Commit 7724b549 by ehebrard

### cactuses

parent 1cfd31cb
 ... ... @@ -1404,6 +1404,15 @@ for low values of \numfeat\ and $\mdepth$. % \numfeat) is low, and for small values of $\mdepth$. When $\numfeat$ grows, however, it often exceeds the memory limit of 50GB (whereas \budalg does not require more memory than the size of the data set). Finally, \binoct does not produce a single optimality proof and very often exceeds the memory limit.%\footnote{In the experiments in \cite{verwer2019learning} not all datapoints were used.} \begin{figure} % \subfloat[depth=3]{\input{src/tables/xscopt3.tex}} \subfloat[depth=4]{\input{src/tables/xscopt4.tex}} % \subfloat[depth=5]{\input{src/tables/xscopt5.tex}} \subfloat[depth=7]{\input{src/tables/xscopt7.tex}} \subfloat[depth=10]{\input{src/tables/xscopt10.tex}} \caption{\label{fig:cactus}Proof ratio over time, averaged across all data sets} \end{figure} % \clearpage ... ... @@ -1458,12 +1467,11 @@ We can see in those graphs that \murtree finds an initial tree extremely quickly \begin{figure} \subfloat[depth=3]{\input{src/tables/xscerror3.tex}} % \subfloat[depth=3]{\input{src/tables/xscerror3.tex}} \subfloat[depth=4]{\input{src/tables/xscerror4.tex}} % \subfloat[depth=5]{\input{src/tables/xscerror5.tex}} \subfloat[depth=7]{\input{src/tables/xscerror7.tex}} \subfloat[depth=10]{\input{src/tables/xscerror10.tex}} % \subfloat[depth=8]{\input{src/tables/xscerror8.tex}} % \subfloat[depth=9]{\input{src/tables/xscerror9.tex}} % \subfloat[depth=10]{\input{src/tables/xscerror10.tex}} \caption{\label{fig:cactus}Accuracy over time, averaged across all data sets} \end{figure} ... ...
This diff is collapsed.
 \begin{tabular}{lrrrrrrrrrrrr} \toprule \multirow{2}{*}{$\mdepth$}& \multicolumn{3}{c}{\budalg} & \multicolumn{3}{c}{\noheuristic} & \multicolumn{3}{c}{\nopreprocessing} & \multicolumn{3}{c}{\nolb}\\ \cmidrule(rr){2-4}\cmidrule(rr){5-7}\cmidrule(rr){8-10}\cmidrule(rr){11-13} & \multicolumn{1}{c}{acc.} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{acc.} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{acc.} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{acc.} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{cpu$^*$} \\ \midrule \texttt{3} & 0.8875 & 0.93 & 67 & 0.8871 & 0.93 & -1.6 & 0.8875 & 0.93 & $\mathsmaller{+}$4.3 & 0.8875 & 0.93 & -2.8\\ \texttt{4} & 0.9095 & 0.78 & 388 & 0.9081 & 0.79 & -61 & 0.9093 & 0.72 & $\mathsmaller{+}$47 & 0.9095 & 0.78 & $\mathsmaller{+}$13\\ \texttt{5} & 0.9275 & 0.64 & 479 & 0.9257 & 0.67 & -30 & 0.9272 & 0.55 & $\mathsmaller{+}$276 & 0.9275 & 0.64 & $\mathsmaller{+}$39\\ \texttt{7} & 0.9517 & 0.48 & 1045 & 0.9455 & 0.48 & $\mathsmaller{+}$17 & 0.9512 & 0.43 & $\mathsmaller{+}$163 & 0.9512 & 0.48 & $\mathsmaller{+}$30\\ \texttt{10} & 0.9667 & 0.62 & 570 & 0.9583 & 0.53 & $\mathsmaller{+}$66 & 0.9656 & 0.43 & $\mathsmaller{+}$63 & 0.9665 & 0.60 & $\mathsmaller{+}$8.0\\ \bottomrule \end{tabular}
