LibLInear库中数据格式介绍

在LibLInear中，读取的数据集的内容格式如下：

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1329472:0.025837 1329473:0.025837 1329474:0.025837 1329475:0.025837 1329476:0.025837 1329477:0.025837 1329478:0.025837 1329479:0.025837 1329480:0.025837 1329481:0.025837 1329482:0.025837 1329483:0.025837 1329484:0.025837 1329485:0.025837 1329486:0.025837 1329487:0.025837 1329488:0.025837 1329489:0.025837 1329490:0.025837 1329491:0.025837 1329492:0.025837 1329493:0.025837 1329494:0.025837 1329495:0.025837 1329496:0.025837 1329497:0.025837 1329498:0.025837 1329499:0.025837 1329500:0.025837 1329501:0.025837 1329502:0.025837 1329503:0.025837 1329504:0.025837 1329505:0.025837 1329506:0.025837 1329507:0.025837 1329508:0.025837 1329509:0.025837 1329510:0.025837 1329511:0.025837 1329512:0.025837 1329513:0.025837 1329514:0.025837 1329515:0.025837 1329516:0.025837 1329517:0.025837 1329518:0.025837 1329519:0.025837 1329520:0.025837 1329521:0.025837 1329522:0.025837 1329523:0.025837 1329524:0.025837 1329525:0.025837 1329526:0.025837 1329527:0.025837 1329528:0.025837 1329529:0.025837 1329530:0.025837 1329531:0.025837 1329532:0.025837 1329533:0.025837 1329534:0.025837 1329535:0.025837 1329536:0.025837 1329537:0.025837 1329538:0.025837 1329539:0.025837 1329540:0.025837 1329541:0.025837 1329542:0.025837 1329543:0.025837 1329544:0.025837 1329545:0.025837 1329546:0.025837 1329547:0.025837 1329548:0.025837 1329549:0.025837 1329550:0.025837 1329551:0.025837 1329552:0.025837 1329553:0.025837 1329554:0.025837 1329555:0.025837 1329556:0.025837 1329557:0.025837 1329558:0.025837 1329559:0.025837 1329560:0.025837 1329561:0.025837 1329562:0.025837 1329563:0.025837 1329564:0.025837 1329565:0.025837 1329566:0.025837 1329567:0.025837 1329568:0.025837 1329569:0.025837 1329570:0.025837 1329571:0.025837 1329572:0.025837 1329573:0.025837 1329574:0.025837 1329575:0.025837 1329576:0.025837 1329577:0.025837 1329578:0.025837 1329579:0.025837 1329580:0.025837 1329581:0.025837 1329582:0.025837 1329583:0.025837 1329584:0.025837 1329585:0.025837 1329586:0.025837 1329587:0.025837
1 97:0.176777 294:0.176777 374:0.176777 1478:0.176777 2004:0.176777 2013:0.176777 3398:0.176777 3688:0.176777 5960:0.176777 6589:0.176777 9072:0.176777 12721:0.176777 17244:0.176777 18123:0.176777 22288:0.176777 25180:0.176777 45553:0.176777 61334:0.176777 116335:0.176777 116336:0.176777 131624:0.176777 192635:0.176777 208232:0.176777 258945:0.176777 258946:0.176777 259390:0.176777 315823:0.176777 346521:0.176777 446783:0.176777 459349:0.176777 459350:0.176777 459351:0.176777
-1 40:0.064282 75:0.064282 89:0.064282 97:0.064282 103:0.064282 114:0.064282 159:0.064282 162:0.064282 171:0.064282 180:0.064282 217:0.064282 244:0.064282 562:0.064282 943:0.064282 951:0.064282 1015:0.064282 1204:0.064282 1603:0.064282 1741:0.064282 1787:0.064282 1792:0.064282 2004:0.064282 2013:0.064282 2018:0.064282 2019:0.064282 2022:0.064282 2107:0.064282 2817:0.064282 2897:0.064282 3379:0.064282 3546:0.064282 3688:0.064282 3721:0.064282 3737:0.064282 3798:0.064282 3886:0.064282 4156:0.064282 4203:0.064282 4259:0.064282 4369:0.064282 4688:0.064282 4711:0.064282 4920:0.064282 4963:0.064282 5097:0.064282 5408:0.064282 5544:0.064282 5545:0.064282 5546:0.064282 5547:0.064282 5779:0.064282 5821:0.064282 5942:0.064282 6392:0.064282 6464:0.064282 7011:0.064282 7014:0.064282 7714:0.064282 8086:0.064282 8087:0.064282 8291:0.064282 8349:0.064282 8527:0.064282 8849:0.064282 8851:0.064282 9490:0.064282 