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ModelmodelIParallelget_RunInParallelset_RunInParallelparallelAccord.MachineLearning.VectorMachines.ISupportVectorMachine.KernelIKernelTKernelAccord.MachineLearning.VectorMachines.ISupportVectorMachine.get_KernelAccord.MachineLearning.VectorMachines.ISupportVectorMachine.set_KernelIRadiallySymmetricKernelCreateKernelEstimateKernelUniformKernelkernelAccord.MachineLearning.dllSPCellallowNullFileStreamget_BaseStreamget_EndOfStreamMemoryStreamstreamTParamparamHistogramget_Itemset_ItemGetKeyForItemitemSystemget_Logarithmset_Logarithmget_UseLogarithmset_UseLogarithmget_Algorithmset_AlgorithmCreateAlgorithmGetDefaultClusteringAlgorithmalgorithmAccord.Math.Randomget_RandomBottomJaggedReducedRowEchelonFormNormUniformuniformAccord.MachineLearning.ICovariantTransform>.TransformAccord.MachineLearning.ICovariantTransform>.TransformAccord.MachineLearning.ICovariantTransform.TransformAccord.MachineLearning.ICovariantTransform.TransformAccord.MachineLearning.ICovariantTransform.TransformITransformanormrenormwnormget_MaxDegreeOfParallelismset_MaxDegreeOfParallelismpartialSumweightSumget_Minimumset_Minimumget_Maximumset_MaximummaximumEnumsumsvmget_Meanset_MeancomputeMeanSquareEuclideanBooleanop_GreaterThanop_LessThanCartesianset_HessianhessianTimeSpanManhattanrandnget_NumberOfInstancesTakenset_NumberOfInstancesTakenget_NumberOfDescriptorsTakenset_NumberOfDescriptorsTakenget_Tokenset_Tokenget_CancellationTokenset_CancellationTokentokenget_Childrenset_Childrenchildrenget_Signget_MinArgMinpartialMinsiftDownMinsiftUpMinsupportMincomputeGaincomputeInfoGaingainAppDomainget_CurrentDomainSeekOriginget_Joinset_JoinjoinGetColumnInsertColumnoutputColumnget_Regionset_RegionsubRegionGetNodesInsideRegiongetNodesInsideRegionregionUnionDecisiondecisionget_Dimensionset_Dimensiondimensionget_VersionAccord.Statistics.Models.RegressionL1RegularizedLogisticRegressionL2RegularizedLogisticRegressionFromLogisticRegressionFromRegressionGeneralizedLinearRegressionMultipleLinearRegressionFanChenLinSupportVectorRegressionregressionSerializerCompressionLambdaExpressionBlockExpressionConditionalExpressionLabelExpressionToExpressionGotoExpressionParameterExpressionConstantExpressionNewExpressionUnaryExpressionBinaryExpressionCodificationSimplificationget_Validationset_ValidationCrossValidationSplitSetValidationget_Creationset_Creationget_StandardDeviationget_PooledStandardDeviationISupportsCancellationget_UseKernelEstimationset_UseKernelEstimationuseKernelEstimationestimationget_ComputeInformationset_ComputeInformationSplitInformationDistanceByEliminationDecideByEliminationProbabilisticOutputCalibrationget_UseAgglomerationset_UseAgglomerationVoronoiIterationget_CurrentIterationset_CurrentIterationTabuSearchExplorationRouletteWheelExplorationBoltzmannExplorationEpsilonGreedyExplorationget_Evaluationset_EvaluationSystem.GlobalizationSystem.Runtime.Serializationget_Initializationset_InitializationDoubleNormalizationAccord.Math.OptimizationFanChenLinQuadraticOptimizationSequentialMinimalOptimizationset_RegularizationregularizationChooseActionpreviousActionGetActionnextActionSetTabuActionSecurityActionwFractionget_Rejectionset_RejectionrejectionSystem.ReflectionICollectionTCollectionDecisionBranchNodeCollectionGridSearchRangeCollectionDecisionVariableCollectionGridSearchParameterCollectionGaussianClusterCollectionKMeansClusterCollectionMeanShiftClusterCollectionKeyCollectioncollectionget_Functionset_FunctionBootstrapFittingFunctionILinkFunctionLogLinkFunctionlinkFunctioncreationFunctionget_IterationFunctionset_IterationFunctionSupportVectorMachineLearningConfigurationFunctionlossFunctionLogProbabilityFunctionLogProbabilityDensityFunctionfunctionSupportVectorReductionop_AdditionGetGenericTypeDefinitionget_Positionset_Positionpositionget_InnerOptionset_InnerOptionNotImplementedExceptionNotSupportedExceptionConvergenceExceptionArgumentOutOfRangeExceptionAggregateExceptionDimensionMismatchExceptionArgumentNullExceptionInvalidOperationExceptionArgumentExceptionReadOnlyExceptionexceptionGetDescriptionget_Proportionset_ProportionuseClassLabelProportionget_TrainingSetProportionset_TrainingSetProportiontrainSetProportionget_ValidationSetProportionset_ValidationSetProportionproportionDistortionTDistributionget_Distributionset_DistributionHypergeometricDistributionChiSquareDistributionToMixtureDistributionIUnivariateDistributionToMultivariateDistributionGeneralDiscreteDistributionMultivariateNormalDistributionToDistributionUnivariateContinuousDistributionMultivariateContinuousDistributionget_Solutionset_Solutionsolutionget_Epsilonset_EpsilonepsilonmostCommontronget_Comparisonset_ComparisoncomparisonAccord.MachineLearning.IUnsupervisedLearning,T[],System.Int32>.LearnAccord.MachineLearning.IUnsupervisedLearning,System.Double[],System.Int32>.LearnAccord.MachineLearning.ISupervisedLearning,TInput,System.Int32>.LearnAccord.MachineLearning.ISupervisedLearning,TInput,System.Int32[]>.LearnAccord.MachineLearning.ISupervisedLearning,TInput,System.Boolean[]>.LearnAccord.MachineLearn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mEventArgsargsmsgsget_MaxEpochsset_MaxEpochsmaxEpochsGetLengths<>4__thisOneSampleHypothesisAccord.Statistics.Analysisget_Axisset_AxismaxCheckslocksAccord.Statistics.LinkslandmarksSystem.Threading.TasksDimensionEqualsSetEqualsget_Labelsset_Labelsget_ComputeLabelsset_ComputeLabelsGetLabelslabelsget_Modelsset_ModelsAllModelsmodelsAccord.Statistics.KernelsAccord.Statistics.Distributions.DensityKernelsallowNegativeSymbolsget_NumberOfSymbolssymbolsToolsitemsno_dimsBalancedKMeansMiniBatchKMeansget_Meanskmeansget_GaussianssignspointGainsContainsget_ColumnscolumnstrackDecisionsget_DimensionsdimensionsTypeExtensionsComparisonExtensionsSystem.Linq.ExpressionsSystem.Text.RegularExpressionsexpressionsSystem.Security.Permissionsget_Iterationsset_Iterationsget_MaximumIterationsset_MaximumIterationsget_MaximumNewtonIterationsset_MaximumNewtonIterationsget_MaxIterationsset_MaxIterationsmaxIterationsGetLastKernelEvaluationsget_MaxEvaluationsset_MaxEvaluationsmaxEvaluationsget_Initializationsset_Initializationsget_NumberOfInitializationsset_NumberOfInitializationsnumberOfInitializationstabuActionsactionsAccord.CollectionsSystem.CollectionsAccord.Math.DecompositionsCreatePartitionspartitionsTOptionsget_Optionsset_OptionsMixtureOptionsGeneralDiscreteOptionsIFittingOptionsget_NormalOptionsset_NormalOptionsGaussianMixtureModelOptionsget_ParallelOptionsset_ParallelOptionsparallelOptionsTInnerOptionsset_InnerOptionsStringSplitOptionsIndependentOptionsget_Exceptionsset_Exceptionsget_AggregateExceptionsset_AggregateExceptionsaggregateExceptionsexceptionsoptionsget_Proportionsget_ComputeProportionsset_ComputeProportionscomputeProportionsget_UseClassProportionsset_UseClassProportionsproportionsAccord.Statistics.Distributionsget_Distributionsset_DistributionsdistributionsZerosepsteachersget_InliersinliersAccord.MachineLearning.Boosting.LearnersAccord.Math.Comparersget_Parametersset_ParametersGetParametersget_BestParametersbestParametersAdaBoostParametersparametersAccord.Statistics.FiltersAccord.MachineLearning.IClusteringAlgorithm.ClustersAccord.MachineLearning.IClusteringAlgorithm.ClustersAccord.MachineLearning.IClusteringAlgorithm.get_ClustersAccord.MachineLearning.IClusteringAlgorithm.get_ClustersclusterspairsKNearestNeighborsGetNearestNeighborsneighborsget_Priorsset_PriorsclassPriorspriorsget_Errorsset_Errorserrorscursorsget_Vectorsset_VectorscomputeSharedVectorssharedVectorsget_RightSingularVectorsget_SupportVectorsset_SupportVectorssupportVectorsget_NumberOfDescriptorsset_NumberOfDescriptorsget_TotalNumberOfDescriptorsset_TotalNumberOfDescriptorsget_CenterOfMassPerClassGetClassifierForClasscenter_of_massFileAccessAssemblyBuilderAccessget_Progressset_ProgressprogressCompresssupressTLossget_Lossset_LossLogisticLossLogLikelihoodLossHingeLossZeroOneLossSquareLossevaluateLossHammingLosslossSyncObjectssyncObjectsProductsupdateSetstargetsSubsetssubsetsTWeightsget_Weightsset_WeightsshuffledWeightsget_NotSupportedWeightssampleWeightsFromWeightsToWeightsweightsGetLastKernelHitsCreateValidationSplitsfoldResultsresultsget_Antecedentsantecedentsget_Coefficientscoefficientsget_NumberOfDocumentsnumberOfDocumentsget_ComponentscomponentstotalNumberOfPointsbuildFromPointspointsget_CountstotalCountsanswerCountstotalDescriptorCountscountscostsget_Inputsset_InputsshuffledInputsAccord.MachineLearning.ITransform.NumberOfInputsAccord.MachineLearning.ITransform.get_NumberOfInputsAccord.MachineLearning.ITransform.set_NumberOfInputsGetNumberOfInputspreCheckInputsinputsget_Outputsset_OutputsshuffledOutputsexpectedOutputsAccord.MachineLearning.ITransform.NumberOfOutputsAccord.MachineLearning.ITransform.get_NumberOfOutputsAccord.MachineLearning.ITransform.set_NumberOfOutputspreCheckOutputsoutputstextsget_Radiusset_RadiusradiusKMeansPlusPluscomputeInfoContinuousRowsget_KeysRemoveAtConcatAppendFormatAccord.Compatget_Compactset_Compactget_IsCompactisCompactleftRectrightRectsyncObjectobjectSelecthyperrectIsCorrectDistinctProductGetget_TrainingSetset_TrainingSetget_IndicesTrainingSetset_IndicesTrainingSettrainingSethasKernelBeenSetDecisionSetget_ValidationSetset_ValidationSetget_IndicesValidationSetset_IndicesValidationSetvalidationSetLabelTargettargetdatasetLearnSubsetoutputSubsetsubsetSystem.Collections.IEnumerator.Resetget_Leftset_LeftMeanShiftget_Rightset_RightGetHeightget_MaxHeightset_MaxHeightmaxHeightget_Weightset_Weightget_NegativeWeightset_NegativeWeightnegativeWeightget_PositiveWeightset_PositiveWeightpositiveWeightheightweightget_Fitset_Fitop_Implicitop_ExplicitfitBinarySplitsplitSystem.Reflection.EmitlimitInitskip_random_initExitget_DefaultGetValueOrDefaultFirstOrDefaultTResultget_ResultIAsyncResultBootstrapResultParallelLoopResultresultget_SignificantToUpperInvariantToLowerInvariantConstantProbabilisticCoordinateDescentProbabilisticDualCoordinateDescentLinearDualCoordinateDescentLinearRegressionCoordinateDescentLinearCoordinateDescentAveragedStochasticGradientDescentAntecedentantecedentget_Coefficientset_GradientconjugateGradientcomputeGradientcomputeE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i = {0}, h = {1}, tih = {2} }}[This solver type is unknown or not supported.solver_type nr_class label nr_feature bias G17wSVsolver_typenr_classnr_feature biasUnknown field: pointsEmpty node)Leaf node; data: {0}ACenter-of-mass: {0}, children: <{0}, {1}> valuesQInsufficient points for creating a tree.distance (null)(,) size1The collection is empty.{0}: {1}[(), {0}])MaxIterations must be higher than zero. The Balanced K-Means algorithm has a high chance of oscillating between possible answers without converging.xNot enough points. There should be more points than the number K of clusters.yData weights vector must be the same length as data samples. dataThe points matrix should be rectangular. The vector at position {} has a different length than previous ones.Batch size for initialization must be greater than the number of clusters.sBatch size for initialization must be a positive integer.yNumber of random initializations must be a positive