 \begin{tabular}{lrrr} \toprule \multirow{2}{*}{}& \multicolumn{3}{c}{\gosdtmed}\\ \cmidrule(rr){2-4} & \multicolumn{1}{c}{error} & \multicolumn{1}{c}{size} & \multicolumn{1}{c}{depth} \\ \midrule \texttt{pendigits} & - & - & -\\ \texttt{vote} & - & - & -\\ \texttt{zoo-1} & - & - & -\\ \texttt{audiology} & 11.0 & 5.0 & 2.0\\ \texttt{german-credit} & 275.0 & 9.0 & 3.0\\ \texttt{weather-aus-un} & - & - & -\\ \texttt{adult\_discretized} & - & - & -\\ \texttt{soybean} & 92.0 & 1.0 & 0.0\\ \texttt{mnist\_4} & - & - & -\\ \texttt{ionosphere} & - & - & -\\ \texttt{hypothyroid} & 70.0 & 5.0 & 2.0\\ \texttt{heart-cleveland} & 60.0 & 7.0 & 2.0\\ \texttt{taiwan\_binarised} & - & - & -\\ \texttt{breast-cancer-un} & 35.0 & 7.0 & 2.0\\ \texttt{wine2-un} & - & - & -\\ \texttt{hepatitis} & 17.0 & 5.0 & 2.0\\ \texttt{tic-tac-toe} & - & - & -\\ \texttt{vehicle} & - & - & -\\ \texttt{anneal} & 119.0 & 13.0 & 5.0\\ \texttt{titanic-un} & - & - & -\\ \texttt{mushroom} & - & - & -\\ \texttt{yeast} & 440.0 & 5.0 & 2.0\\ \texttt{car-un} & 178.0 & 9.0 & 4.0\\ \texttt{letter} & - & - & -\\ \texttt{lymph} & 22.0 & 7.0 & 2.0\\ \texttt{surgical-deepnet-un} & - & - & -\\ \texttt{segment} & 5.0 & 7.0 & 3.0\\ \texttt{wine3-un} & - & - & -\\ \texttt{diabetes} & - & - & -\\ \texttt{compas\_discretized} & 2045.0 & 9.0 & 4.0\\ \texttt{primary-tumor} & - & - & -\\ \texttt{kr-vs-kp} & 494.0 & 7.0 & 2.0\\ \texttt{forest-fires-un} & - & - & -\\ \texttt{splice-1} & - & - & -\\ \texttt{bank-un} & - & - & -\\ \texttt{wine1-un} & - & - & -\\ \texttt{australian-credit} & - & - & -\\ \texttt{mnist\_9} & - & - & -\\ \texttt{mnist\_8} & - & - & -\\ \texttt{mnist\_5} & - & - & -\\ \texttt{breast-wisconsin} & 31.0 & 5.0 & 2.0\\ \texttt{mnist\_7} & - & - & -\\ \texttt{mnist\_6} & - & - & -\\ \texttt{mnist\_1} & - & - & -\\ \texttt{mnist\_0} & - & - & -\\ \texttt{mnist\_3} & - & - & -\\ \texttt{mnist\_2} & - & - & -\\ \bottomrule \end{tabular}
 \alt<2->{\newcolumntype{R}{>{\columncolor{red!30}}r}}{\newcolumntype{R}{r}} \alt<3->{\newcolumntype{B}{>{\columncolor{blue!30}}r}}{\newcolumntype{B}{r}} \tabcolsep=9pt \begin{tabular}{lRBrrrrRB % rrrrrr } \toprule \multirow{2}{*}{$\mdepth$}& \multicolumn{6}{c}{\budalg} % & \multicolumn{6}{c}{\murtree} & \multicolumn{2}{c}{\cart}\\ \cmidrule(rr){2-7} % \cmidrule(rr){8-13}\cmidrule(rr){14-15} \cmidrule(rr){8-9} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{first} & \multicolumn{1}{c}{$\leq$3s} & \multicolumn{1}{c}{$\leq$10s} & \multicolumn{1}{c}{$\leq$1m} & \multicolumn{1}{c}{$\leq$5m} %& \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{first} & \multicolumn{1}{c}{$\leq$3s} & \multicolumn{1}{c}{$\leq$10s} & \multicolumn{1}{c}{$\leq$1m} & \multicolumn{1}{c}{$\leq$5m} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{first} \\ \midrule \texttt{3} & 0.03 & 858 & 808 & 798 & 793 & 793 % & 0.04 & 2156 & 1060 & 974 & 967 & 967 & 1.48 & 871\\ \texttt{4} & 0.03 & 751 & 725 & 714 & 706 & 698 % & 0.04 & 2156 & 1396 & 860 & 853 & 843 & 1.60 & 758\\ \texttt{5} & 0.03 & 670 & 641 & 638 & 632 & 626 % & 0.04 & 2156 & 803 & 788 & 775 & 762 & 2.30 & 674\\ \texttt{7} & 0.04 & 559 & 537 & 531 & 528 & 522 % & 0.05 & 2156 & 872 & 634 & 628 & 618 & 3.55 & 568\\ \texttt{10} & 0.04 & 456 & 437 & 431 & 423 & 418 % & 0.04 & 2156 & 802 & 511 & 507 & 497 & 4.33 & 462\\ \bottomrule \end{tabular}
This diff is collapsed.
