001// 002// This file is auto-generated. Please don't modify it! 003// 004package org.opencv.ml; 005 006import java.util.ArrayList; 007import java.util.List; 008import org.opencv.core.Mat; 009import org.opencv.ml.TrainData; 010import org.opencv.utils.Converters; 011 012// C++: class TrainData 013/** 014 * Class encapsulating training data. 015 * 016 * Please note that the class only specifies the interface of training data, but not implementation. 017 * All the statistical model classes in _ml_ module accepts Ptr<TrainData> as parameter. In other 018 * words, you can create your own class derived from TrainData and pass smart pointer to the instance 019 * of this class into StatModel::train. 020 * 021 * SEE: REF: ml_intro_data 022 */ 023public class TrainData { 024 025 protected final long nativeObj; 026 protected TrainData(long addr) { nativeObj = addr; } 027 028 public long getNativeObjAddr() { return nativeObj; } 029 030 // internal usage only 031 public static TrainData __fromPtr__(long addr) { return new TrainData(addr); } 032 033 // 034 // C++: int cv::ml::TrainData::getLayout() 035 // 036 037 public int getLayout() { 038 return getLayout_0(nativeObj); 039 } 040 041 042 // 043 // C++: int cv::ml::TrainData::getNTrainSamples() 044 // 045 046 public int getNTrainSamples() { 047 return getNTrainSamples_0(nativeObj); 048 } 049 050 051 // 052 // C++: int cv::ml::TrainData::getNTestSamples() 053 // 054 055 public int getNTestSamples() { 056 return getNTestSamples_0(nativeObj); 057 } 058 059 060 // 061 // C++: int cv::ml::TrainData::getNSamples() 062 // 063 064 public int getNSamples() { 065 return getNSamples_0(nativeObj); 066 } 067 068 069 // 070 // C++: int cv::ml::TrainData::getNVars() 071 // 072 073 public int getNVars() { 074 return getNVars_0(nativeObj); 075 } 076 077 078 // 079 // C++: int cv::ml::TrainData::getNAllVars() 080 // 081 082 public int getNAllVars() { 083 return getNAllVars_0(nativeObj); 084 } 085 086 087 // 088 // C++: void cv::ml::TrainData::getSample(Mat varIdx, int sidx, float* buf) 089 // 090 091 public void getSample(Mat varIdx, int sidx, float buf) { 092 getSample_0(nativeObj, varIdx.nativeObj, sidx, buf); 093 } 094 095 096 // 097 // C++: Mat cv::ml::TrainData::getSamples() 098 // 099 100 public Mat getSamples() { 101 return new Mat(getSamples_0(nativeObj)); 102 } 103 104 105 // 106 // C++: Mat cv::ml::TrainData::getMissing() 107 // 108 109 public Mat getMissing() { 110 return new Mat(getMissing_0(nativeObj)); 111 } 112 113 114 // 115 // C++: Mat cv::ml::TrainData::getTrainSamples(int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true) 116 // 117 118 /** 119 * Returns matrix of train samples 120 * 121 * @param layout The requested layout. If it's different from the initial one, the matrix is 122 * transposed. See ml::SampleTypes. 123 * @param compressSamples if true, the function returns only the training samples (specified by 124 * sampleIdx) 125 * @param compressVars if true, the function returns the shorter training samples, containing only 126 * the active variables. 127 * 128 * In current implementation the function tries to avoid physical data copying and returns the 129 * matrix stored inside TrainData (unless the transposition or compression is needed). 130 * @return automatically generated 131 */ 132 public Mat getTrainSamples(int layout, boolean compressSamples, boolean compressVars) { 133 return new Mat(getTrainSamples_0(nativeObj, layout, compressSamples, compressVars)); 134 } 135 136 /** 137 * Returns matrix of train samples 138 * 139 * @param layout The requested layout. If it's different from the initial one, the matrix is 140 * transposed. See ml::SampleTypes. 141 * @param compressSamples if true, the function returns only the training samples (specified by 142 * sampleIdx) 143 * the active variables. 144 * 145 * In current implementation the function tries to avoid physical data copying and returns the 146 * matrix stored inside TrainData (unless the transposition or compression is needed). 147 * @return automatically generated 148 */ 149 public Mat getTrainSamples(int layout, boolean compressSamples) { 150 return new Mat(getTrainSamples_1(nativeObj, layout, compressSamples)); 151 } 152 153 /** 154 * Returns matrix of train samples 155 * 156 * @param layout The requested layout. If it's different from the initial one, the matrix is 157 * transposed. See ml::SampleTypes. 