用于给github.com/linvon/cuckoo-filter提供更多的数据支持，以便于
计算假阳性概率、指纹长度检查、计算项均摊成本、半排序桶占用成本检查
init...
init success
b: 2 f: 16 α: 0.84
计算假阳性概率 2b/2^f< r
0.00006103515625
指纹长度检查
计算指纹应保证 "f ≥ log_2(2b/r)=log_2(1/r) + log_2(2b)"
fingerprint 16 >= 16 && 16 == 16 is ok
计算项均摊成本
计算每个项的均摊成本应保证 "C ≤ [log_2(1/r) + log_2(2b)]/α"
C 每个项均摊成本: "C ≤ 19.047619047619047"
半排序桶占用成本检查
使用半排序时，应保证 "ceil(b*(f-1)/8)<ceil(b*f/8)" 否则是否使用半排序占用的空间是一样大的
半排序桶 4 < 4 is not ok
过滤器大小选择
过滤器的桶总大小一定是 2 的指数倍，因此在设定过滤器大小时，尽量满足 size/α ~=(<) 2^n，size 即为
想要一个过滤器存储的数据量，必要时应选择小一点的过滤器，使用多个过滤器达到目标效果
幂的次数I: 1 2的I次幂: 2 got: 2976190.476190476 近似值: 1.344
幂的次数I: 2 2的I次幂: 4 got: 2976190.476190476 近似值: 2.688
幂的次数I: 3 2的I次幂: 8 got: 2976190.476190476 近似值: 5.376
幂的次数I: 4 2的I次幂: 16 got: 2976190.476190476 近似值: 10.752
幂的次数I: 5 2的I次幂: 32 got: 2976190.476190476 近似值: 21.504
幂的次数I: 6 2的I次幂: 64 got: 2976190.476190476 近似值: 43.008
幂的次数I: 7 2的I次幂: 128 got: 2976190.476190476 近似值: 86.016
幂的次数I: 8 2的I次幂: 256 got: 2976190.476190476 近似值: 172.032
幂的次数I: 9 2的I次幂: 512 got: 2976190.476190476 近似值: 344.064
幂的次数I: 10 2的I次幂: 1024 got: 2976190.476190476 近似值: 688.128
幂的次数I: 11 2的I次幂: 2048 got: 2976190.476190476 近似值: 1376.256
幂的次数I: 12 2的I次幂: 4096 got: 2976190.476190476 近似值: 2752.512
幂的次数I: 13 2的I次幂: 8192 got: 2976190.476190476 近似值: 5505.024
幂的次数I: 14 2的I次幂: 16384 got: 2976190.476190476 近似值: 11010.048
幂的次数I: 15 2的I次幂: 32768 got: 2976190.476190476 近似值: 22020.096
幂的次数I: 16 2的I次幂: 65536 got: 2976190.476190476 近似值: 44040.192
幂的次数I: 17 2的I次幂: 131072 got: 2976190.476190476 近似值: 88080.384
幂的次数I: 18 2的I次幂: 262144 got: 2976190.476190476 近似值: 176160.768
幂的次数I: 19 2的I次幂: 524288 got: 2976190.476190476 近似值: 352321.536
幂的次数I: 20 2的I次幂: 1048576 got: 2976190.476190476 近似值: 704643.072
幂的次数I: 21 2的I次幂: 2097152 got: 2976190.476190476 近似值: 1409286.144
幂的次数I: 22 2的I次幂: 4194304 got: 2976190.476190476 近似值: 2818572.288
幂的次数I: 23 2的I次幂: 8388608 got: 2976190.476190476 近似值: 5637144.576
got: 2.976190476190476e+06 approximation: 5637144.576
穷举b=[2,4,8,16]、f=[9,32]一共为96中组合，并输出测试报告
go test -timeout=100m -v -test.run Test_exhaustion --table-type=0 --size=1000000 --height=10000 --goroutines=100 --loop=1tree -L 3 test_report_2022-09-02_14:12:07
test_report_2022-09-02_14:12:07
├── cuckoo_filter_size_1000000_full_size_1000000_real_size_1761607_tabletype_0_tagsperbucket_2_bitsperitem_10_.html
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├── cuckoo_filter_size_1000000_full_size_1000000_real_size_1992294_tabletype_0_tagsperbucket_4_bitsperitem_10_.html
├── cuckoo_filter_size_1000000_full_size_1000000_real_size_1992294_tabletype_0_tagsperbucket_4_bitsperitem_11_.html
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├── cuckoo_filter_size_1000000_full_size_1000000_real_size_2055208_tabletype_0_tagsperbucket_8_bitsperitem_10_.html
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└── cuckoo_filter_size_1000000_full_size_1000000_real_size_2055208_tabletype_0_tagsperbucket_8_bitsperitem_9_.html
0 directories, 96 files通过传入给定指纹参数--fingerprint，对比在b=[2,4,8,16]对已满的cuckoo进行压力测试，并输出测试报告
go test -timeout=100m -v -test.run Test_pressure --fingerprint=16 --table-type=0 --size=1000000 --height=10000 --goroutines=100 --loop=100tree -L 3 test_report_2022-09-02_14:38:08
test_report_2022-09-02_14:38:08
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├── cuckoo_filter_size_1000000_full_size_1000000_real_size_2055208_tabletype_0_tagsperbucket_16_bitsperitem_16_.html
└── cuckoo_filter_size_1000000_full_size_1000000_real_size_2055208_tabletype_0_tagsperbucket_8_bitsperitem_16_.html