | 0.494949598 | 35861.73737 | 0.909201358 | 4.00E-06 | 12541.92929 | 1241654 | 20.69423333 | 0.984095427 |
| 0.515151421 | 0.484848571 | 40650.67677 | 0.925587887 | 3.00E-06 | 12735.89899 | 1260854 | 21.01423333 | 0.982604374 |
| 0.616161169 | 0.383838835 | 60227.68687 | 0.917906701 | 2.00E-06 | 17665.06061 | 1748841 | 29.14735 | 0.985586481 |
| 0.656565385 | 0.343434615 | 82600.19192 | 0.94293833 | 1.00E-06 | 25140.31313 | 2488891 | 41.48151667 | 0.987077535 |
| 0.585858454 | 0.414141546 | 100350.8889 | 0.960278732 | 9.00E-07 | 30518.93939 | 3021377 | 50.35628333 | 0.986580517 |
| 0.62626245 | 0.37373755 | 100186.5253 | 0.953852642 | 8.00E-07 | 30890.85859 | 3058198 | 50.96996667 | 0.985586481 |
| 0.606060491 | 0.393939508 | 101241.3737 | 0.961071952 | 7.00E-07 | 31049.28283 | 3073879 | 51.23131667 | 0.986580517 |
| 0.545454503 | 0.454545496 | 101789.899 | 0.957125931 | 6.00E-07 | 31263.60606 | 3095099 | 51.58498333 | 0.985586481 |
| 1000-50w-200 | ||||||||
| 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 | 最大准确率p-max |
| 0.49989909 | 0.501402276 | 24.92929293 | 0.538616784 | 0.5 | 757.7373737 | 75017 | 1.250283333 | 0.955765408 |
| 0.511198186 | 0.488955305 | 1977.424242 | 0.75629556 | 0.4 | 1397.292929 | 138332 | 2.305533333 | 0.970178926 |
| 0.549793234 | 0.450735321 | 2351.181818 | 0.829186497 | 0.3 | 1408.171717 | 139409 | 2.323483333 | 0.968687873 |
| 0.589590539 | 0.410519042 | 2482.848485 | 0.87250236 | 0.2 | 1392.060606 | 137816 | 2.296933333 | 0.97166998 |
| 0.521612906 | 0.478503346 | 2954.606061 | 0.87775368 | 0.1 | 1520.353535 | 150515 | 2.508583333 | 0.969681909 |
| 0.574348263 | 0.42565453 | 3905.949495 | 0.854584614 | 0.01 | 1807.959596 | 179004 | 2.9834 | 0.97166998 |
| 0.545357939 | 0.45464297 | 5019.575758 | 0.834924795 | 0.001 | 2220.434343 | 219825 | 3.66375 | 0.97415507 |
| 0.525246044 | 0.474753844 | 7745.818182 | 0.866282105 | 1.00E-04 | 3150.646465 | 311928 | 5.1988 | 0.975149105 |
| 0.404051725 | 0.595948381 | 7963.232323 | 0.882021005 | 9.00E-05 | 3282.707071 | 324989 | 5.416483333 | 0.980119284 |
| 0.343452589 | 0.656547387 | 8365.171717 | 0.881137418 | 8.00E-05 | 3293.636364 | 326070 | 5.4345 | 0.980616302 |
| 0.464650219 | 0.535349756 | 8351.59596 | 0.858093861 | 7.00E-05 | 762.0707071 | 75446 | 1.257433333 | 0.982107356 |
| 0.44444705 | 0.555552934 | 8417.555556 | 0.87027833 | 6.00E-05 | 4767.424242 | 471975 | 7.86625 | 0.980119284 |
| 0.464648267 | 0.535351731 | 9031.434343 | 0.858786674 | 5.00E-05 | 3358.181818 | 332492 | 5.541533333 | 0.978628231 |
| 0.404044535 | 0.595955446 | 9703.252525 | 0.872788521 | 4.00E-05 | 3532.838384 | 349751 | 5.829183333 | 0.978131213 |
