Upload 7 files

cc_pr.txt

@@ -0,0 +1,100 @@
+621A7ZBJO7F2,HL,HM,HL,LL,HL,HM,HL
+Q0Q5L5YDTNG1,HH,LH,MH,LH,LM,MH,MH,LH,LH,LH,LH,LM,LH,LH,MH,HM,LM,HH,MH
+J0NS7HH0YHJL,LH,MM,MM,ML,MM,HM,LL,ML,ML
+1K0FZU9811QO,MM,HH,HM,LL,LM,LM
+OUQH9B59BVCL,HL,LL,ML,LH,LH,HL,HM
+R45CJ1D523DN,MM,LH,LH,LM,HL,MH,LM
+41RU1HRN24XK,LL,ML,HL
+4QJZV7PTND0B,HH,ML
+FVRE3T41YU37,ML,HM,HL,HM,HL,MH,HH,HH,HM,LL,HL
+FY04T54JJIDF,HL,ML,HM,MM,ML,ML
+675AE32IWDG3,HM,HL,HH,ML,HL,HL,HM,ML,LH,HL
+6A065FY6R6D3,MH,HM,HM,HL,HL
+3M05OMHABSYR,HM,HL,LM,HL,ML,MH,HL,MM
+XQHA2HR99ZWF,LM,ML,HH,MM,HL,LL
+4SMFY1E4O8DW,HL,LL,HM,MM,MM,LH,MM,HM,HL,HL,HL
+P64849O9FOI4,HM,HL
+EK1RT9BD5WD3,HH,HL,HH,LH,LM
+CYW22RPT22W5,LM,HH,HM,ML
+N2W3U7X7473N,HH,LM,HL,ML,ML,HL,HL
+HBH3469HC09B,LM,HM,ML
+B8QN63UYMB13,HH,HM,LL,LL,HL,HH,HH,HL
+2WDB10600Q68,MH,HL,MH,LH,HL,HL,HL,LL
+PCHS7V41X672,LH,LL,LL,MM,MM,ML,LH,LH
+ZGY1HSGAM84Q,MM,LM,HM
+494U6FJ52B5Y,ML,HM,ML,HL,HM,LL,HL,HH
+H695CILRQCD8,HH,HL,ML,HM,HH,MH,LL,LL,HL,LM,LL
+7HA1A2GT721A,LL,HL
+1K8E557PJ5WV,LH,HL,HM,HM,HL,HL,LL,MM,LL,MM
+B1MC5MAMKJAJ,HH,HL,HM,ML,LH,HM,LL,HH
+HJ3QPE113PKA,LL,HH,MH,HH,LL,LH,ML,MM,HL,ML
+WLA398Z7KAX5,LH,ML,MH,HM,LH,LH,HL,LH,ML,LM,ML,LH,LM,LM,MM
+19P1P200BWJC,HL,LL,HL,LM,MH,HH,HH,LL,HL,LL
+GYZA6Z6V8BW2,LL,HH,LL,LM,HL,HL,LL,ML,ML,ML,ML
+23021Q6Z6B1A,MM,LH,LH,HM
+H9EQSO5KFTA5,MM,HL,HL,HL,HH
+2Z13CQ0CA7PF,LL,HL,MM,HL
+L931D231FXM9,ML,HH,HH,HL,HM,HH,LM,HL,HL,HH,HL,ML
+EKXZD9JUB1ZU,HL,HL,HL,ML,MM,MH,HL
+E4N64R89O2MQ,HH,HL,HL,MM,HL,HM
+7W56H24BZ41Z,MH,MH,HL,HL,LL,HH
+J1UI5830I4GR,HM,MM,LL,HL,ML,HM,LH
+7S1LN5H9BXGP,HL,HL,HM
+2Q390M1W8JIG,LL,HL,HM,HL,MM
+M92AL00M1VI2,LH,HL,ML,ML
+C1BR8W43HX86,LH,LH,MH,HH,HH,HH,ML,HM,MH,MH,ML,HH
+5YCAMGCNAG25,LL,HL,ML,MH,MH,HH
+P2A6JJ8TV8NN,LM,LH,LM,HH,LH,HH,MH
+43SB25401G64,HL,LL,HL,HL,HM
+N3N8JPS313B6,HM,ML,LL
+360GV1S34Q4O,MM,ML,LL,HM,HL,HH,HM,MM
+9XBTB323MUQ9,LL,LL,HH,ML,MH,HM,MH,HL
+RG2S4M759109,HH,LH,LM,HL,LL,HL,HM
+3F82LXLPHEV4,HM,HL,ML,LL,LL,LL
+J062264U5OW9,ML,MM,ML,HH,LM
+PHZF3M5PM7R7,ML,HH,HL,HL,MM
+E8T87SZ3QQ0M,HL,LM,HL,ML,HL,MH,HL,LL,ML,ML,ML
+GOUY69KDY323,LL,HM,LH,HL,HH,MM,HL,MM,ML,HM,HM
+W44Q1HEA9WPE,LH,HM,ML
+8BG5IU7PE867,HL,LL
+7D331BEDNBK5,HL,LL,LH,HL,HM,ML,LM,HL,HL,HM,HH
+ZYN2411DCAK1,MH,HL,MM,HH,MH,LL,LL,HH,MH,LM,LM,LH,LL,LM,LH,LH
+OSWX98466PH0,MM,HM,HL,HH,HL,MM,HL,HL
+SRKC179LPHVK,HL,LM,LL,LL,ML,MH,HM,LL,LL,LH
+FAX4645CHW07,HH,ML,HL,HL,LL,HL,HL,LL,ML,HL
+8QWWR96ID1LR,HM,LM
+8HU77M101QI1,HL,HL,MM,HH,ML,MM,HL,HL,LL,HL
+8NUHLC02XVZ9,MM,HH,HH,MM,MH,LL,LM
+6A4Y07H7DJV5,LH,HH,HH,HL
+769RA985C6GB,LH,HL,ML,HL
+MAB1FK2IJ4UD,LL,HL,LM,LM,LL
+652FGUVWO5MY,HM,LL,HM,MM
+7M24C49VQB1T,HL,LH,HL,HL,LL,HH,ML,HM,LM,HL
+02V17V7041TI,HM,HM,HL,HM,HH,MM,HL,HL,HL,ML,HL
+VV940J2GPNT3,LH,HL,HL,HM,HH,HM,MH,HM,ML,HL
+93EDJT98VZ06,ML,LL,HH,HM,HH,LM,LL,HL,MM,LL,ML,HL
+933WEULG5Q40,HH,LM,ML,HM,HL,HM,MH,HM,MM,HM
+71F53VE4335L,HL,HM,ML,HL,HH,ML,HH,LL
+JFBNPAF1A07V,LL,HL,ML,ML,LL,HM,ML,LM,LM
+QUU4D4F85BQM,HL,HL,HL,MH,HL,HL,HL,LH,HM,HL,LL
+9ZXIIV039GM1,HL,MM,LH,MH,HL,MM,HL
+S65PQC83412M,LM,LM,HL,MM,LL,ML,HL,HM,HL,HM
+3T32WK39FXI5,HH,LL,HM,HL,LM
+03273QEZ1E5L,HL,HH,HH,HM,HL,HL,HL,HM,HL,HL
+4082T6AV07V0,MH,LH,HM,MH,LH,ML,HL,LH,ML,MH
+U7PO9U27TS3Y,MH,MM
+MCF2RMO71MWE,LL,HL
+VUGD7274GNKW,LL,ML,LM,HH
+A4EKWZ4O222N,HL,HL,LM,MH,HL,MH,LM,HL,HL,HM,HL,MM
+DXD25C37NTT1,HL,HL,LM,HM,ML,MH,HL,LL,HL,LM,LH,MH
+2ZB5H2P5AD4H,HL,HL,ML
+MO6NXK5OG4O7,LL,HH,ML,MM,MM,ML,HM,HL
+6S343G812L5K,HL,HM,HL,LL,HL,HM,LL,ML,HL,LH,HM
+K9PJF4GO3GP9,LH,LH,LL,MM,LL,MH,LL,MM,LH,LH,HM,LH
+23COHBXT5HIF,HM,ML,LL,HL,LH,LH
+TB5G3M753EI2,ML,MH,HL,HM,ML,HL,HM,LM,MH
+4L1NGK4DTPCG,ML,LL,HL,LL,HM,ML,LH,ML,HL,MM
+18E5N7M6NAQR,HL,LH,HH,HH,HL,MH,HL,HL,LH,LH,MH,LH
+6MSUZ2360D65,HL,HH,MM,HL,HL,HM,HM
+B5135265R7H5,LM,ML,LH,LL,HH,MM,HH,MM,ML,HL,MM
+G5N6ZC378200,LL,MM,LL,LH,MH,HL,LL,ML,MM

cc_tr.txt

@@ -0,0 +1,1000 @@
+56C96HWLR9ZO,F,LH,ML,HL,HL,LL
+52731S333A91,F,MM,HH,HH,HL
+6MQBZXG07063,F,LM,HL,HM,HM,ML,HM
+F28306MILIJQ,T,HH,MH,MM,HH
+I6QN23CUG18T,F,LL,HL,ML,LL,HM,HL,HL,HM,HL,LL,HL
+66I54L4BPV3W,T,HL,LL,MM,HL,HL,HL,HL,LL,LL,MM,HH,HL
+NL5R8QB7V2D1,F,HH,HL,HH,MH,HH,HM,HM
+M97M9RW427AU,T,LH,LH,LM,LL,LM,LH,ML
+8PD3415TP28B,F,HM,ML,ML,LL,HH,HM,HM,HL,HM,HL
+80FQYKKK8GEB,F,HL,ML,HH,LL,HM,LL
+T22MQA1Q9FI5,T,LL,LH,LH,MH,HM,LH
+9OE2IIWWH9D2,F,HL,HL,HM,HM,MM,HL,MM,ML,HL
+4G9OK441WL7M,F,ML,MM,HL,LL,HH,HH,HL
+DNLXIKU8196C,F,HH,HL,HM,LL,LL,HL,HL,ML,HL
+J9S6967N9C53,T,HL,HM,HH,LL,ML,LH,HM,LM,LH,MM
+D9ISIS8KQ765,F,LL,HH,HH,HL,LL,LL,HL,ML,HH,LL,ML
+T3QYLV25QX18,F,LL,LH,LL,HL,HL,HL,MH,HH,HL
+IYIP49KT0Q2I,F,MM,LL,LM,HL
+G95GY3EV9YJ9,F,LH,LL
+53S9SW8MOTB3,T,HH,MM,HL,HL,LL,LL,LL,LM,HL,LH,HL
+053AJCSDY2VC,F,HL,ML,HL,HL,HL,HL,LM,HH,HL
+0447N15M19LA,F,HH,HM,MM,LH,LL
+L7OVXEGPLQON,F,ML,ML,HM,ML
+RG1517801E9A,T,MH,HM,HM,ML,LL,LM,HL,LM,HL,LL,HL
+XL53ZVETRAG6,F,LL,ML,HL,MH,HL,ML,HH,HL,LL,HH
+V30OL8KY979H,F,HH,HM,LH,HL,HL,HL,MH,ML,MM,LL
+29BZ8OY9HR5F,F,MM,LL,HM,HH,HH,HL,LL,HL,HL,HL,LL
+6FBD38TRSA96,F,LL,HM,LH,LL,MH,MM,HH,HL,LL,HH,ML
+XPV0C4VJ0UP3,F,LL,LL,HL,HL,MH,HL,ML,HM,HL
+A3THLR7Z7SQ6,T,LL,MM,HL
+VW30AA3240IL,F,LH,HL,LL,LL,HH,ML,ML,HH,HL
+0E1GR4973Z8W,F,LM,ML,HM,LM,LL
+B6992HPDS5T2,F,ML,HL,LM,ML,ML,HL,MM,ML
+M3142693V5LG,F,HL,HL,ML,ML,HH,HL,HM,HM,LL,ML
+19BMEPW3RGO6,F,LL,HM,HL,LL,HL,HL
+UUKL9Y665VT5,F,LL,MH,HL,HL,LH
+BYBAI3HEXSK3,F,MM,HM,HL,HL,LL,LL
+G0XO1USJ4313,F,HM,HL,LH,HL,MH,MM,HL
+813HSN6B600J,T,LM,HH,LH,LH,MH,MM,LL,HH,LH
+A08AWM555ECB,F,MH,ML,HH,ML,HL,HL,LM,HL,LM,HL,HH
+9ART5JAAV4K4,F,LM,LH
+142OY5DC6ZAF,F,HL,HM,HL,ML,LL,HL,LH,LM
+TESQAJ889F54,F,LH,HL,HL,HL,HL,MH,MM,LL,HH,HL,HL
+VEF3D8QG58EN,F,MM,LH,MM,HL,HL
+WK72L5ED0AJL,F,HL,HL
+65QFB88UAY6F,F,HH,ML,HL,LL,HL,HL,HL,LL
+6S8POPSJK682,F,LH,HM,MH,HL,HL,MM,MM,MH,HH,ML,MM,ML
+811X4B8T94J1,T,LM,MH,LM,LM,LH,HM
+157XQ283PK64,F,HM,HL,LL,LH,MH,HM,HL,LH,HL
+45LRT0J36I6E,T,LH,LH
+BQMKR098998X,F,ML,HL,HH,HH,HH,MM,HL,HH
+LO0HLXFXE8X2,F,HL,HL,HL,LM,HL,HH,HH
+10EQ5X8855N0,F,LL,HL
+0651PVCE3864,F,ML,HL,HH
+HJI48P74876E,F,HM,HL,HL,LL
+1ROX8LT61MLB,F,ML,MM,HL,MM,MM,HM,MM
+W0U2Z23YAUXE,F,HH,MM,LL,LL,ML,HH,LM,ML,HL
+P082B59F873N,F,HM,HH,HL,HL,HM,ML,ML,HL,HL,HL,HM,MH
+N752V9XO10LF,T,LH,LH,LM,MH,MM,LL,LH
+KK477Y4DHR67,F,LH,MM,MH,HL,ML,HM,HL,HH,ML,HM,MH
+2KM6A15S2HT7,F,HM,HH,LL,MH,HL,HM,HL,LL
+IQOA8C531N42,T,LH,LM,LH,LH,LH,LH,ML
+8B7630U4S4XM,F,HM,HL
+5677MC0E34M7,T,LL,HM,LH,LL,HH,HH,MH
+1UWK1T3816R2,F,HL,HL,HH,LH,HH,HL,HL
+MAU7DMD48632,F,LH,ML,LM,ML,HL
+579MHG1OQ2P5,F,LL,ML,LL,HL
+RR2OL19852VK,F,MM,HL,HH,HL,LL,LM,HL,LM,LM,LM
+HWQ798EU81DE,F,LL,LH
+051USEJN3QOZ,F,ML,HL,HL,LH,HH,MM,HM,LH
+HO4SIG2TJB7U,F,HM,LL,HL,HL,HL,LL,MM,HL,HH
+GF4A86P2JW22,F,MM,HM,ML,HL,HL
+0XSA5S11UP86,F,MH,HH,HH,HM,LL,LL,MH,HM,LM,HL,HM
+AUI5M5UJ63UH,F,LH,LL,ML,LH,HL,MH
+BYNRU38M2C89,F,HL,HM
+KKW526X8H5W8,F,HL,HM,HH,HH,HL,ML,LM,HM,HL,LL
+6MZRJ14GFL9W,F,ML,MH,HH,HM,LM
+9T509WDDKSPJ,F,HL,HM,MH,HH,HL,ML,HH,HH,ML
+0FWHOWN5M9N8,F,LL,LL,LL,HL,LL,LL,ML
+Z5VJ3PDX2F51,F,HM,HM,HL,LM,LL,HL,LH,LL,HM,LH,LL
+ZCPW0ZNGMC4E,F,HL,HL,LL,HH,MH,ML
+66R7X6ZJ12ED,F,ML,HL,HL,HL,HL,HL,HH,HM,HM,ML,HM,ML
+LW6BLUEOQT03,T,MM,MH,LM,LM
+Q57934F2RLPZ,F,HL,MH,HL,MH,HH
+CM49UPW4NZ6X,F,LL,HL,HH,ML,HM,HL
+M2V6MM915EEE,F,MH,HL
