文件列表:

wekacodenew7\main\java\weka\associations\AbstractAssociator.java (4817, 2013-01-14)
wekacodenew7\main\java\weka\associations\Apriori.java (63485, 2013-01-16)
wekacodenew7\main\java\weka\associations\AprioriItemSet.java (21922, 2013-01-14)
wekacodenew7\main\java\weka\associations\AssociationRule.java (5008, 2013-01-14)
wekacodenew7\main\java\weka\associations\AssociationRules.java (3428, 2013-01-14)
wekacodenew7\main\java\weka\associations\AssociationRulesProducer.java (2173, 2013-01-14)
wekacodenew7\main\java\weka\associations\Associator.java (1640, 2013-01-14)
wekacodenew7\main\java\weka\associations\AssociatorEvaluation.java (8539, 2013-01-14)
wekacodenew7\main\java\weka\associations\BinaryItem.java (2472, 2013-01-14)
wekacodenew7\main\java\weka\associations\CARuleMiner.java (2421, 2013-01-14)
wekacodenew7\main\java\weka\associations\CheckAssociator.java (55644, 2013-01-14)
wekacodenew7\main\java\weka\associations\DefaultAssociationRule.java (9605, 2013-01-14)
wekacodenew7\main\java\weka\associations\FilteredAssociationRules.java (3790, 2013-01-14)
wekacodenew7\main\java\weka\associations\FilteredAssociator.java (15784, 2013-01-14)
wekacodenew7\main\java\weka\associations\FPGrowth.java (79406, 2013-01-14)
wekacodenew7\main\java\weka\associations\Item.java (4456, 2013-01-14)
wekacodenew7\main\java\weka\associations\ItemSet.java (15459, 2013-01-16)
wekacodenew7\main\java\weka\associations\LabeledItemSet.java (12927, 2013-01-14)
wekacodenew7\main\java\weka\associations\NominalItem.java (3499, 2013-01-14)
wekacodenew7\main\java\weka\associations\NumericItem.java (4429, 2013-01-14)
wekacodenew7\main\java\weka\associations\SingleAssociatorEnhancer.java (5703, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\ASEvaluation.java (5110, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\ASSearch.java (3624, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\AttributeEvaluator.java (1345, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\AttributeSelection.java (34439, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\AttributeSetEvaluator.java (3029, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\AttributeTransformer.java (2191, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\BestFirst.java (24657, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\CfsSubsetEval.java (30685, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\CheckAttributeSelection.java (57155, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\CorrelationAttributeEval.java (15162, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\ErrorBasedMeritEvaluator.java (1080, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\GainRatioAttributeEval.java (11178, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\GreedyStepwise.java (23318, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\HoldOutSubsetEvaluator.java (2640, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\InfoGainAttributeEval.java (13899, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\OneRAttributeEval.java (12786, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\PrincipalComponents.java (34327, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\RankedOutputSearch.java (3650, 2013-01-14)
wekacodenew7\main\java\weka\attributeSelection\Ranker.java (17858, 2013-01-14)
... ...

