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libopencv / usr / lib / libopencv_ml.so.3.3.1
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øV&þÒû#ù'þùª:#!R Ð@ ù!¹7ù@ÿ_@ÿ¡ÿÿ5@ùß©´ "ü_CQ#ü¤ÿÿ5_q¡T@ù!@ù ?Ö Õ@ÿ_QAÿ¢ÿÿ5qáàÿT!@ùàª!@ù ?Öÿÿ@ù!@ù ?Öà3@ùÞÿµûþÿ@ù!@ù ?Ö÷þÿàªé(þÿþÿàªæ(þÿÿàªã(þUÿÿàªà(þÿÿ%þáªû#ù"ðB 1óª"øàªf%þàbd%þ.Òàªá&þàªg(þóªàª\%þàªb(þûÿÿóS¼©4ðÒõ{©õªÆGù"@ùâùÒÃ&þóªý'þàª!ð! 2è£øùùQþÿáB©Á´  ü_cü¤ÿÿ5`@ùa©´ "ü_CQ#ü¤ÿÿ5_qTà@ù´ "ü_CQ#ü¤ÿÿ5_qàTҝ&þ"ÐBàáªù #R N©¹ùCü_cCü¤ÿÿ5Cü_dQDü¥ÿÿ5q@TÆGùàªâ@ù@ùAÊµõ{A©óSĨÀ_Ö@ù!@ù ?Ößÿÿ@ù!@ù ?ÖÒÿÿ!@ù!@ù ?Öìÿÿ0%þa@ùôª´  ü_CQü¤ÿÿ5_q¡T"@ùàªA@ù ?Ö!ðàª!ÌGù!@aùh(þÒàªi&þàªï'þôªúÿÿý{¿©ýý{hÀ_ÖÿB¹?@@ñh㈵øä>333333ã?{®Gáz?Y@{®Gáz?É?¹?Àr@{®Gáz?@Zb××çtið?ð?èÿÿÿN2cv6detail8PtrOwnerEN2cv2ml3SVM6KernelEN2cv2ml3SVMEN2cv2ml13SVMKernelImplEN2cv2ml7SVMImpl13TrainAutoBodyEN2cv2ml7SVMImpl11PredictBodyEN2cv2ml7SVMImplEN2cv6detail12PtrOwnerImplINS_2ml9ParamGridENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_2ml13SVMKernelImplENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_2ml7SVMImplENS_14DefaultDeleterIS3_EEEEcheckParamGridoperator<<operator<<getDecisionFunctionMatoperator()predictcalcwriteoperator<<getUncompressedSupportVectorstrainAutocheckParamsread_paramsSolversolve_nu_svrsolve_eps_svrreaddo_trainsortSamplesByClassestrainAutotrainUnknown kernel typeUnknown/unsupported kernel typegamma parameter of the kernel must be positiveThe kernel parameter <coef0> must be positive or zeroThe kernel parameter <degree> must be positiveCustom kernel is not setUnknown/unsupported SVM typeThe parameter C must be positiveThe parameter nu must be between 0 and 1The parameter p must be positivesvm_typeMissing or invalid SVM typeSVM kernel tag is not foundInvalid SVM kernel type (or custom kernel)Array of class labels is missing or invalid(int)sv_node.size() == sv_total(int)uncompressed_sv_node.size() == uncompressed_sv_total(int)df_node.size() == df_countoptimize_linearin the case of classification problem the responses must be categorical; either specify varType when creating TrainData, or pass integer responses(int)_yf.size() == sample_countvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)select_working_set_func != 0calc_rho_func != 0get_row_func != 0_samples.type() == 5sv_count != 0svmType == C_SVC || svmType == NU_SVCparams.class_weights must be 1d floating-point vector containing as many elements as the number of classes_responses.isContinuous() && _responses.checkVector(1, 4) == nsamplesWhile cross-validation one or more of the classes have been fell out of the sample. Try to reduce <Params::k_fold>sv_tab[df_index[i]] > 0k_fold >= 2samples.type() == 5Lower bound of the grid must be less then the upper one/home/nvidia/build_opencv/opencv/modules/ml/src/svm.cppLower bound of the grid must be positiveGrid step must greater then 1INTERNAL ERROR: Unknown SVM type, the SVM structure is probably corruptedC_SVCNU_SVCONE_CLASSEPS_SVRNU_SVRUknown_%dLINEARsvmTypePOLYRBFSIGMOIDCHI2INTERUnknown_%dkernel{degree}gammacoef0Cnuterm_criteria{:epsiloniterationsopencv_ml_svm0 <= i && i < (int)decision_func.size()total() == 0 || data != __null/home/nvidia/build_opencv/opencv/modules/core/include/opencv2/core/mat.inl.hppSVM model data is invalid, check sv_count, var_* and class_count tagsvar_countclass_countclass_labelsclass_weightssv_totalsupport_vectors[[:uncompressed_sv_totaluncompressed_support_vectorsfdecision_functionssv_countrhoalphaindexisv_count == sv_totalsamples.cols == var_count && samples.type() == 5nsamples == 1vector::_M_fill_insertvector::_M_default_appendNo element name has been given/home/nvidia/build_opencv/opencv/modules/core/include/opencv2/core/persistence.hppInvalid type of parameter (use one of SVM::C, SVM::GAMMA et al.)SVM::getDefaultGridthe class is not SVMImpl@ÿBN2cv2ml9TrainDataEN2cv2ml13TrainDataImplEN2cv6detail12PtrOwnerImplINS_2ml13TrainDataImplENS_14DefaultDeleterIS3_EEEEsetTrainTestSplitRatiogetCatCountgetNormCatValuesgetValuesgetSamplesetTrainTestSplitshuffleTrainTestgetSubVectorgetVarSymbolFlagssetVarTypesgetNamespreprocessCategoricalsetDataloadCSVMatstatic cv::Ptr<cv::ml::TrainData> cv::ml::TrainData::create(cv::InputArray, int, cv::InputArray, cv::InputArray, cv::InputArray, cv::InputArray, cv::InputArray)static cv::Ptr<cv::ml::TrainData> cv::ml::TrainData::loadFromCSV(const cv::String&, int, int, int, const cv::String&, char, char)a < ntestb < ntest0 <= count && count < nsamples(data.cols == 1 || data.rows == 1) && (data.type() == 4 || data.type() == 5)(float)idata[i] == fdata[i*istep]0. <= ratio && ratio <= 1./home/nvidia/build_opencv/opencv/modules/ml/src/data.cpprtvector::_M_realloc_insertinvalid CSV format; no data foundridx1 > ridx0nvars == (int)rowvals.size()(!varTypesSet && vtypes[i] == rowtypes[i]) || (varTypesSet && (vtypes[i] == rowtypes[i] || rowtypes[i] == VAR_ORDERED))vsymbolflags[i] == sflag || rowtypes[i] == VAR_MISSEDIf responses are vector values, not scalars, they must be marked as ordered responses_layout == ROW_SAMPLE || _layout == COL_SAMPLEsamples.type() == 5 || samples.type() == 4(sampleIdx.checkVector(1, 4, true) > 0 && checkRange(sampleIdx, true, 0, 0, nsamples)) || sampleIdx.checkVector(1, 0, true) == nsamplessampleWeights.checkVector(1, 5, true) == nsamples(varIdx.checkVector(1, 4, true) > 0 && checkRange(varIdx, true, 0, 0, ninputvars)) || varIdx.checkVector(1, 0, true) == ninputvarsresponses.type() == 5 || responses.type() == 4(layout == ROW_SAMPLE && responses.rows == nsamples) || (layout == COL_SAMPLE && responses.cols == nsamples)varType.checkVector(1, 0, true) == nvars && checkRange(varType, true, 0, VAR_ORDERED, VAR_CATEGORICAL+1)varType.at<uchar>(ninputvars + i) == VAR_ORDEREDmissing.size() == samples.size() && missing.type() == 00 <= vi && vi < ncattype spec is not correct; it should have format "cat", "ord" or "ord[n1,n2-n3,n4-n5,...]cat[m1-m2,m3,m4-m5,...]", where n's and m's are 0-based variable indices0 <= b1 && b1 < nvars0 <= b1 && b1 <= b2 && b2 < nvarstype of some variables is not specifiedordm > 0cmap[idx] == valbuf != 0 && 0 <= sidx && sidx < getNSamples()0 <= j && j < nvars0 <= vi && vi < getNAllVars()n >= 00 <= j && j < nsamplestype == 4 || type == 5 || type == 60 <= k && k < mimpl != 0label > 0 && label <= (int)n�÷ư>µ¾dÈñgý?,Cëâ6ÿN2cv2ml2EMEN2cv2ml6EMImplEN2cv6detail12PtrOwnerImplINS_2ml6EMImplENS_14DefaultDeleterIS3_EEEEsetCovarianceMatrixTypesetClustersNumberoperator<<operator<<operator<<computeLogWeightDivDetcomputeProbabilitiespredict2°<decomposeCovsclusterTrainSamplesdoTraincheckTrainDataread_params!covs.empty()!covs[clusterIndex].empty()nclusters >= 1meansFlt.type() == (((5) & ((1 << 3) - 1)) + (((1)-1) << 3))!clusterSamples.empty()!covsEigenValues.empty()covMatType == COV_MAT_SPHERICAL || covMatType == COV_MAT_DIAGONAL || covMatType == COV_MAT_GENERIC/home/nvidia/build_opencv/opencv/modules/ml/src/em.cppcovMatType >= 0covs!weights || (!weights->empty() && (weights->cols == 1 || weights->rows == 1) && static_cast<int>(weights->total()) == nclusters && (weights->type() == (((5) & ((1 << 3) - 1)) + (((1)-1) << 3)) || weights->type() == (((6) & ((1 << 3) - 1)) + (((1)-1) << 3))))!means || (!means->empty() && means->rows == nclusters && means->cols == dim && means->channels() == 1)!covs || (!covs->empty() && static_cast<int>(covs->size()) == nclusters)!m.empty() && m.size() == covSize && (m.channels() == 1)!samples.empty()samples.channels() == 1nclusters > 0nclusters <= nsamplescovMatType == COV_MAT_GENERIC || covMatType == COV_MAT_DIAGONAL || covMatType == COV_MAT_SPHERICAL!probs || (!probs->empty() && probs->rows == nsamples && probs->cols == nclusters && (probs->type() == (((5) & ((1 << 3) - 1)) + (((1)-1) << 3)) || probs->type() == (((6) & ((1 << 3) - 1)) + (((1)-1) << 3))))probsweights.empty()invCovsEigenValues.empty()opencv_ml_em!means.empty()(stype == 5 || stype == 6) && (ptype == 5 || ptype == 6)sample.size() == Size(means.cols, 1)training_paramsnclusterscov_mat_typesphericaldiagonalgenericunknown_%dweightsmeansisTrained()!sample.empty()getPointsfindNearestMatgetPointbuildmedianPartitionfindOrthoRangeMatidxmat.isContinuous() && idxmat.type() == 4 && (idxmat.cols == 1 || idxmat.rows == 1)/home/nvidia/build_opencv/opencv/modules/ml/src/kdtree.cpplabelsmat.isContinuous()(unsigned)k < (unsigned)points.rowsvecmat.isContinuous() && vecmat.type() == 5 && vecmat.total() == (size_t)points.colsK > 0 && (normType == NORM_L2 || normType == NORM_L1)(unsigned)ptidx < (unsigned)points.rows_points.type() == 5 && !