├── BCHAIN-MKPRU.csv ├── LBMA-GOLD.csv ├── README.md ├── image.png ├── iris.csv ├── pic └── pics.txt ├── 封面.png ├── 第1章-函数极值与规划模型.pptx ├── 第2章-微分方程与差分方程模型.pptx ├── 第3章-数据处理的基本策略.pptx ├── 第4章-评价模型.pptx ├── 第5章-复杂网络与图论建模.pptx ├── 第6章-时间序列分析.pptx ├── 第7章-统计学习与数据科学.pptx ├── 第8章-进化计算与群体智能.pptx ├── 第9章-其他内容.pptx └── 绪论-什么是数学建模.pptx /BCHAIN-MKPRU.csv: -------------------------------------------------------------------------------- 1 | Date,Value 2 | 9/11/16,621.65 3 | 9/12/16,609.67 4 | 9/13/16,610.92 5 | 9/14/16,608.82 6 | 9/15/16,610.38 7 | 9/16/16,609.11 8 | 9/17/16,607.04 9 | 9/18/16,611.58 10 | 9/19/16,610.19 11 | 9/20/16,608.66 12 | 9/21/16,598.88 13 | 9/22/16,597.42 14 | 9/23/16,594.08 15 | 9/24/16,603.88 16 | 9/25/16,601.74 17 | 9/26/16,598.98 18 | 9/27/16,605.96 19 | 9/28/16,605.67 20 | 9/29/16,603.85 21 | 9/30/16,609.39 22 | 10/1/16,614.82 23 | 10/2/16,612.98 24 | 10/3/16,611.85 25 | 10/4/16,609.62 26 | 10/5/16,607.18 27 | 10/6/16,612.08 28 | 10/7/16,617.21 29 | 10/8/16,614.74 30 | 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5/26/18,7342.908333 625 | 5/27/18,7361.831667 626 | 5/28/18,7344.56 627 | 5/29/18,7459.876667 628 | 5/30/18,7385.395 629 | 5/31/18,7386.72 630 | 6/1/18,7535.146667 631 | 6/2/18,7645.146667 632 | 6/3/18,7637.14 633 | 6/4/18,7500.273333 634 | 6/5/18,7615.722142 635 | 6/6/18,7619.43 636 | 6/7/18,7676.271667 637 | 6/8/18,7620.153333 638 | 6/9/18,7615.54 639 | 6/10/18,6776.888333 640 | 6/11/18,6875.678333 641 | 6/12/18,6879.31 642 | 6/13/18,6315.7 643 | 6/14/18,6647.013333 644 | 6/15/18,6637.41 645 | 6/16/18,6509.426667 646 | 6/17/18,6464.411667 647 | 6/18/18,6445.79 648 | 6/19/18,6737 649 | 6/20/18,6714.718333 650 | 6/21/18,6760.83 651 | 6/22/18,6332.573333 652 | 6/23/18,6141.605833 653 | 6/24/18,6170.46 654 | 6/25/18,6211.4475 655 | 6/26/18,6218.595 656 | 6/27/18,6097.46 657 | 6/28/18,6107.896154 658 | 6/29/18,5908.7025 659 | 6/30/18,6223.28 660 | 7/1/18,6374.754167 661 | 7/2/18,6466.069167 662 | 7/3/18,6626.44 663 | 7/4/18,6593.289167 664 | 7/5/18,6603.376667 665 | 7/6/18,6555.51 666 | 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830 | 12/18/18,3556.54 831 | 12/19/18,3808.0425 832 | 12/20/18,3982.850833 833 | 12/21/18,4135.19 834 | 12/22/18,3910.973333 835 | 12/23/18,4027.478333 836 | 12/24/18,3995.17 837 | 12/25/18,3813.88 838 | 12/26/18,3825.379167 839 | 12/27/18,3848.21 840 | 12/28/18,3743.905 841 | 12/29/18,3912.285833 842 | 12/30/18,3770.36 843 | 12/31/18,3791.545833 844 | 1/1/19,3752.271667 845 | 1/2/19,3855.88 846 | 1/3/19,3865.7975 847 | 1/4/19,3822.626667 848 | 1/5/19,3856.62 849 | 1/6/19,3920.456667 850 | 1/7/19,4036.093333 851 | 1/8/19,4032.25 852 | 1/9/19,4034.138333 853 | 1/10/19,3812.585833 854 | 1/11/19,3646.25 855 | 1/12/19,3646.345833 856 | 1/13/19,3599.841667 857 | 1/14/19,3531.96 858 | 1/15/19,3656.785 859 | 1/16/19,3621.270833 860 | 1/17/19,3621.43 861 | 1/18/19,3632.395 862 | 1/19/19,3690.523333 863 | 1/20/19,3703.98 864 | 1/21/19,3548.4275 865 | 1/22/19,3540.02 866 | 1/23/19,3587.35 867 | 1/24/19,3566.4 868 | 1/25/19,3586.25 869 | 1/26/19,3577.79 870 | 1/27/19,3576.3 871 | 1/28/19,3554.5 872 | 1/29/19,3448.58 873 | 1/30/19,3421.6 874 | 1/31/19,3470 875 | 2/1/19,3444.81 876 | 2/2/19,3475.33 877 | 2/3/19,3506.22 878 | 2/4/19,3456.02 879 | 2/5/19,3452.35 880 | 2/6/19,3467.34 881 | 2/7/19,3405.7 882 | 2/8/19,3394.76 883 | 2/9/19,3661.08 884 | 2/10/19,3664.38 885 | 2/11/19,3686.29 886 | 2/12/19,3629.47 887 | 2/13/19,3632.42 888 | 2/14/19,3614.5 889 | 2/15/19,3600.31 890 | 2/16/19,3608.2 891 | 2/17/19,3619.55 892 | 2/18/19,3670.74 893 | 2/19/19,3910.8 894 | 2/20/19,3926.53 895 | 2/21/19,3987.18 896 | 2/22/19,3944.88 897 | 2/23/19,3983.48 898 | 2/24/19,4139.6 899 | 2/25/19,3766.89 900 | 2/26/19,3848.33 901 | 2/27/19,3831.45 902 | 2/28/19,3833.23 903 | 3/1/19,3850.08 904 | 3/2/19,3834.62 905 | 3/3/19,3831.44 906 | 3/4/19,3814.58 907 | 3/5/19,3730.98 908 | 3/6/19,3872.25 909 | 3/7/19,3876.85 910 | 3/8/19,3886.82 911 | 3/9/19,3875.96 912 | 3/10/19,3950.56 913 | 3/11/19,3928.17 914 | 3/12/19,3881.09 915 | 3/13/19,3892.51 916 | 3/14/19,3877.77 917 | 3/15/19,3885.21 918 | 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5/1/19,5269.79 965 | 5/2/19,5326.67 966 | 5/3/19,5390.16 967 | 5/4/19,5657.14 968 | 5/5/19,5771.08 969 | 5/6/19,5717.66 970 | 5/7/19,5684.47 971 | 5/8/19,5755.72 972 | 5/9/19,5936.72 973 | 5/10/19,6146.91 974 | 5/11/19,6348.02 975 | 5/12/19,7248.31 976 | 5/13/19,6978.63 977 | 5/14/19,7823.11 978 | 5/15/19,7992.69 979 | 5/16/19,8206.9 980 | 5/17/19,7885.96 981 | 5/18/19,7362.22 982 | 5/19/19,7265.05 983 | 5/20/19,8193.7 984 | 5/21/19,8007.03 985 | 5/22/19,7952.5 986 | 5/23/19,7625.93 987 | 5/24/19,7880.29 988 | 5/25/19,8003.26 989 | 5/26/19,8071.45 990 | 5/27/19,8744.42 991 | 5/28/19,8770.06 992 | 5/29/19,8719.88 993 | 5/30/19,8662.98 994 | 5/31/19,8272.46 995 | 6/1/19,8553.81 996 | 6/2/19,8554.8 997 | 6/3/19,8739.03 998 | 6/4/19,8134.92 999 | 6/5/19,7675.8 1000 | 6/6/19,7786.04 1001 | 6/7/19,7803.23 1002 | 6/8/19,7998.29 1003 | 6/9/19,7931.34 1004 | 6/10/19,7640.61 1005 | 6/11/19,8020.38 1006 | 6/12/19,7917.58 1007 | 6/13/19,8173.06 1008 | 6/14/19,8237.88 1009 | 6/15/19,8693.6 1010 | 6/16/19,8859.47 1011 | 6/17/19,8978.99 1012 | 6/18/19,9327.48 1013 | 6/19/19,9076.18 1014 | 6/20/19,9281.7 1015 | 6/21/19,9531.35 1016 | 6/22/19,10209.38 1017 | 6/23/19,10668.63 1018 | 6/24/19,10814.48 1019 | 6/25/19,11017.5 1020 | 6/26/19,11766.4 1021 | 6/27/19,12932.55 1022 | 6/28/19,11132.85 1023 | 6/29/19,12374.35 1024 | 6/30/19,11890.38 1025 | 7/1/19,10737.73 1026 | 7/2/19,10578.72 1027 | 7/3/19,10805.4 1028 | 7/4/19,11984.76 1029 | 7/5/19,11160.49 1030 | 7/6/19,10955.16 1031 | 7/7/19,11232.04 1032 | 7/8/19,11477.01 1033 | 7/9/19,12313.08 1034 | 7/10/19,12586.78 1035 | 7/11/19,12099.9 1036 | 7/12/19,11352.87 1037 | 7/13/19,11803.97 1038 | 7/14/19,11389.1 1039 | 7/15/19,10186.96 1040 | 7/16/19,10873.5 1041 | 7/17/19,9397.72 1042 | 7/18/19,9674.28 1043 | 7/19/19,10638.56 1044 | 7/20/19,10535.75 1045 | 7/21/19,10764.9 1046 | 7/22/19,10587.41 1047 | 7/23/19,10323.62 1048 | 7/24/19,9844.3 1049 | 7/25/19,9772.17 1050 | 7/26/19,9875.17 1051 | 7/27/19,9848.65 1052 | 7/28/19,9473.99 1053 | 7/29/19,9530 1054 | 7/30/19,9501.33 