 \begin{tabular}{lrrrrrrrrrrrrr} \toprule \multirow{2}{*}{$\mdepth$}& \multicolumn{6}{c}{\budalg} & \multicolumn{6}{c}{\murtree} & \multicolumn{1}{c}{\cart}\\ \cmidrule(rr){2-7}\cmidrule(rr){8-13}\cmidrule(rr){14-14} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{first} & \multicolumn{1}{c}{$\leq$3s} & \multicolumn{1}{c}{$\leq$10s} & \multicolumn{1}{c}{$\leq$1m} & \multicolumn{1}{c}{$\leq$5m} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{first} & \multicolumn{1}{c}{$\leq$3s} & \multicolumn{1}{c}{$\leq$10s} & \multicolumn{1}{c}{$\leq$1m} & \multicolumn{1}{c}{$\leq$5m} & \multicolumn{1}{c}{first} \\ \midrule \texttt{3} & 0.03 & 0.8743 & 0.8867 & 0.8871 & 0.8874 & 0.8875 & 0.04 & 0.7089 & 0.8821 & 0.8841 & 0.8845 & 0.8845 & 0.8719\\ \texttt{4} & 0.03 & 0.8918 & 0.9069 & 0.9078 & 0.9090 & 0.9092 & 0.04 & 0.7089 & 0.8972 & 0.9046 & 0.9060 & 0.9069 & 0.8909\\ \texttt{5} & 0.03 & 0.9062 & 0.9231 & 0.9249 & 0.9262 & 0.9269 & 0.04 & 0.7089 & 0.9121 & 0.9163 & 0.9205 & 0.9223 & 0.9053\\ \texttt{7} & 0.04 & 0.9299 & 0.9431 & 0.9455 & 0.9471 & 0.9491 & 0.05 & 0.7089 & 0.9270 & 0.9328 & 0.9357 & 0.9414 & 0.9286\\ \texttt{10} & 0.04 & 0.9527 & 0.9613 & 0.9626 & 0.9637 & 0.9647 & 0.04 & 0.7089 & 0.9429 & 0.9509 & 0.9527 & 0.9555 & 0.9521\\ \bottomrule \end{tabular}
 \begin{tabular}{lrrrrrrrrrrrrrrr} \toprule \multirow{2}{*}{$\mdepth$}& \multicolumn{3}{c}{\budalg} & \multicolumn{3}{c}{\murtree} & \multicolumn{3}{c}{\cp} & \multicolumn{4}{c}{\dleight} & \multicolumn{2}{c}{\binoct}\\ \cmidrule(rr){2-4}\cmidrule(rr){5-7}\cmidrule(rr){8-10}\cmidrule(rr){11-14}\cmidrule(rr){15-16} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{sol.} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{error$^*$} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{sol.} & \multicolumn{1}{c}{error$^*$} \\ \midrule &\multicolumn{15}{c}{$\numfeat < 100$ (29 data sets)}\\ \midrule \texttt{3} & 1.00 & 458 & 0.23 & 1.00 & 458 & $\mathsmaller{+}$0.31 & 1.00 & 458 & $\mathsmaller{+}$3.2 & 1.00 & 1.00 & 0 & $\mathsmaller{+}$2.5 & 0.52 & $\mathsmaller{+}$57\\ \texttt{4} & 1.00 & 412 & 14 & 1.00 & 412 & $\mathsmaller{+}$8.0 & 1.00 & 412 & $\mathsmaller{+}$115 & 1.00 & 1.00 & 0 & $\mathsmaller{+}$105 & 0.52 & $\mathsmaller{+}$89\\ \texttt{5} & 0.93 & 379 & 187 & 0.97 & 379 & -12 & 0.62 & 380 & $\mathsmaller{+}$121 & 0.76 & 0.66 & $\mathsmaller{+}$2.5 & $\mathsmaller{+}$2.0 & 0.52 & $\mathsmaller{+}$211\\ \texttt{7} & 0.66 & 329 & 81 & 0.69 & 348 & $\mathsmaller{+}$90 & 0.45 & 682 & $\mathsmaller{+}$193 & 0.66 & 0.55 & $\mathsmaller{+}$93 & $\mathsmaller{+}$6.7 & 0.52 & $\mathsmaller{+}$350\\ \texttt{10} & 0.79 & 270 & 85 & 0.52 & 320 & $\mathsmaller{+}$83 & 0.45 & 766 & $\mathsmaller{+}$2.6 & 0.62 & 0.52 & $\mathsmaller{+}$278 & $\mathsmaller{+}$49 & 0.41 & $\mathsmaller{+}$292\\ \midrule &\multicolumn{15}{c}{$\numfeat \geq 100$ (29 data sets)}\\ \midrule \texttt{3} & 0.86 & 1127 & 100 & 0.86 & 1156 & -42 & 0.72 & 1155 & $\mathsmaller{+}$256 & 0.76 & 0.66 & $\mathsmaller{+}$165 & $\mathsmaller{+}$247 & 0.62 & $\mathsmaller{+}$148\\ \texttt{4} & 0.55 & 979 & 662 & 0.72 & 1023 & $\mathsmaller{+}$64 & 0.28 & 1585 & $\mathsmaller{+}$576 & 0.48 & 0.24 & $\mathsmaller{+}$565 & $\mathsmaller{+}$258 & 0.62 & $\mathsmaller{+}$189\\ \texttt{5} & 0.34 & 870 & 452 & 0.34 & 947 & $\mathsmaller{+}$99 & 0.14 & 1870 & $\mathsmaller{+}$11 & 0.34 & 0.10 & $\mathsmaller{+}$1136 & $\mathsmaller{+}$12 & 0.62 & $\mathsmaller{+}$329\\ \texttt{7} & 0.31 & 688 & 11 & 0.31 & 791 & $\mathsmaller{+}$7.4 & 0.28 & 1857 & $\mathsmaller{+}$571 & 0.34 & 0.14 & $\mathsmaller{+}$1805 & $\mathsmaller{+}$793 & 0.55 & $\mathsmaller{+}$510\\ \texttt{10} & 0.45 & 550 & 101 & 0.41 & 659 & $\mathsmaller{+}$19 & 0.38 & 1827 & $\mathsmaller{+}$85 & 0.45 & 0.28 & $\mathsmaller{+}$1612 & $\mathsmaller{+}$183 & 0.21 & $\mathsmaller{+}$375\\ \bottomrule \end{tabular}
 \begin{tabular}{lrrrrrrrrrrrrrrr} \toprule \multirow{2}{*}{$\mdepth$}& \multicolumn{3}{c}{\budalg} & \multicolumn{3}{c}{\murtree} & \multicolumn{3}{c}{\cp} & \multicolumn{4}{c}{\dleight} & \multicolumn{2}{c}{\binoct}\\ \cmidrule(rr){2-4}\cmidrule(rr){5-7}\cmidrule(rr){8-10}\cmidrule(rr){11-14}\cmidrule(rr){15-16} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{acc.} & \multicolumn{1}{c}{cpu} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{acc.} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{acc.} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{sol.} & \multicolumn{1}{c}{opt.} & \multicolumn{1}{c}{acc.$^*$} & \multicolumn{1}{c}{cpu$^*$} & \multicolumn{1}{c}{sol.} & \multicolumn{1}{c}{acc.$^*$} \\ \midrule &\multicolumn{15}{c}{$\numfeat < 100$ (29 data sets)}\\ \midrule \texttt{3} & 1.00 & 0.8871 & 0.23 & 1.00 & 0.8871 & $\mathsmaller{+}$0.31 & 1.00 & 0.8871 & $\mathsmaller{+}$3.2 & 1.00 & 1.00 & -0.00\% & $\mathsmaller{+}$2.5 & 0.52 & -1.21\%\\ \texttt{4} & 1.00 & 0.9130 & 14 & 1.00 & 0.9130 & $\mathsmaller{+}$8.0 & 1.00 & 0.9130 & $\mathsmaller{+}$115 & 1.00 & 1.00 & $\mathsmaller{+}$0.00\% & $\mathsmaller{+}$105 & 0.52 & -2.62\%\\ \texttt{5} & 0.93 & 0.9344 & 187 & 0.97 & 0.9344 & -12 & 0.62 & 0.9337 & $\mathsmaller{+}$121 & 0.76 & 0.66 & -0.01\% & $\mathsmaller{+}$2.0 & 0.52 & -3.81\%\\ \texttt{7} & 0.66 & 0.9596 & 81 & 0.69 & 0.9564 & $\mathsmaller{+}$90 & 0.45 & 0.9075 & $\mathsmaller{+}$193 & 0.66 & 0.55 & -0.46\% & $\mathsmaller{+}$6.7 & 0.52 & -7.30\%\\ \texttt{10} & 0.79 & 0.9733 & 85 & 0.52 & 0.9623 & $\mathsmaller{+}$83 & 0.45 & 0.8691 & $\mathsmaller{+}$2.6 & 0.62 & 0.52 & -1.34\% & $\mathsmaller{+}$49 & 0.41 & -29.46\%\\ \midrule &\multicolumn{15}{c}{$\numfeat \geq 100$ (29 data sets)}\\ \midrule \texttt{3} & 0.86 & 0.8879 & 100 & 0.86 & 0.8873 & -42 & 0.72 & 0.8875 & $\mathsmaller{+}$256 & 0.76 & 0.66 & -0.25\% & $\mathsmaller{+}$247 & 0.62 & -2.36\%\\ \texttt{4} & 0.55 & 0.9060 & 662 & 0.72 & 0.9050 & $\mathsmaller{+}$64 & 0.28 & 0.8926 & $\mathsmaller{+}$576 & 0.48 & 0.24 & -0.87\% & $\mathsmaller{+}$258 & 0.62 & -4.78\%\\ \texttt{5} & 0.34 & 0.9206 & 452 & 0.34 & 0.9197 & $\mathsmaller{+}$99 & 0.14 & 0.8344 & $\mathsmaller{+}$11 & 0.34 & 0.10 & -1.81\% & $\mathsmaller{+}$12 & 0.62 & -8.55\%\\ \texttt{7} & 0.31 & 0.9439 & 11 & 0.31 & 0.9327 & $\mathsmaller{+}$7.4 & 0.28 & 0.8193 & $\mathsmaller{+}$571 & 0.34 & 0.14 & -2.95\% & $\mathsmaller{+}$793 & 0.55 & -22.33\%\\ \texttt{10} & 0.45 & 0.9602 & 101 & 0.41 & 0.9529 & $\mathsmaller{+}$19 & 0.38 & 0.8612 & $\mathsmaller{+}$85 & 0.45 & 0.28 & -2.68\% & $\mathsmaller{+}$183 & 0.21 & -55.08\%\\ \bottomrule \end{tabular}