integer.QBatch size should be a positive integer.No data supplied to Mini-Batch K-Means. The parameter x cannot be null.weightsNot enough points. There should be more points in the dataset than in a batch. k-Medoid #{0} not found.9Some medoids are not unique.7Some medoids are not unique valueThe value for k should be greater than zero and less than total number of input points.Weights are not supported yet. If are interested in the feature, please submit a request to the proejct's issue tracker. inputsoutputsThe number of input vectors should match the number of corresponding output labelsiThe weights vector should have the same length as x.Not enough data points to cluster. Please try to adjust the feature extraction algorithm to generate more points.There were not enough descriptors to sample the desired amount of samples ({0}). Please either increase the number of images, or increase the number of descriptors that are sampled from each image by adjusting the MaxSamplesPerImage property ({0}).7This property is read-only.KUnknown InverseDocumentFrequency: {0}5Unknown TermFrequency: {0} texts Text cannot be null.Token at text , position ! cannot be null.MThe number of clusters does not match. kmeansqThe number of distributions and clusters does not match.distributionsoThe number of coefficients and clusters does not match.coefficientsYValue must be positive and higher than zero.7Value most be non-negative.wThe weights and points vector must have the same dimension.STemperature should be greather than zero.EEpsilon should be between 0 and 1.aNumber of neighbors should be greater than zero.]Number of classes should be greater than zero.classes{0},{1} winner{0},{1}>{2}]One or more exceptions were thrown when teaching the machines. Please check the InnerException property of this AggregateException to discover what exactly caused this error. paircClass pair ({0}, {1}) does not have any examples.QNumber of classes must be higher than 1. indexgFitting function must have been previously defined.The number of folds can not exceed the total number of samples in the data set folds'validationFoldIndexcomponentskThe number of components should be greater than zero.mixture_All array parameters must have the same length._Index must be higher than 0 and less than size.GArgument should be between 0 and 1.UDiscount factor should be between 0 and 1.minSamplesthreshold[Probability should be a value between 0 and 1probabilityeA model could not be inferred from the data pointsMLearning rate must be between 0 and 1.QDiscount factor must be between 0 and 1. [^\w]Impossible to determine number of inputs because there are no training samples in this set.The weights vector must have the same length as the vector of training samples 'x'.mTraining algorithm needs at least one training vector.KInput vector at position {0} is null.The input vector at index {0} contains a NaN value at position {1}, which is not supported by this algorithm.The input vector at index {0} contains a infinity value at position {1}, which is not supported by this algorithm.The input vector ad index {0} contains a negative value at position {1}, which is not supported by this algorithm.yThe number of output labels should match the number of training samples.qThe output label at index {0} should be either +1 or -1.The output labels should had been either boolean or binary integers.