+0J997HOFCN30,F,HM,HL,HL,HH,ML,HL,HL,HH,MH,LH
+I65DXY46DC78,F,MH,HL,LL,MM,HM,ML,MH,ML,HM,HL,LM
+0N377CO8WGZV,F,LH,MH,HL,HL,HM,LL,MM,HL,HL
+AYT91S72E7B8,T,LM,MH,LM,LM,LM,LH,MH,MH,LM,LH,LL,LM,LH,HH,MH
+P11DSJZLEZL1,F,ML,ML,HH,HM,ML
+3S9J880ZDD2I,F,LM,ML,ML,HL,HM,ML,ML,HL
+AN0D2PJQ9QR2,F,HL,MH,MH,HM,HL
+TP82C07742H7,F,HL,HL,LL,ML,HL,HL,HM,LH,HL
+E675W1QRPPQN,F,LL,HM,HL,LH,ML,HL,LL,LL,HM,HM,HM,LL
+C83XIU539B2U,T,LH,LH,HH,HH,LH,LH,HM,MH,HH,MH,LL
+6JG8FZOJJTFR,F,LL,LM,LL,HM,HH,ML,ML,LL,ML,HL,LL
+2DSUSW8318C8,T,ML,LH
+2N750DE59LI5,F,HM,HH,HL,HM,HL,MH,HL,HH
+W86Z91A1AOHU,T,ML,HL,HM,HH,HH,HL,HH,HH,LL
+V7L09U9882TN,F,HL,MM,ML,HM,HL,HM,ML,HL
+L5L0F89R6Y91,F,LH,HL,HH,ML,HL,LH,HL,LM,HL
+3178CZT97PFW,F,HH,HL,HL,HM,HL,HH,LM,HL
+36BKC8GW7CNL,F,HM,ML,HH,HM
+56XU5SP9FES4,F,HM,HM,ML,HM
+KSKRIV9JV493,F,ML,HH
+252KUB58EVM3,F,HL,HL,HL,HM,ML
+7RYWY52Q92O1,F,HH,LL,HH,ML,HL,HL
+MMNN58S0X0NU,T,HL,HL,HL
+ZE2E325IBLZP,F,LH,MM,MH,HM,ML,HL,LL,MH,LL,MM,HL
+NA1XXCG8MI79,F,HH,LL,MH,HM,HM,MM
+AEKMN02E4EK8,F,ML,MM,ML,HL
+L1VKXC481SO2,F,ML,MM,LM
+0S10S7SKIUXE,F,HM,LM,ML,ML,HH
+WWUZI11PGX8K,F,LL,HL
+WQGY6P4G9VG5,F,LL,HM,HL,MM,HM,HH
+H19Z42J58NEA,F,HM,LL,LL,HM,HL,HL,HM,LH,HL,HL
+HNE96X0231FJ,F,HM,LM,LL,HL,HH,HL,LL
+163PPJ8ELA09,F,HM,LL,HL,LM,HM
+YTKH3BDITL3E,F,HH,LL,HM,MH,HM,HL,LH
+PD10F555M5L1,F,HM,ML,HH,HL,HM,ML,HH,HL,ML,MH
+18Q701G91485,T,LH,LM,LM,MM,LH
+2PG524C7XAHH,F,ML,HL,HM,MM,ML,HL,HL
+R2UWY8DG63U8,F,LL,LL,HL,HL,HH,HH,HM,HL,LL,ML,HM
+LNWOZF33XPLW,F,HL,HH,HM,LM,LH,HL,HH,MM,LL,HH
+NVOD96KGWY20,F,MH,HL,HL,HL,HM,ML
+CYNPB6ZF94R0,F,MM,HL,ML,HL,LL,LL,LH
+1U88JYDCMMLR,F,HM,LM,HL,HM,HH,HM
+2MWRN1AC410J,F,HL,HH,ML,MM,LM,HL,HM,HL,ML,ML
+X6Y4Q5794R74,F,HH,ML,HL,LL,ML,ML,HH,HL,HL,HH,HL,HL
+I097WGS1DWLL,F,HL,LH,ML,MM,LM,MM,LL
+3D8W30UZYK85,F,HL,ML,ML,LM,ML,ML,HL
+1HQH002H44OD,F,HH,MH,ML,LL
+MWJJQ344XW45,F,LL,MH,HH,HM,LM,HL,HH,MM
+MZQ0425QHPIX,F,HL,HL,LH,HM,LL,HL,HL,HH
+TST5Y6766R56,F,HM,HL,LH,HL,HL,HL
+KY9U83KJM2C7,F,HH,LH,ML
+5G266MMKT462,F,HH,LL,HM,HM,ML,ML,MH,LL,HL
+6985N57A719Z,F,LM,HM
+T08LZMK5QL9V,F,LL,ML,HH,HL,LM,HL,MH,HL,HL
+CMK41BAGDHG9,F,HL,HM
+8FJ67U9VJ9NF,F,LL,HL,HL,HH,LM,HM,LM,ML,HL
+80P3HZZD265R,F,HL,HM,MM,HM,HH,ML,LL
+DFP3V0NC3T5S,F,HL,MM,MH,HH,HM,MH,HM,HM
+7E4T9S1YANJY,T,MH,HL,ML,HL,HH,MH,ML
+Z4PY65180T25,T,MM,HH,MH
+84MJ2WVKKV9A,F,HL,ML,HL,HL,LM,HL,HM,LM,HL
+52784Z1S1U2H,F,LL,HH,LH,HH,LH,HL,HM,HM,MM,HH,HL,HH
+KWSW0A8B1742,F,MH,LM,HL,HL,HH,MM,HL,HL,ML
+7T690S5C8GL6,F,HL,HL,HH,LH,MH,HL,HH,HM,HH,ML
+99ZQ4WGKILSP,F,HM,ML,MH,HM,HL,ML,MH,LL
+A3ER7RXBT2DQ,T,HL,ML,HM,LL,HL,HM,MM,ML,LH,LL,LL,LM
+GE0S05U4X865,F,LL,MM,LH,LL,LH,HH,HL,MM,HH,HL,HH
+KH5X0727FWO9,F,ML,HM,HM,LL,HH
+DXYCJP6D87OB,F,HL,LL,HL,HM,HM,LM,ML
+5Q5K9VLCMRZK,F,MM,ML,HL,LL,MH,ML,LL,HL,LL,MH
+A65NC9JJ8MWJ,T,LL,HM,LL,HL
+ZM7XIKYTSG3W,F,HH,HL,LL,LL,LH,HL,ML,HM,HL
+A336SQKFQZ53,T,LL,HL,LL
+AFJ06W15W268,F,LM,HL,HL,ML,LM,MM,HM,LM,LL,MH,MH
+BN34C4Q00XYS,F,HM,LM,LH,HL,LL,HL,MH,LL,HM,HM
+J38QW082JH09,T,LH,MH,LH,LM,LH,LH,LM
+77L909TVBDJ1,T,LL,LH,MH,LM,HM,LL
+7AS8OVNC8Z7K,T,HH,ML
+G36CDJT66T56,F,HL,HL,MH,HL,HL,LM,LM,HM,HL,HL
+WQ6UY3419708,F,LL,HL,HM,HL,HL,LL,HH,HL,ML,LL
+QXF1SBL2V47Y,F,HH,HM
+FYKXEVTH1312,F,HM,HL,MH,LL,HH,HL,HH,HL,HL
+1OODE1O65SSU,F,ML,HM,HL,LL,HH,HM,HL,ML,HL,LL
+576B2Q9WYV2Q,T,HH,LM,HL,HM,LL
+803HO309ZL98,F,HH,HL,HH,LL,HL,ML,LL,LH
+70J5XBT4RK53,T,HL,HL,ML,MH,ML,HM
+VSSCNX77T655,F,ML,MH,HL,LL,HL,HL,HM,HL
+2AX5CR0NJ5X3,F,HH,HL,ML,MM,HH,HH,HL,HL
+4S70N0F492WS,F,ML,LL,LL,ML,HM,HL,ML
+MUZYY7WFDXFB,F,HL,HM,HM,HL,HM
+9VTA782IDYFC,F,LL,HM,LL,HL,HL,LH,LL
+356NG4YJNOIS,F,HL,HL,LL,MM,HL,ML,LH,HL,ML
+6J9X7WL99U76,T,LH,MH,MH,LH,MH,LH,MH,HL,HL,LH,MH,LH,LH,MM,ML,HH,LH,LM
+I7MC2U523DMF,F,HM,HL
+1SHBC2I918TW,F,HL,ML,HH,LL,HL,HL,HH,LM
+MDAUL9PU2DTL,F,HM,HL,LL,HM,HM,HL,HM,MM,HM,HL,HL,HL
+4O30R4O6KV4N,T,HM,ML,HM,ML,HL,ML,HM
+UMB526D7K69Z,F,HL,ML,LL,HL
+71SVMD6H5V99,F,HL,LL,ML,HM,MM,MM,HH,HH,HH,ML,LM
+381Z794F84TK,F,ML,HM,MM,LM,LM,HL,ML,MM,LL,LM,LL,HL
+HI0K87ALQQ6J,T,LH,HH,LH,LH,LH,MH,MH,LM,LM,LL,LH
+XJ4ZJ5686GU4,F,ML,ML,HM,HL,HM,HL,HH,HM
+3V25DJQ9284D,F,HL,LM,MM,MH,HH,LH,HL
+2QZLM2X012NS,F,LL,LM,HL,LH,HL,HL,HH,LH,HL
+MI72DC57CFTG,F,HL,HL,ML,ML
+8MN7R1TOW52I,F,HL,HH,LM,LM,HH,LL,HL,LL,HL,MH,ML
+4JP262EUW7AE,F,MM,LL,HL,HH,HL,LM
+6K99D01III76,T,LM,MH,MH,LL,HM,LH,LH,LH,MH,LH,LH,MH,HH,LL,LL,ML,LH,HM,LH,LH
+W269Y9950TGJ,F,HL,HM,LM,LM,HL
+0Z6SJ2AG5HZ1,F,LH,HL,MM,HL,LL,HL,HM,LM,HL
+AUE0IZ17T2LK,T,LH,LH,MH,LH,LM,ML,HH,LH,LM,LL,LH,LH,MH,LH,ML,MH,LM,LM,LH,MH
+QPP90BUY369K,F,HL,HM,HL,HM,HL,HL
+Z5X318TR8593,T,LH,LM,HH,MH,LM,LL,LM,LH,MM,LM,LH,LH,ML,LM,HL,LH,LH,LH,ML
+7V798HTBAAR4,F,LH,LL,LL,LL,LL,HL
+6J27Z8M2P7UN,F,ML,HM,HL
+R0F566ONCV8Z,F,LM,HM
+99G23Y80F5HL,F,HM,LL,LL,MM,HL,HH,HL
+GR81TUDCURB8,F,LL,HH,LL,MM,LM,HH,LH,MM,MH,HL,ML,HM
+XIR8D618188F,F,HL,HL
+D8X9761N2S64,F,HH,MM,HH,LL,HL,HL
+21BYP348E64A,F,HL,HL,HL,HH,ML,MM
+T6IW1LRGXPIS,F,HH,MH,LL
+464MPTH2835H,F,ML,LM
+9GSB3084F2U0,F,HL,ML
+GFCC6E062T5D,T,LH,MH,LM,LH
+YYY46H3Z2H39,T,LH,LM,LH,LH,LH,MM,LH
+58Q4GN1OZ70O,F,HM,HH,HM
+H5QUVTZO8RG8,F,HL,HM
+3U34C6TB93K5,F,HM,HH,HL,HL
+9SX6IS251C90,T,HL,HL,MH,ML,HL,LM,LH,HL
+T5E1HM4676O6,T,LH,LH,HH,LH,ML,MH,HH,HM,LM
+H62I7W8PPSWI,F,LH,HM,HL,HL,HL,HH,MM,HL,MM,HM,ML
+UU6E24LO3OYN,F,MH,HL,MH,HL,HL,ML,ML,HL,MH,HL,HH,LL
+AHUEW6QBU3T5,F,HL,HH,MH,LH,MH,MM
+581PUEY5J20S,F,MM,HL,MM,HL,HM,HM
+F89676S8YP0N,F,HL,HL,MH,HL,HL,LL,MH
+ESI9QG10NH3J,F,HM,HH,HL,HM,HL,ML,HM
+ZQXMWCT0FDHN,F,ML,ML,LM,LH,HM,HL,HM,LL,ML,HH,HL,LM
+5748A0EEP4D1,T,HH,LH,MM,MH,LM,LH,LM,HH,LM,LH,MH,HL
+P225QFLNL09M,F,HL,MM,HM,HH,HL,MM,HM,HL,ML,HL,LH,HL
+481K7EW98UUL,F,HL,HM,HL,LL,ML,MH,HM,HL,HL
+M0XZRH8P69Y5,F,LL,HH
+KEQ1Q32B23NG,F,HM,ML,HL,LH,LH,ML,ML,HL,ML,LM,ML,LM
+5VFRC1MULOV4,T,ML,HH,HL,HM,ML
+9R5V9GG65LBG,F,LH,HM,HL,HH
+RPNLDETK8653,F,MH,HH,ML,MH,HM,LM,HM,HL,HH,HL,MH,HH
+281QKKV5XG32,F,MM,LL,ML,HH,HM
+0NZ5L25962P0,F,MH,HM,HM,LM,HM,HL,ML,LH
+GSIVK6QW74RS,T,LM,HL,HL,HL,HL,LL,HL
+W3682PF41ANO,F,ML,HL,LL,LH,HH,HH,HH,ML,LL,HH,LM,MH
+OSIMAQTUI040,F,HL,HL,ML
+KQH0I93JR738,F,HL,ML,HH
+K82ML1FMVJDS,F,MH,HH,ML,LL
+C479J534BYDZ,F,HH,HM,HM,MH,ML,LL,HL,HL
+ECXCS2P230JG,T,HL,MH,LM,LM
+3WUKNZ67S9NK,F,HH,HL,HL,LL,HH,HM,ML,HL,HM,ML,HH,HM
+LNXIKU8QKBZB,T,LM,LM,MH,HH,LH,ML,HH,LH,MM
+XH9ZKA8454T7,F,MH,LM,HH,ML,LL,ML,HL,ML,HM,MM
+O2TN8UEG6BXQ,F,MH,HM,HH,ML,HL,HL
+8MJTCW78JUS6,F,MM,ML,HM,MM,LL,MM,HL,HM,HH,ML,HL,LM
+43H78BJF94UY,F,MH,HL,HM,ML,ML,MH,HM,HH,MM,LH,ML
+7FUUNYQ3C9P7,F,ML,HL,HH,HL,HM,HL,LL,HM
+CL8BX0W91B8B,F,LH,HL,HL,MM,HL,ML,LL,HM
+3EVU6K3D92B2,F,HL,LH
+0W9PKAWMQH1W,T,MH,LH,LM,HH,LM,LH,HH,LM,LH,HM,MH,MM,LH,HM,HH,LL
+BWVNY6B892Y8,F,ML,HL,HH,LM,HH,HL
+AO7DEP2BBNV4,T,HL,HH,HL,ML,LL,HM,HL,HM
+LN7S9C9IQQN0,F,HL,HM,HM
+8GOMV9X24B8Q,F,HL,LL,ML,HM,LH,HL,HL,LL
+4UB8P46WGF6H,F,MM,LL,HL,HL,HL,HH,HM,HM,LH,HL
+DL7M93L2CF40,T,MM,HM
+4DHPJ2J01S73,T,HL,LL,MH,LM,HH
+BCWIFAN2Y7SR,F,HL,MM,HL
+RA0L90NH8Y9Q,F,LL,LL,LL,HL,HM,HM,HL
+JJF0386FXTXN,F,HL,HH,HL
+MX2P7Z6G4R3D,F,HL,HL,HL,ML,HL,MM,HL,MH,HM,HH,HL,LL
+KR5LOP7XUGZ4,T,MH,MH,LH,HM,LH,LH,LH,LM,HL
+AKKV72SHOJUJ,F,MH,HM,HH,LL,HM
+2SG57T8227T7,F,HH,HL,LM,LH,ML,LL,LM,HH,ML,MM,HM,LM
+1X16PQSTY42G,F,LH,HL,HM,LM,LH,ML,LL,MM,ML
+M97N345RRCI3,F,HL,MM,HH,LH,MH,ML,LL,HM