| 0.454547878 | 0.545452154 | 10273.36364 | 0.847300038 | 3.00E-05 | 3700.171717 | 366319 | 6.105316667 | 0.978628231 |
| 0.56565451 | 0.434345489 | 11731.25253 | 0.828142258 | 2.00E-05 | 4155.262626 | 411371 | 6.856183333 | 0.978131213 |
| 0.545454248 | 0.454545754 | 18167.29293 | 0.845939514 | 1.00E-05 | 6108.282828 | 604721 | 10.07868333 | 0.979125249 |
| 0.494949631 | 0.505050355 | 18092.37374 | 0.873210234 | 9.00E-06 | 6080.606061 | 601980 | 10.033 | 0.981610338 |
| 0.484848498 | 0.515151499 | 20048.54545 | 0.871227182 | 8.00E-06 | 5920.909091 | 586170 | 9.7695 | 0.98111332 |
| 0.484848532 | 0.51515147 | 19530.54545 | 0.857983413 | 7.00E-06 | 6465.393939 | 640076 | 10.66793333 | 0.978628231 |
| 0.484848802 | 0.515151194 | 24351.9596 | 0.881529008 | 6.00E-06 | 7872.828283 | 779411 | 12.99018333 | 0.982604374 |
| 0.565656041 | 0.434343947 | 29944.24242 | 0.879023837 | 5.00E-06 | 9518.171717 | 942300 | 15.705 | 0.984592445 |
| 0.595958926 | 0.404041068 | 33255.0404 | 0.885003113 | 4.00E-06 | 10485.33333 | 1038052 | 17.30086667 | 0.985586481 |
| 0.515151428 | 0.484848568 | 42423.88889 | 0.923127899 | 3.00E-06 | 13182.83838 | 1305103 | 21.75171667 | 0.98359841 |
| 0.585858301 | 0.414141697 | 59707.63636 | 0.931019941 | 2.00E-06 | 18321.57576 | 1813839 | 30.23065 | 0.986083499 |
| 0.666666374 | 0.333333626 | 90144.52525 | 0.946849208 | 1.00E-06 | 27542.18182 | 2726677 | 45.44461667 | 0.986083499 |
f2[0] f2[1] 迭代次数n 平均准确率p-ave δ 耗时ms/次
0.471610612 0.510345602 4 0.517395626 0.5 1205
0.48972178 0.51117289 4 0.621272366 0.5 876
0.464157499 0.522397641 2 0.51640159 0.5 845
0.499034007 0.514696777 8 0.48359841 0.5 793
0.479386047 0.503039185 6 0.492047714 0.5 813
0.455905821 0.508358873 2 0.510934394 0.5 784
0.460383203 0.50768254 2 0.512922465 0.5 766
0.506071655 0.413810754 1 0.487077535 0.5 768
0.440657068 0.555781736 2 0.512922465 0.5 752
0.570844355 0.478170586 1 0.487077535 0.5 759
0.5118824 0.476449193 1 0.487077535 0.5 751
0.450553156 0.508456472 4 0.512922465 0.5 768
0.495240044 0.501165531 12 0.565109344 0.5 750
0.519483683 0.404071305 1 0.487077535 0.5 768
0.444336158 0.505526292 4 0.718190855 0.5 783
0.436518574 0.507721708 2 0.512922465 0.5 769
0.461973723 0.534900902 10 0.690854871 0.5 768
0.488421566 0.513006507 12 0.574552684 0.5 779
0.48314746 0.523104397 8 0.546222664 0.5 775
0.489038597 0.504752754 8 0.843936382 0.5 797
0.469828691 0.525710462 2 0.512922465 0.5 767
0.49932012 0.513740257 2 0.615805169 0.5 768
0.503480039 0.488127971 7 0.487077535 0.5 766
0.488790355 0.607231555 2 0.512922465 0.5 751
0.513922262 0.488480433 3 0.487077535 0.5 752
0.485686684 0.556449749 2 0.512922465 0.5 758
0.548572611 0.49162887 1 0.487077535 0.5 752
0.439414655 0.533151954 2 0.512922465 0.5 753
0.498015414 0.50187286 12 0.528827038 0.5 783