1055 | 7/31/19,9589.13 1056 | 8/1/19,10084.7 1057 | 8/2/19,10407.17 1058 | 8/3/19,10529.55 1059 | 8/4/19,10814.57 1060 | 8/5/19,10980.23 1061 | 8/6/19,11787.99 1062 | 8/7/19,11465.67 1063 | 8/8/19,11960.82 1064 | 8/9/19,11996.41 1065 | 8/10/19,11856.64 1066 | 8/11/19,11282.22 1067 | 8/12/19,11566.84 1068 | 8/13/19,11386.26 1069 | 8/14/19,10858.12 1070 | 8/15/19,10016.96 1071 | 8/16/19,10302.17 1072 | 8/17/19,10359.44 1073 | 8/18/19,10214.52 1074 | 8/19/19,10317.6 1075 | 8/20/19,10917.26 1076 | 8/21/19,10760.56 1077 | 8/22/19,10129.4 1078 | 8/23/19,10111.98 1079 | 8/24/19,10405.81 1080 | 8/25/19,10143.8 1081 | 8/26/19,10135.06 1082 | 8/27/19,10360.28 1083 | 8/28/19,10171.95 1084 | 8/29/19,9717.82 1085 | 8/30/19,9484.55 1086 | 8/31/19,9577.99 1087 | 9/1/19,9600.9 1088 | 9/2/19,9769.79 1089 | 9/3/19,10386.64 1090 | 9/4/19,10621.29 1091 | 9/5/19,10584.16 1092 | 9/6/19,10577.8 1093 | 9/7/19,10317.47 1094 | 9/8/19,10487.21 1095 | 9/9/19,10406.31 1096 | 9/10/19,10313.66 1097 | 9/11/19,10101.03 1098 | 9/12/19,10159.32 1099 | 9/13/19,10420.16 1100 | 9/14/19,10363.9 1101 | 9/15/19,10361.33 1102 | 9/16/19,10310.43 1103 | 9/17/19,10265.63 1104 | 9/18/19,10190.36 1105 | 9/19/19,10157.59 1106 | 9/20/19,10275.88 1107 | 9/21/19,10173.11 1108 | 9/22/19,9979.49 1109 | 9/23/19,10033.05 1110 | 9/24/19,9683.38 1111 | 9/25/19,8553.61 1112 | 9/26/19,8432.23 1113 | 9/27/19,8055.64 1114 | 9/28/19,8193.9 1115 | 9/29/19,8225 1116 | 9/30/19,8056.74 1117 | 10/1/19,8307.74 1118 | 10/2/19,8322.92 1119 | 10/3/19,8382.03 1120 | 10/4/19,8236.17 1121 | 10/5/19,8155.48 1122 | 10/6/19,8147.69 1123 | 10/7/19,7869.74 1124 | 10/8/19,8212.02 1125 | 10/9/19,8190 1126 | 10/10/19,8587.92 1127 | 10/11/19,8586.9 1128 | 10/12/19,8269.73 1129 | 10/13/19,8308.01 1130 | 10/14/19,8283.76 1131 | 10/15/19,8353.54 1132 | 10/16/19,8162.16 1133 | 10/17/19,8002.51 1134 | 10/18/19,8076.78 1135 | 10/19/19,7954.15 1136 | 10/20/19,7960.04 1137 | 10/21/19,8231.06 1138 | 10/22/19,8222.52 1139 | 10/23/19,8026.76 1140 | 10/24/19,7469.56 1141 | 10/25/19,7431.88 1142 | 10/26/19,8666.79 1143 | 10/27/19,9259.8 1144 | 10/28/19,9551.54 1145 | 10/29/19,9218.76 1146 | 10/30/19,9433.35 1147 | 10/31/19,9164.62 1148 | 11/1/19,9147.98 1149 | 11/2/19,9252.99 1150 | 11/3/19,9301.18 1151 | 11/4/19,9206.16 1152 | 11/5/19,9418.05 1153 | 11/6/19,9310.19 1154 | 11/7/19,9343.34 1155 | 11/8/19,9204.24 1156 | 11/9/19,8766.04 1157 | 11/10/19,8809.41 1158 | 11/11/19,9037.12 1159 | 11/12/19,8717.81 1160 | 11/13/19,8801.52 1161 | 11/14/19,8762.42 1162 | 11/15/19,8632.32 1163 | 11/16/19,8457.69 1164 | 11/17/19,8482.7 1165 | 11/18/19,8503.93 1166 | 11/19/19,8175.99 1167 | 11/20/19,8120.8 1168 | 11/21/19,8081.81 1169 | 11/22/19,7617.07 1170 | 11/23/19,7286.35 1171 | 11/24/19,7324.03 1172 | 11/25/19,6907.4 1173 | 11/26/19,7130.25 1174 | 11/27/19,7163.63 1175 | 11/28/19,7523.83 1176 | 11/29/19,7431 1177 | 11/30/19,7757.47 1178 | 12/1/19,7557.72 1179 | 12/2/19,7402.69 1180 | 12/3/19,7309.59 1181 | 12/4/19,7296.77 1182 | 12/5/19,7192.85 1183 | 12/6/19,7395.97 1184 | 12/7/19,7547.19 1185 | 12/8/19,7504.83 1186 | 12/9/19,7522.39 1187 | 12/10/19,7337.42 1188 | 12/11/19,7220.76 1189 | 12/12/19,7202.31 1190 | 12/13/19,7189.16 1191 | 12/14/19,7251.87 1192 | 12/15/19,7067.74 1193 | 12/16/19,7111.14 1194 | 12/17/19,6879.54 1195 | 12/18/19,6612.12 1196 | 12/19/19,7284.29 1197 | 12/20/19,7150.86 1198 | 12/21/19,7190.17 1199 | 12/22/19,7143.2 1200 | 12/23/19,7514.41 1201 | 12/24/19,7322.08 1202 | 12/25/19,7250.69 1203 | 12/26/19,7192.72 1204 | 12/27/19,7194.4 1205 | 12/28/19,7243.93 1206 | 12/29/19,7301.07 1207 | 12/30/19,7385.36 1208 | 12/31/19,7219.6 1209 | 1/1/20,7168.31 1210 | 1/2/20,7175.68 1211 | 1/3/20,6944.33 1212 | 1/4/20,7326.35 1213 | 1/5/20,7347.89 1214 | 1/6/20,7351.57 1215 | 1/7/20,7759.24 1216 | 1/8/20,8165.47 1217 | 1/9/20,8042.65 1218 | 1/10/20,7817.92 1219 | 1/11/20,8184.66 1220 | 1/12/20,8021.49 1221 | 1/13/20,8173.97 1222 | 1/14/20,8105.24 1223 | 1/15/20,8842.42 1224 | 1/16/20,8813.89 1225 | 1/17/20,8722.03 1226 | 1/18/20,8900.34 1227 | 1/19/20,8910.85 1228 | 1/20/20,8703.36 1229 | 1/21/20,8626.47 1230 | 1/22/20,8722.26 1231 | 1/23/20,8658.94 1232 | 1/24/20,8388.11 1233 | 1/25/20,8428.17 1234 | 1/26/20,8327.36 1235 | 1/27/20,8588.42 1236 | 1/28/20,8895.78 1237 | 1/29/20,9385.69 1238 | 1/30/20,9279.81 1239 | 1/31/20,9502.37 1240 | 2/1/20,9333.77 1241 | 2/2/20,9378.09 1242 | 2/3/20,9314.56 1243 | 2/4/20,9162.14 1244 | 2/5/20,9162.14 1245 | 2/6/20,9614.9 1246 | 2/7/20,9755.66 1247 | 2/8/20,9807.54 1248 | 2/9/20,9907.12 1249 | 2/10/20,10162.41 1250 | 2/11/20,9854.79 1251 | 2/12/20,10275.38 1252 | 2/13/20,10354.3 1253 | 2/14/20,10242.43 1254 | 2/15/20,10368.53 1255 | 2/16/20,9904.17 1256 | 2/17/20,9937.67 1257 | 2/18/20,9703.93 1258 | 2/19/20,10180.65 1259 | 2/20/20,9604.72 1260 | 2/21/20,9606.86 1261 | 2/22/20,9696.58 1262 | 2/23/20,9669.63 1263 | 2/24/20,9989.39 1264 | 2/25/20,9663.75 1265 | 2/26/20,9309.15 1266 | 2/27/20,8785.52 1267 | 2/28/20,8804.72 1268 | 2/29/20,8712.35 1269 | 3/1/20,8534.17 1270 | 3/2/20,8912.82 1271 | 3/3/20,8912.82 1272 | 3/4/20,8754.34 1273 | 3/5/20,8758.9 1274 | 3/6/20,9067.39 1275 | 3/7/20,9155.89 1276 | 3/8/20,8898.63 1277 | 3/9/20,8039.38 1278 | 3/10/20,7931.94 1279 | 3/11/20,7885.46 1280 | 3/12/20,7936.65 1281 | 3/13/20,4830.21 1282 | 3/14/20,5609.03 1283 | 3/15/20,5166.26 1284 | 3/16/20,5348.44 1285 | 3/17/20,5026.35 1286 | 3/18/20,5357.61 1287 | 3/19/20,5410.23 1288 | 3/20/20,6195.2 1289 | 3/21/20,6226.44 1290 | 3/22/20,6189.85 1291 | 3/23/20,5822.62 1292 | 3/24/20,6502.16 1293 | 3/25/20,6768.49 1294 | 3/26/20,6698.46 1295 | 3/27/20,6763.75 1296 | 3/28/20,6369.09 1297 | 3/29/20,6260.95 1298 | 3/30/20,5885.41 1299 | 3/31/20,6405.29 1300 | 4/1/20,6428.28 1301 | 4/2/20,6652.87 1302 | 4/3/20,6809.11 1303 | 4/4/20,6741.67 1304 | 4/5/20,6871.69 1305 | 4/6/20,6777.44 1306 | 4/7/20,7343.2 1307 | 4/8/20,7205.55 1308 | 4/9/20,7365.03 1309 | 4/10/20,7293.69 1310 | 4/11/20,6873.24 1311 | 4/12/20,6891.6 1312 | 4/13/20,6915.37 1313 | 4/14/20,6857.66 1314 | 4/15/20,6871.95 1315 | 4/16/20,6623.8 1316 | 4/17/20,7112.27 1317 | 4/18/20,7034.89 1318 | 4/19/20,7259.36 1319 | 4/20/20,7130.04 1320 | 4/21/20,6840.24 1321 | 4/22/20,6852.52 1322 | 