_points.empty()nlabels == nvals[ofs[k]] <= pivotvals[ofs[k]] >= pivotstd::abs(more - less) <= 1lowerBound.size == upperBound.size && lowerBound.isContinuous() && upperBound.isContinuous() && lowerBound.type() == upperBound.type() && lowerBound.type() == 5 && lowerBound.total() == (size_t)ptdims·Ñ8ÍÌL= ñh㈵øä>¬Å'7ÍÌL=ÿÿÿÿN2cv2ml6SVMSGDEN2cv2ml10SVMSGDImplEN2cv6detail12PtrOwnerImplINS_2ml10SVMSGDImplENS_14DefaultDeleterIS3_EEEEsetOptimalParameterspredictnormalizeSamplescalcShifttrainoperator<<operator<<operator<<writeoperator<<readParamsopencv_ml_svmsgdSVMSGD model data is invalid/home/nvidia/build_opencv/opencv/modules/ml/src/svmsgd.cppsamples.cols == weights_.cols && samples.type() == (((5) & ((1 << 3) - 1)) + (((1)-1) << 3))nSamples == 1average.type() == (((5) & ((1 << 3) - 1)) + (((1)-1) << 3))trainResponses.type() == (((5) & ((1 << 3) - 1)) + (((1)-1) << 3))isClassifier()trainResponses.rows == trainSamples.rowsparams.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS(params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN) && (extendedWeights.type() == (((5) & ((1 << 3) - 1)) + (((1)-1) << 3)))SGDsvmsgdTypeSOFT_MARGINHARD_MARGINmarginTypemarginRegularizationinitialStepSizestepDecreasingPowerSVMSGD model data is invalid, it hasn't been trainedshiftASGDMissing or invalid SVMSGD typeMissing or invalid margin typefn["marginRegularization"].isReal()fn["initialStepSize"].isReal()fn["stepDecreasingPower"].isReal()!tcnode.empty()(params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS)ü©ñÒMbP?¹?jMóStû?ffffffî¿ffffffî?\Âõ(\ï¿\Âõ(\ï?©?ð?{®Gáz?ð?¹?¹?¹?333333ó?à?>N2cv2ml7ANN_MLPEN2cv2ml11ANN_MLPImpl9RPropLoopEN2cv2ml11ANN_MLPImplEN2cv6detail12PtrOwnerImplINS_2ml11ANN_MLPImplENS_14DefaultDeleterIS3_EEEEgetWeightssetActivationFunctionoperator<<operator<<operator<<write_paramspredictMatcalc_activ_functrain_rpropsetLayerSizesread_paramsprepare_to_traincalc_output_scaleMatweights[i].size() == Size(n2, n1+1)there should be at least one input and one output and every hidden layer must have more than 1 neuronUnknown activation functionactiv_func >= 0f_param1f_param2min_val1max_val1BACKPROPdw_scaledw_minusUnknown training method (should be BACKPROP or RPROP)The network has not been created. Use method create or the appropriate constructorinput training data should be a floating-point matrix with the number of rows equal to the number of training samples and the number of columns equal to the size of 0-th (input) layeroutput training data should be a floating-point matrix with the number of rows equal to the number of training samples and the number of columns equal to the size of last (output) layerThe numbers of input and output samples do not matchSome of new output training vector components run exceed the original range too much0 <= layerIdx && layerIdx < (int)weights.size()/home/nvidia/build_opencv/opencv/modules/ml/src/ann_mlp.cppopencv_ml_ann_mlpIDENTITYSIGMOID_SYMGAUSSIANlayer_sizesactivation_functionactivation_function_idmin_valmax_valtrain_methodmoment_scaleRPROPdw0dw_plusdw_mindw_maxUnknown training methodinput_scaleoutput_scaleinv_output_scaleThe network has not been trained or loaded(type == 5 || type == 6) && inputs.cols == layer_sizes[0]n == 1sums.isContinuous()
ÿÿÿ