+There are no samples for class label {0}. Please make sure that class labels are contiguous and there is at least one training sample for each label.This learning algorithm does not support negative class labels. The class label passed for class {0} is {1}.5The input vector at index + contains NaN values.5 contains infinity values.MThe size of the input vector at index does not match the expected number of inputs. All input vectors must have the same length OOnly linear machines can be compressed.Only double-precision linear machines can be converted to linear weights.If you are implementing your own support vector machine type, please override the Create method in your learning algorithm to instruct the framework how to instantiate a type of your new class. - eta  gives loss  -- loss =  -- cost = ! - using eta0 =  Epoch  - Learning -- wNorm:  -- wBias:  -- aNorm:  -- aBias: % -- epoch done in !- Computing loss# -- loss done in AConvergence could not be attained. Please reduce the cost of misclassification errors by reducing the complexity parameter C or try a different kernel function.aWARNING: reaching max number of inner iterationsiter = _#CD cycles 3=========================?optimization finished, #iter = UWARNING: reaching max number of iterations%Objective value = -#nonzeros/#features = /.OUsing -s 0 may be faster (also see FAQ)iOnly linear machines can be created in compact form.Shrinking heuristic can only be used if Strategy is set to SelectionStrategy.SecondOrder.!Unknown strategy#SMO: eta is zero.QKernel type does not support estimation.oPlease set the kernel function before learning a model.eAt least three points are required to fit a circle]At least two points are required to fit a linecAt least three points are required to fit a planePerplexity too large for the number of data points. For {0} points, should be less than {1} Exact?QPerplexity should be lower than K ({0}).data.datresult.datThe number of classses should be higher than 1 (currently detected: {0}).The number of inputs should be higher than 0 (currently detected: {0}).symbols priors -> ?; support: {0}, confidence: {1}[The number of folds must be higher than zero.Please set the Learner property before calling the Learn(x, y) method.The number of folds can not exceed the total number of samples in the data set.Some folds do not have any samples. Please decrease the number of data folds. RangenabstepSizeTrainingValidation7This property is read only.Please specify how the results of the different models should be combined by setting the CombineMethod property.IThe tree expects real-valued inputs.The input vector at index {0} contains an invalid entry ({1}) at column {2}. The tree expected this value to be between [{3}, {4}].{The number of input vectors and output labels does not match.oThe number of input vectors and weights does not match. is null.The input vector at index {0} has {1} dimensions but the tree expects {2}.The output label {0} at index {1} should be >= 0 and < {2} (the number of output classes expected by the tree).==>>=<<=!=AUnexpected node comparison type. nodes3Node collection is empty., !{0} ({1}) => {2}]The decision tree accepts less inputs than {0}eThe decision tree recognizes less classes than {0}aThe decision tree produces less outputs than {0}i// This file has been automatically generated by the//E// Accord Machine Learning Library7// The Accord.NET Framework=// http://accord-framework.net/namespace DecisionTrees{E using System.CodeDom.Compiler;# using System;# /// <summary>a /// Automatically generated decision tree.% /// </summary> /// k [GeneratedCode("Accord.NET DecisionTree", "{0}")]7 public static class {0} {+ /// <summary>k /// Assigns a class label to a given input.