4/23/20,7130.99 1323 | 4/24/20,7477.6 1324 | 4/25/20,7507.08 1325 | 4/26/20,7549.52 1326 | 4/27/20,7699.27 1327 | 4/28/20,7790.66 1328 | 4/29/20,7765.33 1329 | 4/30/20,8777.63 1330 | 5/1/20,8628.77 1331 | 5/2/20,8824.66 1332 | 5/3/20,8973.82 1333 | 5/4/20,8903.95 1334 | 5/5/20,8885.93 1335 | 5/6/20,9030.96 1336 | 5/7/20,9170.79 1337 | 5/8/20,10002.48 1338 | 5/9/20,9821.8 1339 | 5/10/20,9527.39 1340 | 5/11/20,8754.46 1341 | 5/12/20,8617.25 1342 | 5/13/20,8814.53 1343 | 5/14/20,9305.93 1344 | 5/15/20,9790.31 1345 | 5/16/20,9303.59 1346 | 5/17/20,9385.7 1347 | 5/18/20,9669.4 1348 | 5/19/20,9719.37 1349 | 5/20/20,9785.74 1350 | 5/21/20,9510.67 1351 | 5/22/20,9057.57 1352 | 5/23/20,9167.26 1353 | 5/24/20,9178.32 1354 | 5/25/20,8730.73 1355 | 5/26/20,8899.66 1356 | 5/27/20,8842.85 1357 | 5/28/20,9197.54 1358 | 5/29/20,9569.21 1359 | 5/30/20,9425.98 1360 | 5/31/20,9698.1 1361 | 6/1/20,9450.84 1362 | 6/2/20,10204.23 1363 | 6/3/20,9525.57 1364 | 6/4/20,9658.04 1365 | 6/5/20,9794.56 1366 | 6/6/20,9623.75 1367 | 6/7/20,9670.43 1368 | 6/8/20,9753.85 1369 | 6/9/20,9782.59 1370 | 6/10/20,9775.15 1371 | 6/11/20,9892.13 1372 | 6/12/20,9286.42 1373 | 6/13/20,9459.97 1374 | 6/14/20,9473.5 1375 | 6/15/20,9330.07 1376 | 6/16/20,9426.7 1377 | 6/17/20,9526 1378 | 6/18/20,9454.81 1379 | 6/19/20,9380.03 1380 | 6/20/20,9300.15 1381 | 6/21/20,9357.43 1382 | 6/22/20,9284.78 1383 | 6/23/20,9691.9 1384 | 6/24/20,9621.49 1385 | 6/25/20,9276.58 1386 | 6/26/20,9240.85 1387 | 6/27/20,9154.45 1388 | 6/28/20,9004.23 1389 | 6/29/20,9127.47 1390 | 6/30/20,9185.35 1391 | 7/1/20,9133.97 1392 | 7/2/20,9236.38 1393 | 7/3/20,9087.98 1394 | 7/4/20,9072.42 1395 | 7/5/20,9131.31 1396 | 7/6/20,9089.09 1397 | 7/7/20,9348.91 1398 | 7/8/20,9256.23 1399 | 7/9/20,9440.07 1400 | 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8/22/20,11522.8 1444 | 8/23/20,11683.44 1445 | 8/24/20,11653.02 1446 | 8/25/20,11763.93 1447 | 8/26/20,11337.4 1448 | 8/27/20,11467.37 1449 | 8/28/20,11302.01 1450 | 8/29/20,11534.75 1451 | 8/30/20,11481.64 1452 | 8/31/20,11707.78 1453 | 9/1/20,11659.57 1454 | 9/2/20,11923.25 1455 | 9/3/20,11397.44 1456 | 9/4/20,10187.51 1457 | 9/5/20,10467.89 1458 | 9/6/20,10159.62 1459 | 9/7/20,10254.93 1460 | 9/8/20,10367.74 1461 | 9/9/20,10121.52 1462 | 9/10/20,10227.83 1463 | 9/11/20,10352.66 1464 | 9/12/20,10395.44 1465 | 9/13/20,10446.44 1466 | 9/14/20,10330.77 1467 | 9/15/20,10674.64 1468 | 9/16/20,10785.62 1469 | 9/17/20,10948.43 1470 | 9/18/20,10943.89 1471 | 9/19/20,10931.79 1472 | 9/20/20,11081.43 1473 | 9/21/20,10919.65 1474 | 9/22/20,10430.46 1475 | 9/23/20,10532.22 1476 | 9/24/20,10234.48 1477 | 9/25/20,10732.43 1478 | 9/26/20,10692.84 1479 | 9/27/20,10732.4 1480 | 9/28/20,10774.24 1481 | 9/29/20,10692.33 1482 | 9/30/20,10840.8 1483 | 10/1/20,10777.92 1484 | 10/2/20,10619.24 1485 | 10/3/20,10575.06 1486 | 10/4/20,10551.77 1487 | 10/5/20,10673.46 1488 | 10/6/20,10788.56 1489 | 10/7/20,10603.74 1490 | 10/8/20,10670.8 1491 | 10/9/20,10923.3 1492 | 10/10/20,11063.19 1493 | 10/11/20,11302.67 1494 | 10/12/20,11376.61 1495 | 10/13/20,11540.04 1496 | 10/14/20,11428.24 1497 | 10/15/20,11431.32 1498 | 10/16/20,11503.73 1499 | 10/17/20,11327.57 1500 | 10/18/20,11366.51 1501 | 10/19/20,11508.2 1502 | 10/20/20,11758.16 1503 | 10/21/20,11925.46 1504 | 10/22/20,12831.56 1505 | 10/23/20,12990.25 1506 | 10/24/20,12944.52 1507 | 10/25/20,13128.46 1508 | 10/26/20,13036.77 1509 | 10/27/20,13076.37 1510 | 10/28/20,13651.47 1511 | 10/29/20,13289 1512 | 10/30/20,13458.66 1513 | 10/31/20,13564.72 1514 | 11/1/20,13810.32 1515 | 11/2/20,13758.88 1516 | 11/3/20,13575.17 1517 | 11/4/20,14023.31 1518 | 11/5/20,14155.59 1519 | 11/6/20,15591.39 1520 | 11/7/20,15595.77 1521 | 11/8/20,14839.84 1522 | 11/9/20,15490.6 1523 | 11/10/20,15328.53 1524 | 11/11/20,15317.04 1525 | 11/12/20,15708.65 1526 | 11/13/20,16295.57 1527 | 11/14/20,16339.33 1528 | 11/15/20,16091.07 1529 | 11/16/20,15968.16 1530 | 11/17/20,16725.15 1531 | 11/18/20,17679.72 1532 | 11/19/20,17798.45 1533 | 11/20/20,17820.57 1534 | 11/21/20,18687.45 1535 | 11/22/20,18699.75 1536 | 11/23/20,18422.28 1537 | 11/24/20,18398.91 1538 | 11/25/20,19172.52 1539 | 11/26/20,18739.8 1540 | 11/27/20,17151.44 1541 | 11/28/20,17138.87 1542 | 11/29/20,17732.42 1543 | 11/30/20,18191.6 1544 | 12/1/20,19709.73 1545 | 12/2/20,18792.52 1546 | 12/3/20,19226.97 1547 | 12/4/20,19454.54 1548 | 12/5/20,18670.49 1549 | 12/6/20,19155.06 1550 | 12/7/20,19377.66 1551 | 12/8/20,19181.41 1552 | 12/9/20,18318.87 1553 | 12/10/20,18554.15 1554 | 12/11/20,18247.76 1555 | 12/12/20,18029.36 1556 | 12/13/20,18803.44 1557 | 12/14/20,19164.48 1558 | 12/15/20,19276.59 1559 | 12/16/20,19439.75 1560 | 12/17/20,21379.48 1561 | 12/18/20,22847.46 1562 | 12/19/20,23150.79 1563 | 12/20/20,23869.92 1564 | 12/21/20,23490.58 1565 | 12/22/20,22745.48 1566 | 12/23/20,23824.99 1567 | 12/24/20,23253.37 1568 | 12/25/20,23715.53 1569 | 12/26/20,24693.58 1570 | 12/27/20,26443.21 1571 | 12/28/20,26246.58 1572 | 12/29/20,27036.69 1573 | 12/30/20,27376.37 1574 | 12/31/20,28856.59 1575 | 1/1/21,28982.56 1576 | 1/2/21,29393.75 1577 | 1/3/21,32195.46 1578 | 1/4/21,33000.78 1579 | 1/5/21,32035.03 1580 | 1/6/21,34046.67 1581 | 1/7/21,36860.41 1582 | 1/8/21,39486.04 1583 | 1/9/21,40670.25 1584 | 1/10/21,40240.72 1585 | 1/11/21,38240.09 1586 | 1/12/21,35544.94 1587 | 1/13/21,34011.82 1588 | 1/14/21,37393.13 1589 | 1/15/21,39158.47 1590 | 1/16/21,36828.52 1591 | 1/17/21,36065.2 1592 | 1/18/21,35793.01 1593 | 1/19/21,36632.35 1594 | 1/20/21,36020.13 1595 | 1/21/21,35538.98 1596 | 1/22/21,30797.88 1597 | 1/23/21,33002.38 1598 | 1/24/21,32099.74 1599 | 1/25/21,32276.84 1600 | 1/26/21,32243.26 1601 | 1/27/21,32541.8 1602 | 1/28/21,30419.17 1603 | 1/29/21,33403.17 1604 | 1/30/21,34314.26 1605 | 1/31/21,34318.1 1606 | 2/1/21,33136.46 1607 | 2/2/21,33522.9 1608 | 2/3/21,35529.66 1609 | 2/4/21,37676.25 1610 | 2/5/21,37002.09 1611 | 2/6/21,38278.61 1612 | 2/7/21,39323.26 1613 | 2/8/21,38928.1 1614 | 2/9/21,46364.3 1615 | 2/10/21,46589.58 1616 | 2/11/21,44878.17 1617 | 2/12/21,48013.38 1618 | 2/13/21,47471.4 1619 | 2/14/21,47185.19 1620 | 2/15/21,48720.37 1621 | 2/16/21,47951.85 1622 | 2/17/21,49160.1 1623 | 2/18/21,52118.23 1624 | 2/19/21,51608.15 