ÿÿÿÿÿÿÿÿÿÿÿÿÀÿÿÿßAN2cv2ml6DTreesEN2cv2ml10DTreesImplEN2cv6detail12PtrOwnerImplINS_2ml10DTreesImpl8WorkDataENS_14DefaultDeleterIS4_EEEEN2cv6detail12PtrOwnerImplINS_2ml10DTreesImplENS_14DefaultDeleterIS3_EEEEinitCompVarIdxsetRegressionAccuracysetMaxCategoriessetMaxDepthsetCVFoldsreadoperator<<operator<<predictTreesoperator<<operator<<operator<<operator<<operator<<operator<<operator<<predictreadSplitaddTreecalcDirfindBestSplitaddNodeAndTrySplitstartTrainingfindSplitCatClassparams.CVFolds should be =0 (the tree is not pruned) or n>0 (tree is pruned using n-fold cross-validation)tree pruning using cross-validation is not implemented.Set CVFolds to 1max_depth should be >= 0max_categories should be >= 2params.regression_accuracy should be >= 0/home/nvidia/build_opencv/opencv/modules/ml/src/precomp.hppopencv_ml_dtreevector::reservevector::_M_range_insert0 <= vi && vi < nallvars && vi > prevIdx/home/nvidia/build_opencv/opencv/modules/ml/src/tree.cppnodes!fnodes.empty()depthvaluenorm_class_idxsplitssample.type() == 5one of input categorical variable is not an integercmap != __nullc >= 0 && ival == cmap[c]use_surrogatesmax_categoriesregression_accuracymax_depthmin_sample_countcross_validation_foldsuse_1se_rulepriorsinnot_ingtlevarqualityis_classifiervar_allord_var_countcat_var_countglobal_var_idxvar_idxcat_ofscat_mapmissing_subst!roots.empty()0 <= vi && vi <= (int)varType.size()w->wnodes[w_pidx].right == w_nidxw_nidx >= 0(int)_sleft.size() < n && (int)_sright.size() < ncompVarIdx[best_split.varIdx] >= 0 && best_vi >= 0surrogate splits are not implemented yetw->wnodes[nidx].left > 0 && w->wnodes[nidx].right > 0var_typeformatcat_countclass_weights.checkVector(1, 6) == nclasses(int)w->sample_weights.size() == nsamples0 <= ci && ci < nclassessum > 0

void cv::ml::DTreesImplForRTrees::getVotes(cv::InputArray, cv::OutputArray, int) constvoid cv::ml::RTreesImpl::getVotes_(cv::InputArray, cv::OutputArray, int) constcv::ml::DTreesImplForRTrees::DTreesImplForRTrees()virtual const std::vector<int>& cv::ml::DTreesImplForRTrees::getActiveVars()virtual void cv::ml::DTreesImplForRTrees::writeTrainingParams(cv::FileStorage&) constvirtual void cv::ml::DTreesImplForRTrees::write(cv::FileStorage&) constvirtual void cv::ml::RTreesImpl::write(cv::FileStorage&) constvirtual void cv::ml::DTreesImplForRTrees::readParams(const cv::FileNode&)virtual void cv::ml::DTreesImplForRTrees::clear()virtual void cv::ml::DTreesImplForRTrees::read(const cv::FileNode&)virtual void cv::ml::RTreesImpl::read(const cv::FileNode&)virtual void cv::ml::DTreesImplForRTrees::endTraining()virtual void cv::ml::DTreesImplForRTrees::startTraining(const cv::Ptr<cv::ml::TrainData>&, int)virtual bool cv::ml::DTreesImplForRTrees::train(const cv::Ptr<cv::ml::TrainData>&, int)virtual bool cv::ml::RTreesImpl::train(const cv::Ptr<cv::ml::TrainData>&, int)virtual float cv::ml::RTreesImpl::predict(cv::InputArray, cv::OutputArray, int) constN2cv2ml6RTreesEN2cv2ml19DTreesImplForRTreesEN2cv2ml10RTreesImplEN2cv6detail12PtrOwnerImplINS_2ml10RTreesImplENS_14DefaultDeleterIS3_EEEEsetRegressionAccuracysetMaxCategoriessetMaxDepthsetCVFoldswriteoperator<<operator<<operator<<MatgetVotesgetVotestrainreadvoid