158 * sampleIdx) 159 * the active variables. 160 * 161 * In current implementation the function tries to avoid physical data copying and returns the 162 * matrix stored inside TrainData (unless the transposition or compression is needed). 163 * @return automatically generated 164 */ 165 public Mat getTrainSamples(int layout) { 166 return new Mat(getTrainSamples_2(nativeObj, layout)); 167 } 168 169 /** 170 * Returns matrix of train samples 171 * 172 * transposed. See ml::SampleTypes. 173 * sampleIdx) 174 * the active variables. 175 * 176 * In current implementation the function tries to avoid physical data copying and returns the 177 * matrix stored inside TrainData (unless the transposition or compression is needed). 178 * @return automatically generated 179 */ 180 public Mat getTrainSamples() { 181 return new Mat(getTrainSamples_3(nativeObj)); 182 } 183 184 185 // 186 // C++: Mat cv::ml::TrainData::getTrainResponses() 187 // 188 189 /** 190 * Returns the vector of responses 191 * 192 * The function returns ordered or the original categorical responses. Usually it's used in 193 * regression algorithms. 194 * @return automatically generated 195 */ 196 public Mat getTrainResponses() { 197 return new Mat(getTrainResponses_0(nativeObj)); 198 } 199 200 201 // 202 // C++: Mat cv::ml::TrainData::getTrainNormCatResponses() 203 // 204 205 /** 206 * Returns the vector of normalized categorical responses 207 * 208 * The function returns vector of responses. Each response is integer from {@code 0} to `<number of 209 * classes>-1`. The actual label value can be retrieved then from the class label vector, see 210 * TrainData::getClassLabels. 211 * @return automatically generated 212 */ 213 public Mat getTrainNormCatResponses() { 214 return new Mat(getTrainNormCatResponses_0(nativeObj)); 215 } 216 217 218 // 219 // C++: Mat cv::ml::TrainData::getTestResponses() 220 // 221 222 public Mat getTestResponses() { 223 return new Mat(getTestResponses_0(nativeObj)); 224 } 225 226 227 // 228 // C++: Mat cv::ml::TrainData::getTestNormCatResponses() 229 // 230 231 public Mat getTestNormCatResponses() { 232 return new Mat(getTestNormCatResponses_0(nativeObj)); 233 } 234 235 236 // 237 // C++: Mat cv::ml::TrainData::getResponses() 238 // 239 240 public Mat getResponses() { 241 return new Mat(getResponses_0(nativeObj)); 242 } 243 244 245 // 246 // C++: Mat cv::ml::TrainData::getNormCatResponses() 247 // 248 249 public Mat getNormCatResponses() { 250 return new Mat(getNormCatResponses_0(nativeObj)); 251 } 252 253 254 // 255 // C++: Mat cv::ml::TrainData::getSampleWeights() 256 // 257 258 public Mat getSampleWeights() { 259 return new Mat(getSampleWeights_0(nativeObj)); 260 } 261 262 263 // 264 // C++: Mat cv::ml::TrainData::getTrainSampleWeights() 265 // 266 267 public Mat getTrainSampleWeights() { 268 return new Mat(getTrainSampleWeights_0(nativeObj)); 269 } 270 271 272 // 273 // C++: Mat cv::ml::TrainData::getTestSampleWeights() 274 // 275 276 public Mat getTestSampleWeights() { 277 return new Mat(getTestSampleWeights_0(nativeObj)); 278 } 279 280 281 // 282 // C++: Mat cv::ml::TrainData::getVarIdx() 283 // 284 285 public Mat getVarIdx() { 286 return new Mat(getVarIdx_0(nativeObj)); 287 } 288 289 290 // 291 // C++: Mat cv::ml::TrainData::getVarType() 292 // 293 294 public Mat getVarType() { 295 return new Mat(getVarType_0(nativeObj)); 296 } 297 298 299 // 300 // C++: Mat cv::ml::TrainData::getVarSymbolFlags() 301 // 302 303 public Mat getVarSymbolFlags() { 304 return new Mat(getVarSymbolFlags_0(nativeObj)); 305 } 306 307 308 // 309 // C++: int cv::ml::TrainData::getResponseType() 310 // 311 312 public int getResponseType() { 313 return getResponseType_0(nativeObj); 314 } 315 316 317 // 318 // C++: Mat cv::ml::TrainData::getTrainSampleIdx() 319 // 320 321 public Mat getTrainSampleIdx() { 322 return new Mat(getTrainSampleIdx_0(nativeObj)); 323 } 324 325 326 // 327 // C++: Mat cv::ml::TrainData::getTestSampleIdx() 328 // 329 330 public Mat getTestSampleIdx() { 331 return new Mat(getTestSampleIdx_0(nativeObj)); 332 } 333 334 335 // 336 // C++: void cv::ml::TrainData::getValues(int vi, Mat sidx, float* values) 337 // 338 339 public void getValues(int vi, Mat sidx, float values) { 340 getValues_0(nativeObj, vi, sidx.nativeObj, values); 341 } 342 343 344 // 345 // C++: Mat cv::ml::TrainData::getDefaultSubstValues() 346 // 347 348 public Mat getDefaultSubstValues() { 349 return new Mat(getDefaultSubstValues_0(nativeObj)); 350 } 351 352 353 // 354 // C++: int cv::ml::TrainData::getCatCount(int vi) 355 // 356 357 public int getCatCount(int vi) { 358 return getCatCount_0(nativeObj, vi); 359 } 360 361 362 // 363 // C++: Mat cv::ml::TrainData::getClassLabels() 364 // 365 366 /** 367 * Returns the vector of class labels 368 * 369 * The function returns vector of unique labels occurred in the responses. 