0.491326744 0.504592843 12 0.722664016 0.5 752
0.474428347 0.501293277 6 0.784294235 0.5 766
0.429890123 0.527498049 2 0.512922465 0.5 757
0.495806067 0.518518668 2 0.497514911 0.5 768
0.387041179 0.596860264 2 0.512922465 0.5 768
0.505836572 0.489887504 11 0.487077535 0.5 735
0.544215367 0.486051728 1 0.487077535 0.5 799
0.483586864 0.560532994 2 0.513916501 0.5 776
0.477624932 0.507884056 10 0.545228628 0.5 753
0.501680787 0.484450076 11 0.487077535 0.5 750
0.502716665 0.470979728 5 0.487077535 0.5 770
0.500947082 0.491945771 1 0.487077535 0.5 751
0.538892931 0.436438026 1 0.487077535 0.5 768
0.47054908 0.605560948 2 0.512922465 0.5 783
0.462875234 0.50607657 2 0.513419483 0.5 766
0.546671336 0.487599499 3 0.487077535 0.5 814
0.475022888 0.502849277 16 0.527335984 0.5 784
0.507784475 0.485330695 7 0.487077535 0.5 750
0.508634716 0.471640206 5 0.487077535 0.5 799
0.505198797 0.496049613 7 0.487077535 0.5 735
0.480773043 0.511136814 2 0.505964215 0.5 768
0.515451093 0.499331058 9 0.487077535 0.5 751
0.483920258 0.522930988 2 0.596421471 0.5 783
0.517862349 0.491399291 7 0.487077535 0.5 751
0.461191954 0.519102188 10 0.586978131 0.5 787
0.489677439 0.509044128 2 0.488071571 0.5 750
0.515668048 0.481731912 1 0.487077535 0.5 769
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0.999997145 2.86E-06 44215 0.970178926 3.00E-06 13953
0.999997294 2.71E-06 140929 0.975149105 3.00E-06 43454
2.90E-06 0.999997098 68554 0.961729622 3.00E-06 21468
2.85E-06 0.999997171 23314 0.966699801 3.00E-06 7733
0.999997806 2.13E-06 28189 0.974652087 3.00E-06 9175
2.85E-06 0.999997146 200834 0.978131213 3.00E-06 61277
2.77E-06 0.999997239 32360 0.939860835 3.00E-06 10455
0.999997471 2.51E-06 30789 0.956262425 3.00E-06 9975
2.92E-06 0.999997082 79810 0.905069583 3.00E-06 24805
2.07E-06 0.999997976 21644 0.966699801 3.00E-06 7210
2.23E-06 0.999997775 33248 0.94333996 3.00E-06 10632
2.86E-06 0.999997156 152468 0.966699801 3.00E-06 46935
2.33E-06 0.999997662 35158 0.961729622 3.00E-06 11255
1.66E-06 0.999998345 38566 0.931411531 3.00E-06 12417
0.999997894 2.13E-06 16395 0.967196819 3.00E-06 5631
2.11E-06 0.999997828 22844 0.975646123 3.00E-06 7455
2.48E-06 0.999997411 18608 0.952286282 3.00E-06 6318
0.999997246 2.80E-06 68821 0.912027833 3.00E-06 21567
0.999997228 2.75E-06 148585 0.971172962 3.00E-06 45661
0.999997115 2.91E-06 146893 0.96471173 3.00E-06 45016
2.25E-06 0.999997724 16988 0.959741551 3.00E-06 5862
0.999997441 2.58E-06 46067 0.977137177 3.00E-06 14610
2.75E-06 0.999997094 108572 0.966202783 3.00E-06 33472
2.89E-06 0.999997089 29050 0.970675944 3.00E-06 9466
3.00E-06 0.999997011 86436 0.976143141 3.00E-06 26790
5.22E-07 0.999999479 23674 0.945328032 3.00E-06 7892