9871:0.064282 9969:0.064282 9995:0.064282 10513:0.064282 10655:0.064282 10875:0.064282 11310:0.064282 11311:0.064282 11691:0.064282 13600:0.064282 14458:0.064282 14776:0.064282 15065:0.064282 15072:0.064282 16039:0.064282 16041:0.064282 16933:0.064282 17196:0.064282 17820:0.064282 17863:0.064282 17945:0.064282 18293:0.064282 23049:0.064282 23050:0.064282 23051:0.064282 24515:0.064282 24693:0.064282 25731:0.064282 25916:0.064282 26413:0.064282 28785:0.064282 30216:0.064282 30217:0.064282 34416:0.064282 36575:0.064282 37554:0.064282 46535:0.064282 48183:0.064282 49417:0.064282 50678:0.064282 50679:0.064282 50680:0.064282 54401:0.064282 54402:0.064282 58318:0.064282 60021:0.064282 64670:0.064282 64671:0.064282 64672:0.064282 81185:0.064282 104492:0.064282 106693:0.064282 107406:0.064282 107407:0.064282 114608:0.064282 114609:0.064282 136611:0.064282 138388:0.064282 141985:0.064282 158527:0.064282 158528:0.064282 159588:0.064282 174951:0.064282 193073:0.064282 198857:0.064282 203359:0.064282 244792:0.064282 244793:0.064282 269450:0.064282 274928:0.064282 320378:0.064282 324111:0.064282 416228:0.064282 435960:0.064282 599729:0.064282 631217:0.064282 688416:0.064282 700295:0.064282 701085:0.064282 721298:0.064282 747134:0.064282 747135:0.064282 752821:0.064282 801672:0.064282 801792:0.064282 801793:0.064282 802772:0.064282 802773:0.064282 802774:0.064282 802775:0.064282 810673:0.064282 822616:0.064282 826574:0.064282 826582:0.064282 829631:0.064282 832284:0.064282 834474:0.064282 845327:0.064282 846930:0.064282 847558:0.064282 855852:0.064282 855853:0.064282 855854:0.064282 855855:0.064282 861170:0.064282 862201:0.064282 907447:0.064282 923303:0.064282 923304:0.064282 961127:0.064282 1025904:0.064282 1042697:0.064282 1055343:0.064282 1064948:0.064282 1114673:0.064282 1126839:0.064282 1126840:0.064282 1126841:0.064282 1126842:0.064282 1126843:0.064282 1126844:0.064282 1134952:0.064282 1134953:0.064282 1134954:0.064282 1134955:0.064282 1134956:0.064282 1134957:0.064282 1134958:0.064282 1134959:0.064282 1134960:0.064282 1134961:0.064282 1134962:0.064282 1134963:0.064282 1134964:0.064282 1134965:0.064282 1134966:0.064282 1134967:0.064282 1134968:0.064282 1134969:0.064282 1134970:0.064282 1134971:0.064282 1134972:0.064282 1134973:0.064282 1134974:0.064282 1134975:0.064282 1134976:0.064282 1134977:0.064282 1134978:0.064282 1134979:0.064282 1134980:0.064282 1134981:0.064282 1134982:0.064282 1134983:0.064282 1134984:0.064282 1134985:0.064282 1134986:0.064282 1134987:0.064282 1134988:0.064282 1134989:0.064282 1134990:0.064282 1134991:0.064282 1134992:0.064282 1134993:0.064282 1134994:0.064282 1134995:0.064282 1134996:0.064282 1134997:0.064282 1134998:0.064282 1134999:0.064282 1135000:0.064282 1135001:0.064282 1135002:0.064282 1135003:0.064282 1135004:0.064282 1135005:0.064282 1149963:0.064282

上面这个是从数据集news20二分类稀疏数据集中截取的部分数据。其中每行的第一个为标签，之后是(特征号:值)的形式存放，以\n表示换行。

在LibLinear中，数据集是这样存储的。它定义了一个结构体来存放一个特征号和值。

struct FeatureNode{int index;double value;
}

然后定义x_space来存放每个数据

FeatureNode *x_space=new FeatureNode(element+l);

定义y来存放每个标签

double *y=new double[l];

其中element表示数据的个数加上bias的个数(bias的个数等于行数)。
l表示数据集的行数。
x_space数组的存储数据的形式如下：

<54,0.053074>,<75,0.053074>,<97,0.053074>,...,<max_dim+1,bias_value>,<-1,>
<54,0.053074>,<75,0.053074>,<97,0.053074>,...,<max_dim+1,bias_value>,<-1,>
...

上面的例子为x_space的存储形式。element加上l是由于每行的最后的分隔标记。当bias的值小于等于0时，将没有bias位置。

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