370 * @return automatically generated 371 */ 372 public Mat getClassLabels() { 373 return new Mat(getClassLabels_0(nativeObj)); 374 } 375 376 377 // 378 // C++: Mat cv::ml::TrainData::getCatOfs() 379 // 380 381 public Mat getCatOfs() { 382 return new Mat(getCatOfs_0(nativeObj)); 383 } 384 385 386 // 387 // C++: Mat cv::ml::TrainData::getCatMap() 388 // 389 390 public Mat getCatMap() { 391 return new Mat(getCatMap_0(nativeObj)); 392 } 393 394 395 // 396 // C++: void cv::ml::TrainData::setTrainTestSplit(int count, bool shuffle = true) 397 // 398 399 /** 400 * Splits the training data into the training and test parts 401 * SEE: TrainData::setTrainTestSplitRatio 402 * @param count automatically generated 403 * @param shuffle automatically generated 404 */ 405 public void setTrainTestSplit(int count, boolean shuffle) { 406 setTrainTestSplit_0(nativeObj, count, shuffle); 407 } 408 409 /** 410 * Splits the training data into the training and test parts 411 * SEE: TrainData::setTrainTestSplitRatio 412 * @param count automatically generated 413 */ 414 public void setTrainTestSplit(int count) { 415 setTrainTestSplit_1(nativeObj, count); 416 } 417 418 419 // 420 // C++: void cv::ml::TrainData::setTrainTestSplitRatio(double ratio, bool shuffle = true) 421 // 422 423 /** 424 * Splits the training data into the training and test parts 425 * 426 * The function selects a subset of specified relative size and then returns it as the training 427 * set. If the function is not called, all the data is used for training. Please, note that for 428 * each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test 429 * subset can be retrieved and processed as well. 430 * SEE: TrainData::setTrainTestSplit 431 * @param ratio automatically generated 432 * @param shuffle automatically generated 433 */ 434 public void setTrainTestSplitRatio(double ratio, boolean shuffle) { 435 setTrainTestSplitRatio_0(nativeObj, ratio, shuffle); 436 } 437 438 /** 439 * Splits the training data into the training and test parts 440 * 441 * The function selects a subset of specified relative size and then returns it as the training 442 * set. If the function is not called, all the data is used for training. Please, note that for 443 * each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test 444 * subset can be retrieved and processed as well. 445 * SEE: TrainData::setTrainTestSplit 446 * @param ratio automatically generated 447 */ 448 public void setTrainTestSplitRatio(double ratio) { 449 setTrainTestSplitRatio_1(nativeObj, ratio); 450 } 451 452 453 // 454 // C++: void cv::ml::TrainData::shuffleTrainTest() 455 // 456 457 public void shuffleTrainTest() { 458 shuffleTrainTest_0(nativeObj); 459 } 460 461 462 // 463 // C++: Mat cv::ml::TrainData::getTestSamples() 464 // 465 466 /** 467 * Returns matrix of test samples 468 * @return automatically generated 469 */ 470 public Mat getTestSamples() { 471 return new Mat(getTestSamples_0(nativeObj)); 472 } 473 474 475 // 476 // C++: void cv::ml::TrainData::getNames(vector_String names) 477 // 478 479 /** 480 * Returns vector of symbolic names captured in loadFromCSV() 481 * @param names automatically generated 482 */ 483 public void getNames(List<String> names) { 484 getNames_0(nativeObj, names); 485 } 486 487 488 // 489 // C++: static Mat cv::ml::TrainData::getSubVector(Mat vec, Mat idx) 490 // 491 492 /** 493 * Extract from 1D vector elements specified by passed indexes. 494 * @param vec input vector (supported types: CV_32S, CV_32F, CV_64F) 495 * @param idx 1D index vector 496 * @return automatically generated 497 */ 498 public static Mat getSubVector(Mat vec, Mat idx) { 499 return new Mat(getSubVector_0(vec.nativeObj, idx.nativeObj)); 500 } 501 502 503 // 504 // C++: static Mat cv::ml::TrainData::getSubMatrix(Mat matrix, Mat idx, int layout) 505 // 506 507 /** 508 * Extract from matrix rows/cols specified by passed indexes. 