1625 | 2/20/21,55916.5 1626 | 2/21/21,56001.2 1627 | 2/22/21,57487.86 1628 | 2/23/21,54123.4 1629 | 2/24/21,48880.43 1630 | 2/25/21,50624.84 1631 | 2/26/21,46800.42 1632 | 2/27/21,46340.31 1633 | 2/28/21,46155.87 1634 | 3/1/21,45113.92 1635 | 3/2/21,49618.43 1636 | 3/3/21,48356.04 1637 | 3/4/21,50477.7 1638 | 3/5/21,48448.91 1639 | 3/6/21,48861.38 1640 | 3/7/21,48881.59 1641 | 3/8/21,51169.7 1642 | 3/9/21,52299.33 1643 | 3/10/21,54881.52 1644 | 3/11/21,55997.23 1645 | 3/12/21,57764 1646 | 3/13/21,57253.28 1647 | 3/14/21,61258.73 1648 | 3/15/21,59133.47 1649 | 3/16/21,55754.72 1650 | 3/17/21,56872.38 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9/6/18,1205.15 505 | 9/7/18,1198.9 506 | 9/10/18,1196.6 507 | 9/11/18,1189.85 508 | 9/12/18,1195.6 509 | 9/13/18,1209.8 510 | 9/14/18,1201.95 511 | 9/17/18,1201.9 512 | 9/18/18,1200.2 513 | 9/19/18,1203.3 514 | 9/20/18,1208.35 515 | 9/21/18,1198.7 516 | 9/24/18,1202.75 517 | 9/25/18,1201.9 518 | 9/26/18,1194.25 519 | 9/27/18,1185.4 520 | 9/28/18,1187.25 521 | 10/1/18,1189.35 522 | 10/2/18,1204.7 523 | 10/3/18,1201.2 524 | 10/4/18,1203.45 525 | 10/5/18,1203.8 526 | 10/8/18,1186.95 527 | 10/9/18,1185.55 528 | 10/10/18,1188.6 529 | 10/11/18,1205.55 530 | 10/12/18,1219.75 531 | 10/15/18,1229.95 532 | 10/16/18,1230.7 533 | 10/17/18,1229.05 534 | 10/18/18,1223 535 | 10/19/18,1227.85 536 | 10/22/18,1222.3 537 | 10/23/18,1235.95 538 | 10/24/18,1230.55 539 | 10/25/18,1230.8 540 | 10/26/18,1233.85 541 | 10/29/18,1230.8 542 | 10/30/18,1225.4 543 | 10/31/18,1214.95 544 | 11/1/18,1231.15 545 | 11/2/18,1232.1 546 | 11/5/18,1232.25 547 | 11/6/18,1231.6 548 | 11/7/18,1229.95 549 | 11/8/18,1224.15 550 | 11/9/18,1211.4 551 | 11/12/18,1205.55 552 | 11/13/18,1202.1 553 | 11/14/18,1203.25 554 | 11/15/18,1211.85 555 | 11/16/18,1222.4 556 | 11/19/18,1221.6 557 | 11/20/18,1223 558 | 11/21/18,1226.1 559 | 11/22/18,1227.05 560 | 11/23/18,1223.65 561 | 11/26/18,1223.4 562 | 11/27/18,1221.2 563 | 11/28/18,1213.25 564 | 11/29/18,1226.25 565 | 11/30/18,1217.55 566 | 12/3/18,1230.3 567 | 12/4/18,1240.3 568 | 12/5/18,1235.9 569 | 12/6/18,1242.55 570 | 12/7/18,1243.3 571 | 12/10/18,1245.35 572 | 12/11/18,1245.3 573 | 12/12/18,1245.75 574 | 12/13/18,1242.75 575 | 12/14/18,1235.35 576 | 12/17/18,1241.65 577 | 12/18/18,1246.3 578 | 12/19/18,1255 579 | 12/20/18,1259.75 580 | 12/21/18,1258.15 581 | 12/24/18, 582 | 12/27/18,1268 583 | 12/28/18,1279 584 | 12/31/18, 585 | 1/2/19,1282.9 586 | 1/3/19,1290.45 587 | 1/4/19,1279.9 588 | 1/7/19,1292.2 589 | 1/8/19,1286.45 590 | 1/9/19,1288.6 591 | 1/10/19,1291.9 592 | 1/11/19,1288.95 593 | 1/14/19,1292.75 594 | 1/15/19,1294.4 595 | 1/16/19,1292.3 596 | 1/17/19,1290.7 597 | 1/18/19,1284.2 598 | 1/21/19,1279.55 599 | 1/22/19,1282.1 600 | 1/23/19,1279.8 601 | 1/24/19,1283.7 602 | 1/25/19,1293.9 603 | 1/28/19,1302.15 604 | 1/29/19,1307.55 605 | 1/30/19,1310.7 606 | 1/31/19,1323.25 607 | 2/1/19,1318.7 608 | 2/4/19,1312.15 609 | 2/5/19,1314.2 610 | 2/6/19,1312.4 611 | 2/7/19,1310 612 | 2/8/19,1314.85 613 | 2/11/19,1306.4 614 | 2/12/19,1310 615 | 2/13/19,1312.8 616 | 2/14/19,1311.45 617 | 2/15/19,1316.55 618 | 2/18/19,1325.5 619 | 2/19/19,1334.15 620 | 2/20/19,1343.75 621 | 2/21/19,1331.25 622 | 2/22/19,1329.05 623 | 2/25/19,1331.05 624 | 2/26/19,1325.05 625 | 2/27/19,1322.85 626 | 2/28/19,1319.15 627 | 3/1/19,1311.95 628 | 3/4/19,1285.4 629 | 3/5/19,1283.8 630 | 3/6/19,1285.85 631 | 3/7/19,1285.3 632 | 3/8/19,1296.75 633 | 3/11/19,1292.75 634 | 3/12/19,1297.05 635 | 3/13/19,1306.95 636 | 3/14/19,1295.55 637 | 3/15/19,1303.5 638 | 3/18/19,1305.6 639 | 3/19/19,1307.7 640 | 3/20/19,1303.7 641 | 3/21/19,1309.6 642 | 3/22/19,1311.3 643 | 3/25/19,1319.55 644 | 3/26/19,1316.3 645 | 3/27/19,1309.7 646 | 3/28/19,1295.15 647 | 3/29/19,1295.4 648 | 4/1/19,1293.5 649 | 4/2/19,1290.3 650 | 4/3/19,1290.45 651 | 4/4/19,1283.15 652 | 4/5/19,1288.45 653 | 4/8/19,1300 654 | 4/9/19,1303 655 | 4/10/19,1305.45 656 | 4/11/19,1298.8 657 | 4/12/19,1294.3 658 | 4/15/19,1285.65 659 | 4/16/19,1276.35 660 | 4/17/19,1275.85 661 | 4/18/19,1275.7 662 | 4/23/19,1269.5 663 | 4/24/19,1271.65 664 | 4/25/19,1280.8 665 | 4/26/19,1284.2 666 | 4/29/19,1279.5 667 | 4/30/19,1282.3 668 | 5/1/19,1283.8 669 | 5/2/19,1270.95 670 | 5/3/19,1278.55 671 | 5/7/19,1281.2 672 | 5/8/19,1285.2 673 | 5/9/19,1286.1 674 | 5/10/19,1287.1 675 | 5/13/19,1295.6 676 | 5/14/19,1298.4 677 | 5/15/19,1299.1 678 | 5/16/19,1291.7 679 | 5/17/19,1280.8 680 | 5/20/19,1276.85 681 | 5/21/19,1271.15 682 | 5/22/19,1273.8 683 | 5/23/19,1283.65 684 | 5/24/19,1282.5 685 | 5/28/19,1278.3 686 | 5/29/19,1281.65 687 | 5/30/19,1280.95 688 | 5/31/19,1295.55 689 | 6/3/19,1317.1 690 | 6/4/19,1324.25 691 | 6/5/19,1335.05 692 | 6/6/19,1335.5 693 | 6/7/19,1340.65 694 | 6/10/19,1328.6 695 | 6/11/19,1324.3 696 | 6/12/19,1332.35 697 | 6/13/19,1335.9 698 | 6/14/19,1351.25 699 | 6/17/19,1341.3 700 | 6/18/19,1341.35 701 | 6/19/19,1344.05 702 | 6/20/19,1379.5 703 | 6/21/19,1397.15 704 | 6/24/19,1405.7 705 | 6/25/19,1431.4 706 | 6/26/19,1403.95 707 | 6/27/19,1402.5 708 | 6/28/19,1409 709 | 7/1/19,1390.1 710 | 7/2/19,1391.05 711 | 7/3/19,1413.5 712 | 7/4/19,1414.9 713 | 7/5/19,1388.65 714 | 7/8/19,1400.1 715 | 7/9/19,1391.55 716 | 7/10/19,1408.3 717 | 7/11/19,1413.75 718 | 7/12/19,1407.6 719 | 7/15/19,1412.4 720 | 7/16/19,1409.85 721 | 7/17/19,1410.35 722 | 7/18/19,1417.45 723 | 7/19/19,1439.7 724 | 7/22/19,1427.75 725 | 7/23/19,1425.55 726 | 7/24/19,1426.95 727 | 7/25/19,1416.1 728 | 7/26/19,1420.4 729 | 7/29/19,1419.05 730 | 7/30/19,1425.9 731 | 7/31/19,1427.55 732 | 8/1/19,1406.8 733 | 8/2/19,1441.75 734 | 8/5/19,1465.25 735 | 8/6/19,1465.25 736 | 8/7/19,1506.05 737 | 8/8/19,1495.75 738 | 8/9/19,1497.7 739 | 8/12/19,1504.7 740 | 8/13/19,1498.4 741 | 8/14/19,1513.25 742 | 8/15/19,1515.65 743 | 8/16/19,1515.25 744 | 8/19/19,1496.6 745 | 8/20/19,1504.55 746 | 8/21/19,1503.25 747 | 8/22/19,1502.05 748 | 8/23/19,1503.8 749 | 8/27/19,1532.95 750 | 8/28/19,1537.15 751 | 8/29/19,1540.2 752 | 8/30/19,1528.4 753 | 9/2/19,1525.95 754 | 9/3/19,1537.85 755 | 9/4/19,1546.1 756 | 9/5/19,1529.1 757 | 9/6/19,1523.7 758 | 9/9/19,1509.2 759 | 9/10/19,1498.25 760 | 9/11/19,1490.65 761 | 9/12/19,1515.2 762 | 9/13/19,1503.1 763 | 9/16/19,1497.2 764 | 9/17/19,1502.1 765 | 9/18/19,1503.5 766 | 9/19/19,1500.7 767 | 9/20/19,1501.9 768 | 9/23/19,1522.1 769 | 9/24/19,1520.65 770 | 9/25/19,1528.75 771 | 9/26/19,1506.4 772 | 9/27/19,1489.9 773 | 9/30/19,1485.3 774 | 10/1/19,1473.45 775 | 10/2/19,1492.6 776 | 10/3/19,1517.1 777 | 10/4/19,1499.15 778 | 10/7/19,1501.25 779 | 10/8/19,1505.85 780 | 10/9/19,1507.25 781 | 10/10/19,1494.8 782 | 10/11/19,1479.15 783 | 10/14/19,1490.6 784 | 10/15/19,1487.8 785 | 10/16/19,1485.1 786 | 10/17/19,1492.65 787 | 10/18/19,1490 788 | 10/21/19,1491.65 789 | 10/22/19,1485.35 790 | 10/23/19,1494.45 791 | 10/24/19,1496.55 792 | 10/25/19,1513.45 793 | 10/28/19,1492.4 794 | 10/29/19,1486.75 795 | 10/30/19,1492.1 796 | 10/31/19,1510.95 797 | 11/1/19,1508.8 798 | 11/4/19,1509.45 799 | 11/5/19,1488.95 800 | 11/6/19,1486.05 801 | 11/7/19,1484.25 802 | 11/8/19,1464.15 803 | 11/11/19,1458.7 804 | 11/12/19,1452.05 805 | 11/13/19,1462.9 806 | 11/14/19,1466.65 807 | 11/15/19,1466.9 808 | 11/18/19,1467.65 809 | 11/19/19,1468.45 810 | 11/20/19,1471.7 811 | 11/21/19,1467.05 812 | 11/22/19,1464.45 813 | 11/25/19,1458.4 814 | 11/26/19,1454.65 815 | 11/27/19,1454.35 816 | 11/28/19,1454.65 817 | 11/29/19,1460.15 818 | 12/2/19,1461.15 819 | 12/3/19,1477.3 820 | 12/4/19,1475.1 821 | 12/5/19,1475.95 822 | 12/6/19,1459.65 823 | 12/9/19,1461.7 824 | 12/10/19,1464.95 825 | 12/11/19,1466.8 826 | 12/12/19,1467.8 827 | 12/13/19,1466.6 828 | 12/16/19,1477.9 829 | 12/17/19,1475.8 830 | 12/18/19,1474.05 831 | 12/19/19,1476.7 832 | 12/20/19,1479 833 | 12/23/19,1482.1 834 | 12/24/19, 835 | 12/27/19,1511.5 836 | 12/30/19,1514.75 837 | 12/31/19, 838 | 1/2/20,1527.1 839 | 1/3/20,1548.75 840 | 1/6/20,1573.1 841 | 1/7/20,1567.85 842 | 1/8/20,1571.95 843 | 1/9/20,1550.75 844 | 1/10/20,1553.6 845 | 1/13/20,1549.9 846 | 1/14/20,1545.1 847 | 1/15/20,1549 848 | 1/16/20,1554.55 849 | 1/17/20,1557.6 850 | 1/20/20,1560.15 851 | 1/21/20,1551.3 852 | 1/22/20,1556.9 853 | 1/23/20,1562.9 854 | 1/24/20,1564.3 855 | 1/27/20,1580.1 856 | 1/28/20,1574 857 | 1/29/20,1573.45 858 | 1/30/20,1578.25 859 | 1/31/20,1584.2 860 | 2/3/20,1574.75 861 | 2/4/20,1558.35 862 | 2/5/20,1553.3 863 | 2/6/20,1563.3 864 | 2/7/20,1572.65 865 | 2/10/20,1573.2 866 | 2/11/20,1570.5 867 | 2/12/20,1563.7 868 | 2/13/20,1575.05 869 | 2/14/20,1581.4 870 | 2/17/20,1580.8 871 | 2/18/20,1589.85 872 | 2/19/20,1604.2 873 | 2/20/20,1619 874 | 2/21/20,1643.3 875 | 2/24/20,1671.65 876 | 2/25/20,1650.3 877 | 2/26/20,1634.9 878 | 2/27/20,1652 879 | 2/28/20,1609.85 880 | 3/2/20,1599.65 881 | 3/3/20,1615.5 882 | 3/4/20,1641.85 883 | 3/5/20,1659.6 884 | 3/6/20,1683.65 885 | 3/9/20,1672.5 886 | 3/10/20,1655.7 887 | 3/11/20,1653.75 888 | 3/12/20,1570.7 889 | 3/13/20,1562.8 890 | 3/16/20,1487.7 891 | 3/17/20,1536.2 892 | 3/18/20,1498.2 893 | 3/19/20,1474.25 894 | 3/20/20,1494.4 895 | 3/23/20,1525.4 896 | 3/24/20,1605.75 897 | 3/25/20,1605.45 898 | 3/26/20,1634.8 899 | 3/27/20,1617.3 900 | 3/30/20,1618.3 901 | 3/31/20,1608.95 902 | 4/1/20,1576.55 903 | 4/2/20,1616.8 904 | 4/3/20,1613.1 905 | 4/6/20,1648.3 906 | 4/7/20,1649.25 907 | 4/8/20,1647.8 908 | 4/9/20,1680.65 909 | 4/14/20,1741.9 910 | 4/15/20,1718.65 911 | 4/16/20,1729.5 912 | 4/17/20,1692.55 913 | 4/20/20,1686.2 914 | 4/21/20,1682.05 915 | 4/22/20,1710.55 916 | 4/23/20,1736.25 917 | 4/24/20,1715.9 918 | 4/27/20,1714.95 919 | 4/28/20,1691.55 920 | 4/29/20,1703.35 921 | 4/30/20,1702.75 922 | 5/1/20,1686.25 923 | 5/4/20,1709.1 924 | 5/5/20,1699.55 925 | 5/6/20,1691.5 926 | 5/7/20,1704.05 927 | 5/11/20,1702.75 928 | 5/12/20,1702.4 929 | 5/13/20,1708.4 930 | 5/14/20,1731.6 931 | 5/15/20,1735.35 932 | 5/18/20,1734.7 933 | 5/19/20,1737.95 934 | 5/20/20,1748.3 935 | 5/21/20,1724.9 936 | 5/22/20,1733.55 937 | 5/26/20,1720.25 938 | 5/27/20,1694.6 939 | 5/28/20,1717.35 940 | 5/29/20,1728.7 941 | 6/1/20,1730.6 942 | 6/2/20,1742.15 943 | 6/3/20,1705.35 944 | 6/4/20,1700.05 945 | 6/5/20,1683.45 946 | 6/8/20,1690.35 947 | 6/9/20,1713.5 948 | 6/10/20,1722.05 949 | 6/11/20,1738.25 950 | 6/12/20,1733.5 951 | 6/15/20,1710.45 952 | 6/16/20,1719.85 953 | 6/17/20,1724.35 954 | 6/18/20,1719.5 955 | 6/19/20,1734.75 956 | 6/22/20,1761.85 957 | 6/23/20,1768.9 958 | 6/24/20,1766.05 959 | 6/25/20,1756.55 960 | 6/26/20,1747.6 961 | 6/29/20,1771.6 962 | 6/30/20,1768.1 963 | 7/1/20,1771.05 964 | 7/2/20,1777.45 965 | 7/3/20,1772.9 966 | 7/6/20,1787.9 967 | 7/7/20,1789.55 968 | 7/8/20,1811.1 969 | 7/9/20,1812.1 970 | 7/10/20,1803.1 971 | 7/13/20,1807.5 972 | 7/14/20,1801.9 973 | 7/15/20,1804.6 974 | 7/16/20,1807.7 975 | 7/17/20,1807.35 976 | 7/20/20,1815.65 977 | 7/21/20,1842.55 978 | 7/22/20,1852.4 979 | 7/23/20,1878.3 980 | 7/24/20,1902.1 981 | 7/27/20,1936.65 982 | 7/28/20,1940.9 983 | 7/29/20,1950.9 984 | 7/30/20,1957.65 985 | 7/31/20,1964.9 986 | 8/3/20,1958.55 987 | 8/4/20,1977.9 988 | 8/5/20,2048.15 989 | 8/6/20,2067.15 990 | 8/7/20,2031.15 991 | 8/10/20,2044.5 992 | 8/11/20,1939.65 993 | 8/12/20,1931.9 994 | 8/13/20,1944.25 995 | 8/14/20,1944.75 996 | 8/17/20,1972.85 997 | 8/18/20,2008.75 998 | 8/19/20,1981 999 | 8/20/20,1927.15 1000 | 8/21/20,1924.35 1001 | 8/24/20,1943.95 1002 | 8/25/20,1911.15 1003 | 8/26/20,1932.95 1004 | 8/27/20,1923.85 1005 | 8/28/20,1957.35 1006 | 9/1/20,1972.35 1007 | 9/2/20,1947.05 1008 | 9/3/20,1940.45 1009 | 9/4/20,1926.3 1010 | 9/7/20,1928.45 1011 | 9/8/20,1910.95 1012 | 9/9/20,1947.2 1013 | 9/10/20,1966.25 1014 | 9/11/20,1947.4 1015 | 9/14/20,1958.7 1016 | 9/15/20,1949.35 1017 | 9/16/20,1961.8 1018 | 9/17/20,1936.25 1019 | 9/18/20,1950.85 1020 | 9/21/20,1909.35 1021 | 9/22/20,1906 1022 | 9/23/20,1873.4 1023 | 9/24/20,1861.75 1024 | 9/25/20,1859.7 1025 | 9/28/20,1864.3 1026 | 9/29/20,1883.95 1027 | 9/30/20,1886.9 1028 | 10/1/20,1902 1029 | 10/2/20,1903.05 1030 | 10/5/20,1909.6 1031 | 10/6/20,1913.4 1032 | 10/7/20,1884.5 1033 | 10/8/20,1887.45 1034 | 10/9/20,1923.25 1035 | 10/12/20,1925.5 1036 | 10/13/20,1891.3 1037 | 10/14/20,1910.05 1038 | 10/15/20,1891.9 1039 | 10/16/20,1905.05 1040 | 10/19/20,1905.6 1041 | 10/20/20,1898.4 1042 | 10/21/20,1924.15 1043 | 10/22/20,1900.95 1044 | 10/23/20,1903.65 1045 | 10/26/20,1898.45 1046 | 10/27/20,1905.7 1047 | 10/28/20,1869.95 1048 | 10/29/20,1870.3 1049 | 10/30/20,1881.85 