cv::ml::RTrees::getVotes(cv::InputArray, cv::OutputArray, int) conststatic cv::Ptr<cv::ml::RTrees> cv::ml::RTrees::load(const cv::String&, const cv::String&)static cv::Ptr<cv::ml::RTrees> cv::ml::RTrees::create()cv::ml::RTreeParams::RTreeParams(bool, int, cv::TermCriteria)cv::ml::RTreeParams::RTreeParams()Cross validation for RTrees is not implementednactive_varsntrees == (int)trees_node.size()opencv_ml_rtreesRTrees have not been trained/home/nvidia/build_opencv/opencv/modules/ml/src/rtrees.cppoob_errorvar_importancentreestreesthe class is not RTreesImplN2cv2ml21NormalBayesClassifierEN2cv2ml25NormalBayesClassifierImpl13NBPredictBodyEN2cv2ml25NormalBayesClassifierImplEN2cv6detail12PtrOwnerImplINS_2ml25NormalBayesClassifierImplENS_14DefaultDeleterIS3_EEEEoperator<<operator<<predictProbMatreadtrainThe field "var_count" of NBayes classifier is missing or non-positiveNo or invalid "cls_labels" in NBayes classifiercThe new training data is inconsistent with the original training data; varIdx and the class labels should be the sameThe input samples must be 32f matrix with the number of columns = nallvars/home/nvidia/build_opencv/opencv/modules/ml/src/nbayes.cppWhen the number of input samples is >1, the output vector of results must be passedopencv_ml_nbayescls_labelssumproductsumavginv_eigen_valuescov_rotate_matsÿBèN2cv2ml18LogisticRegressionEN2cv2ml22LogisticRegressionImplEN2cv2ml43LogisticRegressionImpl_ComputeDradient_ImplEN2cv6detail12PtrOwnerImplINS_2ml22LogisticRegressionImplENS_14DefaultDeleterIS3_EEEEwriteoperator<<operator<<operator<<readcompute_costcompute_gradientbatch_gradient_descentmini_batch_gradient_descentMatpush_backremap_labelspredicttraincv::Mat cv::ml::LogisticRegressionImpl::batch_gradient_descent(const cv::Mat&, const cv::Mat&, const cv::Mat&)void cv::ml::LogisticRegressionImpl::compute_gradient(const cv::Mat&, const cv::Mat&, const cv::Mat&, double, cv::Mat&)double cv::ml::LogisticRegressionImpl::compute_cost(const cv::Mat&, const cv::Mat&, const cv::Mat&)cv::Mat cv::ml::LogisticRegressionImpl::calc_sigmoid(const cv::Mat&) constvirtual bool cv::ml::LogisticRegressionImpl::train(const cv::Ptr<cv::ml::TrainData>&, int)opencv_ml_lrfile can't open. Check file path/home/nvidia/build_opencv/opencv/modules/ml/src/lr.cppLogistic Regression Classifierclassifiermini_batch_sizelearnt_thetasempty FileNode objectnormo_labelsn_labelscheck training parameters. Invalid training classifier_gradient.rows == _theta.rows && _gradient.cols == _theta.colscheck training parameters (learning rate) for the classifiernumber of iterations cannot be zero or a negative numbercheck training parameters for the classifiertraits::Type<_Tp>::value == type() && cols == 1!lmap.empty()val != lmap.end()classifier should be trained firstthetas.rows > 0data must be of floating typedata_t.cols == thetas.colstemp_pred.cols==1!