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native.win_x64.opencv_ml246.dll Maven / Gradle / Ivy

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???LH?\$WH?? H??H?=??H?H??t??H??H;?r?H?\$0H?? _?H?\$WH?? H??H?=??H?H??t??H??H;?r?H?\$0H?? _??%?S??????????H???MZf9t3??HcH??H??@H??@?????H??@?t??????H??@H??h?????H??h?4>??????H????$>??????H??@?>??????H????̺??????H??h??=??????H?????=??????H??@??=??????H???錺??????@UH?? H??H?UHH?MX?J???H?? ]?????H??X?=??????H??X?????????@UH?? H??H?UHH?MX?
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?e ?H?MX?=??H?? ]???????????@UH?? H??E ????t?e ?H??????V?j?z????????????????~?R?????,?L?T?^?f?x??????X`t????????????d?B??????f?B??????|?`?D?"??????|?H????????j?B??????j?Unknown activation function..\..\..\src\opencv\modules\ml\src\ann_mlp.cppjM?St??UUUUUU????????????????The 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 neuronInner function failed.@???<"???  ???2???"??? Some of new output training vector components run exceed the original range too much?????????????????"??? input 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) layersome of sample weights are negativesample_weights (if passed) must be a valid matrixsample_weights must be 1d floating-point vector containing weights of all or selected training samplesThe numbers of input and output samples do not matchThe network has not been created. Use method create or the appropriate constructor"? iterationsepsilonterm_criteriadw_maxdw_mindw_minusdw_plusdw0RPROPmoment_scaledw_scaletrain_methodBACKPROPtraining_paramsmax_val1min_val1max_valmin_valf_param2f_param1activation_functionGAUSSIANSIGMOID_SYMIDENTITYweightsinv_output_scaleoutput_scaledinput_scalelayer_sizesopencv-ml-ann-mlpThe network has not been initialized???`A????;?C?u??pi?@}? ??}? 9?0?????`!?03??o?`[?`???@J?@????(??-??;?b?"???0"??? ffffff??"?px8?? [??Q?"????"? "?l?@????????333333??I@????????input_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 invalid"??? "?p ?("???0"?? #d!poutput matrix must have the same number of columns as the number of neurons in the output layerinput matrix must have the same number of columns as the number of neurons in the input layerBoth input and output must be floating-point matrices of the same type and have the same number of rows"??"	?"?layer_sizes != 0wJ?????h㈵??>"?0#8# ???@???????????>????`a?0??0*? ??????f?@??g? 8????>???? ?????`g?0?? ????? ?????????p??????? .? 5?@6??6??7??;?@9?@[?`W?p=??????@?????"?<$D$ ??P???????????д?в????0?? ?? ????????????????P????????Unknown/unsupported boosting type..\..\..\src\opencv\modules\ml\src\boost.cpp"??$?$`Boosted trees can only be used for 2-class classification.The boosted tree ensemble has not been trained yetweight_trimming_ratentreessplitting_criteriaboosting_typeSquaredErrMisclassificationGiniDefaultGentleAdaboostLogitBoostRealAdaboostDiscreteAdaboost"?h&p&0 tag is missingThe number of trees stored does not match  tag valuetreesThe classifier has not been trained yetopencv-ml-boost-treevector too long"??&?& "?<'D' "?t'|' "??'?' my_boost_tree"??'?'P"?,(4(0Unknown boosting type"?\(d( "??(?( "?)) "?* *???4"?h*p*`"??*?*P"?+ +?$@??@"?`+h+P"? ,?+0, "??,?, "??,?,X"?T-\-`??"??-?-X"??-?-8"?D.L.8????B"?/;?/In raw mode the input vectors must be continuousone of input categorical variable is not an integerThe output matrix of weak classifier responses must be valid floating-point vector of the same number of components as the length of input slicethe missing data mask must be 8-bit vector of the same size as input samplethe 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 trainingdata is empty..\..\..\src\opencv\modules\ml\src\data.cppdelimited, miss_character and flt_separator must be differentvar_idx is not correctit`s impossible to assign CV_VAR_ORDERED type to categorical variabletype is not correctvariable index is not correct"??2?2 types string is not correctcatord"??3?3 "??3?3 idx value is not correct"??4?4 "??4?4 "?<55L5 "??5?5 "?06@6 "??6?6 "??6?6 "?77 "?47D7 "?t7	?78test_sample_count > 04??train samples count is not correct"??78 "?,8<8 "?d8t8 "??8?8 "??8?8 "?99 map/set too long"?@9X9 "??9?9 invalid map/set iterator"??9?9 "?::0"?h:@:x: "?;,;("?\;d; "??;?;(
