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libopencv-ml2.4 / usr / lib / x86_64-linux-gnu / libopencv_ml.so.2.4.8
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fT f.ÈÀHÅ9l$,ó*ÀóXD$(óD$(H|$ëtHD$¨DkH|$0Ht$@A¸ÚDéè»ïúÿH|$ 'ÿÿÿI$1ÒH@Xé?ÿÿÿWÀóD$(\$,óhÛtóL$(ó*D$,ó^È(ÁHÄ[]A\A]A^A_Ãl$,ó0ítÛóL$(ó*D$,ó^ÈóóYÁë½f.èûíüÿHÃéjüÿÿH)ÊH$üHïD$DD$èDÜüÿL}D$D¶D$éÙüÿÿUHåSHìHXÕ HøÿtHKÕ HëÿÐHHøÿuñHÄ[]ÐHìè÷úÿHÄÃStatModel.EMnclusterscovMatTypemaxItersepsilonmeanscovs/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/ertrees.cppopencv-ml-extremely-randomized-treesCvERTrees do not support surrogate splitsCvERTrees do not support data updateThe array of _responses must be an integer or floating-point vector containing as many elements as the total number of samples in the training data matrixThe memory buffer cannot be allocated since its size exceeds integer fields limitThe many folds in cross-validation for such a small dataset%d-th value of %d-th (categorical) variable is not an integer%d-th value of %d-th (categorical) variable is too large%d-th value of %d-th (ordered) variable (=%g) is too largeEvery class weight should be positive_subsample_idx must be null for extra-trees<nactive_vars> must be non-negative(active_var_mask->cols >= 1) && (params.nactive_vars > 0) && (params.nactive_vars <= active_var_mask->cols)Inner function failed.No training data has been set17CvERTreeTrainData14CvForestERTree9CvERTreesset_datatrain?ÿÿÿ~¿4/4ð?>à?ÿÿÿÿÿÿÿÿÿÿ/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/em.cppstartStep == EM::START_AUTO_STEP || startStep == EM::START_E_STEP || startStep == EM::START_M_STEPcovMatType == EM::COV_MAT_GENERIC || covMatType == EM::COV_MAT_DIAGONAL || covMatType == EM::COV_MAT_SPHERICAL!probs || (!probs->empty() && probs->rows == nsamples && probs->cols == nclusters && (probs->type() == CV_32FC1 || probs->type() == CV_64FC1))!weights || (!weights->empty() && (weights->cols == 1 || weights->rows == 1) && static_cast<int>(weights->total()) == nclusters && (weights->type() == CV_32FC1 || weights->type() == CV_64FC1))!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)/build/opencv/opencv-2.4.8+dfsg1/modules/core/include/opencv2/core/mat.hpp!covsEigenValues.empty()!means.empty()sample.type() == CV_64FC1sample.rows == 1sample.cols == means.colsisTrained()!sample.empty()vector::_M_fill_insert!samples.empty()samples.channels() == 1nclusters > 0nclusters <= nsamples!covs.empty()!covs[clusterIndex].empty()meansFlt.type() == CV_32FC1!clusterSamples.empty()weights.empty()invCovsEigenValues.empty()d.cols == 1 || d.rows == 1N2cv2EMEpredictcheckTrainDatadecomposeCovsclusterTrainSamplescomputeLogWeightDivDetdoTraindiagcomputeProbabilitiesµ¾dÈñgý?ÿÿÿÿÿÿï°<°¼,Cëâ6ÿ/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/boost.cppUnknown/unsupported boosting typeThe boosted tree ensemble has not been trained yetThe classifier has not been trained yetthe input sample must be 1d floating-point vector with the same number of elements as the total number of variables or as the number of variables used for trainingthe missing data mask must be 8-bit vector of the same size as input sampleThe output matrix of weak classifier responses must be valid floating-point vector of the same number of components as the length of input sliceone of input categorical variable is not an integerIn raw mode the input vectors must be continuousThe number of trees stored does not match <ntrees> tag valueBoosted trees can only be used for 2-class classification.boosting_typeDiscreteAdaboostRealAdaboostLogitBoostGentleAdaboostUnknown boosting typesplitting_criteriaDefaultGiniMisclassificationSquaredErrntreesweight_trimming_rateopencv-ml-boost-tree<trees> tag is missingmy_boost_tree11CvBoostTree7CvBoostpredictu<ä7~ñh㈵øä>wJëÿï?À$@Ûíۜ'@?ÿÿÿÈB/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/data.cppdelimited, miss_character and flt_separator must be differentit`s impossible to assign CV_VAR_ORDERED type to categorical variabletrain samples count is not correctdata is emptyvar_idx is not correcttype is not correctordcattypes string is not correcttest_sample_count > 0variable index is not correctidx value is not correct