509 * @param matrix input matrix (supported types: CV_32S, CV_32F, CV_64F) 510 * @param idx 1D index vector 511 * @param layout specifies to extract rows (cv::ml::ROW_SAMPLES) or to extract columns (cv::ml::COL_SAMPLES) 512 * @return automatically generated 513 */ 514 public static Mat getSubMatrix(Mat matrix, Mat idx, int layout) { 515 return new Mat(getSubMatrix_0(matrix.nativeObj, idx.nativeObj, layout)); 516 } 517 518 519 // 520 // C++: static Ptr_TrainData cv::ml::TrainData::create(Mat samples, int layout, Mat responses, Mat varIdx = Mat(), Mat sampleIdx = Mat(), Mat sampleWeights = Mat(), Mat varType = Mat()) 521 // 522 523 /** 524 * Creates training data from in-memory arrays. 525 * 526 * @param samples matrix of samples. It should have CV_32F type. 527 * @param layout see ml::SampleTypes. 528 * @param responses matrix of responses. If the responses are scalar, they should be stored as a 529 * single row or as a single column. The matrix should have type CV_32F or CV_32S (in the 530 * former case the responses are considered as ordered by default; in the latter case - as 531 * categorical) 532 * @param varIdx vector specifying which variables to use for training. It can be an integer vector 533 * (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of 534 * active variables. 535 * @param sampleIdx vector specifying which samples to use for training. It can be an integer 536 * vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask 537 * of training samples. 538 * @param sampleWeights optional vector with weights for each sample. It should have CV_32F type. 539 * @param varType optional vector of type CV_8U and size `<number_of_variables_in_samples> + 540 * <number_of_variables_in_responses>`, containing types of each input and output variable. See 541 * ml::VariableTypes. 542 * @return automatically generated 543 */ 544 public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights, Mat varType) { 545 return TrainData.__fromPtr__(create_0(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, sampleWeights.nativeObj, varType.nativeObj)); 546 } 547 548 /** 549 * Creates training data from in-memory arrays. 550 * 551 * @param samples matrix of samples. It should have CV_32F type. 552 * @param layout see ml::SampleTypes. 553 * @param responses matrix of responses. If the responses are scalar, they should be stored as a 554 * single row or as a single column. The matrix should have type CV_32F or CV_32S (in the 555 * former case the responses are considered as ordered by default; in the latter case - as 556 * categorical) 557 * @param varIdx vector specifying which variables to use for training. It can be an integer vector 558 * (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of 559 * active variables. 560 * @param sampleIdx vector specifying which samples to use for training. It can be an integer 561 * vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask 562 * of training samples. 563 * @param sampleWeights optional vector with weights for each sample. It should have CV_32F type. 564 * <number_of_variables_in_responses>`, containing types of each input and output variable. See 565 * ml::VariableTypes. 566 * @return automatically generated 567 */ 568 public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights) { 569 return TrainData.__fromPtr__(create_1(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, sampleWeights.nativeObj)); 570 } 571 572 /** 573 * Creates training data from in-memory arrays. 574 * 575 * @param samples matrix of samples. It should have CV_32F type. 576 * @param layout see ml::SampleTypes. 577 * @param responses matrix of responses. If the responses are scalar, they should be stored as a 578 * single row or as a single column. The matrix should have type CV_32F or CV_32S (in the 579 * former case the responses are considered as ordered by default; in the latter case - as 580 * categorical) 581 * @param varIdx vector specifying which variables to use for training. It can be an integer vector 582 * (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of 583 * active variables. 584 * @param sampleIdx vector specifying which samples to use for training. It can be an integer 585 * vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask 586 * of training samples. 587 * <number_of_variables_in_responses>`, containing types of each input and output variable. See 588 * ml::VariableTypes. 589 * @return automatically generated 590 */ 591 public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx) { 592 return TrainData.