- /// </summary> /// e public static int Function(double[] input) {e for (int i = 0; i < input.Length; i++) {Y if (Double.IsNaN(input[i]));S throw new ArgumentException("Missing values are not supported at this time. Please open a new issue at the issue tracker if you need this feature."); } } }}if else if /(input[{0}] {1} {2}) {{else throw new ArgumentException("input", "Unexpected value at position .");return ;return -1;{0} : {1} ({2})3List of columns is empty. input returneInput contains a value outside of expected ranges.Attribute  is a constant.subtreeMThe node does not belong to this tree.The tree is degenerated. This is often a sign that the tree is expecting discrete inputs, but it was given only real values.WAccord.MachineLearning.DecisionTrees.CustomCompute Root{0} {1} {2}x[{0}] {1} {2} node{Only leaf nodes that have a parent can be converted to rules.({0}) && ({0}){0} =: {1}x[]1The tree is degenerated.cThe height must be greater than or equal to zero.uThe number of times must be greater than or equal to zero.SThe split step must be greater than zero. treewThe ID3 learning algorithm can only handle discrete inputs. modelsYThe number of models and weights must match.gOnly binary classifiers are supported at this time.)Uknown strategy: {0}Could not convert distances into probabilities that sum up to one during KMeans++ initialization.Could not select medoids. Execution should never had reached this state.[The number of centroids should be equal to K.WDuplicate medoid #{0} detected: {1} and {2}System.Collections.Generic.List`1[[Accord.MachineLearning.KMeansCluster, Accord.MachineLearning, Version=2.;Accord.MachineLearning.KMeans]Accord.MachineLearning.KMeansClusterCollectionIAccord.MachineLearning.KMeansClusteruAccord.MachineLearning.VectorMachines.SupportVectorMachineAccord.MachineLearning.VectorMachines.KernelSupportVectorMachine?SupportVectorMachine+inputCountCSupportVectorMachine+linkFunctionGSupportVectorMachine+supportVectors=SupportVectorMachine+threshold9SupportVectorMachine+weights kernelAccord.MachineLearning.VectorMachines.MulticlassSupportVectorMachineAccord.MachineLearning.VectorMachines.KernelSupportVectorMachine[]Accord.MachineLearning.VectorMachines.MultilabelSupportVectorMachineinputCountlinkFunctionsupportVectorsThe Kernel property has not been set and the UseKernelEstimation property is set to false. 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H  p( }      U ee e h % % hl \d  %  %             (((( (( (( ( ( ($( (, ((( ((%( ( ( ((|((M((Q(U((((( ( ( (  ( ((t( (t(|(|( ( (  ( ( ( ((( ( (( (((A}(%(((((( (((D(H(( ( ((qt(t(\(At( ( (t ((( ( ( ( ( ((h(p(p( (A (A (  (((((( (( ((((4( ( ( (! ( ( (  (e(qe ( (( ((% (( (( (A((X(h (X(d(T((\(<(D(d (t ( ((A}  (( ( (, (( ( ( ( (TWrapNonExceptionThrowsAccord.NET Framework0+Copyright (c) Accord.NET authors, 2009-2017 Accord.NET 3.8.0 3.8.0.6134Accord.MachineLearning*%Accord.NET - Machine Learning Libraryc]Accord.Tests.Math, PublicKey=0024000004800000940000000602000000240000525341310004000001000100039880a76dac76cddb9c85704c8a0e516773c28c0b202d9e0ae60b623b7bc554c7258bbf54ed6d98082964036109d4d970132b761f5b00a83079fbff2fbea283632a420ef5280dd2c5546e3f5da776191f7076a0966c06e7af21754fab55bdbdcddee5520632c3ebdc5908f6cdfb5b78d29123100f41faee0c29645e42455498nhAccord.Tests.MachineLearning, PublicKey=0024000004800000940000000602000000240000525341310004000001000100039880a76dac76cddb9c85704c8a0e516773c28c0b202d9e0ae60b623b7bc554c7258bbf54ed6d98082964036109d4d970132b761f5b00a83079fbff2fbea283632a420ef5280dd2c5546e3f5da776191f7076a0966c06e7af21754fab55bdbdcddee5520632c3ebdc5908f6cdfb5b78d29123100f41faee0c29645e42455498keAccord.Tests.Math.Interop, PublicKey=0024000004800000940000000602000000240000525341310004000001000100039880a76dac76cddb9c85704c8a0e516773c28c0b202d9e0ae60b623b7bc554c7258bbf54ed6d98082964036109d4d970132b761f5b00a83079fbff2fbea283632a420ef5280dd2c5546e3f5da776191f7076a0966c06e7af21754fab55bdbdcddee5520632c3ebdc5908f6cdfb5b78d29123100f41faee0c29645e42455498keAccord.Tests.Interop.Math, PublicKey=0024000004800000940000000602000000240000525341310004000001000100039880a76dac76cddb9c85704c8a0e516773c28c0b202d9e0ae60b623b7bc554c7258bbf54ed6d98082964036109d4d970132b761f5b00a83079fbff2fbea283632a420ef5280dd2c5546e3f5da776191f7076a0966c06e7af21754fab55bdbdcddee5520632c3ebdc5908f6cdfb5b78d29123100f41faee0c29645e42455498G.NETFramework,Version=v4.0TFrameworkDisplayName.NET Framework 4QLPlease use the default constructor and pass the texts to the Learn() method.