1050 | 11/2/20,1889.9 1051 | 11/3/20,1908.3 1052 | 11/4/20,1900.15 1053 | 11/5/20,1938.45 1054 | 11/6/20,1940.8 1055 | 11/9/20,1867.3 1056 | 11/10/20,1878.7 1057 | 11/11/20,1860.95 1058 | 11/12/20,1874.85 1059 | 11/13/20,1890.9 1060 | 11/16/20,1885.6 1061 | 11/17/20,1889.05 1062 | 11/18/20,1876.1 1063 | 11/19/20,1857.35 1064 | 11/20/20,1875.7 1065 | 11/23/20,1840.2 1066 | 11/24/20,1799.6 1067 | 11/25/20,1810.2 1068 | 11/26/20,1807.4 1069 | 11/27/20,1779.3 1070 | 11/30/20,1762.55 1071 | 12/1/20,1810.75 1072 | 12/2/20,1822.6 1073 | 12/3/20,1832.35 1074 | 12/4/20,1843 1075 | 12/7/20,1859.95 1076 | 12/8/20,1868.15 1077 | 12/9/20,1841.75 1078 | 12/10/20,1844.35 1079 | 12/11/20,1842 1080 | 12/14/20,1831.15 1081 | 12/15/20,1850.65 1082 | 12/16/20,1851.95 1083 | 12/17/20,1890.75 1084 | 12/18/20,1879.75 1085 | 12/21/20,1880 1086 | 12/22/20,1877.1 1087 | 12/23/20,1875 1088 | 12/24/20, 1089 | 12/29/20,1874.3 1090 | 12/30/20,1887.6 1091 | 12/31/20, 1092 | 1/4/21,1943.2 1093 | 1/5/21,1940.35 1094 | 1/6/21,1931.95 1095 | 1/7/21,1920.1 1096 | 1/8/21,1862.9 1097 | 1/11/21,1847.25 1098 | 1/12/21,1841.25 1099 | 1/13/21,1858.85 1100 | 1/14/21,1841.75 1101 | 1/15/21,1839 1102 | 1/18/21,1833.05 1103 | 1/19/21,1834.7 1104 | 1/20/21,1856.6 1105 | 1/21/21,1862.1 1106 | 1/22/21,1852.7 1107 | 1/25/21,1856.85 1108 | 1/26/21,1856.6 1109 | 1/27/21,1843 1110 | 1/28/21,1853.7 1111 | 1/29/21,1863.8 1112 | 2/1/21,1862.95 1113 | 2/2/21,1833.1 1114 | 2/3/21,1835.45 1115 | 2/4/21,1785.9 1116 | 2/5/21,1802.95 1117 | 2/8/21,1835.25 1118 | 2/9/21,1839.6 1119 | 2/10/21,1842.65 1120 | 2/11/21,1840.1 1121 | 2/12/21,1816.35 1122 | 2/15/21,1817.3 1123 | 2/16/21,1794.25 1124 | 2/17/21,1780.7 1125 | 2/18/21,1773.15 1126 | 2/19/21,1786.2 1127 | 2/22/21,1807.45 1128 | 2/23/21,1799.65 1129 | 2/24/21,1788 1130 | 2/25/21,1779.65 1131 | 2/26/21,1742.85 1132 | 3/1/21,1734.15 1133 | 3/2/21,1723.85 1134 | 3/3/21,1711.4 1135 | 3/4/21,1712.1 1136 | 3/5/21,1696.25 1137 | 3/8/21,1687.05 1138 | 3/9/21,1716.9 1139 | 3/10/21,1716.2 1140 | 3/11/21,1724.25 1141 | 3/12/21,1704.8 1142 | 3/15/21,1723.65 1143 | 3/16/21,1735 1144 | 3/17/21,1729.65 1145 | 3/18/21,1725.9 1146 | 3/19/21,1735.2 1147 | 3/22/21,1736.15 1148 | 3/23/21,1726.2 1149 | 3/24/21,1730.5 1150 | 3/25/21,1737.3 1151 | 3/26/21,1731.8 1152 | 3/29/21,1705.95 1153 | 3/30/21,1683.95 1154 | 3/31/21,1691.05 1155 | 4/1/21,1726.05 1156 | 4/6/21,1744.65 1157 | 4/7/21,1738.05 1158 | 4/8/21,1755.5 1159 | 4/9/21,1741.2 1160 | 4/12/21,1732.85 1161 | 4/13/21,1747.95 1162 | 4/14/21,1735.55 1163 | 4/15/21,1757.2 1164 | 4/16/21,1774.45 1165 | 4/19/21,1774.5 1166 | 4/20/21,1777.85 1167 | 4/21/21,1798.2 1168 | 4/22/21,1787.75 1169 | 4/23/21,1781.8 1170 | 4/26/21,1773.35 1171 | 4/27/21,1784.15 1172 | 4/28/21,1772.2 1173 | 4/29/21,1762.65 1174 | 4/30/21,1767.65 1175 | 5/4/21,1797.75 1176 | 5/5/21,1782.25 1177 | 5/6/21,1813.15 1178 | 5/7/21,1836.55 1179 | 5/10/21,1840.45 1180 | 5/11/21,1829.1 1181 | 5/12/21,1830.7 1182 | 5/13/21,1821.8 1183 | 5/14/21,1838.1 1184 | 5/17/21,1853.7 1185 | 5/18/21,1866.75 1186 | 5/19/21,1888.45 1187 | 5/20/21,1878.3 1188 | 5/21/21,1875.9 1189 | 5/24/21,1880.15 1190 | 5/25/21,1887 1191 | 5/26/21,1899.75 1192 | 5/27/21,1891.45 1193 | 5/28/21,1899.95 1194 | 6/1/21,1899.35 1195 | 6/2/21,1902.75 1196 | 6/3/21,1866.55 1197 | 6/4/21,1890.6 1198 | 6/7/21,1888.4 1199 | 6/8/21,1893.15 1200 | 6/9/21,1894.6 1201 | 6/10/21,1888.65 1202 | 6/11/21,1881.05 1203 | 6/14/21,1865.6 1204 | 6/15/21,1865.1 1205 | 6/16/21,1860.75 1206 | 6/17/21,1778.7 1207 | 6/18/21,1773.1 1208 | 6/21/21,1775.05 1209 | 6/22/21,1775.05 1210 | 6/23/21,1791.6 1211 | 6/24/21,1784.85 1212 | 6/25/21,1786.65 1213 | 6/28/21,1780.3 1214 | 6/29/21,1755.45 1215 | 6/30/21,1763.15 1216 | 7/1/21,1781.5 1217 | 7/2/21,1786.15 1218 | 7/5/21,1791.35 1219 | 7/6/21,1809.85 1220 | 7/7/21,1804.65 1221 | 7/8/21,1807.7 1222 | 7/9/21,1806 1223 | 7/12/21,1792.4 1224 | 7/13/21,1813.85 1225 | 7/14/21,1823.2 1226 | 7/15/21,1823.75 1227 | 7/16/21,1824.3 1228 | 7/19/21,1814.9 1229 | 7/20/21,1823.05 1230 | 7/21/21,1802.15 1231 | 7/22/21,1799.45 1232 | 7/23/21,1799.6 1233 | 7/26/21,1800.2 1234 | 7/27/21,1800.35 1235 | 7/28/21,1796.6 1236 | 7/29/21,1829.3 1237 | 7/30/21,1825.75 1238 | 8/2/21,1811.45 1239 | 8/3/21,1812.65 1240 | 8/4/21,1829.1 1241 | 8/5/21,1800.75 1242 | 8/6/21,1762.9 1243 | 8/9/21,1738.85 1244 | 8/10/21,1723.35 1245 | 8/11/21,1743.6 1246 | 8/12/21,1747.4 1247 | 8/13/21,1773.85 1248 | 8/16/21,1786.35 1249 | 8/17/21,1789.45 1250 | 8/18/21,1783.45 1251 | 8/19/21,1782.05 1252 | 8/20/21,1779.05 1253 | 8/23/21,1802 1254 | 8/24/21,1808.45 1255 | 8/25/21,1788.7 1256 | 8/26/21,1786.6 1257 | 8/27/21,1798.5 1258 | 8/31/21,1814.85 1259 | 9/1/21,1811.8 1260 | 9/2/21,1812.55 1261 | 9/3/21,1823.7 1262 | 9/6/21,1821.6 1263 | 9/7/21,1802.15 1264 | 9/8/21,1786 1265 | 9/9/21,1788.25 1266 | 9/10/21,1794.6 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 数学建模导论：基于python语言（2022秋） 2 | 3 | ## 最新版（2024版）教程的新地址https://github.com/datawhalechina/intro-mathmodel，欢迎大家去那边观看并star！ 4 | 5 | #### 介绍 6 | 本实验是《数学建模导论：基于Python语言》的配套实验内容，马马将在这次实验中带大家一步步从小白到竞赛实战，让数学建模不再是一项可怕的竞赛。 7 | 8 | ![输入图片说明](%E5%B0%81%E9%9D%A2.png) 9 | 10 | ## Python环境的安装 11 | 在开始实验之前，大家可以先去anaconda官网安装python大礼包：www.anaconda.com 12 | 13 | 当然如果想安装原生Python也可以直接上python官网：www.python.org 14 | 15 | 但是Python如果是原生状态下的话里面啥都没有，所以如果碰到了库不存在的话，将命令行打开，键入pip install xxxx 能够解决大多数问题。 16 | 不过我想应该没有什么同学愿意为了节约一点表面上的空间去安装原生Python吧，不会吧不会吧？一如你去集贸的呷哺呷哺吃火锅你难道只会点一个锅底不点菜的吗？ 17 | anaconda实际上就是提供锅底的同时还会提供你毛肚、鸭血、鸭肠等。所以，为什么不去安装一个anaconda呢？ 18 | 19 | 如果觉得编程环境不好用的话可以加装一个VSCode或者Pycharm，但是华科的学生装正版Pycharm可能还有一点点问题因为学校邮箱出了点事。 20 | 21 | ## 实验一：Python的基础语法 22 | 23 | 本次实验的基本目的是熟悉Python的基础语法。首先我们可以打开命令行（windows用户可以在cortana中键入cmd，会弹出命令提示符），然后键入python回车就可以切换到python命令行模式了。这一模式下我们可以试试它的hello world和C的hello world有多大差距： 24 | 25 | ![输入图片说明](image.png) 26 | 27 | 可以看到Python的hello world仅需要一行。但是在更多情况下，我们和C一样会在文本文件中写程序然后运行。 28 | 29 | 这一次实验我们的目的就是熟悉基本的控制流和集合等内容。 30 | 31 | ### 1. 熟悉Python的输入输出操作，并实现对一个输入序列的冒泡排序 32 | 现在对计算机1915班的前五名同学模电成绩想排个序，要求： 33 | 34 | - 输入成绩个数为5，用input()函数实现 35 | - 将输入的五个数保存成列表，并进行冒泡排序 36 | - 不允许使用list自带的sort()方法，必须自己设置冒泡排序 37 | - 将五个数排序后的结果排成一行输出到控制台 38 | 39 | ### 2. 