+8S9B1UST1E95,F,MH,HL,ML,LM,MH,ML,LL
+3WSXE8TCM12D,T,HH,LL,LH,LH,MH,LH,LH,MH
+RYPH1GE8NI47,F,ML,MM,HL,HM
+82QT1K5IW0Y8,T,LH,LH,MM,LH,LL,LH,ML,LL,LL,LH,LH,HH,MH,LH
+CW1J2MU0I2BF,T,HM,LH,LH,LM,ML,LH,LH,LM,LM,LH,MM,HM,HH,LM,LH,LM,HM,LH,LL,LH
+81LCYO9H732P,F,HM,HM,HL,HH,LM,HL,ML
+55C0NM8X2N4I,F,HL,ML
+458H7TI4G946,F,HM,LL,HL,ML,HM
+WSKJ0L5B546Q,F,LL,LL,LL,HL,HL,ML
+W26091VI5BR6,F,HM,HM,MH,HL,HM,HH
+DHK3P48MP1UB,F,LL,MH,HL
+8L4483MN29T3,F,MH,LL,HM,LL,HM,HL
+76JDBY4IT3O6,F,HL,HL
+T8BKTOMZ47F2,F,HM,HL,LH,LL,HL,HL,HL,LH,LH,HL,HM
+AQQ3AC31J2RG,F,ML,MM,ML,HL,ML,HM
+94M7OFYRIW87,F,ML,HL,HH,HM,HL,HH,LH,ML,MH,HM,ML
+JV9ED980JNXP,F,HL,HL,HL,LL,HL,HH
+97F4HVBGH5SQ,F,MH,ML,HL,LL,MH,HL,HL,ML,LL
+ENRR2PG33COS,F,HL,HH,HL,HL,HL,HL
+11KFL56M8DRT,F,LL,LL,HH,MM,HH,ML,LM,MH,ML,HH,HL,HM
+Q21M0GHZGRXG,T,HM,LL,HL,HM,ML,LL,MM
+Q8R1Y0CGMOE6,T,LM,HM,MM,MM,HM,HM,HH,HM,HL,HL,HH
+04M4EZFF2D30,F,HM,HL,ML,HM,HL,LM,LL,HM,LL,HH
+Q0F8Q0MSB8H2,F,ML,ML,HM,ML,HH,MM
+8J03F5TSK1T3,F,LM,HM
+OP6B2L814SIM,F,LM,HL,MM
+6Z8SIBMYKW67,F,HL,LL,ML,HL,ML,HH,ML,ML,HL,LL,LM
+E2W71FHE21Z0,F,HL,HH,MM,HH,LM,LL,LL,HL,HH,HL
+6H049C9GU9TI,T,HH,MM,ML,ML
+WX3WE9R79OHL,T,LH,HH,LM,HH,LH,LH,LH,ML
+6Y4AM0525B66,F,LH,LH,ML,LM
+HO59JUQ4GY4E,F,HL,HM,HL,LL,LM
+ZL4B35I451AQ,F,HH,HH
+39PWJ9N6DW4D,T,LL,MH
+1S7AYM556Y5Z,F,LL,HH,LM,ML,HM,HM,HL,HM,HL,LM,LH,HL
+93F2A681TAA8,T,LH,MH,HM,LH,MH,LH,HH,HM,LL,LH,LH,LH,LM,LH,LH,MH
+UZMS9J344X2K,F,HL,HL,ML,HH,HL,ML
+B59VDB8451UQ,F,MH,HL
+QAW186WM426V,F,ML,MH,ML,LL,HL,HL,HM,LM,LL,MH
+SV9D75WR3SU3,F,MH,ML,HL,HL,ML,LM,HL
+KVPEQKW1HJJ0,F,HL,LM,HL,HL
+LS060UU211C8,T,LL,LL,MM
+KKH3GT1UIJL1,F,HH,LM,LL,HL,HH,HM,ML,HL,LL,HH
+THVPK1EO925L,F,LL,HL,HH,ML,HM,ML,HL
+JVU5I18RJOR5,F,LM,HL,HH,LL,HL,HH,HL,HM,LL,HM
+88Y58NDMP0BQ,F,HM,HM,HM,HM,HM,HL,ML,HL,HL,HM,MH
+9RBO7PPLQRCB,T,HL,LH,LM,MH,LM,HM,HL,HL
+67R27DB34565,F,HL,LM,HM,HM
+0PBTI2525930,F,LL,HH,MH,HL,MH,ML,HM
+9O4AU9AZ9I1C,T,MM,LL,HL,HL,HH,LL,HL,HL
+9V10S6BS7Q1J,T,HH,HM,LH,HL,LH,LH,LH,LL,HH,HH,LH,MH,LM,MH
+S9BAD6T88ORS,F,LL,HH,HH,ML,HL,HL,HH
+07K796S2HS8F,F,HM,HM,HH,HH
+462AO22J69BM,T,LL,LL,HM,HL,HM,HM,HL,HH
+8R2S99513C8B,F,HM,HH,HM,MH,HM,HL,HL,HM,LL,LL
+PX1V6Z52SP5J,F,HH,HL,HM,HH,HL,MH
+3125LZW47Y22,F,MH,MM,HM,LM,LL,MM,MH,LL,HM,MH,HL
+RA3X280O2C19,F,LL,LM,LH,ML,MH,LH
+E3KQPSTVUMAA,F,HM,ML,LL,MH,HL
+EDHPA594ZLGA,T,HH,MH,LM,HL,ML,MM,LL,LL,HL,ML,HL
+8447K6C6G983,F,HH,HH,ML
+M6LVPJ8B9GHW,F,HL,LM,HL,LM,MH,HL,LH,HL,HM,ML
+IHACW95BW3LX,F,LL,ML,HL,ML,LL,LL,LL,HM,MH,ML,MH
+76SCLIS5NMNC,F,LL,MM,MH,HL,HH,HL
+F6W5X1L5FUYY,F,HH,HL,LM,HM,HL,LM,HL,HM,ML,MM,HL
+TY3I5EB84BJ4,F,HH,LL,LH,LH,LM,LM
+YPNN2805570S,T,ML,LL,LL,ML,MH,HL,MM,HH,LM,HL,HL,HM
+8NL2I00VENDK,T,ML,HL,MH
+956L482DTMR9,F,MH,LH,ML,LM,MH
+25RUCLQ3VO20,F,ML,LH,HL,HM,HL,HM,LL
+6VLF4NKJH7H2,F,HL,HL,HL,HH,MM,HL,ML,MH,HL,HL,HL,LM
+13MDC3LR1KT3,F,MM,HL,ML,HL,HL,ML,HH,MM,HM,HH,ML
+5X15WI2899F5,F,LM,LM,HH,LL
+2146CVYQZV2K,F,ML,HL,LL,LL
+H57U2MQ50TC9,F,MH,LL,HL,ML,HH,HM,HL,LM,ML,LH
+GVLS515GCPZ4,F,ML,HL,HM
+I8W48N5R9F45,F,LL,HL,LM,HM,MM
+EB8XIK191D17,F,HH,HL,LH,LH,LL,HL,HL,MH,HH,HL,HH,HH
+ES00MHAQ1G09,F,HL,HM,ML,LH,MH,HL,MM,HM
+DT468DQRDO0C,T,HH,LH,HM,LH,LH,LM,LM,ML,HL,MH,LL,HM,LM,LH,LH,LM,LM,HH
+JLZS1KZCG662,F,HH,ML,HL,MH,HM,LH,HM,HM,HM
+7B10551KB361,F,ML,HL,HM,HL,HL,HL,HL,LM,ML,HH
+R6I6AOHBQ6LS,F,HL,HM,LH,HL,HL,HM,HM,HL
+67TFJ4TZMUT7,T,LH,LM,MH,LL
+UIV9LKH84235,T,HL,HL,MM,HM,HM,HH,MH,ML,MH,LL,LH
+30OCX5UJYF9S,F,LL,HL,HM,HL,HL,HM,HM,HL,LM,HL,HH
+1JOTQAANQMXT,F,MM,LL,LL,MH,HH,HM,LL,ML,HL
+2H0OWPYECAKT,F,HH,MH
+7W3K7GOGA111,F,LL,HM
+AH12RQ7RK09N,F,HL,HL,HH,HL,MH,HM,MH,LL
+KNZC30P1M9D8,F,HL,MM,MH,HL
+8YG0DF4M6PGL,T,MH,MH,MH,LL,LH,LM,LH,HH,HH,MH,MM,LM,LM,LH,LH,HM,LL,MH
+NEF825BV1W94,T,HH,LL,MM,MM,HL,HH,HL,ML,LM
+JH82Z659JO6A,F,MH,HL
+OVB1T7Y4X2BL,F,HL,LM,LM,HM,HM,HM,LL,ML,LM,HL,HL,ML
+436Z1XD0T87H,F,MM,HH,HL
+I3A8B8J0F058,F,LM,HL,ML,HH,HL,HL,ML,HL
+OLJV57EIAOGB,F,HL,LL,HL,ML,HL,HL,HL,MM,LH,HM
+AUQ1RD7DGF6Y,F,MM,ML,HL,LH,LL,LL,HL,HH,ML,HL
+4Y5AKYCK5R7C,F,MH,HL,HH
+JNV05PZJ5YO2,F,LL,HL,LL,LL,LL,MH,LM,HL
+QIW7XM3RH9J6,T,HL,LM,MH,HH,HM,HL,HM,ML,HL,HM
+UYS388QOO174,F,HL,ML,HH,HH,HH,HL,HH,MH,HL,ML,HM
+Y42QFG3CV97D,F,HL,HL
+88FF9BW76MS0,F,LL,HM,HL,ML,LM,HM,HM,HH
+1XQPJ3PQ7XY3,F,HL,ML,MM,HL,HL,LH,HH,LL,MM,LL,HL,LL
+P29RDJY06IM2,F,HL,LL,HM,LH,HM,MH,HL,HH,HL,LL,HM
+ZWY88E0ZAX4O,F,HL,LL,LL,HL,HL,LM,MM,HL,HH,LL
+LQ2675682V2N,F,LL,HM,ML,LM,HH,LM,HL,HM
+52RYL5S13HGD,F,HL,HL,HL,HM,HL,HM,HM,HM,HM,HM
+T3I244PN5R0F,F,HL,LL,ML,HL,LL,HL
+5FCT1IP8JWBA,T,HL,LL,LH,ML
+WZELXTQGGM4I,F,HM,MH,HM
+A0DQX43TSHKB,F,HL,HL,HM,HL,MH,HL,HL,HL,HH,HL,LM
+14305DYNMYGD,T,HM,ML,HL,LL,LL,HL,HM,LH
+UC9M9W4603ME,F,HL,HL,HL,ML,MH,HM
+9P3DZG65OC3R,F,LL,HL,LH,HH,HH
+TAQVBN8A4H37,F,HL,ML
+9XYD17YRXE68,F,ML,LL,HL
+22ZV4LDJWQQJ,T,MH,ML,HL,HL,HH,HM,LL,LL,HL,LM
+R1JAU01KA73X,T,HM,HM,HL
+RIT30509BYWP,T,HL,HH,HL,HH,LL,LM,ML,HH
+C51FPM3OEWK0,T,LM,LL,HH,LM,LL,MH,LH,LH,MM,LH,LH,HH,LM
+H3D0XLK6LH8C,F,ML,HM,HH,LM,HL,HM,ML,LL,ML,ML
+095R3WLL9E8D,T,MH,LM,LH,LM,LM,LH,MH,MM,MM,LL,LH,LL,LL,LH,HH,LH,LH,LM
+2AC3FU9DV25O,F,HM,HL,LH,MM,HL,ML,HM,HH,HM,MM,LL,HL
+RJFYY053G4J4,T,HL,LL,LL
+RC0M85Y0PD7B,F,HH,HL,MM,LL,LH,ML,HM
+94E9189LT26P,F,LL,ML,HM,MM,ML,HL,MM,LL
+IAXME86GD6RK,F,LM,HM
+5FV8KN6665V6,F,HL,HL,HM,MH,HL,HL,HL,ML,HL,LL
+4V408AIWPT2Q,F,HH,HH
+IB3INGD2L0EJ,F,LL,LH,HM,HH
+S09R35PA6A42,F,HH,HL,HL,MH,HL
+HWIDU8NY4X0J,F,HL,HM,LM,LL,ML,HL,HH,HM,HM,ML
+9M8Y2YR615OG,F,HL,LM,MH
+G11Y3Q47AHHB,F,HM,ML,HL,HL,MH,HL,HM,LM,HM,HL,HM
+FF49P05HV17C,F,HM,HM,HM,HL,ML,HM,ML,LL,HL,ML,LM,LL
+UJT134U75MHV,T,MH,LM,MH,LL,HH,MM,HH,HH,HH,MH
+UQW635W98S4K,T,MM,LL,HH,HL,HH
+T6G7XSNM1YCV,F,ML,HL,HM,HL,MM,HL
+96F5JGY58R2J,T,MM,ML,LL,LL,MM
+W5SEV822GYAD,T,HL,LL,LL,LL,ML,HL
+SC4E1ENX655T,F,LH,LL,HH,LH,HH,HH,HH,HL,LL,HM,HH,LL
+HV1C6874P081,F,MM,LL
+B2YCO118VXYW,F,MH,HM
+U73H3R99F9NN,T,LH,LL,LH,LM,LM,MM,MH,LM,LH
+I3X29FSEC89J,F,HL,LM,LH,MM,MM,HL,HM,HL,HM
+I5BSKPO39HT7,F,ML,HL,HH,LM,LL
+E1ZLU3G3C5F0,T,LH,LH
+2R82J0U41A2L,T,HL,LH,HL,ML,HL,LL
+K92WOC3SC83Z,F,HM,LL,HL,HL,HL,HM,ML,HH,MH
+CV8Z192369K8,F,MM,LL,HL
+OR0O8QI3HY9O,T,LL,HL,LH,LH,LH,MH,HH,HH,MH,HH,LL,LL,LL,MH,ML,LM,MM,LM,ML
+8HS1M3S2U35M,F,HL,HL,HL,HL,ML,LL,HL
+Y5Y3F08M2PNV,F,HH,HL,HL,LL,LL,ML,LL,HL,MH,HM,HL
+S1I2228G3DA0,F,HL,HL,HH,LL,HL,MM,HH,HM,LL,MM
+V9AIK5ZZ5352,F,MH,HM,ML,LL,MM,LL,LL,HL
+IPB57X5CMRPW,F,ML,HM,HM,ML,LL
+3KCK41WRUK2G,T,MH,LL,LH,MM,LH,LM,HM,ML
+Q2W9BJPNBNW8,F,LL,HM
+9D3E3104G384,T,HH,MH,LM,LH,LH,LM,LH,ML,LH,MM,MM,MH,HH,LH,MM,MH,HH,LM,LM,HH
+WZH1CTQKD95G,F,HH,MM,HL,HL,ML,LL,ML,HL,MM,ML,MH,MH
+9K6QYWZ0N8Z7,F,MM,LL,HL,LM
+S733L2X35R0F,F,MH,LH,LL,MH
+A0HIR806Y2XK,F,LH,HL,HM,MM,LL,HM,MM,MH,HL,HL,HL
+81NPYEH1N5I2,F,LL,MH,HM,LH,HL,ML,HM,HL
+50594P6ONJX3,F,HL,LL,HL,LL,ML,ML
+5D8O9NO4KPM7,F,ML,LL,MH,HH,LH
+W0T8GWE1G2C3,F,HL,HH,HL
+R332F6E7Z2E7,F,HH,ML,MM
+LRY35R3JU972,T,LH,HM,LL,HM,HH,HM,HL,HL,LL,MM,HM
+2IQJB1TVEX8Q,F,HL,ML,ML,MM,HH,HL,ML,MH,LH,HH
+K37UO034ARXJ,F,MM,ML
+3UPRU65J42P7,F,ML,LL,LL,LM,LH,HH,HH,MM,HM,HH,HL,HH
+I110YBP6VMA1,F,HH,MM,HL
+469UD3SID6S0,F,HL,HM,HM,HM,HL,LM,ML,LL,MM
+2JZG3T8M4MJL,F,LH,LL,LL,ML,HM,HL,LL
+F7WRL3W6088O,F,HM,HL,HM,LL,LH,LL,HH
+PLNKN0OXRFJV,F,HL,HL,HM,MM,MH
+PORPE9I46202,T,HH,HL,HL,HL,HM,HL,HM,LL,LM,LL,HL,HM