2.72E-06 0.999997266 25162 0.971172962 3.00E-06 8249
0.999997624 2.38E-06 168891 0.938369781 3.00E-06 51829
1793295
1.52E-06 0.999998474 33410 0.961729622 2.00E-06 -88217
1.65E-06 0.999998348 32212 0.929920477 2.00E-06 10363
0.999998801 1.17E-06 41283 0.953280318 2.00E-06 13063
1.43E-06 0.999998571 17600 0.969681909 2.00E-06 5971
0.99999806 1.93E-06 70293 0.972166998 2.00E-06 21686
1.71E-06 0.999998286 83632 0.970675944 2.00E-06 25713
0.999998551 1.45E-06 58063 0.952286282 2.00E-06 18043
1.92E-06 0.999998083 172302 0.931908549 2.00E-06 52815
0.9999981 1.90E-06 87265 0.963220676 2.00E-06 27191
0.999998481 1.52E-06 30213 0.826043738 2.00E-06 9785
1.52E-06 0.999998488 18266 0.969184891 2.00E-06 6142
0.99999852 1.47E-06 34893 0.892644135 2.00E-06 11222
0.999998138 1.86E-06 86375 0.956759443 2.00E-06 26839
0.999998878 1.13E-06 24545 0.959741551 2.00E-06 8018
0.999998537 1.47E-06 48181 0.979622266 2.00E-06 15242
0.999998213 1.79E-06 216305 0.951789264 2.00E-06 66015
1.08E-06 0.999998915 28588 0.907554672 2.00E-06 9509
0.999998227 1.77E-06 37457 0.895129225 2.00E-06 12016
1.48E-06 0.999998509 43016 0.970675944 2.00E-06 13612
1.68E-06 0.999998308 23882 0.903578529 2.00E-06 7950
0.99999822 1.79E-06 40305 0.919980119 2.00E-06 12941
0.999998012 1.99E-06 116655 0.973161034 2.00E-06 35919
1.97E-06 0.999998019 75254 0.916998012 2.00E-06 23411
1.28E-06 0.999998651 78840 0.981610338 2.00E-06 24509
1.84E-06 0.999998154 91432 0.963220676 2.00E-06 28319
1.67E-06 0.999998335 33312 0.967693837 2.00E-06 10630
0.999998583 1.43E-06 21345 0.887176938 2.00E-06 7037
0.999999204 7.98E-07 30213 0.912524851 2.00E-06 9722
1.98E-06 0.999998016 545552 0.977634195 2.00E-06 165064
1.40E-06 0.999998628 34092 0.972664016 2.00E-06 10943
0.999998919 1.09E-06 100655 0.915506958 2.00E-06 30792
1.90E-06 0.999998099 16724 0.97415507 2.00E-06 5646
0.999998014 1.99E-06 277365 0.986083499 2.00E-06 84291
0.999998036 1.96E-06 102965 0.979125249 2.00E-06 31604
1.84E-06 0.999998106 25886 0.965705765 2.00E-06 8400
1.33E-06 0.999998679 40500 0.968190855 2.00E-06 12914
0.999998116 1.89E-06 154393 0.977137177 2.00E-06 47202
0.999998016 1.98E-06 68021 0.972166998 2.00E-06 21536
1.80E-06 0.999998188 39696 0.973161034 2.00E-06 12559
1.98E-06 0.99999803 35120 0.968687873 2.00E-06 11133
0.999998501 1.50E-06 30643 0.942345924 2.00E-06 9915
0.99999807 1.93E-06 54931 0.957256461 2.00E-06 17139
1.94E-06 0.999998064 49798 0.957256461 2.00E-06 15696
0.999998039 1.96E-06 65061 0.9527833 2.00E-06 20304
1.37E-06 0.999998632 22748 0.959741551 2.00E-06 7591
1.71E-06 0.999998261 56474 0.982107356 2.00E-06 17561
1.87E-06 0.999998128 195320 0.976640159 2.00E-06 59315
1.95E-06 0.999998068 60156 0.977634195 2.00E-06 18772