This diff is collapsed.
 \cactus{Average Accuracy}{CPU time}{\budalg, \murtree, \cart}{{{(0.8662064701436945, 0) [a] (0.8700043377060156, 0.001) [a] (0.8702714609936868, 0.002) [a] (0.8765154118753802, 0.003) [a] (0.8767551379027775, 0.006) [a] (0.8767811653000378, 0.008) [a] (0.8767976036562022, 0.009) [a] (0.8831415798503431, 0.01) [a] (0.8831813058777404, 0.011) [a] (0.8837758264256855, 0.012) [a] (0.8867170325349597, 0.015) [a] (0.8867184023979734, 0.017) [a] (0.8867197722609871, 0.019) [a] (0.8879462562792519, 0.02) [a] (0.8879489960052793, 0.021) [a] (0.8879613247724026, 0.025) [a] (0.8880092699778821, 0.026) [a] (0.8880311877861012, 0.027) [a] (0.8880846124436355, 0.028) [a] (0.8884740644984299, 0.03) [a] (0.8885261192929504, 0.033) [a] (0.8885617357313066, 0.034) [a] (0.8885740644984299, 0.035) [a] (0.8887761192929505, 0.04) [a] (0.8888939275121286, 0.043) [a] (0.8889720097039094, 0.044) [a] (0.8889774891559642, 0.048) [a] (0.8890822836765121, 0.05) [a] (0.8890850234025395, 0.058) [a] (0.8891639275121286, 0.06) [a] (0.8892317357313066, 0.08) [a] (0.889264475457334, 0.09) [a] (0.8892740644984299, 0.096) [a] (0.8892768042244573, 0.098) [a] (0.8893884480600738, 0.11) [a] (0.8894048864162382, 0.138) [a] (0.8894499549093888, 0.14) [a] (0.8894526946354162, 0.155) [a] (0.8894787220326765, 0.157) [a] (0.8895557083340464, 0.16) [a] (0.8895598179230875, 0.167) [a] (0.8895625576491148, 0.168) [a] (0.8895639275121285, 0.174) [a] (0.8895652973751422, 0.176) [a] (0.8895689960052792, 0.2) [a] (0.8904115342533334, 0.22) [a] (0.8904653698697718, 0.23) [a] (0.8904749589108677, 0.239) [a] (0.89049619178758, 0.24) [a] (0.8905057808286759, 0.248) [a] (0.8905113972670321, 0.25) [a] (0.8905144109656622, 0.26) [a] (0.890519890417717, 0.305) [a] (0.8919044380401684, 0.311) [a] (0.8919427942045519, 0.32) [a] (0.8919681366703054, 0.33) [a] (0.8920037531086615, 0.339) [a] (0.8920112873552368, 0.34) [a] (0.8920126572182505, 0.359) [a] (0.892029506533319, 0.36) [a] (0.8920541640675655, 0.403) [a] (0.8920671777661957, 0.41) [a] (0.8920740270812642, 0.492) [a] (0.8920767668072915, 0.493) [a] (0.8921537531086614, 0.51) [a] (0.8922153969442779, 0.539) [a] (0.8951170574714661, 0.54) [a] (0.8951204821290003, 0.55) [a] (0.8951276054166716, 0.56) [a] (0.8951307561016031, 0.57) [a] (0.8951362355536578, 0.62) [a] (0.8952143177454387, 0.64) [a] (0.8953047287043429, 0.739) [a] (0.895557123287671, 0.83) [a] (0.8955573972602737, 0.86) [a] (0.8955628767123285, 0.907) [a] (0.8955630136986299, 0.96) [a] (0.8955709589041093, 1.17) [a] (0.8955750684931504, 1.213) [a] (0.8955764383561641, 1.219) [a] (0.8955765753424655, 1.24) [a] (0.8955771232876709, 1.25) [a] (0.8955780821917805, 1.26) [a] (0.8955808219178079, 1.39) [a] (0.8956130136986298, 1.43) [a] (0.8956131506849312, 1.44) [a] (0.8956143835616435, 1.65) [a] (0.8956145205479449, 1.78) [a] (0.8956150684931503, 1.8) [a] (0.895615342465753, 