+J4N3KU5HBBTD,T,HL,HL,HL,HL,MM,LL,LL,MM,ML,ML
+V7NU174H5W6U,F,MM,ML,HM
+8FE930E6O3AY,F,ML,HL,HH,HM,HL,HL,LM,HH
+FST55G9QNO29,T,LH,HL,LL,HM,HL,HM,MH
+O3L37OKH95WH,F,HM,HL
+3PP5SZ9DJ2BZ,F,HL,MM,HL
+148823MCWCGT,T,HM,ML,ML,HH,LL,LH,LH,LM,LH,HH,LM,MH,LL,MH
+7V120S49PTG0,F,LM,HM,ML,HH,HL,HL,HL,HH
+159MWZ25NVOC,F,HM,HL,HL,HM,HH,HL,LH,MM,LM,MM,MM
+BW01FQV12RP4,F,HH,ML
+640IP2BHHR34,F,HL,HH,HH,HM,HL,LL,MM,HM
+B48822826KCE,F,HL,HL,HL,MH,LL,MM,ML
+CL922KA326YP,F,HL,HL,HM,HH,LM,HH,ML,HH,LL,HL,LL
+E5YM4040LYOS,F,HL,MH,LH,HH,HM,MM
+GU5RY7H302Q2,F,HL,LH,ML,ML,MH,LL,HL,HM,HL
+FZ5SFU287D7I,F,LH,HL,HL,HM
+QJ6J7F98EB4B,F,HH,ML,HL
+ZKK1PE395UGI,T,ML,HM,HL,HH,HM
+2V1E8C51H4YR,F,HM,HL,LL
+819446O2A2J2,F,HL,MM,HL,ML
+EBMY476882WR,F,LM,ML,HL,LM,HL,ML,ML,HM,MM,LL
+1M6LRUW0K91U,F,MM,LH,HM,HL,HL,LL,ML,HH,MM,HL,HL,HM
+F093WH50G41V,T,HL,HL,ML
+XC146FN6L3R3,F,LH,HM,HL,HL,LL,MM,LM,ML
+QDKH1MOR9D66,T,LH,LH,HH,LH,LL,LM,LH,MH,LM,MM,MH,HM,LH,HM,MM,LH,LM,LL,LH,MH
+888O3AVT6XJ2,F,HM,HH,MH,HM,HM
+AZ3DS5VFI570,F,LM,HM,HL,LL,HL,HL,HH,ML,HM,HM,HH
+2QIAFV1I2PT3,F,HL,HH,HL,ML,HM,LL,HL
+3F4892KVJ372,F,HL,HH,LL,HM,HH,HM,LL,LM,HL,HH
+L2POF1F66KMJ,T,MH,LL,LM,LH,LH,MH,LH,HM,MH,MH
+LY48N24R23BN,F,HL,HM,LM,HL,HL,HH,ML
+3GQJNFUACR44,F,HM,HH,HL
+1MUT10I0R24D,F,LM,LL,HL
+6BDFGXHUVMWQ,F,HH,MH,LM,HM,HL,HM
+A5R6SB7LW6L3,F,ML,MM,HL,HM,HL,MM,ML,HL
+7C3AI433MJI7,F,MM,HL,HH,HM,LL,HL
+JSUJI13PJEZV,T,HM,HM,LL,HM,ML,HM
+B01SYG68WAX2,F,MM,HL,HL,MH,HL,MH,HL,HH,HM,HH,ML
+29W2746572OO,F,ML,HM,MM,LM,ML,HM,ML,LL,HM,MM,MM,LL
+036K3I5AOBH7,F,LL,HH,HH,HL,HL,ML,HL,HM
+Y7PA67Q6W70P,F,HH,HL,ML,ML,LL,HM,HL
+9PW8QBX5E5LG,F,HM,HL,HH,ML,HM,HL,HL,HL,LL,HL,HH,HM
+LKMWB815WVSE,T,HH,ML,HL,HL,HL,HM,ML,LL,LH,HL
+8J089IG45VMB,F,HL,HM,HM,LM,LL,HL
+AMMUWK8WGCJ8,F,HH,ML,HL,HM,HL,HL
+D174Q0A05J61,F,HH,LM,HL,HL,LM,HL,HL,HL,LM,HL,HH
+Y1AH86FVX967,F,MM,MH,MM,LH,HL,HL,HM,HL,LH,LH,LH,LM
+Z64GDK8I7636,F,HH,HM,LL,MM,HM,LM,MM,ML,ML,HH,HM,HH
+W24OV3V4M54E,T,HH,MH,MH
+KAJR6KF6LOT6,F,LH,HM,HH,MM,LH
+ZA264WSION23,F,HM,MH,ML,ML,ML,HL,ML
+8V1E0EH69194,F,HL,HL,ML,MH,LL,MH
+WX73BK1Y2O78,F,LH,MH,HM,ML,ML,HL
+EBV1NWX33IZ4,F,MH,HM,LM,ML,HL,HH,HL,MH,LL,LM
+T1WP5TH8QM2M,F,HM,HL,ML,MH,HL,HH,HL,HM,HL,MH,HL
+AP6FMAOXED5F,F,LM,LL,MH,HM,LH,HH,HL,HH,LL,LL
+KG8BJZA4N2TZ,T,MM,HL
+C2WR1OH86K58,F,MH,MH,HM,LH,LM
+B819L2P7H8M5,F,HL,ML,ML,ML,ML
+DIUZML20R9L7,F,ML,LL,HM,HM,LM,HL,LL,MH,LM,HM,LL,HM
+J743BGNNZVN1,F,LL,HL,HL,HH,LL,HL,LL,MM,HL,MH
+PZJ3P56UB986,F,HL,MM,MH,HL,HL,HL
+L1FXQ437AFHP,T,HL,MM,ML,MM,HL,HL,ML,HH,HL,MH
+ZT109202PXOJ,F,MH,HM,HH,MM,ML,HL,ML,ML,HL,HL,LH
+85D11T0PXFYW,F,HL,HM,LL,LL,LH,HL
+8VY64559HD42,F,HL,MM
+BB8AS4Y3CR3N,F,LL,HM,HL,HH,HL,LM,HL,HH,HM,LL,ML
+9V32Z3O1UIVA,T,HH,LH,MM,MM,MH,MM,MH,LM,HL,LH,MH,HH,HH,MH,LL,LM
+8888QPOGNJ4C,F,HM,LL,MM
+5G8238W9HEBD,F,HH,LL,HL,LH
+H9Q8P89NP46R,F,LL,LL,HM,HL,HM
+ZLT95AJX058W,F,HL,HL,MM,MH,LH,LH,HL,HH,HL,HM,ML,HL
+3EVWRUHZY2K3,T,HM,HL,HH,HM,MM,HL,LL,MH,HM,HM,HH,HL
+1R7QKRSLM8K0,F,LL,HL,HM,MM
+OC25P8B29LP4,F,HM,MH,ML,HL,HL,LL,LL,ML
+W7642HEOWAD7,T,LL,HM,ML,HL
+793B6LKK3E9N,F,LM,ML
+PBOH62789653,F,HL,HL,LM,HM,HL,HL
+SLGRZQ81Z07F,F,MM,ML,HH,HL,ML,MM,HH,HL,MM,MM
+ECH13H37FC2J,T,LM,LH,HL
+OKSSD47I9L1J,F,LL,MH,LH,ML,HL
+4IB2LU0EZX0J,F,HL,MH,HM,HL,ML,LL,ML,HL,HM,HH
+JK710T8T11XT,F,HL,HM,MH,HL,HL,HM,LL,HM,HH
+VQA2KX2QCI3R,F,LM,HM,MM,HM
+J7YE79FMFDBB,F,LM,ML,ML,HM,HL,ML,ML,HL,MH
+9QG0ITM77Q0F,F,HH,MH,LL,HH,HL,LL
+BI42QB3U7W43,F,HM,MM,LL
+S9MN7F49OWAK,T,MH,HM,HL
+R90IJ6N3Y06M,T,HL,LH,HM,HL,HL,LL,HL,ML,LM,LM
+9TAW33H78TY0,F,HH,MM,LL,HH,HH,LH,HM,ML,ML,MM
+O74VGOAZC25Y,T,MM,HL,MM,ML,LM,HH,LL,HL,LM,LH
+O262J7Q94I58,F,HM,HM,ML,HL,LL,LM,ML,HL,HL,ML,MH,ML
+AKO4ZNEEKEML,F,LL,HL,HM,HL,HL,HM,HH,ML,HM,HH
+A4N5AH762UA3,F,HH,LH,LM,HL,HL,HL,HL,HM,LL,MH,HH,LL
+ELHJ74XX1187,F,MH,HM,ML,HL,HH,HH,HL,LH
+A0Z1JCG1F41I,F,HM,LM,ML,HL,HL,HL,HM,HL,HL
+OF00LWM0Q06C,F,ML,MH,HL,ML,HL,LH,ML,HL,ML
+E0KY757JSJ9N,F,HM,HL,HL,LL,ML,HH,LM,LL,HL
+B2Q1H69Q1MOC,F,HM,HM,HL,MM,ML,LM,HL,ML,LH,HL
+2SG7LXH0W7N8,F,HL,ML,ML,HM
+O50BPU5US1AA,F,HH,LH
+HE58T250M4KT,F,HH,HL,HL,HH,HL,MM,HH,MH,HL,LL,ML
+O9TI5O1PU9S9,F,HL,LM,HH,HL
+I0W38FIHYVJF,F,LL,ML,ML,MM,HL,HM,MM,HL,HM,HL,HL,ML
+P8VE2Z57LXV6,F,ML,LL,HL,HL,HL,HL,HL,MH,HL
+4JQWD63S7I87,F,HL,HM
+UA16X91Y8JZG,F,ML,HL,HH,HH,HL,LL,MM,HL
+8OP9R4YIK953,F,LL,HL
+1IU0EH9K0DX2,T,LH,LM,LH,HM,MM,MH,HM,LL,HH,LH,MH,LH,MH
+3O6194QV96LR,F,HL,MH,HL,HL,LL,HL
+XNBA3A27ASFB,F,HH,LM,HL,HM,HM
+HXS703619KQP,F,ML,LL
+X2M6GYB9B2I3,T,MH,LL,LH,HH,LH,HH,MH,HL,LM,MH,LH,MH,LH,LL,LL
+7YJ4LX4ZF0BB,T,HL,HL,HH
+D11CCV130207,F,HL,HL,HL,HM,LH,ML,LH,HL,HH
+0J13Q2OZ5IP4,F,MH,HL,HM,HM,LH,HL
+5VYK0A90GJ6V,F,MM,HH,LL,HL
+ZR2PG03CN6AK,F,MM,MM,HM,LM,MH,ML,HM,HM,ML,HL,HL,MM
+9PGPI8ZBJFIQ,T,LL,ML,HM,HM,LH,LH,LM,HL
+YW5H44O00R5B,F,ML,LL,LM,LL,HM,HL,MH,LL,HL,HL,ML,HL
+YA42Q3MDCG0E,T,LL,HL,LL,ML,LH,HL,LL
+T222938PUREH,T,HL,MH
+5DY1307XN4WD,F,HL,HL,LL,LL
+LW6I325H6464,F,MM,HH,HL,LL,HL,HL,HL,HH,LL,ML,HM
+EB23A7B91W82,F,HM,HL,MM,LL,HM,MH,MH,LL
+SZTX30F0440C,F,HL,MM,HH,HL,HL,HL,HL,LL,HH,LM,HM
+Z6SR877M0D75,T,MH,LM,LH,LH,HH,LL,LH,LM,HH
+V49PY9B9AV4F,F,MM,HL,ML,HL,HL,MM,LM,HL,LL,HL
+H2MOIO05L4EJ,F,MH,HL,ML,ML,HL,MM,ML,HH,LM
+44590GFJG617,F,HM,ML
+C1SV0K93641N,F,HL,ML,LL,HH,LH,MM,ML,HH,HL,HL,HL,ML
+WKKSM3JZ8MLB,T,HL,ML,HL,MM,HL,HL,ML,HM,HL
+F9D38027M3OE,T,HM,MH,HL,LL,LL,LM,HL,HL,HL,HM,LL,MH
+8FJ7N8LB2WDX,F,HH,MH,HH,HL
+35899TOQ4CT2,F,MH,LL,HL,HL,MM,HM
+1SYJGAI0ZP02,F,MM,HL,ML,ML,LM,HM,MM,MH,MH,LL,HL
+QM98W6Q2J290,F,LL,LH,HL,HL,HH,HL,HH,HL
+6Z2SS2VI3C43,F,HL,HL
+PJRM2VUUR1CB,F,HL,ML,ML,HL
+P0IQSTQVC1IT,F,MM,HM,HM,HM,ML,HM,HM
+8W5SIO44X454,F,HL,MM,HH,LL,HL,HM,MM,LL,HH
+841PLQ8NFPJV,T,LM,HH,LM,LM,HH,LH,LH,HH,HH,LL,HH,LH,HL,LH,MH,LM
+PD9GR00VAR76,F,HL,HM,HL,HH,HL,LH
+H1GAOD95MM45,F,HL,HM,LL,MH,HM,ML,HL,HL,HH
+MRV0R0N7F808,F,HM,LL,HL,MM,HM,LM,HH
+85IFB1TM9VRY,F,LL,HM,LH,HH,HL,HL,HM
+5YNROKJJ5891,F,MH,ML,ML,HL,HL,HL,HH,HM,LM,MH,HL,MH
+9DDO3EW6516Y,F,HH,LL,LL,MH
+U4HE7HFSF783,T,HH,LH,LL,HM,HL,LH,LH,LH,LH,MH,LL,MH,LM,LH,LH,HM,HM,LH,HL,LL
+CG3N3U3RRK3T,T,MH,HH,LL,MM,HH,LH,MM,MH
+B7CI8H9S09K8,F,LL,HL,LL,HH,ML,LL,HM,MH,LH
+0Y4P75K6OFW3,F,HL,HL,HL,HL,MH,LL,HM,MM,HL,LM,MM,HL
+B0Z59TXHD9P0,F,HL,HL,HM
+L7EVPJM40JY5,F,LM,HM,LL,HH,HL,MM,ML,MH,HL,LL,HL,HH
+QB3J88G31VLO,F,LL,HH,LM
+OUBYY469W2IX,F,HL,HH,HM,HM,HM
+K249VY1EOR0C,F,HL,HL,HH,ML,ML,HH,HL
+NI2W911RBHG9,T,MH,ML,MM,LL,ML,LL,MH,LM
+A6UUFG3Q86FJ,F,LL,LM,HL,LH
+IR4898A4EKKP,F,HM,HL,HL,HL
+2R6297QUZ263,F,HL,HM,LM,HL,HL,HL,MH,HM,HL,ML
+48379M6LXVIP,T,MH,LM,HM,LL,ML,ML,HL
+E7K50DVHH1P1,F,HL,LM,ML,LL,HL,HH,MH,LL,HL,HH,LM
+3J64HMG41L3G,F,LH,HM
+C1BT390A7Z00,F,LH,HL,LL,LH,HM,LM,MH,ML,HL,LM
+CSCC5GF0AMYX,F,HH,HL,HL,HL,HL,LM,LL,HL,HM
+6V0FT7W1VHQS,F,HL,ML,ML,HM,HL,HH
+NQ874MIWQN68,F,MM,HL,LL,HL,HL,HM,HM,HM,HM
+H8VJFG329SV6,F,MH,LL,LL,HL,HL,HH,HH,LH,HM
+RM2X7XH09H35,T,HL,MM,ML,HL,LM
+27T6WQ0SO50B,F,HM,LL,HL,HL,LH,HL,LL,MH,HL,MH,HL
+1JWI39OP469W,F,ML,HL,LL,LL,ML,HL,HL,LH,HL
+97R2PRKLR72K,F,LL,ML,HL,HL,LL,LL,MH,HL,LH,LL
+R34VEL2GWY29,F,HH,HM,HL,ML,MM,HL,HH,HL,LM,LH
+J94839807GKZ,F,MH,LM,HL,LL,HL,HH,LL,HL,HM,HM
+BL0U0873P25D,F,HL,HH,LL,ML,HL,MM,HM,HL,HL,HH,HM,LH
+PHC125W1BL5L,F,HM,HL,HM,HL,ML,HL,LL,HL,HL,HL
