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Change Parameter Type data : Dataset to data : Dataframe in method protected _fit(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.XMinMaxNormalizer |
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Change Parameter Type newdata : Dataset to newdata : Dataframe in method protected abstract filterFeatures(newdata Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.FeatureSelection |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _convert(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXYMinMaxNormalizer |
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Change Parameter Type newData : Dataset to newData : Dataframe in method public transform(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.DataTransformer |
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Change Parameter Type testDataset : Dataset to testDataset : Dataframe in method public validate(testDataset Dataframe) : BaseMLmodel.ValidationMetrics in class com.datumbox.applications.nlp.TextClassifier |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.Kmeans |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected filterFeatures(newData Dataframe) : void in class com.datumbox.framework.machinelearning.featureselection.continuous.PCA |
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Change Parameter Type data : Dataset to data : Dataframe in method private removeRareFeatures(data Dataframe, featureCounts Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.CategoricalFeatureSelection |
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Change Parameter Type validationDataset : Dataset to validationDataset : Dataframe in method protected updateObservationAndClassifierWeights(validationDataset Dataframe, observationWeights AssociativeArray, idMapping FlatDataList) : Status in class com.datumbox.framework.machinelearning.ensemblelearning.Adaboost |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.Kmeans |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _denormalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXMinMaxNormalizer |
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Change Parameter Type newdata : Dataset to newdata : Dataframe in method protected filterFeatures(newdata Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.CategoricalFeatureSelection |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.regression.StepwiseRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private LibSVMTrainer(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.SupportVectorMachine |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _fit(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.XYMinMaxNormalizer |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected abstract predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLmodel |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.BernoulliNaiveBayes |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.SupportVectorMachine |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method public kFoldCrossValidation(trainingData Dataframe, trainingParameters TrainingParameters, k int) : BaseMLregressor.ValidationMetrics in class com.datumbox.framework.machinelearning.regression.StepwiseRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.topicmodeling.LatentDirichletAllocation |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.recommendersystem.CollaborativeFiltering |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private calculateClusters(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.HierarchicalAgglomerative |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected abstract predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLrecommender |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.BernoulliNaiveBayes |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.topicmodeling.LatentDirichletAllocation |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseBoostingBagging |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _fit(data Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.CategoricalFeatureSelection |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.regression.StepwiseRegression |
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Change Parameter Type testDataset : Dataset to testDataset : Dataframe in method public predict(testDataset Dataframe) : void in class com.datumbox.applications.nlp.TextClassifier |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected filterFeatures(newData Dataframe) : void in class com.datumbox.framework.machinelearning.featureselection.scorebased.TFIDF |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method public kFoldCrossValidation(trainingData Dataframe, trainingParameters TP, k int) : VM in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLmodel |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseDPMM |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected abstract _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.baseobjects.BaseTrainable |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseDPMM |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private calculateError(trainingData Dataframe, previousThitaMapping Map<Object,Object>, weights Map<Object,Double>, thitas Map<Object,Double>) : double in class com.datumbox.framework.machinelearning.classification.OrdinalRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private calculateFeatureWeights(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.Kmeans |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected validateModel(validationData Dataframe) : VM in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLclassifier |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private calculateClusters(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.Kmeans |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.regression.NLMS |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method public fit(trainingData Dataframe, trainingParameters TP) : void in class com.datumbox.framework.machinelearning.common.bases.baseobjects.BaseTrainable |
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Change Parameter Type dataSet : Dataset to dataSet : Dataframe in method private bivariateMatrix(dataSet Dataframe, type BivariateType) : DataTable2D in class com.datumbox.framework.statistics.descriptivestatistics.Bivariate |
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Change Parameter Type dataSet : Dataset to dataSet : Dataframe in method public pearsonMatrix(dataSet Dataframe) : DataTable2D in class com.datumbox.framework.statistics.descriptivestatistics.Bivariate |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.SoftMaxRegression |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected calculateSSE(validationData Dataframe) : double in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseLinearRegression |
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Change Parameter Type data : Dataset to data : Dataframe in method private filterData(data Dataframe, dbc DatabaseConnector, featureScores Map<Object,Double>, ignoringNumericalFeatures boolean) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.CategoricalFeatureSelection |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.BinarizedNaiveBayes |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.MaximumEntropy |
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Change Parameter Type newDataset : Dataset to newDataset : Dataframe in method public parseDataset(newDataset Dataframe, featureIdsReference Map<Object,Integer>) : MatrixDataset in class com.datumbox.common.dataobjects.MatrixDataset |