2.07) [a] (0.895617534246575, 2.37) [a] (0.8956631506849312, 2.39) [a] (0.8956868493150681, 2.72) [a] (0.8957173972602737, 2.73) [a] (0.8957254794520545, 2.76) [a] (0.8957268493150682, 2.81) [a] (0.8957753424657532, 2.83) [a] (0.8957761643835614, 2.84) [a] (0.8957912328767121, 2.85) [a] (0.8957931506849314, 3.35) [a] (0.8957932876712328, 3.52) [a] (0.8957946575342465, 3.54) [a] (0.8957960273972602, 3.541) [a] (0.8957965753424656, 3.65) [a] (0.895796712328767, 3.66) [a] (0.8957999999999999, 4.07) [a] (0.8959006849315068, 5.84) [a] (0.8959253424657534, 5.926) [a] (0.8959561643835616, 5.96) [a] (0.8959639726027397, 6.09) [a] (0.8959742465753425, 6.1) [a] (0.8959761643835618, 6.47) [a] (0.8959808219178084, 6.71) [a] (0.8959849315068494, 6.714) [a] (0.8959920547945207, 6.74) [a] (0.8959967123287673, 6.98) [a] (0.8959995890410961, 7.17) [a] (0.8960178082191783, 7.21) [a] (0.896032876712329, 7.22) [a] (0.8960349315068495, 7.27) [a] (0.8960395890410962, 7.39) [a] (0.8960442465753428, 7.73) [a] (0.8960757534246578, 7.769) [a] (0.8960880821917812, 7.772) [a] (0.89609095890411, 7.82) [a] (0.8960956164383566, 8.06) [a] (0.8960993150684935, 8.18) [a] (0.8961031506849318, 8.38) [a] (0.8961095890410963, 8.71) [a] (0.8961105479452058, 8.74) [a] (0.8961115068493154, 8.81) [a] (0.8961152054794523, 9) [a] (0.8961190410958907, 9.17) [a] (0.8961217808219181, 9.32) [a] (0.8961271232876715, 9.54) [a] (0.8961291780821921, 9.55) [a] (0.8961305479452057, 9.56) [a] (0.8961375342465756, 9.57) [a] (0.8961504109589044, 9.58) [a] (0.896159178082192, 9.61) [a] (0.8961630136986304, 9.86) [a] (0.896167671232877, 10.04) [a] (0.8961723287671236, 10.32) [a] (0.8961769863013702, 10.56) [a] (0.896179863013699, 10.77) [a] (0.896183561643836, 10.93) [a] (0.8962205479452058, 11.06) [a] (0.8962209589041099, 11.35) [a] (0.8962212328767126, 11.38) [a] (0.8962357534246578, 13.79) [a] (0.8962360273972605, 13.8) [a] (0.8962401369863016, 13.83) [a] (0.896240273972603, 14.27) [a] (0.8962512328767126, 14.87) [a] (0.8962963013698633, 16.51) [a] (0.896338767123288, 16.52) [a] (0.8963652054794523, 17.05) [a] (0.8963653424657537, 17.57) [a] (0.8963673972602743, 17.6) [a] (0.896380273972603, 17.61) [a] (0.8963816438356167, 17.65) [a] (0.896382465753425, 17.68) [a] (0.89641397260274, 19.18) [a] (0.8964268493150688, 22.11) [a] (0.8964405479452058, 22.31) [a] (0.8964426027397263, 24.38) [a] (0.8964430136986304, 28.2) [a] (0.896449178082192, 28.55) [a] (0.8964645205479455, 36.23) [a] (0.8964867123287674, 36.84) [a] (0.8964980821917811, 37.77) [a] (0.8965009589041099, 39.14) [a] (0.8965243835616441, 40.23) [a] (0.8965247945205482, 40.39) [a] (0.896535068493151, 41.84) [a] (0.8965378082191784, 42.39) [a] (0.8965397260273976, 42.4) [a] (0.8965406849315072, 42.41) [a] (0.8965426027397264, 42.42) [a] (0.8965480821917812, 48.04) [a] (0.8965504109589044, 48.05) [a] (0.8965541095890414, 48.51) [a] (0.8965550684931509, 48.62) [a] (0.8965560273972605, 48.93) [a] (0.8965597260273974, 48.99) [a] (0.896564383561644, 49.44) [a] (0.8965672602739728, 49.95) [a] (0.8965887671232879, 50.22) [a] (0.8965891780821921, 72.07) [a] (0.8965895890410962, 72.18) [a] (0.8965952054794524, 