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fracpete / multisearch-weka-package

Weka package for parameter optimization, similar to GridSearch, but with arbitrary number of parameters.
GNU General Public License v3.0
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What is the name of this package? #2

Closed xraywu closed 9 years ago

xraywu commented 9 years ago

Hello, I am trying to install the multisearch pacakge from package manager but seems I can not find the package. Is it available now and what the name is it? Following is the package list I found - 

Installed   Repository  Loaded  Package
=========   ==========  ======  =======
-----       1.2.1       No  AnDE: Averaged N-Dependence Estimators (includes A1DE and A2DE)
-----       1.0.0       No  ArabicStemmers_LightStemmers: Arabic Stemmer / Light Stemmer 
-----       1.0         No  CAAR: Context Aware Case-Based Regression Learner
-----       1.0.1       No  CHIRP: CHIRP: A new classifier based on Composite Hypercubes on Iterated Random Projections
-----       1.0.1       No  CLOPE: CLOPE: a fast and effective clustering algorithm for transactional data
-----       1.0.0       No  CVAttributeEval: An Variation degree Algorithm to explore the space of attributes.
-----       1.0.1       No  DMNBtext: Class for building and using a Discriminative Multinomial Naive Bayes classifier
-----       1.0.3       No  DTNB: Class for building and using a decision table/naive Bayes hybrid classifier.
-----       1.0.1       No  DilcaDistance: Learning distance measure for categorical data
-----       1.0.0       No  DistributionBasedBalance: Distribution-based balancing of datasets
-----       1.0         No  EAR4: Case-Based Regression Learner
-----       1.0.1       No  EMImputation: Replaces missing numeric values using Expectation Maximization with a multivariate normal model.
-----       1.0.0       No  EvolutionarySearch: An Evolutionary Algorithm (EA) to explore the space of attributes.
-----       1.0.0       No  GPAttributeGeneration: Genetic Programming Attribute Generation
-----       1.0.1       No  IBkLG: Log and Gaussian kernel for K-NN
-----       1.0.0       No  IWSS: Incremental Wrapper Subset Selection
-----       1.0.0       No  IWSSembeddedNB: Incremental Wrapper Subset Selection with embedded NB classifier
-----       3.0         No  J48Consolidated: Class for generating a pruned or unpruned C45 consolidated tree
-----       1.0.3       No  J48graft: Class for generating a grafted (pruned or unpruned) C4.5 decision tree
-----       1.0.0       No  JDBCDriversDummyPackage: Dummy package that provides a place to drop JDBC driver jar files so that they get loaded by the system.
-----       1.0.1       No  LVQ: Cluster data using the Learning Vector Quantization algorithm.
-----       1.9.5       No  LibLINEAR: A wrapper class for the liblinear classifier
-----       1.0.6       No  LibSVM: A wrapper class for the libsvm tools
-----       1.0.0       No  MODLEM: MODLEM rule algorithm
-----       1.0.0       No  MTreeClusterer: MTree Clusterer
-----       1.0.1       No  NNge: Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules)
-----       0.9.1       No  OpenmlWeka: Openml Weka
-----       1.0.0       No  PCP: Parallel Coordinates Plot
-----       1.2.0       No  PSOSearch: An implementation of the Particle Swarm Optimization (PSO) algorithm to explore the space of attributes.
-----       1.0.8       No  RBFNetwork: Classes that implement radial basis function networks.
-----       1.1.6       No  RPlugin: Execute R Scripts
-----       1.1.0       No  RerankingSearch: Meta-Search algorithm which performs a Hybrid feature selection based on re-ranking
-----       1.0.3       No  SMOTE: Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).
-----       1.0.1       No  SPegasos: Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007).
-----       1.0.1       No  SSF: Simplified Silhouette Filter
-----       1.0.1       No  SVMAttributeEval: Evaluates the worth of an attribute by using an SVM classifier.
-----       1.0.3       No  SelfOrganizingMap: Cluster data using the Kohonen's Self-Organizing Map algorithm.