8CvMLDataset_train_test_split4ÿÿopencv-ml-svmvar_allclass_countclass_labelsclass_weightsvar_idxsv_totalsupport_vectorsfdecision_functionssv_countrhoalphaindexNU_SVCC_SVCONE_CLASSEPS_SVRNU_SVRPOLYLINEARRBFSIGMOIDsvm_typekerneldegreegammacoef0nuterm_criteriaiterationsToo small storage block sizeCvSVM::trainUnknown/unsupported SVM typeGrid step must greater then 1CvSVM::train_autoCvSVM::get_default_gridsvm_type tag is not foundSVM kernel tag is not foundMissing of invalid SVM typemy_svmrho is missingoptimize_linearsv_count != 0/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/svm.cppAssertion: sv_count == sv_total failedThe SVM should be trained firstUnknown/unsupported kernel typegamma parameter of the kernel must be positiveThe kernel parameter <coef0> must be positive or zeroThe kernel parameter <degree> must be positiveThe parameter C must be positiveThe parameter nu must be between 0 and 1The parameter p must be positiveINTERNAL ERROR: Unknown SVM type, the SVM structure is probably corruptedLower bound of the grid must be less then the upper oneLower bound of the grid must be positiveParameter <k_fold> must be > 1Invalid type of parameter (use one of CvSVM::C, CvSVM::GAMMA et al.)SVM kernel type tag is not foundMissing of invalid SVM kernel typeThe requested element is not foundSVM model data is invalid, check sv_count, var_* and class_count tagsArray of class labels is missing or invalidvar_idx array is missing or invalidMissing or invalid sequence of support vectorsAssertion: var_count == 1 || (CV_NODE_IS_SEQ(sv_elem->tag) && sv_elem->data.seq->total == var_count) faileddecision_functions is missing or is not a collection or has a wrong number of elementssv_count is missing or non-positivealpha is missing in the decision functionAssertion: sv_count == 1 || (CV_NODE_IS_SEQ(alpha_node->tag) && alpha_node->data.seq->total == sv_count) failedindex is missing in the decision functionAssertion: sv_count == 1 || (CV_NODE_IS_SEQ(index_node->tag) && index_node->data.seq->total == sv_count) failedparams.class_weights must be 1d floating-point vector containing as many elements as the number of classesWhile cross-validation one or more of the classes have been fell out of the sample. Try to enlarge <CvSVMParams::k_fold>11CvSVMKernel11CvSVMSolver5CvSVMdo_trainpredict16predict_body_svmZb××çtiÿÿÿÿÿÿïÿð¿ ð?nz/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/testset.cppsamples parameter must be not NULLresponses parameter must be not NULLnum_samples parameter must be positivenum_features parameter must be positivenum_classes parameter must be positiveInvalid type parameter/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/inner_functions.cppThe function supports only single-channel arraysUnsupported combination of input and output vectorsCould not open the file storage. Check the path and permissionsthe index array must be 1-dimensionalComponent mask should contain as many elements as the total number of input variablesNo components/input_variables is selected!index array may not contain more elements than the total number of input variablesUnsupported index array data type (it should be 8uC1, 8sC1 or 32sC1)the index array elements are out of rangeThere are duplicated index array elementsInvalid or absent var_type arrayvar_type array must be 1-dimensionaltype mask must be 8uC1 or 8sC1 arraytype mask must be of <input var count> + 1 sizevar index array should be continuous 1-dimensional integer vectorResponse array must be 1-dimensionalResponse array must contain as many elements as the total number of samplessample index array should be continuous 1-dimensional integer vectorsample index array is too largeout_response_map pointer is NULLInvalid or NULL training data matrixtrain data must be floating-point matrixmissing value mask must be 8-bit matrix of the same size as training dataUnknown training data layout (must be CV_ROW_SAMPLE or CV_COL_SAMPLE)output pointer to train samples is NULLoutput response pointer is NULLINTERNAL ERROR: some of the args are NULL pointersclasses