"??;< "?(<8< "?`<h< ?h?X?"??<?< "?=	=???rt"?d=|= ?<"??=?= "?(>0>0"?P>h> "??>?>("??>?> "? ?(? "?????p"???@ "?0@H@ "??@?@ "? A?@8A0"??A?A?A("?4lBiD8probsmeans!m.empty() && m.size() == covSize && (m.channels() == 1)!covs || (!covs->empty() && static_cast(covs->size()) == nclusters)!means || (!means->empty() && means->rows == nclusters && means->cols == dim && means->channels() == 1)!weights || (!weights->empty() && (weights->cols == 1 || weights->rows == 1) && static_cast(weights->total()) == nclusters && (weights->type() == CV_32FC1 || weights->type() == CV_64FC1))!probs || (!probs->empty() && probs->rows == nsamples && probs->cols == nclusters && (probs->type() == CV_32FC1 || probs->type() == CV_64FC1))covMatType == EM::COV_MAT_GENERIC || covMatType == EM::COV_MAT_DIAGONAL || covMatType == EM::COV_MAT_SPHERICALstartStep == EM::START_AUTO_STEP || startStep == EM::START_E_STEP || startStep == EM::START_M_STEPnclusters <= nsamplesnclusters > 0samples.channels() == 1!samples.empty()..\..\..\src\opencv\modules\ml\src\em.cpp"??G?GH!covsEigenValues.empty()"? \H4\Ix??d??g??sample.cols == means.colssample.rows == 1sample.type() == CV_64FC1!means.empty()"?(K8K "??K?K@d.cols == 1 || d.rows == 1C:/slave/builds/WinInstallerMegaPack/src/opencv/modules/core/include\opencv2/core/mat.hpp"?|L?L@!sample.empty()isTrained()"??M?MN0"??N?N ??p????0???Q?`??`??p?? ?0??Ь???"?hO
?O "??P@Q8!covs[clusterIndex].empty()!covs.empty()"?,R!?RX!clusterSamples.empty()meansFlt.type() == CV_32FC1"?	?S8TP,C??6???invCovsEigenValues.empty()weights.empty()"?U?U?"??V?V@"??V	Wx"?xW?W@???~..\..\..\src\opencv\modules\ml\src\ertrees.cpp_subsample_idx must be null for extra-treesNo training data has been set"??X?X `???H????????t?px??#???t?Z?n?`Z? q??Z??I?J??^??j??t??"??X?X ?/"?YY @? ??_?"??*?`??%? L??"??#??#?0?? ???%?`??B??D?E?`F?@"??E?"?4Y??G?`a?@G?0*?0G?????f?@??g? 8??G??>?????+?PX?pb?P9??D? %? Q?@l???? 3?p??????? .? 5?@6??6??7??;?@9?@[?`W?p=????F???PG?"??Y?Y "??Y?Y "??YZ "?dZtZX"??Z?ZX4"?,[4[?"??[?[P"??[\?"?x\	?\?"?]]?"?X]h]?"??]	?]?The 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 matrixEvery class weight should be positive%d-th value of %d-th (ordered) variable (=%g) is too large%d-th value of %d-th (categorical) variable is not an integer%d-th value of %d-th (categorical) variable is too largeThe many folds in cross-validation for such a small datasetThe memory buffer cannot be allocated since its size exceeds integer fields limitCvERTrees do not support data updateCvERTrees do not support surrogate splits"?<^D^(opencv-ml-extremely-randomized-trees"?x^
?^x(active_var_mask->cols >= 1) && (params.nactive_vars > 0) && (params.nactive_vars <= active_var_mask->cols) must be non-negative??L>"?Ta\a ??????"?xa?a class_labelssubsample_portionshrinkageensemble_lengthloss_functionDevianceLossHuberLossAbsoluteLossSquaredLoss"??a?a ?????p??"??a?a "?tb|b ?????????????@??????0?????????????p??`??М????`?? ??@????"??b?b"?cc "?Pcxc "??c?c0..\..\..\src\opencv\modules\ml\src\gbt.cppUnknown loss functionclass_labels must stored as a matrix"?dd"??d?d "??d?dP"?e e ????????p??@??????????????????????"??f?f P????"?gg "?Lg\g "??g?gP"??g?g(??"?,h tag is missingmy_stat_model@??*??????????`?????Could not open the file storage. Check the path and permissions..\..\..\src\opencv\modules\ml\src\inner_functions.cppCvStatModel::writeCvStatModel::readInvalid index arrayUnsupported index array data type (it should be 8uC1, 8sC1 or 32sC1)the index array elements are out of rangeThere are duplicated index array elementsNo components/input_variables is selected!Component mask should contain as many elements as the total number of input variablesindex array may not contain more elements than the total number of input variablesthe index array must be 1-dimensionalInvalid or absent var_type arrayvar index array should be continuous 1-dimensional integer vectorvar index array is too largetype mask must be of  + 1 sizetype mask must be 8uC1 or 8sC1 arrayvar_type array must be 1-dimensionalInvalid response arraysample index array should be continuous 1-dimensional integer vectorsample index array is too largeUnsupported response typeResponse array must contain as many elements as the total number of samplesResponse array must be 1-dimensionalThere is only a single classresponse #%d is not integralout_response_map pointer is NULLInvalid or NULL training data matrixtrain data must be floating-point matrixUnknown training data layout (must be CV_ROW_SAMPLE or CV_COL_SAMPLE)missing value mask must be 8-bit matrix of the same size as training dataoutput response pointer is NULLoutput pointer to train samples is NULLINTERNAL