102.49) [a] (0.8965971232876716, 108.77) [a] (0.896597260273973, 108.8) [a] (0.8966043835616443, 146.63) [a] (0.896605753424658, 180.9) [a] (0.896609863013699, 198.87) [a] (0.8966123287671237, 235.62) [a] (0.8966132876712333, 259.14) [a] (0.8966217808219182, 570.34) [a] (0.896628356164384, 953.22) [a] (0.8966320547945209, 953.29) [a] (0.8966330136986305, 953.31) [a] },{(0.817408444061846, 0) [b] (0.8384010790637917, 0.001) [b] (0.8391064481252993, 0.002) [b] (0.8417932760083486, 0.003) [b] (0.8458168057858171, 0.004) [b] (0.8479885373510532, 0.005) [b] (0.8481468078130099, 0.006) [b] (0.8491866560216892, 0.007) [b] (0.8503493854895878, 0.008) [b] (0.8520215428122333, 0.009) [b] (0.8544867603200413, 0.01) [b] (0.8546307386305436, 0.011) [b] (0.8592859266156418, 0.012) [b] (0.8610235662789468, 0.013) [b] (0.8612124198702003, 0.015) [b] (0.8615598602431475, 0.016) [b] (0.8616379151157229, 0.017) [b] (0.8616980847942162, 0.019) [b] (0.8617980747952161, 0.02) [b] (0.8618349983265826, 0.022) [b] (0.8648811823884713, 0.023) [b] (0.8651471494591998, 0.024) [b] (0.8661520019520069, 0.028) [b] (0.8661857424203246, 0.032) [b] (0.8662194828886424, 0.033) [b] (0.8667971139432578, 0.039) [b] (0.8673560180528468, 0.046) [b] (0.8676655431245913, 0.047) [b] (0.8681532136417611, 0.049) [b] (0.8685642373148075, 0.05) [b] (0.8686131054618829, 0.051) [b] (0.8688525212211616, 0.052) [b] (0.8701710338855647, 0.053) [b] (0.8701910904450625, 0.06) [b] (0.870353525207397, 0.064) [b] (0.8710689275549766, 0.066) [b] (0.8712207596624062, 0.067) [b] (0.8712260609898586, 0.071) [b] (0.871273667002498, 0.075) [b] (0.8723982050290713, 0.077) [b] (0.872496972581783, 0.082) [b] (0.872710457726775, 0.084) [b] (0.873065664596167, 0.085) [b] (0.8730866425902511, 0.097) [b] (0.8730874079623674, 0.101) [b] (0.873703076732569, 0.108) [b] (0.8743957040990458, 0.109) [b] (0.8763582296855675, 0.11) [b] (0.87643919085659, 0.112) [b] (0.8774396526081676, 0.114) [b] (0.8774449539356199, 0.116) [b] (0.8774457193077362, 0.121) [b] (0.8778871917481699, 0.122) [b] (0.8782269548406317, 0.123) [b] (0.8782659822769194, 0.13) [b] (0.8782956329915017, 0.132) [b] (0.8784016185244764, 0.133) [b] (0.8784225965185606, 0.134) [b] (0.878825336244588, 0.141) [b] (0.8788295551020371, 0.148) [b] (0.8789761304445028, 0.16) [b] (0.8790877807214814, 0.165) [b] (0.8793060081100991, 0.167) [b] (0.8798513564188701, 0.186) [b] (0.8799937664202943, 0.221) [b] (0.8800567004025469, 0.231) [b] (0.8801869904038498, 0.243) [b] (0.8804392470036503, 0.259) [b] (0.8809428249342071, 0.282) [b] (0.880984871622307, 0.297) [b] (0.8810618302185822, 0.326) [b] (0.8810636581649576, 0.334) [b] (0.8810901545482013, 0.335) [b] (0.8812440717407517, 0.344) [b] (0.8812540215782632, 0.383) [b] (0.8812731996604549, 0.455) [b] (0.8812822896605458, 0.474) [b] (0.8813019243637421, 0.523) [b] (0.8813788829600173, 0.543) [b] (0.8813829925490584, 0.727) [b] (0.8817224902659534, 0.818) [b] (0.8824706637819352, 0.822) [b] (0.8824966327490195, 0.907) [b] (0.882713984347193, 0.936) [b] (0.8851077697713563, 0.98) [b] (0.8851847283676315, 1.027) [b] (0.885602079965805, 