1.94E-06 0.999998085 30914 0.963717694 2.00E-06 9876
0.999998694 1.30E-06 56959 0.979125249 2.00E-06 17798
1.40E-06 0.999998597 21252 0.835984095 2.00E-06 7048
1.53E-06 0.999998425 54084 0.971172962 2.00E-06 16939
1.53E-06 0.999998472 35204 0.927435388 2.00E-06 11167
0.999998695 1.36E-06 30685 0.91500994 2.00E-06 9847
1.81E-06 0.999998125 29472 0.972166998 2.00E-06 9528
1.38E-06 0.99999857 26158 0.965705765 2.00E-06 8586
0.999998466 1.53E-06 88529 0.950298211 2.00E-06 27293
1.80E-06 0.999998202 427424 0.987077535 2.00E-06 129662
1.95E-06 0.999998024 141608 0.979125249 2.00E-06 43378
0.999998795 1.23E-06 33925 0.947813121 2.00E-06 10940
1.96E-06 0.999998028 18676 0.960238569 2.00E-06 6242
1.63E-06 0.999998359 153238 0.979622266 2.00E-06 47165
1.66E-06 0.99999834 81430 0.960238569 2.00E-06 25254
0.999998026 1.97E-06 46383 0.96222664 2.00E-06 14697
1.81E-06 0.999998183 219796 0.936878728 2.00E-06 67159
0.999998139 1.85E-06 29111 0.96471173 2.00E-06 9394
0.999998454 1.55E-06 30773 0.930417495 2.00E-06 10036
0.99999826 1.74E-06 44277 0.939860835 2.00E-06 14080
1.83E-06 0.999998161 116726 0.974652087 2.00E-06 36040
9.27E-07 0.999999074 41536 0.808648111 2.00E-06 13287
5.44E-07 0.99999946 24606 0.954771372 2.00E-06 8113
0.999998133 1.87E-06 271137 0.986083499 2.00E-06 82702
1.19E-06 0.999998801 42298 0.940357853 2.00E-06 13424
0.999998524 1.48E-06 96827 0.97415507 2.00E-06 29975
0.999998363 1.64E-06 22329 0.946322068 2.00E-06 7427
0.999998181 1.81E-06 75345 0.978131213 2.00E-06 23432
0.999998229 1.77E-06 23963 0.961232604 2.00E-06 7998
1.45E-06 0.99999852 18516 0.953777336 2.00E-06 6352
1.82E-06 0.99999817 148386 0.980616302 2.00E-06 45930
1.72E-06 0.999998277 63172 0.960238569 2.00E-06 19695
0.999998367 1.64E-06 83347 0.939363817 2.00E-06 25938
0.999998242 1.76E-06 68901 0.961729622 2.00E-06 21741
1.57E-06 0.999998433 38316 0.968687873 2.00E-06 12241
0.999998027 1.98E-06 41369 0.850397614 2.00E-06 13126
1.66E-06 0.999998338 28994 0.971172962 2.00E-06 9504
0.999999069 9.43E-07 28061 0.932902584 2.00E-06 9190
1.86E-06 0.999998223 69440 0.962723658 2.00E-06 21803
0.999998137 1.88E-06 37799 0.978628231 2.00E-06 12084
0.999998788 1.21E-06 23867 0.948310139 2.00E-06 7850
1.85E-06 0.999998147 298304 0.977137177 2.00E-06 90697
0.999998213 1.83E-06 77583 0.986083499 2.00E-06 23977
1.02E-06 0.99999897 23252 0.964214712 2.00E-06 7573
1.64E-06 0.999998364 58074 0.979622266 2.00E-06 18140
1.62E-06 0.999998382 75094 0.934393638 2.00E-06 23402
0.999998723 1.29E-06 18295 0.961232604 2.00E-06 6122
1.89E-06 0.999998113 41548 0.97415507 2.00E-06 13223
0.999998085 1.92E-06 119029 0.951292247 2.00E-06 36303
0.999998196 1.81E-06 27293 0.953280318 2.00E-06 8915
0.999998001 2.00E-06 225561 0.956262425 2.00E-06 68421