-----       1.4.4       No  SparseGenerativeModel: Sparse Generative Model
-----       1.0.0       No  StudentFilters: Student Filters
-----       1.0.6       No  TPP: Targeted Projection Pursuit
-----       1.0.5       No  WekaExcel: WEKA MS Excel loader/saver
-----       1.0.4       No  WekaODF: WEKA ODF loader/saver
-----       1.0.4       No  XMeans: Cluster data using the X-means algorithm.
-----       1.0.6       No  alternatingDecisionTrees: Binary-class alternating decision trees and multi-class alternating decision trees.
-----       1.0.0       No  alternatingModelTrees: Alternating Model Trees
-----       1.0.1       No  anonymizationPackage: A Filter to apply k-anonymization and l-diversity
-----       1.0.1       No  associationRulesVisualizer: A visualization component for displaying association rules that uses a modified version of the Association Rules Viewer from DESS IAGL of Lille.
-----       1.0.7       No  attributeSelectionSearchMethods: Four search methods for attribute selection: ExhaustiveSearch, GeneticSearch, RandomSearch and RankSearch.
-----       1.2.1       No  averagedOneDependenceEstimators: Family of methods for learning ensembles of naive Bayes-like classifiers (with relaxed independence assumption) - includes AODE, WAODE and AODEsr
-----       1.0.0       No  baggedLocalOutlierFactor: Filter implementing the Bagged LOF outlier/anomaly detection algorithm.
-----       1.0.2       No  bayesianLogisticRegression: Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors
-----       1.0.4       No  bestFirstTree: Class for building a best-first decision tree classifier.
-----       1.0.3       No  cascadeKMeans: k-means clustering with automatic selection of k
-----       1.0.1       No  cassandraConverters: Loader and saver for the cassandra NoSQL database
-----       1.0.4       No  chiSquaredAttributeEval: Attribute evaluator that evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.
-----       1.0.1       No  citationKNN: Modified version of the Citation kNN multi instance classifier
-----       1.0.3       No  classAssociationRules: Class association rules algorithms (including an implementation of the CBA algorithm).
-----       1.0.3       No  classificationViaClustering: A simple meta-classifier that uses a clusterer for classification.
-----       1.0.1       No  classificationViaRegression: Class for doing classification using regression methods.
-----       1.0.5       No  classifierBasedAttributeSelection: A subset evaluator and an attribute evaluator for evaluating the merit of subsets and single attributes respectively using a classifier.
-----       1.0.1       No  classifierErrors: A visualization component for displaying errors from numeric schemes using the JMathTools library.
-----       1.0.1       No  clojureClassifier: Wrapper classifiers for classifiers implemented in the Clojure programming language
-----       1.0.3       No  complementNaiveBayes: Class for building and using a Complement class Naive Bayes classifier.
-----       1.0.4       No  conjunctiveRule: This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
-----       1.0.4       No  consistencySubsetEval: Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
-----       1.0.3       No  costSensitiveAttributeSelection: Two meta attribute selection evaluators (one attribute-based and the other subset-based) for performing cost-sensitive attribute selection.
-----       1.0.3       No  dagging: This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
-----       1.0.2       No  decorate: DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples
-----       1.0.3       No  denormalize: An instance filter that collapses instances with a common grouping ID value into a single instance.
-----       1.0.0       No  discriminantAnalysis: Code for discriminant analysis
-----       1.0.12      No  distributedWekaBase: Generic configuration classes and distributed map/reduce type tasks for Weka
-----       1.0.14      No  distributedWekaHadoop: Hadoop wrappers for Weka
-----       1.0.0       No  distributedWekaSpark: Spark wrappers for Weka
-----       1.0.5       No  ensembleLibrary: Manages a libary of ensemble classifiers
-----       1.0.3       No  ensemblesOfNestedDichotomies: A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
-----       1.0.1       No  extraTrees: Package for generating a single Extra-Tree
-----       1.0.1       No  fastCorrBasedFS: Feature selection method based on correlation measureand relevance and redundancy analysis
-----       1.0.1       No  filteredAttributeSelection: Two meta attribute selection evaluators (one attribute-based and the other subset-based) for filtering data before performing attribute selection.
