A B C D E F G H I J K L M N O P Q R S T U V W

M

m_Assignments - Variable in class codebook_generation.SimpleKMeansWithOutput

Assignments obtained.

m_centroidIndex - Variable in class codebook_generation.SimpleKMeansWithOutput.KMeansComputeCentroidTask

m_cluster - Variable in class codebook_generation.SimpleKMeansWithOutput.KMeansComputeCentroidTask

m_clusterAssignments - Variable in class codebook_generation.SimpleKMeansWithOutput.KMeansClusterTask

m_ClusterCentroids - Variable in class codebook_generation.SimpleKMeansWithOutput

holds the cluster centroids.

m_ClusterMissingCounts - Variable in class codebook_generation.SimpleKMeansWithOutput

m_ClusterNominalCounts - Variable in class codebook_generation.SimpleKMeansWithOutput

For each cluster, holds the frequency counts for the values of each nominal attribute.

m_ClusterSizes - Variable in class codebook_generation.SimpleKMeansWithOutput

The number of instances in each cluster.

m_ClusterStdDevs - Variable in class codebook_generation.SimpleKMeansWithOutput

Holds the standard deviations of the numeric attributes in each cluster.

m_completed - Variable in class codebook_generation.SimpleKMeansWithOutput

m_displayStdDevs - Variable in class codebook_generation.SimpleKMeansWithOutput

Display standard deviations for numeric atts.

m_DistanceFunction - Variable in class codebook_generation.SimpleKMeansWithOutput

the distance function used.

m_dontReplaceMissing - Variable in class codebook_generation.SimpleKMeansWithOutput

Replace missing values globally?

m_end - Variable in class codebook_generation.SimpleKMeansWithOutput.KMeansClusterTask

m_executionSlots - Variable in class codebook_generation.SimpleKMeansWithOutput

m_executorPool - Variable in class codebook_generation.SimpleKMeansWithOutput

For parallel execution mode

m_failed - Variable in class codebook_generation.SimpleKMeansWithOutput

m_FastDistanceCalc - Variable in class codebook_generation.SimpleKMeansWithOutput

whether to use fast calculation of distances (using a cut-off).

m_FullMeansOrMediansOrModes - Variable in class codebook_generation.SimpleKMeansWithOutput

Stats on the full data set for comparison purposes.

m_FullMissingCounts - Variable in class codebook_generation.SimpleKMeansWithOutput

m_FullNominalCounts - Variable in class codebook_generation.SimpleKMeansWithOutput

m_FullStdDevs - Variable in class codebook_generation.SimpleKMeansWithOutput

m_initializeWithKMeansPlusPlus - Variable in class codebook_generation.SimpleKMeansWithOutput

Whether to initialize cluster centers using the k-means++ method

m_inst - Variable in class codebook_generation.SimpleKMeansWithOutput.KMeansClusterTask

m_Iterations - Variable in class codebook_generation.SimpleKMeansWithOutput

Keep track of the number of iterations completed before convergence.

m_MaxIterations - Variable in class codebook_generation.SimpleKMeansWithOutput

Maximum number of iterations to be executed.

m_NumClusters - Variable in class codebook_generation.SimpleKMeansWithOutput

number of clusters to generate.

m_PreserveOrder - Variable in class codebook_generation.SimpleKMeansWithOutput

Preserve order of instances.

m_ReplaceMissingFilter - Variable in class codebook_generation.SimpleKMeansWithOutput

replace missing values in training instances.

m_squaredErrors - Variable in class codebook_generation.SimpleKMeansWithOutput

Holds the squared errors for all clusters.

m_start - Variable in class codebook_generation.SimpleKMeansWithOutput.KMeansClusterTask

main(String[]) - Static method in class codebook_generation.CodebookGeneration

main(String[]) - Static method in class codebook_generation.SampleLocalFeatures

main(String[]) - Static method in class codebook_generation.SimpleKMeansWithOutput

Main method for executing this class.

main(String[]) - Static method in class dimensionality_reduction.PCALearning

This method can be used for learning a PCA projection matrix.

main(String[]) - Static method in class dimensionality_reduction.PCAProjection

main(String[]) - Static method in class evaluation.EvaluationFromFile

main(String[]) - Static method in class experimental_data_creation.BestFeatureData

main(String[]) - Static method in class experimental_data_creation.BestVocSizeData

main(String[]) - Static method in class experimental_data_creation.FeatureFilteringIntrinsicData

main(String[]) - Static method in class experimental_data_creation.FeatureFilteringVocData

main(String[]) - Static method in class experimental_data_creation.MultiVocData

main(String[]) - Static method in class experimental_data_creation.SumVsMeanAggregationData

main(String[]) - Static method in class feature_extraction.ImageScaling

main(String[]) - Static method in class feature_extraction.SURForSIFTExtractionExample

main(String[]) - Static method in class feature_filtering.AbstractFeatureFiltering

main(String[]) - Static method in class feature_filtering.DiscretizationFilterCreation

This method uses the DiscretizationFilterCreation class to learn a discretization filter from an arff formatted dataset file containing real-valued features (e.g.

main(String[]) - Static method in class product_quantization.CoarseQuantizerLearning

main(String[]) - Static method in class product_quantization.ProductQuantizerLearning

main(String[]) - Static method in class utilities.DescriptorsIO

main(String[]) - Static method in class utilities.IndexTransformation

main(String[]) - Static method in class utilities.Normalization

main(String[]) - Static method in class vector_aggregation.VladAggregatorWithFiltering

This method can be used for calculating the percentage values in the distribution method.

maxFeaturesPerScale - Variable in class feature_extraction.SURFExtractor

The maximum features extracted per scale.

maxIterationsTipText() - Method in class codebook_generation.SimpleKMeansWithOutput

Returns the tip text for this property.

maxNumVectors - Variable in class data_structures.AbstractSearchStructure

The maximum number of vectors that can be indexed.

maxPixelsScaling(BufferedImage) - Method in class feature_extraction.ImageScaling

This method returns a scaled instance of the provided BufferedImage.

mean - Variable in class dimensionality_reduction.PCA

mean values of each element across all the samples

method - Variable in class utilities.ImageIOGreyScale.ContainsFilter

minFeatureIntensity - Variable in class feature_extraction.SURFExtractor

The minimum intensity threshold.

modified - Variable in class feature_extraction.SURFExtractor

The type of SURF to extract.

moveCentroid(int, Instances, boolean, boolean) - Method in class codebook_generation.SimpleKMeansWithOutput

Move the centroid to it's new coordinates.

msurf(Class<T>) - Static method in class utilities.boofcv_extensions.FactoryDescribePointAlgsNormalization

MultiVocData - Class in experimental_data_creation

This class can be use to generate the datasets of the multiple vocabularies experiment.

MultiVocData() - Constructor for class experimental_data_creation.MultiVocData