+4DD6B417ZF8U,F,HL,HL,HL,HH,ML,HL,HL,HL,HL
+D6XT49C1S2K9,F,HL,HM,MH,LM,MM
+8NVM85DHHIZW,F,LL,HM,ML
+07DG236C723H,F,HH,HM,HL,ML,LM,HH,HM,LH,HM,MM,HM
+BY715OR9487Y,T,MM,HH,ML,LH,MH,LL,MH,LL
+3FVHT8P1AT0I,F,HM,MH,HH,HL,HH,HM,HL,HL,HM,MH,HL,ML
+Y97874JWB0KR,F,HL,HL,HH,LM,LM,HM,MM,MM,HL
+JW6BK3SR4IEF,T,MM,HH,HL,MH,HL
+25UP6FU0O25J,F,MH,MM,HH,HL,HM,MH,HH,HL,LH,MM
+OR2ZU3HV3XS8,F,HL,ML,MM,HL,ML
+3YBUZ1LEPHT2,F,HH,HL,HL,ML,HL,HL,HL,HL,HM,HL,HL,LL
+FFUW9HWG54UR,F,HL,HL,HM,ML,MM,LL,HM,HH,HL,LM,HH
+1PRQ135U9281,F,ML,HH,LL
+59E6YI2LT121,F,HL,MM,MM,LL,HL,LL
+XBWT9FPTJPRF,F,MM,HM,HH,HM,HL
+YND422JHMZP3,F,HH,HM
+JEKB7MZB2E60,T,LL,MH,MH,HH,MH,HH,HH,HM,HL
+B4IGX8V7S3J4,F,LL,HL,LL,LL,ML
+13XV5K1NGY9T,T,HM,HH,HM,LL,LM,HH,HL,LL,HH
+U747X4PSQ9OI,F,HH,LM,HM,ML,LH,HL,LL,MH,ML,LM,LM,HL
+FYB89GAD90MO,T,LH,MH,LH,HL,MM,MM,LH
+UF1Z1MI1LL63,T,HH,ML,LH,HM,LL,HL,HL,HL,HM
+2JHOBC0702Q3,F,HM,ML,MM,ML
+EGYCSY29L2C0,F,MM,HM,HM,ML,HL,HL,HL,MH
+O4836V62J5WO,F,ML,HL,LL,HL,LH,HL,HL,HM,HL,MM,HH
+2ZSQYH4B8175,F,LH,LL,LH,HM,HH,MM
+XNQ28KP0L512,F,MM,HM,HM,HL
+0OQ4A3F5XZC7,T,LH,LL,ML,MM,HH,ML,HM
+NJ3QS73Z8KP6,F,ML,LM,HM,ML,HL,HM,LM,HH,MH,LL,ML
+TK58V0CYUXT7,F,LH,MH,LH,HM,HH,ML,LM,LM,HL,LH,MH
+E09NGCN8ODHO,T,HM,HL,HL,ML,HH,LL,HL,HL,HH,HL,LM
+T0DU8VF7YDNU,T,LH,HH,LM,LH,MM,LH,MH,LH,LL,ML,LH,LH,LH,LH
+O4W7RCWO1BTM,F,HM,LH
+7TNU1682Q3C7,F,HH,LL,HL,MM,ML,HH,ML
+P65HS4PHEJPK,F,MM,ML,MM,HL,ML,HM,HM,LL,HL,LH,LL,LH
+2BL8G6MZXRPW,F,HL,HL,LL
+X03EDPHNG139,T,HL,ML,HM
+766GI2M1ENHS,F,LM,HL,MH,HL,HM
+CKWG8E29VDA8,F,HH,HM,LL,HL,HH,HM,ML,LH
+536E8O1PLZG8,F,HM,MM,HH,ML,HL,HL,LM,ML,LM
+TQ0F3SG2YYJ3,F,HM,HL
+KMVAYYV8S19Q,F,MM,MH,MM,HM,HM,HL,LM,ML
+IFE7Y78LMH2Q,F,HL,LL,HH,HM,HL,ML,HL,HL
+7488FY5W7M0B,T,HH,LM,HH
+VG0H6C4S7UZM,F,MH,LL,LH,LL,LL,LM,HL,LH
+0373NDJ0K59O,F,HL,HM
+AZ587FWM1936,F,MM,HM,HL,LL
+UX8YGOMD7N18,F,HL,HL,ML,HL,HH,HM
+678039A346WH,F,MM,HH,HH,HH,HL,MM,HH,ML,ML
+S86TJKN4262S,F,HL,ML,HL,LM,ML
+O49NX9JRUDAS,T,LH,LH,LH,HL
+M331QB3FV39H,F,ML,HM,MH,MM,HL,ML,ML
+7M7A2S5QQ3LM,F,ML,MM,LH,MH,LH,HL,HL,HL
+HJB4PM449N80,F,ML,HH,HM,HL,LM,ML,HL,HL
+387J1825I21Y,F,HH,HM,HL,LL,HM,HL,MH
+IW1L92OB6LMU,F,HL,HH,HM,MM,LL,LL,HH,MM
+0MGM82AJXZ4C,F,HL,HL,HM,HL,HM
+6HV6BWZG35G3,F,HM,MH,HM,HL,HM
+I7ED9DU71A93,F,HM,HL,HH
+T1KT55G6S5QQ,F,ML,LL,HL,HL
+7KIA2M630ZAK,T,HL,MM,HL,HL,MM,MH
+4H8Q5798YRFC,T,LH,LH,LH,HH,MH,MH,LH,LH,MH,MH,LM,HL
+2800FCP95Q9N,F,MH,HL,MH,HH,LL,LL,ML,HH,HM,HL,HH,LH
+EA90407W814E,T,MM,HL,LM,MH,ML,MH,HL,HL,LL
+QR2C22W3V8Y9,T,LH,MH,MM,ML,HH,HL,ML,HM,HL,HL
+ULIR228XESOC,F,HL,ML,HL,HM,ML,ML
+6S7I9OUA8678,F,ML,MM,MM,MM,LH,HH,LH,HH
+H5O3K5USL3G0,F,MM,LM,LM,LH,ML,ML
+GLV3H822C8HM,F,MM,HL,HH,MM,ML,MM,LL,HH,ML,HM,LL
+CMGXSV59LKNB,F,MM,HH,LH,LL
+AN9L3K306756,T,HM,HM,HM,HL
+CT8GW1X2RTX0,F,LH,LL,HM
+LD3TBJ4BUFIV,F,LL,HL,HH,HM,HH,HM,MH,HL,ML
+TY5U469F6TES,F,HH,LL,LL,HL,LL,HH
+MTO3399BBWON,F,HL,HH,HL,HL,LM,HL,HH,HL,LH
+9JNVRDWP671N,F,HL,HL,LL
+EK9VJ72H8951,F,ML,MM,HM,LL
+VC6T40P8H0F5,F,HL,HL,HL,HH,ML
+6KE3QKO1CP22,F,HM,ML,LM,HL,HL
+MPRI1GSFVR7T,F,HM,HH,HM,HM,ML
+258APGT3AFJG,F,HM,LL,HL,HL
+M2C60AK80182,T,MM,HH,MH,LH,HL,HL,HL,ML,HH
+TUTDH5WZ38I4,F,LL,LL,HL,ML,HL,LL,ML,HL,ML,HM,HL
+O944M4TT06QO,F,ML,HM,HL,HL,LM,HH,ML
+9LG76BSVCMF7,F,MM,HM,LH,MH,LL,HH,MM,HL,LM,LL,HM
+X36ISHGNUU6E,F,HM,ML,HH
+V19C3JBU5N1X,F,HL,LH,MH,HH,HM,HL,HH,LL,HL,HL,HL
+UTY69R3P95ES,T,HL,ML,LM
+SCNY38CFY3U5,F,HH,HL,HL,HM,LL,HH
+71X0TI745R1W,F,ML,LL,HH,HL,MM,MH
+UXPV49367M8G,F,HH,HL,MM,MM,ML,LL,HH,HH
+5UM1ZZ9461RC,T,HM,HM,HL,ML,LL,LL,HL,HM,HM,ML,HL
+7Y7DX9LW9KX0,F,HH,LM,LM,HH,HH,HM,MH,ML,HM
+622R96HS20S1,F,ML,HH,ML,HM,HM,HH,HM,LL,HL,LM,HM
+39MP023RZ92U,F,HL,ML,HL,LL,HM,HL,HL,HM,HL,HL
+TY58JG959G97,F,LL,LM
+1WR7UJOSZED6,T,HH,HL,ML,LH,LL,HL,LL,MM
+Z9Q0C4VA8BXE,F,ML,HM,HL,ML,HH,HL,ML,HH,HM,LL
+ITDFORO53520,T,MH,LL,LL,LH,MH
+VQVR5XO889X0,F,HH,HL,HL,LL,HM,HL,MM,HL
+3F5MT34ZK853,F,HM,LL,ML,ML,HL,LL,HL,HH,LL,HM,LM
+MN343G5OPT5X,F,HL,HL,HL,ML,HL,ML,HM,HL,HL,HL,HL,HL
+U575HX7VT8WQ,F,HH,HH,HL,LH
+AWH0C20TELFK,F,ML,LL,HM,HL,HH,HL,LL,HL,HM,ML,MH
+9WMZ58EAKJ77,F,HM,HL
+12NCXGRJWDJ5,F,HH,LL,HH,HM,MH,HL,MM,LL,MM,LM,HL
+O1OZM842M896,F,HH,LL
+2TI3B285ND17,T,HL,HH,HL,LM,MH
+S1AE7Y76G284,F,HH,MH,HM,ML,HH,LL,HH,LM
+O6V0VM1FQ4HH,F,ML,HL,LH,HL,LM,HM,HL,HM
+D365X5QN7G9P,F,LL,HM,ML,HH,HM,ML,HM,HL,HH,LH,LL
+23VMS5AUF5NW,F,HL,LM,LL,LM,HH
+T7ANICFQHLYJ,F,HM,MH,HL,HL,ML,ML,ML,LH,HL
+8CV7V9CJXZ66,F,HL,LH,MH,HH,LM
+YDG1XL1M96C5,F,HL,HH,HL,LL,MM,HL,MM,LL,HL,ML
+XE9NDX8AE0LN,F,LM,HL,LL,LL,HM,HL,LL
+68BCEC66625J,T,LL,MH,HL,HL,HL,HM,HM,LL,HL,HL,HL
+Z1EK5BQM8PTB,T,LM,LM,LH,LM,MH
+9A4IDZ34L08X,F,MM,HL,HH
+XQ5DWKQOBE2D,F,HL,HM,HL,HH,HM,HH,MH,LL,HM,HL
+82K97ZSSVTFY,F,HL,ML,MM
+E6UPZXPU34E2,T,HL,HH,ML,HL,HH,HL,HM,ML
+0CQ99526HF5X,F,HL,LH,LL,HL,MH,ML,HM,ML,HL,MM,HM
+TXHD39A6C56D,F,HL,HL,ML,HL
+HCR4B1BBB22M,F,HL,HL,HH,LL,HL,HL
+P86VOKQBZ3W0,T,LH,LH
+PCMVGBX1SP50,F,HL,LL,HL,LL,MH,HL,LL,HL,ML,ML,MH
+CH12P5554LD6,F,LL,LL,LM,LL,HM,HH,LH,HM
+E9YEE274T46X,F,HM,HL,HL,HL,ML,HL,HH,HM,HL,MM,ML
+5GWFFH4GM8P4,F,HM,ML
+53Z853WG4G2U,F,MM,HM,ML,HL,ML
+9GT94XP5SP39,F,HL,HL,HM,LM,HL,HL
+FXAYJ0H971V0,F,MM,HL,HL,ML,MM,HH,HL,MH,HL,LL
+M02FMHTP491A,F,HH,HL,HL,LL,ML,HM,HM,MM,HL,ML,LH,HL
+O64U281ZJK8X,F,LL,HL
+90HGWC324593,F,LL,HM,LH,MM,MH
+6C54PBJYEKIB,F,LL,HL,HH
+4M7SOH8746YA,T,HM,LH,MH
+GNQ7OW245XFI,F,LH,HL,HL,HH,HM,ML,ML,LL,LL,ML,ML
+6X2SNN48D5P1,F,HM,LL,HL,HH
+NTFDQKU6TRY9,T,MH,LH,LH
+X6K292QB3RGN,F,HL,MM
+268F7DTMIDKJ,T,MM,HL,HL,HM,LL,HL,HL
+VT35Q89B74MT,F,MH,HM,ML,HL,LH
+T328CI1X913L,F,HL,ML
+3BPD15RBE50R,F,LM,HH,MM
+H7BH4FCMO7VI,T,MM,LH,HL,HL,HM,HM
+O4T856CR48QG,F,MM,ML,LH,LM,ML,HL,LH,HM,HL,HH,HL,MM
+R96IV46XXB20,F,HL,HH,HL,HL,LL,ML,HM,LH,HM
+B4AS044OLV50,F,HM,HH,HL,MH,LL,HM,HL
+IT19IPO77N35,F,LM,HM,HM,LM,HM,HH,HM
+K0EAYQL57B7A,T,LL,LH,MH,LH,LM,ML,LL,MM,LH,LL,MM,MH,HH,LH,MH
+98AYD64DW8I9,F,ML,HM,LM,ML,HL,ML,MM,ML,HM,HL,HM,HL
+7HGSNLM6OWBX,F,HM,HL,ML,LH,LL,MM,HL,HM,HL,ML,HH,ML
+K23T8497O35F,F,HL,HL,LH,LH,ML,HL,ML,HL,HL,HM,HL,ML
+Y69LM12ZO73I,F,HL,HL,HL,LL,HL,MM
+1BCJ322SO33R,F,ML,HL,ML
+TU9H3MV6NH4X,F,HH,LL,HM,HL,LL,LM,HM,ML,HL,HL,HM
+0P4S6LH53NX3,F,HL,HL,ML,HM,HL,MM,HH,HL,HL
+5L3R0W4NT4U3,F,MM,MH,HM,HL,LL,LH,HM,LM,HL,HL,HM
+XLKXSRN3CQ60,F,HL,MH
+6T7T0N7YDD56,T,ML,MH
+NRYQRTUR7UXI,F,ML,HM
+B191GF5W73QE,F,HM,HL,LL,HM,LL,HM,HM,HL,MM,HH
+K1675U7RO1L7,F,HH,LM,ML,MH,LH,HM
+A13GG6896IG3,F,ML,ML
+81PU3R2O87C0,T,LM,HH,LH,LM,MM,LL,LM,HH,ML,LH,LH,LM,MH,MH,LL
+4A6VBCGV1AQ0,F,HL,LL,HH,LH,HL,HL,HL,HM,HL,HL,ML,LM
+7B04OU07YZ41,T,HL,LL,HL,LM,ML,LL
+LKM606SWH1SB,T,MM,MM,HH,LH,LH,LM,LL,LM,MH
+1K9W59QYL0OA,F,LM,HH,HL,ML
+TAJ85Q6G1T77,F,MM,MH,HL
+Q80Y8NL1VR6T,F,LM,LL
+MLGD58660NVT,F,LM,HL,LL,ML,HL,LL,ML,ML,MM,MM
+H414WG06AFQ7,T,HL,HH,HM,ML,HL,LM,HL
+SKS7874E4MT8,F,HL,HL,MH,HM
+GC78D2KZZ339,F,HL,HL,HL,HL,HL
+56W47XANIR9S,F,HM,HL,LH,MM,HM,HL
+BLJ2S7P3KC5J,F,HL,LL,ML,HH,HL,HH,HL,LH
+26E28R2L8ZFX,F,HL,MM,HM,HL,ML,HH,LL,LL,ML
+LG6WU90N036S,T,MM,HM,HM,LH,LL,HL,HL,HL,LL,HL
+85EW93FB9X2Q,F,LM,MH,HM,LL,HH,MH,MH
+E7QL50EC9O9A,F,HL,MM,LH,HL,LL,HH
+87D7M1KT6QQU,T,LL,LM,LM,LH,MH,LH,MM,LH,MH,MH,MM,LH,MH
+QN698ZL60TS3,F,HM,ML,ML,LL,HL,LM,LL,HL,HH
+7I69NNQNX1Z8,F,HL,LL,ML,HM,MM
+Y3226E051CNL,T,MM,LM,HH,HH,MM,HM,HH,HH,HM,ML,LH,LH,MH,HH,LH,LL,HH,LL