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Change Parameter Type data : Dataset to data : Dataframe in method protected denormalizeY(data Dataframe, minColumnValues Map<Object,Double>, maxColumnValues Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.SoftMaxRegression |
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Change Parameter Type newData : Dataset to newData : Dataframe in method public predict(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLmodel |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected validateModel(validationData Dataframe) : BaseMLregressor.ValidationMetrics in class com.datumbox.framework.machinelearning.regression.StepwiseRegression |
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Change Parameter Type data : Dataset to data : Dataframe in method protected denormalizeX(data Dataframe, minColumnValues Map<Object,Double>, maxColumnValues Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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Change Parameter Type originalData : Dataset to originalData : Dataframe in method protected _fit(originalData Dataframe) : void in class com.datumbox.framework.machinelearning.featureselection.continuous.PCA |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private calculateError(trainingData Dataframe, thitas Map<Object,Double>) : double in class com.datumbox.framework.machinelearning.regression.NLMS |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _fit(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXMinMaxNormalizer |
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Change Parameter Type dataset : Dataset to dataset : Dataframe in method private collapsedGibbsSampling(dataset Dataframe) : int in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseDPMM |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private batchGradientDescent(trainingData Dataframe, newThitas Map<Object,Double>, learningRate double) : void in class com.datumbox.framework.machinelearning.regression.NLMS |
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Change Parameter Type newData : Dataset to newData : Dataframe in method private predictAndValidate(newData Dataframe) : ValidationMetrics in class com.datumbox.framework.machinelearning.topicmodeling.LatentDirichletAllocation |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.recommendersystem.CollaborativeFiltering |
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Change Parameter Type validationDataset : Dataset to validationDataset : Dataframe in method protected abstract updateObservationAndClassifierWeights(validationDataset Dataframe, observationWeights AssociativeArray, idMapping FlatDataList) : Status in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseBoostingBagging |
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Change Parameter Type data : Dataset to data : Dataframe in method protected fitY(data Dataframe, minColumnValues Map<Object,Double>, maxColumnValues Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _fit(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXYMinMaxNormalizer |
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Change Parameter Type newData : Dataset to newData : Dataframe in method public predict(newData Dataframe) : void in class com.datumbox.applications.datamodeling.Modeler |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _denormalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.XMinMaxNormalizer |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.OrdinalRegression |
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Change Parameter Type data : Dataset to data : Dataframe in method protected fitX(data Dataframe, minColumnValues Map<Object,Double>, maxColumnValues Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.MaximumEntropy |
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Change Parameter Type dataSet : Dataset to dataSet : Dataframe in method public kendalltauMatrix(dataSet Dataframe) : DataTable2D in class com.datumbox.framework.statistics.descriptivestatistics.Bivariate |
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Change Parameter Type data : Dataset to data : Dataframe in method private buildFeatureStatistics(data Dataframe, classCounts Map<Object,Integer>, featureClassCounts Map<List<Object>,Integer>, featureCounts Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.CategoricalFeatureSelection |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _denormalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXYMinMaxNormalizer |
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Change Parameter Type validationDataset : Dataset to validationDataset : Dataframe in method protected updateObservationAndClassifierWeights(validationDataset Dataframe, observationWeights AssociativeArray, idMapping FlatDataList) : Status in class com.datumbox.framework.machinelearning.ensemblelearning.BootstrapAggregating |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseNaiveBayes |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.HierarchicalAgglomerative |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.regression.MatrixLinearRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method public fit_transform(trainingData Dataframe, trainingParameters TP) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.FeatureSelection |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private batchGradientDescent(trainingData Dataframe, newThitas Map<List<Object>,Double>, learningRate double) : void in class com.datumbox.framework.machinelearning.classification.SoftMaxRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.OrdinalRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method public fit_transform(trainingData Dataframe, trainingParameters TP) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.DataTransformer |
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Change Parameter Type trainingDataset : Dataset to trainingDataset : Dataframe in method protected _fit(trainingDataset Dataframe) : void in class com.datumbox.applications.nlp.TextClassifier |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private runRegression(trainingData Dataframe) : Map<Object,Double> in class com.datumbox.framework.machinelearning.regression.StepwiseRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private batchGradientDescent(trainingData Dataframe, previousThitaMapping Map<Object,Object>, newWeights Map<Object,Double>, newThitas Map<Object,Double>, learningRate double) : void in class com.datumbox.framework.machinelearning.classification.OrdinalRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.applications.datamodeling.Modeler |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private initializeClusters(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.Kmeans |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.featureselection.scorebased.TFIDF |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _normalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.XYMinMaxNormalizer |
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Change Parameter Type data : Dataset to data : Dataframe in method protected abstract _convert(data Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.DataTransformer |
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Change Parameter Type data : Dataset to data : Dataframe in method protected abstract _denormalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.DataTransformer |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.clustering.HierarchicalAgglomerative |
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Change Parameter Type dataSet : Dataset to dataSet : Dataframe in method public spearmanMatrix(dataSet Dataframe) : DataTable2D in class com.datumbox.framework.statistics.descriptivestatistics.Bivariate |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseBoostingBagging |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected validateModel(validationData Dataframe) : ValidationMetrics in class com.datumbox.framework.machinelearning.classification.OrdinalRegression |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _normalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXYMinMaxNormalizer |
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Change Parameter Type testData : Dataset to testData : Dataframe in method public validate(testData Dataframe) : BaseMLmodel.ValidationMetrics in class com.datumbox.applications.datamodeling.Modeler |
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Change Parameter Type dataSet : Dataset to dataSet : Dataframe in method public covarianceMatrix(dataSet Dataframe) : DataTable2D in class com.datumbox.framework.statistics.descriptivestatistics.Bivariate |
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Change Parameter Type data : Dataset to data : Dataframe in method protected abstract _normalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.DataTransformer |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.regression.NLMS |
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Change Parameter Type newData : Dataset to newData : Dataframe in method protected predictDataset(newData Dataframe) : void in class com.datumbox.framework.machinelearning.classification.SupportVectorMachine |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected validateModel(validationData Dataframe) : VM in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLclusterer |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.regression.MatrixLinearRegression |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected validateModel(validationData Dataframe) : VM in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseLinearRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method public fit(trainingData Dataframe, trainingParameters TP) : void in class com.datumbox.common.objecttypes.Trainable |
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Change Parameter Type dataset : Dataset to dataset : Dataframe in method public kFoldCrossValidation(dataset Dataframe, k int, dbName String, dbConf DatabaseConfiguration, aClass Class<? extends BaseMLmodel>, trainingParameters TP) : VM in class com.datumbox.framework.machinelearning.common.bases.validation.ModelValidation |
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Change Parameter Type data : Dataset to data : Dataframe in method protected transformDummy(data Dataframe, referenceLevels Map<Object,Object>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected validateModel(validationData Dataframe) : ValidationMetrics in class com.datumbox.framework.machinelearning.topicmodeling.LatentDirichletAllocation |
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Change Parameter Type newData : Dataset to newData : Dataframe in method public predict(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLrecommender |
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Change Parameter Type data : Dataset to data : Dataframe in method public denormalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.DataTransformer |
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Change Parameter Type newData : Dataset to newData : Dataframe in method public transform(newData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.featureselection.FeatureSelection |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private calculateError(trainingData Dataframe, thitas Map<List<Object>,Double>) : double in class com.datumbox.framework.machinelearning.classification.SoftMaxRegression |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.ensemblelearning.BayesianEnsembleMethod |
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Change Parameter Type data : Dataset to data : Dataframe in method private evaluateData(data Dataframe, estimateValidationMetrics boolean) : BaseMLmodel.ValidationMetrics in class com.datumbox.applications.datamodeling.Modeler |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private stochasticGradientDescent(trainingData Dataframe, newThitas Map<Object,Double>, learningRate double) : void in class com.datumbox.framework.machinelearning.regression.NLMS |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _normalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.XMinMaxNormalizer |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _denormalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.XYMinMaxNormalizer |
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Change Parameter Type dataset : Dataset to dataset : Dataframe in method public newInstance(dataset Dataframe, addConstantColumn boolean, featureIdsReference Map<Object,Integer>) : MatrixDataset in class com.datumbox.common.dataobjects.MatrixDataset |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _normalize(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXMinMaxNormalizer |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected validateModel(validationData Dataframe) : SoftMaxRegression.ValidationMetrics in class com.datumbox.framework.machinelearning.classification.SoftMaxRegression |
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Change Parameter Type validationData : Dataset to validationData : Dataframe in method protected abstract validateModel(validationData Dataframe) : VM in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLmodel |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method private IIS(trainingData Dataframe, EpFj_observed Map<List<Object>,Double>, Cmax double) : void in class com.datumbox.framework.machinelearning.classification.MaximumEntropy |
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Change Parameter Type dataset : Dataset to dataset : Dataframe in method private performClustering(dataset Dataframe, numberOfClusters int) : void in class com.datumbox.applications.nlp.CETR |
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Change Parameter Type data : Dataset to data : Dataframe in method protected normalizeY(data Dataframe, minColumnValues Map<Object,Double>, maxColumnValues Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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Change Parameter Type testingData : Dataset to testingData : Dataframe in method public validate(testingData Dataframe) : VM in class com.datumbox.framework.machinelearning.common.bases.mlmodels.BaseMLmodel |
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Change Parameter Type trainingData : Dataset to trainingData : Dataframe in method protected _fit(trainingData Dataframe) : void in class com.datumbox.framework.machinelearning.common.bases.basemodels.BaseNaiveBayes |
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Change Parameter Type data : Dataset to data : Dataframe in method protected _convert(data Dataframe) : void in class com.datumbox.framework.machinelearning.datatransformation.DummyXMinMaxNormalizer |
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Change Parameter Type testDataset : Dataset to testDataset : Dataframe in method private preprocessTestDataset(testDataset Dataframe) : void in class com.datumbox.applications.nlp.TextClassifier |
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Change Parameter Type data : Dataset to data : Dataframe in method protected normalizeX(data Dataframe, minColumnValues Map<Object,Double>, maxColumnValues Map<Object,Double>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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Change Parameter Type data : Dataset to data : Dataframe in method protected fitDummy(data Dataframe, referenceLevels Map<Object,Object>) : void in class com.datumbox.framework.machinelearning.common.bases.datatransformation.BaseDummyMinMaxTransformer |
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