=============================================================== KnowledgeFlow GUI Quick Primer =============================================================== What's new in the KnowledgeFlow: The KnowledgeFlow has undergone a major UI overhaul in Weka 3.7.4. New features include: * Move from tabbed toolbars of components to a tree * Multiple flow layouts (each in its own tab) * New icons for general controls * Support for cut, copy, paste and delete of multiple components * "Save as" as well as "Save" * Informational notes can now be added to the layout * Undo buffer * "Select all" button * Drag multiple components * Snap-to-grid * New buttons to run flow in parallel mode (all data sources launched simultaneously) and sequential mode (data sources launched one after the other in an order specified by the user) * Template flows demonstrating example learning processes * Plugin "perspectives" that allow additional functionality to take over the main UI Introduction: The KnowledgeFlow provides an alternative to the Explorer as a graphical front end to Weka's core algorithms. It presents a "data-flow" inspired interface to Weka. The user can select Weka components from a pallete, place them on a layout canvas and connect them together in order to form a "knowledge flow" for processing and analyzing data. At present, all of Weka's classifiers, filters, clusterers, loaders and savers are available in the KnowledgeFlow along with some extra tools. The KnowledgeFlow can handle data either incrementally or in batches (the Explorer handles batch data only). Of course learning from data incrementally requires a classifier that can be updated on an instance by instance basis. There are a number of schemes that can handle data incrementally: NaiveBayesUpdateable, IB1, IBk, LWR (locally weighted regression), SGD, SPegasos, Cobweb and RacedIncrementalLogitBoost. Features of the KnowledgeFlow: * intuitive data flow style layout * process data in batches or incrementally * process multiple batches or streams in parallel! (each separate flow executes in its own thread). Alternatively, multiple streams can be executed sequentially, in a user-specified order * chain filters together * view models produced by classifiers for each fold in a cross validation * visualize performance of incremental classifiers during processing (scrolling plots of classification accuracy, RMS error, predictions etc) * access additional non flow-based functionality through plugin "perspectives" Components available in the KnowledgeFlow: DataSources: All of Weka's loaders are available DataSinks: All of Weka's savers are available Filters: All of Weka's filters are available Classifiers: All of Weka's classifiers are available Clusterers: All of Weka's clusterers are available Evaluation: TrainingSetMaker - make a data set into a training set TestSetMaker - make a data set into a test set CrossValidationFoldMaker - split any data set, training set or test set into folds TrainTestSplitMaker - split any data set, training set or test set into a training set and a test set ClassAssigner - assign a column to be the class for any data set, training set or test set ClassValuePicker - choose a class value to be considered as the "positive" class. This is useful when generating data for ROC style curves (see below) ClassifierPerformanceEvaluator - evaluate the performance of batch trained/tested classifiers IncrementalClassifierEvaluator - evaluate the performance of incrementally trained classifiers ClustererPerformanceEvaluator - evaluate the performance of batch trained/tested clusterers PredictionAppender - append classifier predictions to a test set. For discrete class problems, can either append predicted class labels or probability distributions SerializedModelSaver - save a classifier out to a file for later use. Visualization: DataVisualizer - component that can pop up a panel for visualizing data in a single large 2D scatter plot ScatterPlotMatrix - component that can pop up a panel containing a matrix of small scatter plots (clicking on a small plot pops up a large scatter plot) AttributeSummarizer - component that can pop up a panel containing a matrix of histogram plots - one for each of the attributes in the input data ModelPerformanceChart - component that can pop up a panel for visualizing threshold (i.e. ROC style) curves. TextViewer - component for showing textual data. Can show data sets, classification performance statistics etc. GraphViewer - component that can pop up a panel for visualizing tree based models StripChart - component that can pop up a panel that displays a scrolling plot of data (used for viewing the online performance of incremental classifiers) CostBenefitAnalysis - interactively and graphically explore the effects of changing costs/benefits and adjusting prediction thresholds. --------------- Launching the KnowledgeFlow: The Weka GUI Chooser window is used to launch Weka's graphical environments. Select the button labeled "KnowledgeFlow" to start the KnowledgeFlow. Alternatively, you can launch the KnowledgeFlow from a terminal window by typing "java weka.gui.beans.KnowledgeFlow". EXAMPLE: ----------------- Setting up a flow to load an arff file (batch mode) and perform a cross validation using J48 (Weka's C4.5 implementation). NOTE, this example ("Cross validation") can be accessed from the Templates button (third in from the right in the toolbar) in the KnowledgeFlow UI. First start the KnowlegeFlow. Next expand the DataSources entry in the tree and choose "ArffLoader" from the toolbar (the mouse pointer will change to a "cross hairs"). Next place the ArffLoader component on the layout area by clicking somewhere on the layout (A copy of the ArffLoader icon will appear on the layout area). Next specify an arff file to load by first right clicking the mouse over the ArffLoader icon on the layout. A pop-up menu will appear. Select "Configure" under "Edit" in the list from this menu and browse to the location of your arff file. Alternatively, you can double-click on the icon to bring up the configuration dialog (if the component in question has one). Next expand the "Evaluation" entry in the tree and choose the "ClassAssigner" (allows you to choose which column to be the class) component from the toolbar. Place this on the layout. Now connect the ArffLoader to the ClassAssigner: first right click over the ArffLoader and select the "dataSet" under "Connections" in the menu. A "rubber band" line will appear. Move the mouse over the ClassAssigner component and left click - a red line labeled "dataSet" will connect the two components. Next right click over the ClassAssigner and choose "Configure" from the menu. This will pop up a window from which you can specify which column is the class in your data (last is the default). Next grab a "CrossValidationFoldMaker" component from Evaluation and place it on the layout. Connect the ClassAssigner to the CrossValidationFoldMaker by right clicking over "ClassAssigner" and selecting "dataSet" from under "Connections" in the menu. Next expand the "Classifiers" entry in the tree, then the "trees" sub-entry and select the "J48" component. Place it on the layout. Connect the CrossValidationFoldMaker to J48 TWICE by first choosing "trainingSet" and then "testSet" from the pop-up menu for the CrossValidationFoldMaker. Next go back to the "Evaluation" entry and place a "ClassifierPerformanceEvaluator" component on the layout. Connect J48 to this component by selecting the "batchClassifier" entry from the pop-up menu for J48. Next expand the "Visualization" entry and place a "TextViewer" component on the layout. Connect the ClassifierPerformanceEvaluator to the TextViewer by selecting the "text" entry from the pop-up menu for ClassifierPerformanceEvaluator. Now start the flow executing by pressing the blue "play" icon at the top-left of the display. Progress information for the executing components willa appear in the "Status" area and "Log" at the bottom of the window. When finished you can view the results by choosing show results from the pop-up menu for the TextViewer component. Other cool things to add to this flow: connect a TextViewer and/or a GraphViewer to J48 in order to view the textual or graphical representations of the trees produced for each fold of the cross validation (this is something that is not possible in the Explorer). -----------------------------

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