+H7OWV0N669D8,F,HL,LH,MM,HL,HL,ML
+9S88HZYN8QH9,F,HM,LM,ML,HH,LM,LL,MM,MM
+O6L367N0131F,T,LM,LH,LH,LL,LM,LH,LH,LH,MM
+7W2V0LWG88OZ,F,HL,MH,ML,ML,ML,HH,MH,HH,LL,HL,LL,MM
+YF332PP10YU1,F,MM,HL,ML,HL,HL,HL,HM,HH,HM,HL
+VIRGWGJ8MI2O,T,HH,LL,HL,HM,MH
+WTMRB18P5QIB,F,ML,ML,LL,LL,HM,HH,HH,LL,ML,MM
+8Q7K0TSPDOT1,F,HL,MM,HL,MM,ML,HL,LL,HL
+D5K748D93NOV,F,HL,HM,HM,LM
+1Q32981XB3GS,F,HM,HL,HL,HL,HM,LH,HL,HM,ML
+560MF631B3PU,F,HL,HH,ML,MH,HM,LL,HL,HL,HL,LH
+CKQVZ90N87ED,T,HH,HL,HM,ML,LL,HL,HH,ML,MH,HH,HL
+LRI33N98UFSC,F,HL,HH,HH,LM,HL,MH,LM,HH,HL,ML,MM
+13837VI8G28Q,F,HH,MH,LM,ML,MM,LH,LL,LL
+TFGBWRD2KEEJ,F,MH,ML,ML,HL
+YKUGY6MZ179I,F,ML,HH,LL,MM,HH,ML,HL
+539SS59I22V5,F,HL,HM,HM,ML,HL,HL,HL,ML,HL
+CHF9VH7648MX,F,LL,MH,ML,HL,LL,HL,LL,HH,HL,MH,HH
+L5EJIP437ZXL,F,HM,LH,HH,HH,LM,MM,ML,MH,LL,MH,LL
+MDB0OLY6TYR6,F,MH,ML,ML,MM,HM,HL,HL,LL
+QD7M2R1739ME,F,HH,HM,HH,ML,LM
+ZHQ972R264UU,F,MM,HH,MM,LL,LM,HM,HL,HL,ML
+W1W8RCY9A7ZN,F,LL,HM,HL,HL,MH,HL,HM,HH,HL,HM,ML
+G0TT32SAG704,T,ML,HL
+FY6P40VF374F,T,LL,MH,LL,LH,LH,MH,LM,LL,HM,MH,LH,MH,LH,HH
+0WR6KTMD17SO,F,HL,LM,ML,LL,HL,LH,MM,HH,LH,LM
+45ER8VGAX940,F,HL,HL,HL,HH,HM,LH,HM
+DC482I056K4K,F,HM,HL,HL
+CSKOUCDZJFWU,F,LL,HL,HM,LL,HL,LH
+5TVITL07B873,F,LL,LM,HM,ML,HL,LL,HL,LL,HM
+EV78X4BSY85N,T,LL,HH,ML,LH,MM,HH,MH,ML,LM,LM,LM,LH,MH,LM,LM,MH,LM,LH,LM,LH
+ROW002BDNAKJ,F,LL,HL,ML,HL,HM,ML,HM
+EQWR1F83564A,F,ML,HL,MM,HH,HM,HL
+Z9K8X45656V5,F,HM,ML,LL,LL,HL,HL
+VU7823H5RLTI,F,ML,HL,MH,MM,LL,HL,MH,HM,HM
+402C9G26IZ6Q,F,MH,HL,MM,ML
+I8P0FX1WDYE8,F,ML,LM,HH,HH,HL,MM,HL,HM
+WSH5E7Q7L9OT,F,ML,HL,LM,HM
+83NZ1R633X75,F,HL,LL
+I4BS42B715R6,T,LM,ML,LL,HL,HL,MM,LH
+RW9Z6T1T4UBI,F,ML,HL
+X44G40P8K012,T,LM,MH,HM,HL,MH,LH,MH,ML,HL,LH,LH,LH,LH
+E1C0V8L6IW75,F,LH,HL,HM,LL,LL,HL
+T0VY9W5MBI55,F,HL,LL,LH,HM,HM,HH
+Y570ZLWRVO82,F,HL,LH,HL,HM,HL,HL,HM,MM,ML,LM,LL
+7GZODJ68522H,F,LL,LH,HL,HH,HL
+VQ2V573W2GQD,F,HL,LH
+07IE0F0QFTF4,F,HL,HL,HL,HM,LH,HL,HL
+DLTEGA7A4DA0,F,LM,HH,ML,ML,MM,LH,HL,ML,HL
+0P135ELYG208,F,ML,ML
+ADAB506MV71A,F,ML,MH,MH,LL,HM,ML,HL,LL,HL,HL,ML,LL
+72CAA93FNTLL,F,ML,MH,HM,LL,MM,HL,MM,ML
+2J538Q7JHOS8,F,HM,HH,ML,ML,HL,MM,ML,ML,HL,ML,HM,HH
+C07T1MPRN987,F,LL,HL,MM,LL,LH,HL,LL,LL,HL,ML,HL,ML
+LSFILC958R3X,F,LM,ML,LH,HL,HM,LM,HH,HM,HH
+UKF6OBXUS35F,F,HL,HL,LL
+WYJQEV867U1M,F,ML,HH,LH,HL,LL,MH,HL,HL,ML,HH
+P37RNROA10L3,F,ML,HH,HL,LH,LL,MM,LL,HL,HM,LH,HL
+HQ7K1VL4VDCC,F,LH,HH
+RYQ25C4EXTDQ,F,HH,ML,MM,HL,LH,HH
+079T3B91NCIG,F,HH,HM,HL,HL,ML,HH,HL,HM,HL,HM,ML
+V3AX8Q5OKC89,F,ML,MH
+QW19BL8NMU9L,F,LH,LL
+H50X32YKU5N0,F,HM,LH,HH,HL,HL,HM
+89K758FAGU6V,F,LL,HL,HH,HM,LL,MM,LM,HH,HM
+2W7TO4MR31Q4,F,HL,HM,HH,HL,HH,HH,ML,HM,MM,MM
+1PC9E467Q3JK,F,HM,HH,MM,HM,HM,LL,ML,ML,MH,HL
+5Y4O417D75J6,F,MH,LM,LL,LH,HM,HH,MM,LL,LL,LH,HH,LM
+96R2XNU6R37C,T,LH,LL
+BCLGS42G4V4T,F,HM,MH,HL,HL,HL,LL
+O2N4IKSH3FHZ,F,ML,ML,HL,LM
+VAGLTZGRJTIN,F,HM,HH,HH,LL,HL,ML,HL,ML,HH,HL,LL,HM
+441W2S5D429O,F,MM,LL,MM,HM,HL,HL,HH,LM
+Q7LIK381P8Z9,F,HL,LL,LL,MM,ML,LL,HH,HL,HH,LL
+DO7V69I32S4L,T,LH,ML,MH,LL,HL,HH,ML
+2CX4NM6F9MF9,T,LH,LH,HM,MH,MM,LH
+DNGWVFQVZQBH,F,HH,LH,HL,HL,HL,LL,HH,MM,LL,HH,HM,MH
+Z9FH7UK3G0CV,F,MM,ML,HL,MH,HL,LH,MM
+02BWA104WVQ4,T,LL,LM,LM,MH,LH
+4VJ8OR2KPM11,T,LH,LM,LH,LH,LH,LH,LH,LM,LL,LM,MM,LM,LH
+5K8NFV0RTD9K,F,HL,HL,HL,MM,LL,HM,ML,HM,HL,HH,HM,HL
+63HAJ5Z3QLK7,F,LL,ML,HL,LH,MM
+M17JU3V91K1B,T,LL,HH,MH,HL,LH,LL,HM,HH
+AHXI666CM03W,F,MH,LH,MH,HL,HL,HL,LL,MM,HM
+5Q60552CHJMI,F,MM,MM,ML,LL
+L5B9S5RXSZ4K,F,LL,HM,HL,HM,LH,HL,HL,ML,LM,ML,HM,HM
+TO6X975XATCL,F,HM,HL,ML,LH,LM
+M34B48C9302O,T,HL,HH,LL,HM,HM,HH,ML
+2N7EOY0A89QA,F,HM,HM,HL
+TEP0Q41H1KFG,F,HL,HM,HL,ML
+4RJ2Y0XQ72E8,F,HL,HL,MH,LL
+AVS182DEYH89,F,ML,LM,MH,HL,ML,LH,HL,HL
+L9FQ3I36N72I,F,HM,ML
+TJL4V0R27R3L,F,HL,HL,HL,MH,HL,HH,HM,MH,HM
+FO0HARNR1QSL,F,HH,LM
+BQT3OZ631Q18,F,MM,HL,ML,MM
+DL1APYIFAZN5,F,MM,LL,MM,HM,MH,MH,HL,ML,LM,HL,ML,HM
+E3DGL2CCU7QZ,F,HH,MM,HM,LL,ML,HH,HH
+YPDO5PKH0M4G,F,ML,HL,HM,ML,HL,ML,HL,MM,HL,ML,HL
+E0H1R40IC431,F,MM,HM,LL,HM,HH,HL,HL,MM,ML,LL,HH
+T02J6Z214KWO,F,LL,LH,HL,HH,MM,LM,ML,HH,HL
+JDEW41795S72,T,LH,LH,LH,HL,LH,ML,LM,LH,ML,LM,LH,MM
+W8TL9CWR38H0,F,HM,LM,LL
+17VD606H4ZN1,F,HM,HL,HM
+MJLQOL9ZN2DU,F,MH,LL,MM,LL,MH,HL,MH,HH
+289ARPF97B7G,F,HL,HL,HH,HL,HH
+4N18P9KD23AA,F,LH,HH,HH
+UD3Y19WU05FJ,F,HL,HL,HH,HM,LL,ML,HM,HH,LM,LL,HL,HL
+8U104564V1OG,F,MM,HL,LL,HM,HM,ML,HL,HL,ML,HL,HL,HL
+HNPI0NG1UC6S,F,LL,ML,HH,HL,MM,HM
+TU3UDFQKYY30,F,ML,ML,HL,HH,ML,LH,HH,ML
+IIQL0D1ZO507,F,HL,HL,HL,MM,HM,HM,ML,HL
+8YR038E6S2Y9,F,HM,ML,LM,LL,ML,HL,HL,HH,HL,LL,LL
+308VT8033KWG,F,ML,HL,HL,HM,HL,HH,LM
+3EE014D4SC3V,F,ML,HL,HH,HL,HL,LM,HH,HM,HL,HL,HL
+LQT33IBUTY5E,F,HM,LH,HL,HM
+PUY8TOLHZBHT,T,HM,HH,HM,HM,LL,HM,MM,LM
+92PNLX9BJ949,F,LL,ML,LL,HL,MM,ML,HL,LH,ML
+08L19X1L1PUM,F,MM,LM,MH,HL,HL,HL,HH,HH,ML
+L10YA0S19MN1,F,LL,HM,HL,HM,HH
+HG97IHOPMLJ0,F,LL,HL,HM,HL,HH,HM
+1X08OY35C032,T,MH,MM,ML,LM
+N6CZ4FA87I01,T,MH,ML,MH,MM,LM,LM,LM,LL,LH,LL,LH,LH,LH,LM,LM,LL
+T92VU83VD680,F,HM,HM,ML,MM,HL,HH,MM,HM,MH,HL
+Y2F6AC3R83SH,F,HM,LH,LL,HL,HM
+GL7M00TYN662,F,LL,HM
+197PSCX98V0H,F,LL,HH,ML,HL,HM,ML,LH,HM,HL,HL,HL,HH
+ZM79362V27ZR,F,ML,LM,HL,HH,HH,LH,HL,HL,HL,LH,HL
+9019ZN73B6EE,F,ML,LM,HL,HL,HL
+RO0Q5J8U1PU3,F,HL,LL,HH,LL,ML,ML,LM,HL,LM
+KNQFGM505063,F,MM,LL,HL,LM,ML,ML,HM
+60DJHUZ23C40,F,LH,HL,ML,HL,LH,HL,HM,LH,HM,LL
+ZFG8F9CEK53Y,F,LL,ML,HH,HM,HM,LH,HL,HL,LL,MM
+SY13A004T042,F,MH,LL,MM,MM,LM,LL,ML,HM,MH,MH
+D9LY3ZB4AY8B,F,ML,ML,ML,LL,HM
+775WP9716LK9,F,HM,HL,HM,HH,HL,ML,HL,MM,LM,ML
+AL7RVGGK5W2Q,T,LH,MH,LH,HL,HM,LH,LM
+AS1B5B8R565B,F,HL,HL,MH,HM,HH,HM,MH
+31LFT80C4854,F,ML,MH,ML,HH,LM,LM
+32552G3N91Q3,F,HL,HM,ML,LL,HH,ML
+73HOJFWM1DEI,T,LH,LH,LH,LH,MH,LH,LL,ML,LL,HH,MM,HH,HH,MM
+T1QRO242LWNW,F,LH,LL,HH,HM,LL,LL
+9RKME67360LS,F,HL,LH,HL,HL,ML,LL,HM,HL
+V12SY88KTUR4,F,HH,LL,MH,MH,ML,HM,LL,MM,HM,LH,HH,HL
+25P4H3UR1B78,F,MH,ML,ML
+SJV8K83JM6U6,F,HL,LL,HM,ML,MH,ML,HL,HL,ML,LL,HL,HH
+126NQ0414BCU,F,ML,HL,ML,HM,ML,HL,HH,HM,LH,LL,HL,ML
+N7UDND4RP3LT,F,LL,HL,ML,HL
+6V3CE9G73I0R,F,ML,HM,HM,HL,HM,ML,HM,HM,HL
+94TN69N3488T,F,HM,HL
+HIV8E3NTTL75,F,HL,HH,LH,ML,ML,HH,MH,LL
+5E31248J0IMF,F,HL,HL,HL,ML,MH,HH
+K9FVAFOKJA1U,F,HL,ML,LL,HH,HM,HM
+85740U3PS1XK,F,HM,HL,HL
+X5IOT4X04G04,F,HL,HM,HL,ML,ML,LL,HM
+E1624NI3H017,F,HL,LL,HH,HL,MM,LL,HH
+0UP40JSGONTB,F,MH,HH,LH,HM,HM,ML,HL,HL,HH
+G73S2GOJIC5D,F,HM,ML,HM,HL,MM
+L2L80MA0Z2MT,F,HL,HL,HH,LL,ML,HL,HL,HL,HL
+U1FNOXSBIII5,F,HL,HL,HM,HM,HL,LM,HL,ML,HL,HL,HL
+7IZCEMO5P52Z,F,HL,ML,MH
+NLY11O5BLQWZ,F,MH,LL,HM,ML,MM,LH,HH,LL,ML,HL
+GK3VKWZAH0K7,F,MM,HL,MH,HL,LL,ML,MM,MH,HH
+8PJ98109AK0B,F,LL,LL,HL,HL,HL,LH,HL,HM
+KLEYFUE4T84C,F,LL,MH,HH,ML,HL,HH,LM,HL,HH
+SNQ0UQINSKB7,F,HL,HL,HH,LL,HL,HL,HM,LM,HL
+859PY4Z1S2AV,F,HL,HL,ML,ML,HL,MH,HL,MM
+70KYW2VX37F7,F,HH,HM,HH,HL
+0A2D95P8QY2L,T,ML,LM
+943D02L08SLR,F,HH,HL,LM,HL,HL,LM,HL,ML,HL,LM,LH
+WBDBY483R4NK,T,LM,LM,HL,MM,HL
+6UYAC126ZYP8,F,HM,LL,HM,HM,HM,HL,LL,ML,HH,HM
+Z57LBO31NPO7,F,ML,HH,MM,HL,HL,LL,LL,HL,HM,LH,ML,ML
+RQQYT7CRCDP2,F,HM,HH,HH,LL,HL,ML
+J45FOFHV8O8P,T,LH,ML
+N1250OPA5VAO,T,LH,HL,LM,ML,HL,HL,HH
+4KNA188K648J,F,MH,ML,HL,ML,MH,LH
+E4845AV7QWIN,F,LM,MH,ML,HL,ML,HL,LM,HL,LH,MH,MM
+P12M42038288,F,MH,LM,ML
