data.datasets.RdThis dataset is a subset of the full datasets, consisting of 1,000 samples from the original 10,000,000-sample datasets.
A 1,000×55 matrix, where the first 54 columns represent feature values and the last column represents the labels, which correspond to the number of factors associated with the features.
Methods for generating and extracting features from the dataset can be found in DNN_predictor.
data(data.datasets)
head(data.datasets)
#> F1 F2 F3 F4 F5 F6 F7
#> [1,] 3.449469 2.139801 1.753022 1.6549324 1.4588590 1.2709600 1.2434970
#> [2,] 2.046535 1.948081 1.838298 1.7785074 1.6814405 0.8880175 0.8586191
#> [3,] 6.127758 4.647768 3.652419 3.1679118 3.0464664 2.3754557 1.3103738
#> [4,] 4.465966 2.371172 1.934378 1.3809833 1.3540222 1.3175061 1.2700367
#> [5,] 6.119797 4.432873 3.585771 2.8901987 2.5978858 2.4803066 2.3080562
#> [6,] 1.643637 1.036650 0.978414 0.9418461 0.9262909 0.8735311 0.8369349
#> F8 F9 F10 F11 F12 F13 F14
#> [1,] 1.1812912 1.154440 1.1322633 2.276308 1.4933437 1.2595695 1.2147629
#> [2,] 0.8467365 0.785946 0.7780678 1.565069 1.5766180 1.5810167 1.6320681
#> [3,] 1.2652531 1.241975 1.2185048 3.482829 2.8301176 2.3469144 2.1242156
#> [4,] 1.2444636 1.235412 1.2004347 2.603034 1.4920944 1.2599636 0.9233445
#> [5,] 1.6562822 1.567374 1.4032901 3.420261 2.5950409 2.1697192 1.7954553
#> [6,] 0.7626967 -10.000000 -10.0000000 1.297257 0.9010351 0.8709756 0.8561513
#> F15 F16 F17 F18 F19 F20 F21
#> [1,] 1.0927157 0.9674518 0.9577057 0.9204475 0.9089693 0.9006963 0.07498845
#> [2,] 1.6607115 0.8880175 0.8586191 0.8467365 0.7859460 0.7780678 0.10232674
#> [3,] 2.1212501 1.7183386 0.9752948 0.9513048 0.9428778 0.9339789 0.07856100
#> [4,] 0.9174991 0.9048367 0.8838607 0.8771113 0.8818615 0.8683156 0.09304096
#> [5,] 1.6469920 1.6010473 1.5160348 1.1056165 1.0560137 0.9535403 0.05368243
#> [6,] 0.8602380 0.8363100 0.8258897 0.7626967 -10.0000000 -10.0000000 0.20545458
#> F22 F23 F24 F25 F26 F27 F28
#> [1,] 0.12150586 0.1596150 0.1955918 0.2273061 0.2549357 0.2819682 0.3076485
#> [2,] 0.19973077 0.2916457 0.3805710 0.4646430 0.5090439 0.5519749 0.5943117
#> [3,] 0.13814777 0.1849737 0.2255879 0.2646452 0.2950997 0.3118994 0.3281206
#> [4,] 0.14244037 0.1827399 0.2115104 0.2397192 0.2671672 0.2936263 0.3195527
#> [5,] 0.09256728 0.1240214 0.1493740 0.1721625 0.1939196 0.2141657 0.2286945
#> [6,] 0.33503579 0.4573375 0.5750683 0.6908547 0.8000460 0.9046629 1.0000000
#> F29 F30 F31 F32 F33 F34 F35
#> [1,] 0.3327450 0.3573594 0.9287634 0.8130818 0.7564490 0.7288969 0.7048936
#> [2,] 0.6336090 0.6725124 0.9871831 0.9565823 0.9442346 0.4674613 0.4575068
#> [3,] 0.3440433 0.3596652 0.9333449 0.7976198 0.7597060 0.6908303 0.5449600
#> [4,] 0.3452904 0.3702995 0.8155033 0.8047656 0.7867454 0.7724195 0.7146654
#> [5,] 0.2424434 0.2547529 0.9162504 0.7845118 0.7577174 0.7046553 0.6049087
#> [6,] -10.0000000 -10.0000000 0.9802790 0.9230530 0.9018478 0.8646608 0.8595795
#> F36 F37 F38 F39 F40 F41 F42
#> [1,] 0.6807052 0.6728978 0.6630069 0.7671335 0.6649367 0.6121396 0.6038095
#> [2,] 0.4558560 0.4514142 0.4513808 0.9687435 0.9330316 0.9164982 0.4195942
#> [3,] 0.5050962 0.4942649 0.4847605 0.7472143 0.6967277 0.6508977 0.5307449
#> [4,] 0.6835665 0.6717350 0.6665090 0.9066257 0.8897387 0.7725550 0.7046632
#> [5,] 0.4849563 0.4557727 0.3172019 0.9377238 0.8260271 0.7181912 0.6983433
#> [6,] 0.8554727 -10.0000000 -10.0000000 0.9802790 0.9230530 0.9018478 0.8646608
#> F43 F44 F45 F46 F47 F48 F49
#> [1,] 0.5986990 0.5927710 0.5433224 0.5353361 0.9555157 0.9256488 0.8912933
#> [2,] 0.4106590 0.4091773 0.4051903 0.4051603 0.8959872 0.7941371 0.6940292
#> [3,] 0.3735811 0.3330064 0.3142246 0.3101160 0.9507523 0.9139840 0.8833414
#> [4,] 0.6510777 0.6428085 0.6385483 0.6348384 0.9581877 0.9215224 0.8871146
#> [5,] 0.6089951 0.4334195 0.4266654 0.2911213 0.9642651 0.9363030 0.9147150
#> [6,] 0.8595795 0.8554727 -10.0000000 -10.0000000 0.8433803 0.6962485 0.5452810
#> F50 F51 F52 F53 F54 labels
#> [1,] 0.8673939 0.8389446 0.8155203 0.7905784 0.7668220 5
#> [2,] 0.5973912 0.5531480 0.5157129 0.4657190 0.4286671 5
#> [3,] 0.8489665 0.8179203 0.8021961 0.7870601 0.7732634 6
#> [4,] 0.8597419 0.8362937 0.8105712 0.7877528 0.7651553 3
#> [5,] 0.8907437 0.8661540 0.8611705 0.8319804 0.8225548 9
#> [6,] 0.4040339 0.2727843 0.1339839 -10.0000000 -10.0000000 1