1.072) [b] (0.8856025365868095, 1.095) [b] (0.8856131392417144, 1.106) [b] (0.8857181569248389, 1.167) [b] (0.8857951155211141, 1.184) [b] (0.8862000304740875, 1.251) [b] (0.8862301674603889, 1.253) [b] (0.886251788748091, 1.354) [b] (0.8863298436206664, 1.408) [b] (0.8863545011549129, 1.459) [b] (0.886388477464159, 1.759) [b] (0.8863925870532001, 1.792) [b] (0.8864877925326522, 1.876) [b] (0.8869433218547471, 1.887) [b] (0.8898446457258623, 1.936) [b] (0.8900375681002916, 2.06) [b] (0.8902814037167299, 2.109) [b] (0.890286887555856, 2.154) [b] (0.8906576638115639, 2.247) [b] (0.8906622300216095, 2.263) [b] (0.8910229606152168, 2.279) [b] (0.8912784400672716, 2.28) [b] (0.8912969332179566, 2.301) [b] (0.8916309514827967, 2.313) [b] (0.891631179793299, 2.32) [b] (0.8917688510261756, 2.353) [b] (0.8917709058206962, 2.407) [b] (0.8918921386974085, 2.471) [b] (0.8920572071905591, 2.482) [b] (0.8921679377841665, 2.497) [b] (0.8923040108435272, 2.603) [b] (0.8923346044508331, 2.611) [b] (0.8923476181494633, 2.726) [b] (0.8923777551357647, 2.732) [b] (0.8924028692910159, 2.823) [b] (0.8924259286517464, 2.871) [b] (0.8924752437202396, 2.892) [b] (0.8925074355010615, 2.944) [b] (0.8925104128168921, 3.062) [b] (0.8925279927255679, 3.103) [b] (0.8925318740041067, 3.17) [b] (0.8925366685246546, 3.237) [b] (0.892551508707303, 3.319) [b] (0.8925521936388099, 3.96) [b] (0.892643085023605, 4.039) [b] (0.8926622631057968, 4.282) [b] (0.8926976512336506, 4.557) [b] (0.892712491416299, 4.645) [b] (0.8927321261194954, 4.753) [b] (0.8927638612793127, 5.057) [b] (0.8927668385951434, 5.623) [b] (0.893709052419738, 5.752) [b] (0.8937218378078658, 7.42) [b] (0.8937225227393727, 7.815) [b] (0.8937490191226164, 8.91) [b] (0.8937737291657045, 9.007) [b] (0.8938046167195647, 9.064) [b] (0.8938482240255008, 10.628) [b] (0.893854845030067, 11.149) [b] (0.8938735664912542, 11.604) [b] (0.8939168090666584, 12.525) [b] (0.8939202337241926, 13.263) [b] (0.8939222885187131, 15.528) [b] (0.8939593535833453, 18.716) [b] (0.8940600385148522, 21.544) [b] (0.8940604951358567, 22.093) [b] (0.8941042677700876, 22.653) [b] (0.8941145417426903, 23.043) [b] (0.894148103386526, 23.828) [b] (0.894153141963128, 24.777) [b] (0.8941537717852033, 25.594) [b] (0.8941690685888563, 27.185) [b] (0.8941720366253859, 27.745) [b] (0.8942152792007901, 31.283) [b] (0.8942230417578677, 32.109) [b] (0.8942233566689053, 37.731) [b] (0.8942542442227654, 42.9) [b] (0.8942551574647746, 50.679) [b] (0.8942583065751509, 58.189) [b] (0.8942653842007217, 73.439) [b] (0.8942674389952422, 76.434) [b] (0.8942699504107673, 98.767) [b] (0.8942740599998084, 99.756) [b] (0.8942825074883929, 311.363) [b] (0.8946803153331636, 489.37) [b] (0.896160949650166, 507.799) [b] (0.8963453452864479, 564.835) [b] (0.8963509613971976, 577.042) [b] (0.8963514430886359, 611.124) [b] (0.896382315217506, 1117.87) [b] (0.8963828414469753, 1133.81) [b] (0.8963940736684748, 3522.86) [b] },{(0.8813585753424655, 0.001) [c] (0.8813585753424655, 1.4935057671232883) [c] (0.8813585753424655, 3600) [c] }}}{legend pos=north west} \ No newline at end of file
This diff is collapsed.
This diff is collapsed.