BUG in Naive Bayes

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BUG in Naive Bayes

xiaohui
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I use the data provided by the official Bayesian classification model training. When the training is completed, if the maximum index number of the data record is not the same as the maximum index number provided in the classification training, there is no way to predict it. If the maximum index number of this data record is the same as the maximum index number provided during the classification training, the classification prediction can be performed correctly. So I think this piece of BUG is due to take the largest index number where there is a problem. Please help look, thank you.
The following is my use of the data:
0 128:87 129:208 130:249 155:27 156:212 157:254 158:195 182:118 183:225 184:254 185:254 186:232 187:147 188:46 209:115 210:248 211:254 212:254 213:254 214:254 215:254 216:230 217:148 218:12 236:18 237:250 238:254 239:245 240:226 241:254 242:254 243:254 244:254 245:254 246:148 263:92 264:205 265:254 266:250 267:101 268:20 269:194 270:254 271:254 272:254 273:254 274:229 275:53 291:152 292:254 293:254 294:94 297:14 298:124 299:187 300:254 301:254 302:254 303:213 318:95 319:252 320:254 321:206 322:15 327:3 328:6 329:51 330:231 331:254 332:94 345:50 346:246 347:254 348:254 349:20 358:200 359:254 360:96 372:21 373:184 374:254 375:254 376:147 377:2 386:200 387:254 388:96 400:177 401:254 402:254 403:218 404:33 413:16 414:211 415:254 416:96 427:11 428:219 429:254 430:251 431:92 441:84 442:254 443:232 444:44 455:101 456:254 457:254 458:141 469:162 470:254 471:231 472:42 483:235 484:254 485:227 486:42 496:51 497:238 498:254 499:213 511:235 512:254 513:199 524:160 525:254 526:229 527:52 539:235 540:254 541:199 549:10 550:84 551:150 552:253 553:254 554:147 567:235 568:254 569:213 570:20 575:17 576:63 577:158 578:254 579:254 580:254 581:155 582:12 595:122 596:248 597:254 598:204 599:98 600:42 601:177 602:180 603:200 604:254 605:254 606:253 607:213 608:82 609:10 624:203 625:254 626:254 627:254 628:254 629:254 630:254 631:254 632:251 633:219 634:94 652:35 653:221 654:254 655:254 656:254 657:254 658:254 659:217 660:95

error:
Caused by: java.lang.IllegalArgumentException: requirement failed: The columns of A don't match the number of elements of x. A: 692, x: 660
  at scala.Predef$.require(Predef.scala:224)
  at org.apache.spark.ml.linalg.BLAS$.gemv(BLAS.scala:539)
  at org.apache.spark.ml.linalg.Matrix$class.multiply(Matrices.scala:118)
  at org.apache.spark.ml.linalg.DenseMatrix.multiply(Matrices.scala:184)
  at org.apache.spark.ml.classification.NaiveBayesModel.multinomialCalculation(NaiveBayes.scala:300)
  at org.apache.spark.ml.classification.NaiveBayesModel.predictRaw(NaiveBayes.scala:319)
  at org.apache.spark.ml.classification.NaiveBayesModel.predictRaw(NaiveBayes.scala:252)
  at org.apache.spark.ml.classification.ProbabilisticClassificationModel$$anonfun$1.apply(ProbabilisticClassifier.scala:117)
  at org.apache.spark.ml.classification.ProbabilisticClassificationModel$$anonfun$1.apply(ProbabilisticClassifier.scala:116)
  ... 16 more
-----------------------------------------------------------------------------------------------------
0 152:56 153:105 154:220 155:254 156:63 178:18 179:166 180:233 181:253 182:253 183:253 184:236 185:209 186:209 187:209 188:77 189:18 206:84 207:253 208:253 209:253 210:253 211:253 212:254 213:253 214:253 215:253 216:253 217:172 218:8 233:57 234:238 235:253 236:253 237:253 238:253 239:253 240:254 241:253 242:253 243:253 244:253 245:253 246:119 260:14 261:238 262:253 263:253 264:253 265:253 266:253 267:253 268:179 269:196 270:253 271:253 272:253 273:253 274:238 275:12 288:33 289:253 290:253 291:253 292:253 293:253 294:248 295:134 297:18 298:83 299:237 300:253 301:253 302:253 303:14 316:164 317:253 318:253 319:253 320:253 321:253 322:128 327:57 328:119 329:214 330:253 331:94 343:57 344:248 345:253 346:253 347:253 348:126 349:14 350:4 357:179 358:253 359:248 360:56 371:175 372:253 373:253 374:240 375:190 376:28 385:179 386:253 387:253 388:173 399:209 400:253 401:253 402:178 413:92 414:253 415:253 416:208 427:211 428:254 429:254 430:179 442:135 443:255 444:209 455:209 456:253 457:253 458:90 470:134 471:253 472:208 483:209 484:253 485:253 486:178 497:2 498:142 499:253 500:208 511:209 512:253 513:253 514:214 515:35 525:30 526:253 527:253 528:208 539:165 540:253 541:253 542:253 543:215 544:36 553:163 554:253 555:253 556:164 567:18 568:172 569:253 570:253 571:253 572:214 573:127 574:7 580:72 581:232 582:253 583:171 584:17 596:8 597:182 598:253 599:253 600:253 601:253 602:162 603:56 607:64 608:240 609:253 610:253 611:14 625:7 626:173 627:253 628:253 629:253 630:253 631:245 632:241 633:239 634:239 635:246 636:253 637:225 638:14 639:1 654:18 655:59 656:138 657:224 658:253 659:253 660:254 661:253 662:253 663:253 664:240 665:96 685:37 686:104 687:192 688:255 689:253 690:253 691:182 692:73



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