2254576
9.45E-07 0.99999905 416004 0.970675944 1.00E-06 125374
0.999999323 6.78E-07 48839 0.956262425 1.00E-06 15416
6.76E-07 0.999999316 32766 0.980119284 1.00E-06 10467
7.39E-07 0.999999276 52032 0.953280318 1.00E-06 16229
8.36E-07 0.999999164 25316 0.932405567 1.00E-06 8208
5.90E-07 0.999999405 45266 0.96471173 1.00E-06 14336
8.20E-07 0.999999165 33864 0.952286282 1.00E-06 10879
9.12E-07 0.999999087 49812 0.967693837 1.00E-06 15649
0.999999056 9.42E-07 40209 0.962723658 1.00E-06 12778
0.999999239 7.57E-07 98819 0.942842942 1.00E-06 30258
0.999999012 9.90E-07 234681 0.979125249 1.00E-06 71077
8.82E-07 0.999999118 119298 0.990059642 1.00E-06 36659
0.99999923 7.71E-07 90619 0.960735586 1.00E-06 27632
9.76E-07 0.999999018 480722 0.975646123 1.00E-06 145414
8.87E-07 0.999999091 31458 0.95526839 1.00E-06 10199
9.79E-07 0.999999021 142556 0.978131213 1.00E-06 43638
9.63E-07 0.999999036 343050 0.97166998 1.00E-06 103729
8.47E-07 0.999999159 60886 0.959244533 1.00E-06 19136
0.999999205 7.98E-07 66383 0.957256461 1.00E-06 20820
0.999999076 9.25E-07 247531 0.984592445 1.00E-06 75407
7.09E-07 0.999999289 22494 0.966202783 1.00E-06 7453
8.55E-07 0.99999915 55112 0.975646123 1.00E-06 17407
4.90E-07 0.99999951 32362 0.925944334 1.00E-06 10447
8.84E-07 0.999999121 39826 0.973161034 1.00E-06 12689
0.999999007 9.97E-07 189053 0.977634195 1.00E-06 57894
9.27E-07 0.99999907 157216 0.96471173 1.00E-06 48724
8.73E-07 0.999999055 82338 0.972664016 1.00E-06 25419
0.99999906 9.49E-07 65573 0.984095427 1.00E-06 20484
0.999999246 7.55E-07 51587 0.938866799 1.00E-06 16258
9.88E-07 0.999999013 71692 0.958250497 1.00E-06 22399
0.999999081 9.19E-07 128795 0.919483101 1.00E-06 39508
0.999999043 9.56E-07 127819 0.954274354 1.00E-06 39220
9.52E-07 0.999999048 50476 0.941351889 1.00E-06 15911
0.99999901 9.90E-07 600129 0.923459245 1.00E-06 119308
0.999999161 8.39E-07 90763 0.920974155 1.00E-06 28154
8.60E-07 0.999999142 31774 0.944333996 1.00E-06 10381
0.999999004 9.99E-07 83327 0.928429423 1.00E-06 25895
9.48E-07 0.999999058 258980 0.975646123 1.00E-06 78878
0.999999363 6.35E-07 80193 0.922465209 1.00E-06 25033
0.999999138 8.63E-07 129335 0.972664016 1.00E-06 39740
0.999999055 9.44E-07 251079 0.928429423 1.00E-06 76290
9.87E-07 0.999999009 39486 0.952286282 1.00E-06 12558
0.999999023 9.77E-07 951485 0.964214712 1.00E-06 286201
0.999999188 8.12E-07 65545 0.948807157 1.00E-06 20375
0.9999991 9.00E-07 180297 0.949801193 1.00E-06 54639
0.999999285 7.88E-07 13905 0.963220676 1.00E-06 4820
8.95E-07 0.999999105 58484 0.976640159 1.00E-06 18242
0.999999055 9.44E-07 117827 0.972664016 1.00E-06 35841
9.83E-07 0.999999017 380774 0.980119284 1.00E-06 115022