+P80E9741Q764,F,HL,LL,HH,HL,HH,HM,HM,LL,HM,MM
+K37S50CC4G92,F,LL,MH,HL,LH,HL,LL,HL,HL
+1K9H4F1MK0DS,F,LL,ML,HL
+C1CG4U2L7965,F,HH,ML
+4LB890C4R5D2,F,HM,LM,HH,HM,MH,MM,HM
+7EPRH4IDQ0P6,F,MH,ML,MM,ML,ML,HL,ML,HM,HL,LM
+M37VW5WNK6SE,F,ML,HL,LH,LH,LM,ML
+Y9ZNJS532JNA,F,HL,HL,MM,MM
+D573M50ARUS3,F,LL,HL,ML,HL,ML,HL

mcclf.py

@@ -0,0 +1,188 @@
+import os
+import sys
+import matplotlib.pyplot as plt
+import numpy as np
+import random
+import jprops
+from random import randint
+from matumizi.util import *
+from matumizi.mlutil import *
+
+"""
+Markov chain classifier
+"""
+class MarkovChainClassifier():
+	def __init__(self, configFile):
+		"""
+		constructor
+		
+		Parameters
+			configFile: config file path
+		"""
+		defValues = {}
+		defValues["common.model.directory"] = ("model", None)
+		defValues["common.model.file"] = (None, None)
+		defValues["common.verbose"] = (False, None)
+		defValues["common.states"] = (None, "missing state list")
+		defValues["train.data.file"] = (None, "missing training data file")
+		defValues["train.data.class.labels"] = (["F", "T"], None)
+		defValues["train.data.key.len"] = (1, None)
+		defValues["train.model.save"] = (False, None)
+		defValues["train.score.method"] = ("accuracy", None)
+		defValues["predict.data.file"] = (None, None)
+		defValues["predict.use.saved.model"] = (True, None)
+		defValues["predict.log.odds.threshold"] = (0, None)
+		defValues["validate.data.file"] = (None, "missing validation data file")
+		defValues["validate.use.saved.model"] = (False, None)
+		defValues["valid.accuracy.metric"] = ("acc", None)
+		self.config = Configuration(configFile, defValues)
+		
+		self.stTranPr = dict()
+		self.clabels = self.config.getStringListConfig("train.data.class.labels")[0]
+		self.states = self.config.getStringListConfig("common.states")[0]
+		self.nstates = len(self.states)
+		for cl in self.clabels:
+			stp = np.ones((self.nstates,self.nstates))
+			self.stTranPr[cl] = stp
+		
+	def train(self):
+		"""
+		train model
+		"""	
+		#state transition matrix
+		tdfPath = self.config.getStringConfig("train.data.file")[0]
+		klen = self.config.getIntConfig("train.data.key.len")[0]
+		for rec in fileRecGen(tdfPath):
+			cl = rec[klen]
+			rlen = len(rec)
+			for i in range(klen+1, rlen-1, 1):
+				fst = self.states.index(rec[i])
+				tst = self.states.index(rec[i+1])
+				self.stTranPr[cl][fst][tst] += 1
+		
+		#normalize to probability
+		for cl in self.clabels:
+			stp = self.stTranPr[cl]
+			for i in range(self.nstates):
+				s = stp[i].sum()
+				r = stp[i] / s
+				stp[i] = r
+		
+		#save		
+		if 	self.config.getBooleanConfig("train.model.save")[0]:
+			mdPath = self.config.getStringConfig("common.model.directory")[0]
+			assert os.path.exists(mdPath), "model save directory does not exist"
+			mfPath = self.config.getStringConfig("common.model.file")[0]
+			mfPath = os.path.join(mdPath, mfPath)
+
+			with open(mfPath, "w") as fh:
+				for cl in self.clabels:
+					fh.write("label:" + cl +"\n")
+					stp = self.stTranPr[cl]
+					for r in stp:
+						rs = ",".join(toStrList(r, 6)) + "\n"
+						fh.write(rs)
+
+	def validate(self):
+		"""
+		validate using  model
+		"""	
+		useSavedModel = self.config.getBooleanConfig("predict.use.saved.model")[0]
+		if useSavedModel:
+			self.__restoreModel()
+		else:
+			self.train() 
+			
+		vdfPath = self.config.getStringConfig("validate.data.file")[0]	
+		accMetric = self.config.getStringConfig("valid.accuracy.metric")[0]
+		
+		yac, ypr = self.__getPrediction(vdfPath, True)
+		if type(self.clabels[0]) == str:
+			yac = self.__toIntClabel(yac)
+			ypr = self.__toIntClabel(ypr)
+		score = perfMetric(accMetric, yac, ypr)
+		print(formatFloat(3, score, "perf score"))
+
+			
+	def predict(self):
+		"""
+		predict using  model
+		"""	
+		useSavedModel = self.config.getBooleanConfig("predict.use.saved.model")[0]
+		if useSavedModel:
+			self.__restoreModel()
+		else:
+			self.train() 
+			
+		#predict
+		pdfPath = self.config.getStringConfig("predict.data.file")[0]
+		_ , ypr = self.__getPrediction(pdfPath)
+		return ypr
+		
+	def __restoreModel(self):
+		"""
+		restore model
+		"""
+		mdPath = self.config.getStringConfig("common.model.directory")[0]
+		assert os.path.exists(mdPath), "model save directory does not exist"
+		mfPath = self.config.getStringConfig("common.model.file")[0]
+		mfPath = os.path.join(mdPath, mfPath)
+		stp = None
+		cl = None
+		for rec in fileRecGen(mfPath):
+			if len(rec) == 1:
+				if stp is not None:
+					stp = np.array(stp)
+					self.stTranPr[cl] = stp
+				cl = rec[0].split(":")[1]
+				stp = list()
+			else:
+				frec = asFloatList(rec)
+				stp.append(frec)
+				
+		stp = np.array(stp)
+		self.stTranPr[cl] = stp
+				
+	def __getPrediction(self, fpath, validate=False):
+		"""
+		get predictions
+		
+		Parameters
+			fpath : data file path
+			validate: True if validation
+		"""
+	
+		nc = self.clabels[0]
+		pc = self.clabels[1]
+		thold = self.config.getFloatConfig("predict.log.odds.threshold")[0]
+		klen = self.config.getIntConfig("train.data.key.len")[0]
+		offset = klen+1 if validate else klen
+		ypr = list()
+		yac = list()
+		for rec in fileRecGen(fpath):
+			lodds = 0
+			rlen = len(rec)
+			for i in range(offset, rlen-1, 1):
+				fst = self.states.index(rec[i])
+				tst = self.states.index(rec[i+1])
+				odds = self.stTranPr[pc][fst][tst] / self.stTranPr[nc][fst][tst]
+				lodds += math.log(odds)
+			prc = pc if lodds > thold else nc
+			ypr.append(prc)
+			if validate:
+				yac.append(rec[klen])
+			else:
+				recp = prc + "\t" + ",".join(rec)
+				print(recp)
+
+		re = (yac, ypr)
+		return re
+	
+	def __toIntClabel(self, labels):
+		"""
+		convert string class label to int
+		
+		Parameters
+			labels : class label values
+		"""
+		return list(map(lambda l : self.clabels.index(l), labels))

mcclf_cc.properties

@@ -0,0 +1,15 @@
+common.model.directory=./model
+common.model.file=cc.mod
+common.verbose=_
+common.states=HH,HM,HL,MH,MM,ML,LH,LM,LL
+train.data.file=cc_tr.txt
+train.data.class.labels=_
+train.data.key.len=1
+train.model.save=True
+train.score.method=_
+predict.data.file=cc_pr.txt
+predict.use.saved.model=True
+predict.log.odds.threshold=_
+validate.data.file=cc_va.txt
+validate.use.saved.model=True
+validate.score.method=_

model/cc.mod

@@ -0,0 +1,20 @@
+label:T
+0.103226,0.096774,0.129032,0.148387,0.045161,0.077419,0.180645,0.096774,0.122581
+0.106061,0.143939,0.174242,0.068182,0.053030,0.136364,0.136364,0.030303,0.151515
+0.116505,0.135922,0.266990,0.043689,0.063107,0.106796,0.072816,0.067961,0.126214
+0.106918,0.056604,0.069182,0.094340,0.081761,0.075472,0.194969,0.194969,0.125786
+0.137255,0.078431,0.137255,0.127451,0.078431,0.107843,0.156863,0.078431,0.098039
+0.100000,0.109091,0.218182,0.100000,0.045455,0.045455,0.136364,0.109091,0.136364
+0.068182,0.058442,0.058442,0.175325,0.064935,0.051948,0.305195,0.142857,0.074675
+0.104651,0.040698,0.093023,0.122093,0.040698,0.058140,0.290698,0.168605,0.081395