__fromPtr__(create_2(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj)); 593 } 594 595 /** 596 * Creates training data from in-memory arrays. 597 * 598 * @param samples matrix of samples. It should have CV_32F type. 599 * @param layout see ml::SampleTypes. 600 * @param responses matrix of responses. If the responses are scalar, they should be stored as a 601 * single row or as a single column. The matrix should have type CV_32F or CV_32S (in the 602 * former case the responses are considered as ordered by default; in the latter case - as 603 * categorical) 604 * @param varIdx vector specifying which variables to use for training. It can be an integer vector 605 * (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of 606 * active variables. 607 * vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask 608 * of training samples. 609 * <number_of_variables_in_responses>`, containing types of each input and output variable. See 610 * ml::VariableTypes. 611 * @return automatically generated 612 */ 613 public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx) { 614 return TrainData.__fromPtr__(create_3(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj)); 615 } 616 617 /** 618 * Creates training data from in-memory arrays. 619 * 620 * @param samples matrix of samples. It should have CV_32F type. 621 * @param layout see ml::SampleTypes. 622 * @param responses matrix of responses. If the responses are scalar, they should be stored as a 623 * single row or as a single column. The matrix should have type CV_32F or CV_32S (in the 624 * former case the responses are considered as ordered by default; in the latter case - as 625 * categorical) 626 * (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of 627 * active variables. 628 * vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask 629 * of training samples. 630 * <number_of_variables_in_responses>`, containing types of each input and output variable. See 631 * ml::VariableTypes. 632 * @return automatically generated 633 */ 634 public static TrainData create(Mat samples, int layout, Mat responses) { 635 return TrainData.__fromPtr__(create_4(samples.nativeObj, layout, responses.nativeObj)); 636 } 637 638 639 @Override 640 protected void finalize() throws Throwable { 641 delete(nativeObj); 642 } 643 644 645 646 // C++: int cv::ml::TrainData::getLayout() 647 private static native int getLayout_0(long nativeObj); 648 649 // C++: int cv::ml::TrainData::getNTrainSamples() 650 private static native int getNTrainSamples_0(long nativeObj); 651 652 // C++: int cv::ml::TrainData::getNTestSamples() 653 private static native int getNTestSamples_0(long nativeObj); 654 655 // C++: int cv::ml::TrainData::getNSamples() 656 private static native int getNSamples_0(long nativeObj); 657 658 // C++: int cv::ml::TrainData::getNVars() 659 private static native int getNVars_0(long nativeObj); 660 661 // C++: int cv::ml::TrainData::getNAllVars() 662 private static native int getNAllVars_0(long nativeObj); 663 664 // C++: void cv::ml::TrainData::getSample(Mat varIdx, int sidx, float* buf) 665 private static native void getSample_0(long nativeObj, long varIdx_nativeObj, int sidx, float buf); 666 667 // C++: Mat cv::ml::TrainData::getSamples() 668 private static native long getSamples_0(long nativeObj); 669 670 // C++: Mat cv::ml::TrainData::getMissing() 671 private static native long getMissing_0(long nativeObj); 672 673 // C++: Mat cv::ml::TrainData::getTrainSamples(int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true) 674 private static native long getTrainSamples_0(long nativeObj, int layout, boolean compressSamples, boolean compressVars); 675 private static native long getTrainSamples_1(long nativeObj, int layout, boolean compressSamples); 676 private static native long getTrainSamples_2(long nativeObj, int layout); 677 private static native long getTrainSamples_3(long nativeObj); 678 679 // C++: Mat cv::ml::TrainData::getTrainResponses() 680 private static native long getTrainResponses_0(long nativeObj); 681 682 // C++: Mat cv::ml::TrainData::getTrainNormCatResponses() 683 private static native long getTrainNormCatResponses_0(long nativeObj); 684 685 // C++: Mat cv::ml::TrainData::getTestResponses() 686 private static native long getTestResponses_0(long nativeObj); 687 688 // C++: Mat cv::ml::TrainData::getTestNormCatResponses() 