2.程式碼：

/**
  * @author xubo
  *         ref:Spark MlLib機器學習實戰
  *         more code:https://github.com/xubo245/SparkLearning
  *         more blog:http://blog.csdn.net/xubo245
  */
package org.apache.spark.mllib.learning.basic

import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.{SparkContext, SparkConf}

/**
  * Created by xubo on 2016/5/23.
  * 從文字中讀取帶標籤的資料
  */
 

object LabeledPointLoadlibSVMFile {
  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster("local").setAppName(this.getClass().getSimpleName().filter(!_.equals('$')))
    //  println(this.getClass().getSimpleName().filter(!_.equals('$')))
    //設定環境變數
    val sc = new SparkContext(conf)

    val
 
 mu = MLUtils.loadLibSVMFile(sc, "file/data/mllib/input/basic/sample_libsvm_data.txt") //讀取檔案
    mu.foreach(println) //列印內容

    sc.stop
  }
}

資料：
一行

0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10
 
 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271:47 272:79 273:255 274:168 290:48 291:238 292:252 293:252 294:179 295:12 296:75 297:121 298:21 301:253 302:243 303:50 317:38 318:165 319:253 320:233 321:208 322:84 329:253 330:252 331:165 344:7 345:178 346:252 347:240 348:71 349:19 350:28 357:253 358:252 359:195 372:57 373:252 374:252 375:63 385:253 386:252 387:195 400:198 401:253 402:190 413:255 414:253 415:196 427:76 428:246 429:252 430:112 441:253 442:252 443:148 455:85 456:252 457:230 458:25 467:7 468:135 469:253 470:186 471:12 483:85 484:252 485:223 494:7 495:131 496:252 497:225 498:71 511:85 512:252 513:145 521:48 522:165 523:252 524:173 539:86 540:253 541:225 548:114 549:238 550:253 551:162 567:85 568:252 569:249 570:146 571:48 572:29 573:85 574:178 575:225 576:253 577:223 578:167 579:56 595:85 596:252 597:252 598:252 599:229 600:215 601:252 602:252 603:252 604:196 605:130 623:28 624:199 625:252 626:252 627:253 628:252 629:252 630:233 631:145 652:25 653:128 654:252 655:253 656:252 657:141 658:37
。。。

3.結果：

(0.0,(692,[127,128,129,130,131,154,155,156,157,158,159,181,182,183,184,185,186,187,188,189,207,208,209,210,211,212,213,214,215,216,217,235,236,237,238,239,240,241,242,243,244,245,262,263,264,265,266,267,268,269,270,271,272,273,289,290,291,292,293,294,295,296,297,300,301,302,316,317,318,319,320,321,328,329,330,343,344,345,346,347,348,349,356,357,358,371,372,373,374,384,385,386,399,400,401,412,413,414,426,427,428,429,440,441,442,454,455,456,457,466,467,468,469,470,482,483,484,493,494,495,496,497,510,511,512,520,521,522,523,538,539,540,547,548,549,550,566,567,568,569,570,571,572,573,574,575,576,577,578,594,595,596,597,598,599,600,601,602,603,604,622,623,624,625,626,627,628,629,630,651,652,653,654,655,656,657],[51.0,159.0,253.0,159.0,50.0,48.0,238.0,252.0,252.0,252.0,237.0,54.0,227.0,253.0,252.0,239.0,233.0,252.0,57.0,6.0,10.0,60.0,224.0,252.0,253.0,252.0,202.0,84.0,252.0,253.0,122.0,163.0,252.0,252.0,252.0,253.0,252.0,252.0,96.0,189.0,253.0,167.0,51.0,238.0,253.0,253.0,190.0,114.0,253.0,228.0,47.0,79.0,255.0,168.0,48.0,238.0,252.0,252.0,179.0,12.0,75.0,121.0,21.0,253.0,243.0,50.0,38.0,165.0,253.0,233.0,208.0,84.0,253.0,252.0,165.0,7.0,178.0,252.0,240.0,71.0,19.0,28.0,253.0,252.0,195.0,57.0,252.0,252.0,63.0,253.0,252.0,195.0,198.0,253.0,190.0,255.0,253.0,196.0,76.0,246.0,252.0,112.0,253.0,252.0,148.0,85.0,252.0,230.0,25.0,7.0,135.0,253.0,186.0,12.0,85.0,252.0,223.0,7.0,131.0,252.0,225.0,71.0,85.0,252.0,145.0,48.0,165.0,252.0,173.0,86.0,253.0,225.0,114.0,238.0,253.0,162.0,85.0,252.0,249.0,146.0,48.0,29.0,85.0,178.0,225.0,253.0,223.0,167.0,56.0,85.0,252.0,252.0,252.0,229.0,215.0,252.0,252.0,252.0,196.0,130.0,28.0,199.0,252.0,252.0,253.0,252.0,252.0,233.0,145.0,25.0,128.0,252.0,253.0,252.0,141.0,37.0]))
。。。