+0.080460,0.091954,0.172414,0.086207,0.080460,0.068966,0.160920,0.114943,0.143678
+label:F
+0.091892,0.172973,0.275676,0.043243,0.063063,0.126126,0.054054,0.070270,0.102703
+0.104749,0.136872,0.300279,0.055866,0.054469,0.136872,0.053073,0.065642,0.092179
+0.123711,0.136082,0.285223,0.047423,0.057045,0.134708,0.049485,0.051546,0.114777
+0.106529,0.161512,0.298969,0.041237,0.044674,0.127148,0.054983,0.041237,0.123711
+0.102167,0.154799,0.229102,0.068111,0.061920,0.139319,0.046440,0.052632,0.145511
+0.106870,0.135878,0.305344,0.059542,0.071756,0.129771,0.030534,0.058015,0.102290
+0.103306,0.128099,0.314050,0.066116,0.066116,0.099174,0.045455,0.041322,0.136364
+0.105660,0.139623,0.271698,0.052830,0.037736,0.154717,0.052830,0.060377,0.124528
+0.107509,0.143345,0.286689,0.066553,0.069966,0.122867,0.044369,0.039249,0.119454

readme.md

@@ -0,0 +1,59 @@
+Customer Conversion Prediction with Markov Chain Classifier
+
+Bussiness Requirement: For online users, conversion generally refers to the user action that results in some tangible gain for a business e.g., a user opening an account or a user making his or her first purchase. Next to drawing a large number of users to a website, getting a user to convert is the most critical event in a user’s relationship with an online business. Being able to predict when a user will convert to become a customer should be an important tool that online businesses should have at their disposal. A business could initiate a targeted marketing campaign based on the prediction result.
+
+
+There are many relevant attributes for the web session. We will be considering only the following as part of the demo.
+1. Time elapsed since the last visit
+2. Time spent in the session
+
+To keep the state transition matrix manageable, we will discretize the attributes into 3 levels; High, Medium, and Low. With two attributes, we will end up with 9 states in our problem. Each session will be characterized by two symbols, which stand for a state. For example, HM will imply that time elapsed since the last session is high and time spent in the current session is medium.
+
+
+Here is some sample input data:
+4F014156K07N,LL,ML,HH,HL,LL,HM,HL,LH,ML,HH,HL,LH
+G7C0M9H5SUZ1,HL,LM,HL,MH,HH,HH,ML,HL
+GWBX875AD31D,LL,HM,HL,HL,HM
+KRO2F24JUDE5,HL,HM,HM,HL,HM,MH,HM,HL,HL
+3J0G4BB9BI1Q,LM,LH,LH,MH,LM,MH,LH
+
+Here is the output for the above data:
+4F014156K07N,F,LL,ML,HH,HL,LL,HM,HL,LH,ML,HH,HL,LH
+G7C0M9H5SUZ1,F,HL,LM,HL,MH,HH,HH,ML,HL
+GWBX875AD31D,F,LL,HM,HL,HL,HM
+KRO2F24JUDE5,T,HL,HM,HM,HL,HM,MH,HM,HL,HL
+3J0G4BB9BI1Q,F,LM,LH,LH,MH,LM,MH,LH
+
+Each line in our output will consist of the following
+1. Cookie ID (or User ID)
+2. Class variable indicating whether the user converted or not (True or False)
+3. Sequence of session data where each element of the sequence is a 2 alphabet symbol
+
+Setup
+=====
+Install matumizi which is a package for data exploration and various other utilities
+pip3 install -i https://test.pypi.org/simple/ matumizi==0.0.3
+
+Make sure you have the supv directory at the same level as your working directory containing 
+visit_history.py mcclf_cc.properties
+
+
+Generate training data
+======================
+python3 visit_history.py --op gen --nuser 1000 --crate 10 --label true >> cc_tr.txt
+
+nuser = num of users
+crate = conversion rate
+label = whether class label should be created
+
+Train model
+===========
+python3 visit_history.py --op train --mlfpath mcclf_cc.properties
+
+Generate prediction data
+========================
+python3 visit_history.py --op gen --nuser 100 --crate 10 --label false >> cc_pr.txt
+
+Predict
+=======
+python3 visit_history.py --op pred --mlfpath mcclf_cc.properties

visit_history.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import os
+import sys
+from random import randint
+import time
+import uuid
+import argparse
+sys.path.append(os.path.abspath("../supv"))
+from matumizi.util import *
+from mcclf import *
+
+def  genVisitHistory(numUsers, convRate, label):
+    for i in range(numUsers):
+        userID = genID(12)
+        userSess = []
+        userSess.append(userID)
+
+        conv = randint(0, 100)
+        if (conv < convRate):
+            #converted
+            if (label):
+                if (randint(0,100) < 90):
+                    userSess.append("T")
+                else:
+                    userSess.append("F")
+
+
+            numSession = randint(2, 20)
+            for j in range(numSession):
+                sess = randint(0, 100)
+                if (sess <= 15):
+                    elapsed = "H"
+                elif (sess > 15 and sess <= 40):
+                    elapsed = "M"
+                else:
+                    elapsed = "L"
+
+                sess = randint(0, 100)
+                if (sess <= 15):
+                    duration = "L"
+                elif (sess > 15 and sess <= 40):
+                    duration = "M"
+                else:
+                    duration = "H"
+
+                sessSummary = elapsed + duration
+                userSess.append(sessSummary)
+
+
+        else:
+            #not converted
+            if (label):
+                if (randint(0,100) < 90):
+                    userSess.append("F")
+                else:
+                    userSess.append("T")
+
+            numSession = randint(2, 12)
+            for j in range(numSession):
+                sess = randint(0, 100)
+                if (sess <= 20):
+                    elapsed = "L"
+                elif (sess > 20 and sess <= 45):
+                    elapsed = "M"
+                else:
+                    elapsed = "H"
+
+                sess = randint(0, 100)
+                if (sess <= 20):
+                    duration = "H"
+                elif (sess > 20 and sess <= 45):
+                    duration = "M"
+                else:
+                    duration = "L"
+
+                sessSummary = elapsed + duration
+                userSess.append(sessSummary)
+
+        print(",".join(userSess))
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--op', type=str, default = "none", help = "operation")
+    parser.add_argument('--nuser', type=int, default = 100, help = "num of users")
+    parser.add_argument('--crate', type=int, default = 10, help = "concersion rate")
+    parser.add_argument('--label', type=str, default = "false", help = "whether to add label")
+    parser.add_argument('--mlfpath', type=str, default = "false", help = "ml config file")
+    args = parser.parse_args()
+    op = args.op
+
+    if op == "gen":	
+        numUsers = args.nuser
+        convRate = args.crate
+
+        label = args.label == "true"
+        genVisitHistory(numUsers, convRate, label)
+
+    elif op == "train":
+        model = MarkovChainClassifier(args.mlfpath)
+        model.train()
+
+    elif op == "pred":
+        model = MarkovChainClassifier(args.mlfpath)
+        model.predict()
+
+    else:
+        exitWithMsg("invalid command)")
+