-----       1.0.3       No  functionalTrees: Classifier for learning Functional Trees
-----       1.0.1       No  fuzzyLaticeReasoning: The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment
-----       1.0.2       No  fuzzyUnorderedRuleInduction: Fuzzy Unordered Rule Induction Algorithm
-----       1.0.1       No  gaussianProcesses: Implements Gaussian Processes for regression without hyperparameter-tuning.
-----       1.0.1       No  generalizedSequentialPatterns: Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set
-----       1.0.1       No  grading: Implements Grading. The base classifiers are "graded".
-----       1.0.0       No  graphgram: GraphGram - Visualization for Clusterings
-----       1.0.9       No  gridSearch: Performs a grid search of parameter pairs for the a classifier.
-----       1.0.1       No  hiddenNaiveBayes: Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC
-----       1.0.0       No  hiveJDBC: A package containing the JDBC driver and dependencies for the Apache Hive database, along with a DatabaseUtils.props file for use with Weka.
-----       1.0.5       No  hotSpot: HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest.
-----       1.0.1       No  hyperPipes: Class implementing a HyperPipe classifier.
-----       1.0.0       No  imageFilters: A package that contains filters to process image files.
-----       1.0.1       No  isolationForest: Class for building and using a classifier built on the Isolation Forest anomaly detection algorithm.
-----       1.0.1       No  isotonicRegression: Learns an isotonic regression model.
-----       1.0.2       No  jfreechartOffscreenRenderer: Offscreen chart renderer plugin for the Knowledge Flow that uses JFreeChart
-----       1.0.0       No  jsonFieldExtractor: Extract fields from repeating JSON structures.
-----       1.0.0       No  kernelLogisticRegression: A package that contains a class to train a two-class kernel logistic regression model.
-----       1.0.9       No  kfGroovy: A Knowledge Flow plugin that provides a Knowledge Flow step that wraps around a Groovy script.
-----       1.0.3       No  kfKettle: A Knowledge Flow plugin that serves as a data source for data coming from the Kettle ETL tool.
-----       1.0.2       No  kfPMMLClassifierScoring: A Knowledge Flow plugin that provides a Knowledge Flow step for scoring test sets or instance streams using a PMML classifier.
-----       1.0.2       No  latentSemanticAnalysis: Performs latent semantic analysis and transformation of the data
-----       1.0.0       No  lazyAssociativeClassifier: Lazy Associative Classifier
-----       1.0.1       No  lazyBayesianRules: Lazy Bayesian Rules Classifier
-----       1.0.1       No  leastMedSquared: Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions.
-----       1.0.1       No  levenshteinEditDistance: Computes the Levenshtein edit distance between two strings
-----       1.0.1       No  linearForwardSelection: Extension of BestFirst that takes a restricted number of k attributes into account.
-----       1.0.4       No  localOutlierFactor: Filter implementing the Local Outlier Factor (LOF) outlier/anomaly detection algorithm.
-----       1.0.2       No  massiveOnlineAnalysis: MOA (Massive On-line Analysis).
-----       1.0.3       No  metaCost: This metaclassifier makes its base classifier cost-sensitive using Pedro Domingo's method.
-----       1.0.1       No  multiBoostAB: Class for boosting a classifier using the MultiBoosting method.
-----       1.0.5       No  multiInstanceFilters: A collection of filters for manipulating multi-instance data.
-----       1.0.7       No  multiInstanceLearning: A collection of multi-instance learning classifiers.
-----       1.0.7       No  multiLayerPerceptrons: This package currently contains classes for training multilayer perceptrons with one hidden layer for classification and regression, and autoencoders.
-----       1.0.1       No  multilayerPerceptronCS: An extension of the standard MultilayerPerceptron classifier in Weka that adds context-sensitive Multiple Task Learning (csMTL)
-----       1.0.1       No  naiveBayesTree: Class for generating a decision tree with naive Bayes classifiers at the leaves.