分支语句判断 40 | 现在马马考完了模电，老师想在控制台里面根据我的成绩评价我的学习水平，要求： 41 | 42 | - 控制台输入马马的模电分数 43 | - 如果分数高于95，输出“马马+是卷王”；如果在80-95之间，输出“马马+牛牛”；如果在60-80之间，输出“马马+摆烂了”；如果低于60，输出“马马+老师，你不会又得重修了吧” 44 | - 异常处理：如果分数是一个小于零的数或者输入的根本就不是数字如何handle？ 45 | 46 | ### 3. 循环语句 47 | 老师手里有一份光之国模电考试的成绩单，保存成了txt文件，每一行分别是班里同学的名字和分数。现在需要： 48 | 49 | - 利用open和read函数循环读取每一行，将结果保存到字典中 50 | - 首先输出杰克、雷欧和贝利亚的模电成绩，如果检索不到人名需要报警“找不到这位同学” 51 | - 然后循环遍历这个字典，对于每个同学的模电成绩按照第二关中的操作输出这位同学的状态 52 | 53 | ### 4. 函数编写 54 | 试着将第三关的语句改写为一个函数，函数参数为成绩单文件的路径与文件名。 55 | 56 | 57 | ## 实验二：Python规划求解 58 | 59 | 本次实验的目的是为了熟悉基本的规划问题求解策略。求解规划问题需要用到numpy和scipy库，这两个库我建议先去熟悉一些基本操作会比较好。 60 | 61 | 这里我附上numpy和scipy的官方文档： 62 | 63 | numpy官方文档：https://numpy.org/ 64 | 65 | scipy官方文档：https://scipy.org/ 66 | 67 | 主要的方法就在scipy.optimize库当中，包括minimize、linear_sum_assignment等方法。 68 | 69 | ### 1. 线性规划问题求解 70 | 求这样一个线性规划的最优解： 71 | 72 | ![image](https://user-images.githubusercontent.com/61874427/172415401-32d55e34-d877-4a70-8656-830339c75566.png) 73 | 74 | 75 | ### 2. 非线性规划问题求解 76 | 求这样一个非线性规划的最优解，要求使用scipy.optimize.minimize求解： 77 | 78 | ![image](https://user-images.githubusercontent.com/61874427/172414855-dc1b4e38-c59d-497f-b811-0606beb10136.png) 79 | 80 | 81 | ### 3. 指派问题求解 82 | 现在光之国的五位奥特曼与阿布索留特族的五个小金人开始了1v1的车轮战，如果你现在安排奥特兄弟与他们1v1的战斗，每一项代表一个奥解决一个小金人所需要的时间。只有在最短的时间内打败所有小金人才能救出尤莉安公主。请问对于给定的小金人ABCDE，应该按照怎样的战斗策略使得五场战斗的时间总和最短？ 83 | 84 | | 奥特曼 | A | B | C | D | E | 85 | | ------ | ---- | ---- | ---- | ---- | ---- | 86 | | 佐菲 | 22 | 16 | 20 | 35 | 18 | 87 | | 初代 | 20 | 12 | 35 | 40 | 26 | 88 | | 赛文 | 11 | 19 | 15 | 17 | 21 | 89 | | 杰克 | 25 | 30 | 21 | 37 | 40 | 90 | | 艾斯 | 22 | 26 | 35 | 30 | 19 | 91 | 92 | ### 4. 综合建模 93 | 原题的链接附上：https://blog.csdn.net/OK_XIAOCHEN/article/details/124523838?utm_term=matlab%E4%B8%AD%E7%9A%84%E6%8C%87%E6%B4%BE%E9%97%AE%E9%A2%98&utm_medium=distribute.pc_aggpage_search_result.none-task-blog-2~all~sobaiduweb~default-0-124523838-null-null&spm=3001.4430 94 | 95 | 现在光之国开通了从M78星云（A）到L77星云（B）和U40（C）星云的宇宙航线，这些航线每天班次起飞与到达时间如下表。 设飞船在机场停留的损失费用大致与停留时间的平方成正比. 又每架飞船从降落到下班起飞至少需 2 小时准备时间， 试决定一个使停留费用损失为最小的分配飞行方案。 96 | 97 | ![image](https://user-images.githubusercontent.com/61874427/172405114-de0bd77a-416d-4836-913b-635d27573470.png) 98 | 99 | 到M78星云损失费用： 100 | 101 | ![img](https://img-blog.csdnimg.cn/7c421911268f43b1a17a5126430bd535.png) 102 | 103 | 到L77星云损失费用： 104 | 105 | ![image](https://img-blog.csdnimg.cn/7c421911268f43b1a17a5126430bd535.png) 106 | 107 | 到U40星云损失费用： 108 | 109 | ![image](https://img-blog.csdnimg.cn/68617b292cf34e79a0da7db521ccc6ca.png) 110 | 111 | 对于问题四，写出你的分析思路，建立的模型表达式，程序和求解结果。最终形成一个不超过两页A4纸的文档。没啥格式要求，你认为你将你的思路表达清楚就够了。 112 | 113 | ## 实验三：Python微分方程建模 114 | 115 | 本次实验的目的是对视频中讲到的微分方程模型进行进一步探究和补充。题目并不太多。 116 | 117 | ### 1. 计算微分方程 118 | 计算这样一个微分方程的符号解和数值解：y''-4y'+3y=x\*exp(x)，并且探究不同初值条件对数值解的影响 119 | 120 | ### 2. 计算微分方程组 121 | 计算这样一个微分方程组的数值解： 122 | 123 | ![image](https://user-images.githubusercontent.com/61874427/173194058-6395aa4a-b501-4238-bd8c-a26d2aa83d71.png) 124 | 125 | 三个方程定义了三维空间中的各个坐标点上度速度矢量，其中ρ、σ、β为常数，不同的参数可以算出不同的轨迹：x(t)、y(t)、z(t)。当参数为某些值时，轨迹出现混沌现象。即最小的初值差别也会显著地影响运动轨迹。 126 | 127 | ### 3. 对SEIR模型的进一步探讨 128 | 为了巩固经济增长基础、保障国内国际双循环，中国政府已决定在全国进行 129 | 全人群的新冠病毒疫苗接种，以建立最大规模的国民群体新冠免疫屏障。但目前多地爆发二次疫情，在二次疫情中流行了一种名为“奥密克戎”的病毒。这种病毒是普通COVID-19效果的9-11倍，能在14秒之内使人感染，传播速度极快。对于某一个城市而言，请考虑这样一些问题： 130 | 131 | 1. 目前疫苗对“奥密克戎”病毒并无免疫作用，现在考虑在没有对“奥密克戎”有效疫苗的情况下病毒的传播情况。 132 | 2. 若现在已经研制出针对“奥密克戎”的疫苗，并且接种以后就可以不被感染，讨论在这一情况下某个城市内“奥密克戎”的传播情况。 133 | 3. 事实上，疫苗并不能保证接种后不被感染，而是降低其感染后危重症的概率和死亡率，那么讨论在这一情况下病毒传播的效果。 134 | 4. 以任意一个城市为例，考虑采取怎样的步骤、需要多大成本、多长时间可完成群体免疫屏障的建立。 135 | 136 | ### 4. 捕食者模型的分析 137 | 在白垩纪时期潜伏在地底的怪兽双尾怪和古墩是一对捕食者和被捕食者关系。白垩纪的某一天，美菲拉斯星人的飞船队从太空带来了2000只双尾怪和3000只古墩来地球。假设双尾怪和古墩每年生育率为0.004和0.006，古墩会以双尾怪为食，试着分析 138 | 1. 设置不同的参数绘制古墩和双尾怪在1000年内的变化情况 139 | 2. 试着画出古墩和双尾怪变化的相轨图 140 | 3. 1972年的一天在东京K地区，潜伏在地底的一只古墩和双尾怪复活了并打败了杰克奥特曼。地球防卫军准备开发一枚浓缩核弹将东京K地区炸毁。这枚核弹的爆炸威力是100万吨TNT当量，假设采用U235作为核弹原料，请查找资料并分析在引爆核弹以后半径多少以外是安全的？并给出半径与放射强度之间的数学建模与仿真。 141 | 142 | ![image](https://user-images.githubusercontent.com/61874427/173193648-72174f07-39d0-4d52-b068-013b7131ef5e.png) 143 | 144 | 145 | 146 | ## 实验四：鸢尾花的数据预处理与可视化 147 | 这次实验内容很简单，我会上传一份iris.csv，大家试着去对鸢尾花数据做数据预处理，包括数据的填充，min-max规约和z-score规约，然后尽可能画一些描述性统计图表。 148 | 149 | ## 实验五：多维度评价 150 | 这次实验内容就一个问题，但我希望各位能用不同的方法解决，并给出一些有意思的结论。 151 | 152 | 宇宙帝王莫尔德和宙达准备率领大军攻打光之国，在此之前他们通过黑客技术从宇宙警备队科学局偷到了奥特六兄弟的资料。他们选取了一系列重要的评价指标对奥特兄弟的战力进行评估： 153 | | | 身高/m | 体重/t | 速度/马赫 | 年龄/万岁 | 光线杀伤力 | 腕力/万吨 | 光线技能 | 154 | | ---------- | ------ | ------ | --------- | --------- | ---------- | --------- | ------------ | 155 | | 炎头队长 | 45 | 45000 | 10 | 2.5 | 1000 | 16 | M87光线 | 156 | | 硫酸脸 | 40 | 35000 | 5 | 2 | 800 | 4 | 斯派修姆光线 | 157 | | 莫西干 | 40 | 35000 | 7 | 1.7 | 750 | 5 | 艾梅丽姆光线 | 158 | | 致敬 | 40 | 35000 | 6 | 1.7 | 850 | 12 | 希拉奈玛光线 | 159 | | 肉联厂主任 | 40 | 45000 | 20 | 1.5 | 900 | 14 | 梅塔利姆光线 | 160 | | 太子爷 | 53 | 55000 | 20 | 1.2 | 1000 | 16 | 斯托里姆光线 | 161 | 162 | 请对六位奥特曼的能力进行评估，你认为谁最难搞定？如果安排车轮战，消耗顺序应该是怎样的？ 163 | ![image](https://user-images.githubusercontent.com/61874427/175906282-2374eebf-2337-4ed0-81ee-7a39fa42b03f.png) 164 | 165 | 166 | ## 实验六：比特币价格预测 167 | 这一节的实验我们希望能够通过对2022美赛C题比特币价格预测问题的求解，熟悉arima模型的编程细节。 168 | 根据附件Bitcoin.csv，预测比特币在未来30日内的收盘价变化。 169 | ![原始数据](https://user-images.githubusercontent.com/61874427/186624937-cd91a281-c37a-4fc6-813d-5a77b4f9bfc9.png) 170 | 171 | 172 | ## 实验七：最短路径问题 173 | 这一节的实验包括单向最短路径问题和TSP问题的求解，其中TSP问题的求解建议使用遗传算法求解，如果使用动态规划也是可以的。如果不太熟悉遗传算法的同学可以参考第八章。 174 | 175 | ### 1. 普通最短路径 176 | ![路径](https://img-blog.csdnimg.cn/2020112415370673.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dheW5lX2xlZV9sd2M=,size_16,color_FFFFFF,t_70#pic_center) 177 | 如图所示，这是从光之国（节点1）到其他几个行星的路线图，如果我想从节点1走到节点6，应该以怎样的路线最短？ 178 | 179 | 我知道这个问题你们手算都可以算出来，我希望你们能够用Floyd和Dijsktra两种算法进行计算。 180 | ### 2. 周游最短路径 181 | 这里我可以给出34个城市的坐标，希望能够求解一条路径，它能不重复走完34个城市，然后总的路径长是最短的。 