+&Please use the Learn() method instead.$Please use Transform() instead.4/Please use the Transform(value) method instead.&!Please use Learn(inputs) instead.PKIt is not necessary to specify a value for the dimension parameter anymore.!Please use Learn(x) instead.TOPlease use KNearestNeighbors(int k, IDistance distance) constructor instead.1,Please use the Decide(input) method instead.>9Please use the Score(input, out decision) method instead.?:Please use the Scores(input, out decision) method instead.TOPlease specify the distance function using classes instead of lambda functions.83Please use CrossValidation instead.4/Please use Classes.Random(size, folds) instead.?:Please use Classes.Random(labels, classes, folds) instead.VQPlease set the properties of this class directly and use Learn(double[]) instead.gbPlease set the tolerance threshold using the Tolerance property and use Compute(double[]) instead.=8Please use KNearestNeighbors(int k) constructor instead.C>Please get the error value through this class' Error property.;6Please use the Decide, Distance or Probability methodsWRPlease use Accord.IO.Serializer.Save() instead (or use it as an extension method).ZUPlease use Accord.IO.Serializer.Load(stream) instead.XSPlease use Accord.IO.Serializer.Load(path) instead.]XPlease use Accord.IO.Serializer.Save(stream) instead (or use it as an extension method).[VPlease use Accord.IO.Serializer.Save(path) instead (or use it as an extension method).ZUPlease use Accord.IO.Serializer.Load(stream) instead.XSPlease use Accord.IO.Serializer.Load(path) instead.*%Please use the Decide method instead.83Please use the Decide or Transform methods instead. Please use Learn() instead.Please use Accord.Math.Optimization.ZeroOneLoss or any other losses of your choice from the Accord.Math.Optimization namespace.~Please use Accord.Math.Optimization.SquareLoss or any other losses of your choice from the Accord.Math.Optimization namespace.pkPlease do not pass parameters in the constructor. Use the default constructor and the Learn method instead.Please use Accord.Math.Optimization.BinaryCrossEntropyLoss or any other losses of your choice from the Accord.Math.Optimization namespace.:5Please use NaiveBayesLearning instead.-(Please use ZeroOneLoss function instead.Please use Decide instead.0+Please use Decide or LogLikelihood instead.50Please specify priors using the Priors property.@;Please specify the prior by writing to the Priors property.+&Please use NaiveBayesLearning instead.FAPlease use Accord.IO.Serializer.Load(stream) instead.D?Please use Accord.IO.Serializer.Load(path) instead.UPPlease use Accord.IO.Serializer.Load>(stream) instead.SNPlease use Accord.IO.Serializer.Load>(path) instead.% Please specify an owner instead./*Please use the Learn(x, y) method instead.!Please use Decide() instead.HCPlease use Accord.IO.Serializer.Load(stream) instead.FAPlease use Accord.IO.Serializer.Load(path) instead.,'Please use the Decide() method instead.$Please use Learn(x, y) instead..)Please use the ZeroOneLoss class instead.61Please use the parameterless constructor instead.