_labels_i.empty() && !_data_i.empty()labels should be a column matrixdata and labels must be a floating point matrixnumber of rows in data and labels should be equaldata should have atleast 2 classesdÙãۜ'@*zðiø@wJëÿï?ºW\GÃøä>N2cv2ml5BoostEN2cv2ml18DTreesImplForBoostEN2cv2ml9BoostImplEN2cv6detail12PtrOwnerImplINS_2ml9BoostImplENS_14DefaultDeleterIS3_EEEEsetRegressionAccuracysetMaxDepthsetMaxCategoriessetCVFoldswriteoperator<<MatupdateWeightsAndTrimreadoperator<<boosting_typeDiscreteAdaboostRealAdaboostLogitBoostGentleAdaboostweight_trimming_ratestd::abs(w->ord_responses[si]) == 1Unknown boosting typeUnknownopencv_ml_boost/home/nvidia/build_opencv/opencv/modules/ml/src/boost.cppN2cv2ml9StatModelEtrainrandMVNormalCholeskyvoid cv::ml::Cholesky(const cv::Mat&, cv::Mat&)void cv::ml::randMVNormal(cv::InputArray, cv::InputArray, int, cv::OutputArray)virtual float cv::ml::StatModel::calcError(const cv::Ptr<cv::ml::TrainData>&, bool, cv::OutputArray) constvirtual bool cv::ml::StatModel::train(cv::InputArray, int, cv::InputArray)virtual bool cv::ml::StatModel::train(const cv::Ptr<cv::ml::TrainData>&, int)cv::ml::ParamGrid::ParamGrid(double, double, double)/home/nvidia/build_opencv/opencv/modules/ml/src/inner_functions.cppmean.rows == 1 || mean.cols == 1cov.rows == dim && cov.cols == dimA.type() == 5createConcentricSpheresTestSetnum_samples parameter must be positive/home/nvidia/build_opencv/opencv/modules/ml/src/testset.cppnum_features parameter must be positivenum_classes parameter must be positiveÿÿÿÿÿÿÿÿN2cv2ml8KNearestEN2cv2ml4ImplEN2cv2ml14BruteForceImpl19findKNearestInvokerEN2cv2ml14BruteForceImplEN2cv2ml10KDTreeImplEN2cv2ml12KNearestImplEN2cv6detail12PtrOwnerImplINS_2ml14BruteForceImplENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_2ml10KDTreeImplENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_2ml12KNearestImplENS_14DefaultDeleterIS3_EEEEoperator<<operator<<findNearestfindNearesttrainnew_samples.type() == 5new_samples.cols == samples.cols && new_responses.cols == responses.colsdefault_kresponses0 < k/home/nvidia/build_opencv/opencv/modules/ml/src/knearest.cpptest_samples.type() == 5 && test_samples.cols == samples.colsopencv_ml_knnopencv_ml_knn_kd;$£ÈGøÿ°+Høÿ´<HHøÿÀHHøÿ@I¸Høÿ0LðHøÿS(Iøÿ _`IøÿìuIøÿ}ÐIøÿäJøÿ@JøÿxJøÿh °Jøÿ8¢èJøÿԩHLøÿ<PLøÿPXLøÿd`LøÿxhLøÿpLøÿ xLøÿ´hNøÿÈ`OøÿÜ8Pøÿð`Qøÿ¸RøÿÀRøÿ,ÈRøÿ@ÐRøÿTØRøÿhàRøÿ|èRøÿðRøÿ¤øRøÿ¸SøÿÌSøÿàSøÿôSøÿ Søÿ(Søÿ00SøÿD@SøÿXHSøÿl`SøÿhSøÿpSøÿ¨xSøÿ¼SøÿÐ°Søÿð¸SøÿÀSøÿÈSøÿ,ðSøÿLTøÿ`(Tøÿ8Tøÿ`Tøÿ´°WøÿÈÀXøÿðpYøÿ Zøÿ@ZøÿTØZøÿ@[øÿ¨ø[øÿÜ(]øÿÈ`øÿ@aøÿhPbøÿ¨bøÿÀ døÿì0eøÿ @føÿTpføÿhðføÿ|gøÿ¨àjøÿløÿDnøÿhqøÿärøÿ søÿ(søÿ<ðzøÿ|øÿÌxøÿ4øÿHøÿàøÿ<`øÿdhøÿ øÿ, ¨øÿl à¥øÿH!8§øÿp!ªøÿØ!+øÿ"®øÿd"³øÿ"èºøÿè"ð¼øÿ#½øÿ<#è¾øÿ#¿øÿ¤#@Àøÿä#Áøÿ8$¸Âøÿ$Äøÿð$8Èøÿ@%øÎøÿ%@Óøÿà%ÈÖøÿ8&ÐÚøÿ&PÜøÿ¼&Häøÿl' åøÿ 'hòøÿ(póøÿÐ(øøÿ)pùøÿ`)¨úøÿt)üøÿ¸) !ùÿ*6ùÿ´* ?ùÿp+ØCùÿÐ+°Eùÿ0,¸EùÿD,ÐEùÿX,ØEùÿl,àEùÿ,¸FùÿØ,0Gùÿì,ØGùÿ-Hùÿ(-8IùÿP-èIùÿx- Jùÿ -PKùÿÈ-Lùÿð-°Lùÿ.`Mùÿ@.Nùÿh.ÀNùÿ.pOùÿ¸. 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