182 | ![image](https://user-images.githubusercontent.com/61874427/186627079-f38cac9e-0fb9-466b-b22e-81aa7f1ba647.png) 183 | | 经度 | 纬度 | 184 | | ------ | ----- | 185 | | 116.46 | 39.92 | 186 | | 117.2 | 39.13 | 187 | | 121.48 | 31.22 | 188 | | 106.54 | 29.59 | 189 | | 91.11 | 29.97 | 190 | | 87.68 | 43.77 | 191 | | 106.27 | 38.47 | 192 | | 111.65 | 40.82 | 193 | | 108.33 | 22.84 | 194 | | 126.63 | 45.75 | 195 | | 125.35 | 43.88 | 196 | | 123.38 | 41.8 | 197 | | 114.48 | 38.03 | 198 | | 112.53 | 37.87 | 199 | | 101.74 | 36.56 | 200 | | 117 | 36.65 | 201 | | 113.6 | 34.76 | 202 | | 118.78 | 32.04 | 203 | | 117.27 | 31.86 | 204 | | 120.19 | 30.26 | 205 | | 119.3 | 26.08 | 206 | | 115.89 | 28.68 | 207 | | 113 | 28.21 | 208 | | 114.31 | 30.52 | 209 | | 113.23 | 23.16 | 210 | | 121.5 | 25.05 | 211 | | 110.35 | 20.02 | 212 | | 103.73 | 36.03 | 213 | | 108.95 | 34.27 | 214 | | 104.06 | 30.67 | 215 | | 106.71 | 26.57 | 216 | | 102.73 | 25.04 | 217 | | 114.1 | 22.2 | 218 | | 113.33 | 22.13 | 219 | 220 | 221 | ## 实验八：量化投资选股问题 222 | 这一实验我们基于实验六对比特币的预测，分析比特币和黄金股价，如果每一次投资都必须至少经过一周才能重新调仓，在你有十万美金的前提下应该如何安排每次的投资策略才能在五年后盈利最大？ 223 | 224 | ## 实验九：鸢尾花的分类与聚类 225 | 对于附件中提供的iris.csv（也可以通过sklearn.datasets去load_iris()），对其进行以下基本操作： 226 | ### 1. 绘图 227 | 尽可能想到更多的可视化表现形式，绘制成图表并附带文字描述 228 | ### 2. 特征分析 229 | 对于鸢尾花的数据，分析每个特征对鸢尾花类别的影响，你认为用怎样的假设检验去分析最为合适？ 230 | ### 3. 分类 231 | 尝试利用不同的分类器去分类鸢尾花数据，并将结果用不同的衡量指标或可视化图表表示。 232 | ### 4. 聚类 233 | 尝试使用Kmeans聚类方法去对鸢尾花进行聚类，并利用轮廓系数等方法测量K最优的取值。 234 | 235 | ## 实验十： 波士顿房价预测 236 | 与实验九对应，可以使用sklearn.datasets.load_boston()获取波士顿房价数据集进行预测。这里我们更多的是做回归分析。 237 | ### 1. 绘图 238 | 尽可能想到更多的可视化表现形式，绘制成图表并附带文字描述 239 | ### 2. 特征分析 240 | 对于房价的数据，分析每个特征对房价类别的影响，你认为用怎样的假设检验去分析最为合适？能否建立这些量之间的回归方程并进行检验？ 241 | ### 3. 分类 242 | 尝试利用不同的回归器去分类鸢尾花数据，并将结果用不同的衡量指标或可视化图表表示。 243 | 244 | ## 实验十一：34城市的TSP问题 245 | 参考实验七的问题2中给出的34城市TSP问题，要求从遗传算法、蚁群算法、粒子群算法、模拟退火算法中至少选择两种进行实现。如果有更多的想法也欢迎使用你们看到的资料。 246 | 247 | ## 实验十二：元胞自动机 248 | 试着从网上查找资料，复现一个生命游戏的元胞自动机。这里我给出一个案例： 249 | ``` 250 | """ 251 | 元胞自动机 Python 实现 252 | """ 253 | import numpy as np 254 | import matplotlib.pyplot as plt 255 | 256 | 257 | class GameOfLife(object): 258 | 259 | def __init__(self, cells_shape): 260 | """ 261 | Parameters 262 | ---------- 263 | cells_shape : 一个元组，表示画布的大小。 264 | 265 | Examples 266 | -------- 267 | 建立一个高20，宽30的画布 268 | game = GameOfLife((20, 30)) 269 | 270 | """ 271 | 272 | # 矩阵的四周不参与运算 273 | self.cells = np.zeros(cells_shape) 274 | 275 | real_width = cells_shape[0] - 2 276 | real_height = cells_shape[1] - 2 277 | 278 | self.cells[1:-1, 1:-1] = np.random.randint(2, size=(real_width, real_height)) 279 | self.timer = 0 280 | self.mask = np.ones(9) 281 | self.mask[4] = 0 282 | 283 | def update_state(self): 284 | """更新一次状态""" 285 | buf = np.zeros(self.cells.shape) 286 | cells = self.cells 287 | for i in range(1, cells.shape[0] - 1): 288 | for j in range(1, cells.shape[0] - 1): 289 | # 计算该细胞周围的存活细胞数 290 | neighbor = cells[i-1:i+2, j-1:j+2].reshape((-1, )) 291 | neighbor_num = np.convolve(self.mask, neighbor, 'valid')[0] 292 | if neighbor_num == 3: 293 | buf[i, j] = 1 294 | elif neighbor_num == 2: 295 | buf[i, j] = cells[i, j] 296 | else: 297 | buf[i, j] = 0 298 | self.cells = buf 299 | self.timer += 1 300 | 301 | def plot_state(self): 302 | """画出当前的状态""" 303 | plt.title('Iter :{}'.format(self.timer)) 304 | plt.imshow(self.cells) 305 | plt.show() 306 | 307 | def update_and_plot(self, n_iter): 308 | """更新状态并画图 309 | Parameters 310 | ---------- 311 | n_iter : 更新的轮数 312 | """ 313 | plt.ion() 314 | for _ in range(n_iter): 315 | plt.title('Iter :{}'.format(self.timer)) 316 | plt.imshow(self.cells) 317 | self.update_state() 318 | plt.pause(0.2) 319 | plt.ioff() 320 | 321 | 322 | if __name__ == '__main__': 323 | game = GameOfLife(cells_shape=(60, 60)) 324 | game.update_and_plot(200) 325 | ``` 326 | -------------------------------------------------------------------------------- /image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShituoMa/mathematical_modeling_python/3283df48f67c88017ba7f1948bc289d136aad902/image.png -------------------------------------------------------------------------------- /iris.csv: -------------------------------------------------------------------------------- 1 | X1,X2,X3,X4,label 2 | 5.1,3.5,1.4,0.2,Iris-setosa 3 | 4.9,3,1.4,0.2,Iris-setosa 4 | 4.7,3.2,1.3,0.2,Iris-setosa 5 | 4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,3.6,1.4,0.2,Iris-setosa 7 | 5.4,3.9,1.7,0.4,Iris-setosa 8 | 4.6,3.4,1.4,0.3,Iris-setosa 9 | 5,3.4,1.5,0.2,Iris-setosa 10 | 4.4,2.9,1.4,0.2,Iris-setosa 11 | 4.9,3.1,1.5,0.1,Iris-setosa 12 | 5.4,,1.5,0.2,Iris-setosa 13 | 4.8,3.4,1.6,0.2,Iris-setosa 14 | 4.8,3,1.4,0.1,Iris-setosa 15 | 4.3,3,1.1,0.1,Iris-setosa 16 | 5.8,4,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setosa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5,3,1.6,0.2,Iris-setosa 28 | 5,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.1,Iris-setosa 37 | 5,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,,1.5,0.1,Iris-setosa 40 | 4.4,3,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | ,3.2,1.3,0.2,Iris-setosa 45 | 5,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5,3.3,1.4,0.2,Iris-setosa 52 | 7,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5,2,3.5,1,Iris-versicolor 63 | 5.9,3,4.2,1.5,Iris-versicolor 64 | 6,2.2,4,1,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 6.7,3,5,1.7,Iris-versicolor 80 | 6,2.9,4.5,1.5,Iris-versicolor 81 | 5.7,2.6,3.5,1,Iris-versicolor 82 | 5.5,2.4,3.8,1.1,Iris-versicolor 83 | 5.5,2.4,3.7,1,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6,2.7,5.1,1.6,Iris-versicolor 86 | 5.4,3,4.5,1.5,Iris-versicolor 87 | 6,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4,1.2,Iris-versicolor 95 | 5,2.3,3.3,1,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3,4.2,1.2,Iris-versicolor 98 | 5.7,2.9,4.2,1.3,Iris-versicolor 99 | 6.2,2.9,4.3,1.3,Iris-versicolor 100 | 5.1,2.5,3,1.1,Iris-versicolor 101 | 5.7,2.8,4.1,1.3,Iris-versicolor 102 | 6.3,3.3,6,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3,5.8,2.2,Iris-virginica 107 | 7.6,3,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | ,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5,2,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6,2.2,5,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2,Iris-virginica 124 | 7.7,2.8,6.7,2,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6,3,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5,1.9,Iris-virginica 149 | 6.5,3,5.2,2,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3,5.1,1.8,Iris-virginica 152 | 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