-(Please used the Decide() method instead.0+Please use the default constructor instead.4/Please use the ThresholdLearning class instead. Unknown L2R_LRL2R_L2LOSS_SVC_DUALL2R_L2LOSS_SVCL2R_L1LOSS_SVC_DUAL MCSVM_CSL1R_L2LOSS_SVC L1R_LR L2R_LR_DUALL2R_L2LOSS_SVRL2R_L2LOSS_SVR_DUALL2R_L1LOSS_SVR_DUAL ItemThis class will be removed$Please use IClusterCollectionEx@;Please refer to Bootstrap instead.;6Please use Bootstrap instead.GBPlease use CrossValidationResult instead.=8Please refer to CrossValidation instead.A instead. This class will be removed.;6Please use SplitSetValidation instead.@;Please refer to SplitSetValidation instead.,'Please use SplitResult{TModel} instead.<7Please use GridSearch instead./*Accord.MachineLearning.KMeans+KMeansBinder.)Please use SupportVectorMachine.faAccord.MachineLearning.VectorMachines.KernelSupportVectorMachine+KernelSupportVectorMachineBinder@;Please use MulticlassSupportVectorMachine instead.niAccord.MachineLearning.VectorMachines.MulticlassSupportVectorMachine+MulticlassSupportVectorMachineBinder@;Please use MultilabelSupportVectorMachine instead.niAccord.MachineLearning.VectorMachines.MultilabelSupportVectorMachine+MultilabelSupportVectorMachineBinderZUAccord.MachineLearning.VectorMachines.SupportVectorMachine+SupportVectorMachineBindergbPlease use the InnerLearning property to configure OneVsOneLearning and OneVsRestLearning classes.HCPlease specify the desired kernel function as a template parameter.?:Please use OneclassSupportVectorLearning instead.=8Accord.MachineLearning.Bayes.NaiveBayes+NaiveBayesBinderIDAccord.MachineLearning.DecisionTrees.DecisionTree+DecisionTreeBinder.)Please use the .Learner property instead.$This interface will be removed.(#Please use NumberOfClasses instead.'"Please use NumberOfInputs instead.D?Please set ParallelOptions.MaxDegreeOfParallelism to 1 instead.(#Please use NumberOfOutputs instead.!Please use Gaussians instead#Please use the Count property.,'Please use the Models property instead.% Use the Models property instead.Please use Models instead.PKA machine in compact form is simply a machine with a single support vector.&!Please use MaxIterations instead.3.Please set the InnerLearning property instead.-(Please use the Configure method instead.QLTo generate compact linear machines, call the Compact method after creation.(#Please use NumberOfSymbols instead.&!Please use NumberOfTrees instead.-(Please use the Learner property instead.0+Please use the Comparison property instead.D.󆐨]8׆^Mh'1N|]%qzG~[j38+$-왰WyL?_X#,3p$z2VhjRwm`giD}(SnYTTRSDS˴ p@G%C:\Projects\Accord.NET\framework\Sources\Accord.MachineLearning\obj\net40\Accord.MachineLearning.pdb _CorDllMainmscoree.dll% 0HX4VS_VERSION_INFO?DVarFileInfo$Translation|StringFileInfoX000004b0d&CommentsAccord.NET - Machine Learning Library6 CompanyNameAccord.NETVFileDescriptionAccord.MachineLearning6 FileVersion3.8.0.6134VInternalNameAccord.MachineLearning.dll|,LegalCopyrightCopyright (c) Accord.NET authors, 2009-2017*LegalTrademarks^OriginalFilenameAccord.MachineLearning.dllJProductNameAccord.NET Framework0ProductVersion3.8.08Assembly Version3.8.0.0 8*0*  *H )0)10  `He0\ +7N0L0 +70 010  `He 숆ё ־mnʣN<ME䠂0o0WH`U6Ɋ֔0  *H  0{1 0 UGB10U Greater Manchester10U Salford10U Comodo CA Limited1!0U AAA Certificate Services0 210525000000Z 281231235959Z0V1 0 UGB10U Sectigo Limited1-0+U$Sectigo Public Code Signing Root R460"0  *H 0 "$t.#6G#+>}1QoRЇc$;}'keӥ iI9mn䠃'ښ +PK[d6~J=qas< 3W47]=v?fҔ1im4zxܒPdǪ-j/n!@ ٞ v' ֺ)p6.O2tFkG5w3Oc.-ekqm>,ugmcc6iE>nZ_[˝00@ 8gS0eoti>h;g3H@dFsleU[WxF|p@)\+EgO(^Lz>2F 6귪F : fX\) 08)00U2뒚5H/(@Bp 6\0U0U00U% 0 +0U 00U 0g 0CU<0:08642http://crl.comodoca.com/AAACertificateServices.crl04+(0&0$+0http://ocsp.comodoca.com0  *H  tDiF$ g:E}RN!9k^- y;SʹD\cg@Dr*2]@gB'r#8L FO&|fQ6TXl. 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