array must be a single row of integersThe sample is not a valid vectorInput sample must have 32fC1 typeInput sample must be 1-dimensional vectorThe sample size is different from what has been used for trainingINTERNAL ERROR: The row_sample pointer is NULLINTERNAL ERROR: invalid comp_idxThe output matrix of probabilities is invalidThe matrix of probabilities must be 1-dimensional vector of 32fC1 typeThe vector of probabilities must contain as many elements as the number of classes in the training setArray of output labels is not a valid matrixArray of cluster centers is not a valid matrixProbability matrix is not validAssertion: sample_idx->rows == 1 && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 failedAssertion: comp_idx->rows == 1 && CV_MAT_TYPE(comp_idx->type) == CV_32SC1 failedAssertion: labels->rows == 1 failedArray of output labels should be 1d vectorSize of vector of output labels is not equal to the total number of input samplesAssertion: labels->cols == samples_selected failedInvalid number of rows in matrix of output centersNumber of columns in matrix of output centers is not equal to the total number of components in the input samplesAssertion: centers->cols == dims_selected failedInvalid number of columns in output probability matrixNumber of rows in output probability matrix is not equal to the total number of input samplesAssertion: probs->rows == samples_selected failedSome of input arguments is not the CvMatAssertion: free_response == out_n failedAssertion: ICV_IS_MAT_OF_TYPE(probs, CV_64FC1) failedAssertion: ICV_IS_MAT_OF_TYPE(labels, CV_32SC1) failedAssertion: nsamples == labels->cols failedAssertion: (int)cvSum(counts).val[0] == nsamples failedAssertion: maxprob_loc >= 0 failedCvStatModel::readCvStatModel::writemy_stat_modelInvalid index arrayvar index array is too largeInvalid response arrayUnsupported response typeresponse #%d is not integralThere is only a single classNULL labelsNULL centersNULL probsAssertion: prob_vector failed11CvStatModel/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/ann_mlp.cppSome of new output training vector components run exceed the original range too muchinput_scale tag is not found or is invalidoutput_scale tag is not found or is invalidinv_output_scale tag is not found or is invalidweights tag is not found or is invalidThe array of layer neuron counters must be an integer vectorthere should be at least one input and one output and every hidden layer must have more than 1 neuronThe network has not been created. Use method create or the appropriate constructorinput training data should be a floating-point matrix withthe 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 withthe 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 matchsample_weights (if passed) must be a valid matrixsample_weights must be 1d floating-point vector containing weights of all or selected training samplessome of sample weights are negativeThe network has not been initializedBoth input and output must be floating-point matrices of the same type and have the same number of rowsinput matrix must have the same number of columns as the number of neurons in the input layeroutput matrix must have the same number of columns as the number of neurons in the output layerUnknown activation functionlayer_sizesinput_scaleinv_output_scaleopencv-ml-ann-mlpSIGMOID_SYMIDENTITYGAUSSIANactivation_functionf_param1f_param2min_valmax_valmin_val1max_val1training_paramsBACKPROPtrain_methoddw_scalemoment_scaleRPROPdw0dw_plusdw_minusdw_mindw_maxlayer_sizes != 0my_nn9CvANN_MLPpredictpredict10rprop_loopUUUUUUå?jMóStû?ÿÿÿÿÿÿß@ffffffæ?