ERROR: some of the args are NULL pointersclasses array must be a single row of integersThe function supports only single-channel arraysUnsupported combination of input and output vectorsNULL probsAssertion: probs->rows == samples_selected failedNumber of rows in output probability matrix is not equal to the total number of input samplesNULL centersInvalid number of columns in output probability matrixAssertion: centers->cols == dims_selected failedNumber of columns in matrix of output centers is not equal to the total number of components in the input samplesInvalid number of rows in matrix of output centersNULL labelsAssertion: labels->cols == samples_selected failedSize of vector of output labels is not equal to the total number of input samplesArray of output labels should be 1d vectorAssertion: comp_idx->rows == 1 && CV_MAT_TYPE(comp_idx->type) == CV_32SC1 failedAssertion: sample_idx->rows == 1 && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 failedAssertion: labels->rows == 1 failedProbability matrix is not validArray of cluster centers is not a valid matrixArray of output labels is not a valid matrixSome of input arguments is not the CvMatAssertion: free_response == out_n failedAssertion: num_of_clusters >= 0 failedAssertion: prob_vector failedAssertion: ICV_IS_MAT_OF_TYPE(probs, CV_64FC1) failedAssertion: ICV_IS_MAT_OF_TYPE(labels, CV_32SC1) failedAssertion: maxprob_loc >= 0 failedAssertion: (int)cvSum(counts).val[0] == nsamples failedAssertion: nsamples == labels->cols failedThe sample is not a valid vectorInput sample must be 1-dimensional vectorThe output matrix of probabilities is invalidThe vector of probabilities must contain as many elements as the number of classes in the training setThe matrix of probabilities must be 1-dimensional vector of 32fC1 typeINTERNAL ERROR: invalid comp_idxINTERNAL ERROR: The row_sample pointer is NULLThe sample size is different from what has been used for trainingInput sample must have 32fC1 typemax_k must be a positive numberThe newly added data have different dimensionalityCould not allocate memory for responses..\..\..\src\opencv\modules\ml\src\knearest.cppCvKNearest::train"??r?r "??r?r h?`;?@+???????`??????;??+??8??<?D??3??.?"?ss "?@sHs@"?psxs???:?`B?"??s?s "??s?s "??t?t@"??t?t`"?u<v?The distances from the neighbors (if present) must be floating-point matrix of  x  sizeThe neighbor responses (if present) must be floating-point matrix of  x  sizek must be within 1..max_k rangeThe results must be floating-point or integer (in case of classification) vectorThe results must be 1d vector containing as much elements as the number of samplesInput samples must be floating-point matrix (x)The search tree must be constructed first using train method"? x(x "?hxxx "??x?x StatModel.EM"?yPyhcovsmaxIterscovMatTypenclusters"??y?y my_nbh?c??U???????e?`k?`u??V? v?0}?"?tz|z In the current implementation the new training data must have absolutely the same set of class labels as used in the original training data???..\..\..\src\opencv\modules\ml\src\nbayes.cppThe new training data is inconsistent with the original training data"??z?z "?{{ "?@{H{0"?p{x{???d??w?"??{?{ ccov_rotate_matsinv_eigen_valuesavgproductsumsumcountcls_labelsvar_idxvar_allvar_countopencv-ml-bayesianThe field "var_count" of NBayes classifier is missingNumber of classes is less 1No "cls_labels" in NBayes classifier"?,|4|0"??|?|?????G"? }?}?The output array must be integer or floating-point vector with the number of elements = number of rows in the input matrixWhen the number of input samples is >1, the output vector of results must be passedThe input samples must be 32f matrix with the number of columns = var_all	 ? @? 0@??0@? h
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CvSVM::train"?H?P? pnuCcoef0gammadegreetypekernelsvm_typeSIGMOIDRBFPOLYLINEARNU_SVREPS_SVRONE_CLASSNU_SVCC_SVCCvSVM::get_default_gridInvalid type of parameter (use one of CvSVM::C, CvSVM::GAMMA et al.)Too small storage block size"????? my_svm"? ?(? "?`?h?0The SVM should be trained first"?????h??? ??"??? "?0?H? Assertion: sv_count == sv_total failediindexalpharhosv_countdecision_functionsfsupport_vectorssv_totalclass_weightsopencv-ml-svmMissing of invalid SVM kernel typeSVM kernel type tag is not foundMissing of invalid SVM typeSVM kernel tag is not foundsvm_type tag is not found"??? 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