-----       1.0.1       No  normalize: An instance filter that normalize instances considering only numeric attributes and ignoring class index
-----       1.0.4       No  oneClassClassifier: Performs one-class classification on a dataset.
-----       1.0.5       No  optics_dbScan: The OPTICS and DBSCAN clustering algorithms
-----       1.0.1       No  ordinalClassClassifier: Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
-----       1.0.1       No  ordinalLearningMethod: An implementation of the Ordinal Learning Method (OLM)
-----       1.0.1       No  ordinalStochasticDominance: An implementation of the Ordinal Stochastic Dominance Learner
-----       1.0.1       No  paceRegression: Class for building pace regression linear models and using them for prediction.
-----       1.0.1       No  partialLeastSquares: Partial least squares filter and classifier for performing PLS regression.
-----       1.0.4       No  predictiveApriori: Class implementing the predictive apriori algorithm for mining association rules.
-----       1.0.2       No  prefuseGraph: A visualization component for displaying graphs that uses the prefuse visualization toolkit.
-----       1.0.2       No  prefuseGraphViewer: A Knowledge Flow visualization component for displaying trees and graphs that uses the prefuse visualization toolkit.
-----       1.0.2       No  prefuseTree: A visualization component for displaying trees that uses the prefuse visualization toolkit.
-----       1.0.1       No  probabilisticSignificanceAE: Evaluates the worth of an attribute by computing the Probabilistic Significance as a two-way function
-----       1.0.1       No  raceSearch: Races the cross validation error of competing attribute subsets.
-----       1.0.1       No  racedIncrementalLogitBoost: Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
-----       1.0.1       No  realAdaBoost: Class for boosting a 2-class classifier using the Real Adaboost method.
-----       1.0.1       No  regressionByDiscretization: A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
-----       1.0.1       No  ridor: An implementation of a RIpple-DOwn Rule learner.
-----       1.0.2       No  rotationForest: Ensembles of decision trees trained on rotated subsamples of the training data.
-----       1.0.0       No  sasLoader: SAS sas7bdat file reader
-----       1.0.5       No  scatterPlot3D: A visualization component for displaying a 3D scatter plot of the data using Java 3D.
-----       1.0.1       No  scriptingClassifiers: Wrapper classifiers for Jython and Groovy scripting code.
-----       1.0.1       No  sequentialInformationalBottleneckClusterer: Cluster data using the sequential information bottleneck algorithm.
-----       1.0.4       No  simpleCART: Class implementing minimal cost-complexity pruning.
-----       1.0.1       No  simpleEducationalLearningSchemes: Simple learning schemes for educational purposes (Prism, Id3, IB1 and NaiveBayesSimple).
-----       1.0.1       No  stackingC: Implements StackingC (more efficient version of stacking)
-----       1.0.0       No  supervisedAttributeScaling: A simple filter to rescale attributes to reflect their discriminative power.
-----       1.0.1       No  tabuAndScatterSearch: Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch)
-----       1.0.1       No  tertius: Finds rules according to confirmation measure (Tertius-type algorithm)
-----       1.0.3       No  thresholdSelector: A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
-----       1.0.0       No  tigerjython: TigerJython
-----       1.0.0       No  timeSeriesFilters: Time Series Filters
-----       1.0.15      No  timeseriesForecasting: Time series forecasting environment.
-----       1.0.3       No  userClassifier: Interactively classify through visual means.
-----       1.0.1       No  votingFeatureIntervals: Classification by voting feature intervals.
-----       1.0.1       No  wavelet: A filter for wavelet transformation.
-----       1.0.4       No  wekaServer: Simple server for executing Weka tasks.
-----       1.0.1       No  winnow: Implements Winnow and Balanced Winnow algorithms by Littlestone
fracpete commented 9 years ago

The multi-search package is an unofficial package and not listed in the "official" package repository (see this list here). You have to download a release zip file and then install it in the package manager using the "File/URL" button.