9.70E-07 0.99999903 41786 0.92693837 1.00E-06 13122
0.999999214 7.76E-07 92109 0.976640159 1.00E-06 28239
0.999999021 9.80E-07 113303 0.931908549 1.00E-06 34547
9.29E-07 0.999999071 381310 0.978131213 1.00E-06 114771
0.999999106 8.92E-07 80201 0.96471173 1.00E-06 24636
9.36E-07 0.999999068 71442 0.963220676 1.00E-06 21965
8.41E-07 0.999999159 26346 0.977634195 1.00E-06 8555
7.86E-07 0.999999215 56068 0.981610338 1.00E-06 17474
5.72E-07 0.999999426 44770 0.975149105 1.00E-06 14126
7.75E-07 0.999999226 78804 0.972166998 1.00E-06 24330
0.999999233 7.68E-07 63323 0.972664016 1.00E-06 19736
7.76E-07 0.999999225 188326 0.984095427 1.00E-06 57271
3.59E-07 0.99999964 28672 0.957256461 1.00E-06 9376
8.19E-07 0.999999181 33270 0.939363817 1.00E-06 10685
0.999999085 9.15E-07 86077 0.973658052 1.00E-06 26445
8.86E-07 0.999999114 95132 0.970675944 1.00E-06 29432
0.999999309 6.90E-07 90263 0.970178926 1.00E-06 27871
0.999999103 8.98E-07 74203 0.973658052 1.00E-06 23123
0.999999017 9.87E-07 109331 0.972166998 1.00E-06 33778
0.999999071 9.29E-07 398597 0.980616302 1.00E-06 121089
9.18E-07 0.999999083 291842 0.98359841 1.00E-06 88771
6.67E-07 0.999999331 58592 0.947316103 1.00E-06 18265
8.20E-07 0.999999194 53882 0.975646123 1.00E-06 16898
0.999999025 9.74E-07 348719 0.976143141 1.00E-06 106091
0.999999141 8.62E-07 98817 0.953280318 1.00E-06 30603
0.999999009 9.91E-07 151341 0.959244533 1.00E-06 46584
9.18E-07 0.999999076 57164 0.940854871 1.00E-06 18190
6.27E-07 0.999999374 31450 0.904572565 1.00E-06 10281
9.97E-07 0.999999008 106518 0.972166998 1.00E-06 32776
7.55E-07 0.999999245 97294 0.96471173 1.00E-06 30068
9.33E-07 0.999999062 50658 0.98359841 1.00E-06 16062
7.21E-07 0.999999279 41454 0.94333996 1.00E-06 13194
8.06E-07 0.999999191 98560 0.977137177 1.00E-06 30677
9.72E-07 0.999999032 17166 0.918489066 1.00E-06 5857
9.32E-07 0.999999084 157294 0.98359841 1.00E-06 48163
0.999999101 8.98E-07 189281 0.970178926 1.00E-06 57857
0.999999206 7.90E-07 50781 0.96471173 1.00E-06 16029
9.69E-07 0.99999901 19834 0.973161034 1.00E-06 6632
5.30E-07 0.99999947 40940 0.966699801 1.00E-06 13003
1.00E-06 0.999999023 15994 0.959741551 1.00E-06 5427
9.16E-07 0.999999084 142318 0.968190855 1.00E-06 43677
0.999999034 9.66E-07 278191 0.976143141 1.00E-06 83619
0.999999103 8.92E-07 75541 0.946819085 1.00E-06 24300
0.999999233 7.61E-07 21353 0.960238569 1.00E-06 9036
7.51E-07 0.999999246 45106 0.926441352 1.00E-06 17931
9.01E-07 0.999999101 20670 0.975646123 1.00E-06 8703
7.17E-07 0.999999264 27690 0.970675944 1.00E-06 11248
0.999999237 7.63E-07 204357 0.958250497 1.00E-06 77831
0.999999308 6.92E-07 42395 0.972664016 1.00E-06 16912
0.999999064 9.36E-07 411597 0.963717694 1.00E-06 155732
3819484
本次数据比较多有感兴趣的朋友到我的资源里下载