ð;ü©ñÒMbP?CvKNearest::train/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/knearest.cppCould not allocate memory for responsesThe newly added data have different dimensionalitymax_k must be a positive numberThe search tree must be constructed first using train methodInput samples must be floating-point matrix (<num_samples>x<var_count>)The results must be 1d vector containing as much elements as the number of samplesThe results must be floating-point or integer (in case of classification) vectork must be within 1..max_k rangeThe neighbor responses (if present) must be floating-point matrix of <num_samples> x <k> sizeThe distances from the neighbors (if present) must be floating-point matrix of <num_samples> x <k> size10CvKNearestfind_nearest2P1opencv-ml-bayesiancls_labelsproductsumavginv_eigen_valuescov_rotate_matscNumber of classes is less 1my_nb/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/nbayes.cppThe new training data is inconsistent with the original training dataIn the current implementation the new training data must have absolutely the same set of class labels as used in the original training dataNo "cls_labels" in NBayes classifierThe field "var_count" of NBayes classifier is missingThe input samples must be 32f matrix with the number of columns = var_allWhen the number of input samples is >1, the output vector of results must be passedThe output array must be integer or floating-point vector with the number of elements = number of rows in the input matrix23CvNormalBayesClassifierpredict12predict_bodyàÀàÿÿïGopencv-ml-random-treesInvalid CvRTrees objectnclassesnactive_varsoob_errorvar_importancemy_random_treesvar_count == data->var_count/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/rtrees.cppThis method is not supported for regression problemsSome <nclasses>, <nsamples>, <var_count>, <nactive_vars>, <oob_error>, <ntrees> of tags are missing<ntrees> is not equal to the number of trees saved in file0 <= class_idx && class_idx < nclassesThis function works for binary classification problems only...12CvForestTree8CvRTreesN2cv25ForestTreeBestSplitFinderEfind_best_splittrainget_train_errorpredictpredict_probwritereadAbsoluteLossSquaredLossHuberLossDevianceLossloss_functionensemble_lengthshrinkagesubsample_portionUnknown loss functionbase_valuetrees_<trees_x> tag is missing/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/gbt.cppclass_labels must stored as a matrixResponse should be a 32fC1 or 32sC1 vector._sample_idx should be a 32sC1, 8sC1 or 8uC1 vector.basic_string::_S_construct null not validopencv-ml-gradient-boosting-treesThe model has not been trained yet9CvGBTreestrain14Tree_predictor16Sample_predictor¹?ÍÌL>/build/opencv/opencv-2.4.8+dfsg1/modules/ml/src/tree.cppparams.max_categories should be >= 2params.max_depth should be >= 0params.cv_folds should be =0 (the tree is not pruned) or n>0 (tree is pruned using n-fold cross-validation)params.regression_accuracy should be >= 0some of the tree elements are not stored properlysplits tag must stored as a sequencepriors must stored as a matrixvar_idx (if exist) must be valid 1d integer vector containing <var_count> elementssome of var_idx elements are out of rangevar_type must exist and be a sequence of 0's and 1'sBoth cat_count and cat_map must exist and be valid 1d integer vectors of an appropriate sizesome of cat_count elements are out of rangecat_map vector length is not equal to the total number of categories in all categorical varssome of the splits are not stored properlySplit variable index is out of rangeEither 'in' or 'not_in' tags should be inside a categorical split datasome of in/not_in elements are out of rangeThe new training data must have the same types and the input and output variables and the same categories for categorical variablesThe tree has not been trained yetthe input sample must be 1d floating-point vector with the same number of elements as the total number of variables used for trainingbest_tree_idxnodesopencv-ml-treenorm_class_idxTncomplexitynode_risktree_risktree_errorsplitsis_classifierord_var_countcat_var_countuse_surrogatesmax_categoriesregression_accuracymax_depthmin_sample_countcross_validation_foldsuse_1se_ruletruncate_pruned_treepriorsvar_typecat_countcat_mapincorrect node depthnodes tag is missingroot->leftroot->rightvarnot_ingtqualitymy_tree16CvDTreeTrainData7CvDTreeN2cv20DTreeBestSplitFinderEset_datado_trainpredict;ð¸¢úÿ¨®úÿ X®úÿ®úÿ¸®úÿ0(è®úÿà-¯úÿÀ:H¯úÿ ;x¯úÿØ@¨¯úÿÀGدúÿØG°úÿK8°úÿÐNh°úÿxV°úÿVȰúÿH_(±úÿ`p(²úÿð²úÿ¨³úÿ8H»úÿpø»úÿ¸¼úÿÐ¨½úÿ 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