689 private static native long getTestNormCatResponses_0(long nativeObj); 690 691 // C++: Mat cv::ml::TrainData::getResponses() 692 private static native long getResponses_0(long nativeObj); 693 694 // C++: Mat cv::ml::TrainData::getNormCatResponses() 695 private static native long getNormCatResponses_0(long nativeObj); 696 697 // C++: Mat cv::ml::TrainData::getSampleWeights() 698 private static native long getSampleWeights_0(long nativeObj); 699 700 // C++: Mat cv::ml::TrainData::getTrainSampleWeights() 701 private static native long getTrainSampleWeights_0(long nativeObj); 702 703 // C++: Mat cv::ml::TrainData::getTestSampleWeights() 704 private static native long getTestSampleWeights_0(long nativeObj); 705 706 // C++: Mat cv::ml::TrainData::getVarIdx() 707 private static native long getVarIdx_0(long nativeObj); 708 709 // C++: Mat cv::ml::TrainData::getVarType() 710 private static native long getVarType_0(long nativeObj); 711 712 // C++: Mat cv::ml::TrainData::getVarSymbolFlags() 713 private static native long getVarSymbolFlags_0(long nativeObj); 714 715 // C++: int cv::ml::TrainData::getResponseType() 716 private static native int getResponseType_0(long nativeObj); 717 718 // C++: Mat cv::ml::TrainData::getTrainSampleIdx() 719 private static native long getTrainSampleIdx_0(long nativeObj); 720 721 // C++: Mat cv::ml::TrainData::getTestSampleIdx() 722 private static native long getTestSampleIdx_0(long nativeObj); 723 724 // C++: void cv::ml::TrainData::getValues(int vi, Mat sidx, float* values) 725 private static native void getValues_0(long nativeObj, int vi, long sidx_nativeObj, float values); 726 727 // C++: Mat cv::ml::TrainData::getDefaultSubstValues() 728 private static native long getDefaultSubstValues_0(long nativeObj); 729 730 // C++: int cv::ml::TrainData::getCatCount(int vi) 731 private static native int getCatCount_0(long nativeObj, int vi); 732 733 // C++: Mat cv::ml::TrainData::getClassLabels() 734 private static native long getClassLabels_0(long nativeObj); 735 736 // C++: Mat cv::ml::TrainData::getCatOfs() 737 private static native long getCatOfs_0(long nativeObj); 738 739 // C++: Mat cv::ml::TrainData::getCatMap() 740 private static native long getCatMap_0(long nativeObj); 741 742 // C++: void cv::ml::TrainData::setTrainTestSplit(int count, bool shuffle = true) 743 private static native void setTrainTestSplit_0(long nativeObj, int count, boolean shuffle); 744 private static native void setTrainTestSplit_1(long nativeObj, int count); 745 746 // C++: void cv::ml::TrainData::setTrainTestSplitRatio(double ratio, bool shuffle = true) 747 private static native void setTrainTestSplitRatio_0(long nativeObj, double ratio, boolean shuffle); 748 private static native void setTrainTestSplitRatio_1(long nativeObj, double ratio); 749 750 // C++: void cv::ml::TrainData::shuffleTrainTest() 751 private static native void shuffleTrainTest_0(long nativeObj); 752 753 // C++: Mat cv::ml::TrainData::getTestSamples() 754 private static native long getTestSamples_0(long nativeObj); 755 756 // C++: void cv::ml::TrainData::getNames(vector_String names) 757 private static native void getNames_0(long nativeObj, List<String> names); 758 759 // C++: static Mat cv::ml::TrainData::getSubVector(Mat vec, Mat idx) 760 private static native long getSubVector_0(long vec_nativeObj, long idx_nativeObj); 761 762 // C++: static Mat cv::ml::TrainData::getSubMatrix(Mat matrix, Mat idx, int layout) 763 private static native long getSubMatrix_0(long matrix_nativeObj, long idx_nativeObj, int layout); 764 765 // C++: static Ptr_TrainData cv::ml::TrainData::create(Mat samples, int layout, Mat responses, Mat varIdx = Mat(), Mat sampleIdx = Mat(), Mat sampleWeights = Mat(), Mat varType = Mat()) 766 private static native long create_0(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long sampleWeights_nativeObj, long varType_nativeObj); 767 private static native long create_1(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long sampleWeights_nativeObj); 768 private static native long create_2(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj); 769 private static native long create_3(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj); 770 private static native long create_4(long samples_nativeObj, int layout, long responses_nativeObj); 771 772 // native support for java finalize() 773 private static native void delete(long nativeObj); 774 775}