文件列表:

weka-3-7-7 (0, 2012-08-15)
weka-3-7-7\changelogs (0, 2012-08-15)
weka-3-7-7\data (0, 2012-08-15)
weka-3-7-7\doc (0, 2012-08-15)
weka-3-7-7\doc\resources (0, 2012-08-15)
weka-3-7-7\doc\weka (0, 2012-08-15)
weka-3-7-7\doc\weka\associations (0, 2012-08-15)
weka-3-7-7\doc\weka\attributeSelection (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes\net (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes\net\estimate (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes\net\search (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes\net\search\ci (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes\net\search\fixed (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes\net\search\global (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\bayes\net\search\local (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\evaluation (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\evaluation\output (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\evaluation\output\prediction (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\functions (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\functions\neural (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\functions\supportVector (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\lazy (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\lazy\kstar (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\meta (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\misc (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\pmml (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\pmml\consumer (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\rules (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\rules\part (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\trees (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\trees\j48 (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\trees\lmt (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\trees\m5 (0, 2012-08-15)
weka-3-7-7\doc\weka\classifiers\xml (0, 2012-08-15)
weka-3-7-7\doc\weka\clusterers (0, 2012-08-15)
weka-3-7-7\doc\weka\core (0, 2012-08-15)
weka-3-7-7\doc\weka\core\converters (0, 2012-08-15)
weka-3-7-7\doc\weka\core\json (0, 2012-08-15)
... ...

===================================================================== ====== README ====== WEKA 3.7.7 17 Aug 2012 Java Programs for Machine Learning Copyright (C) 19***-2012 University of Waikato web: http://www.cs.waikato.ac.nz/~ml/weka ===================================================================== Contents: --------- 1. Using one of the graphical user interfaces in Weka 2. Weka packages and the package manager 3. The Weka data format (ARFF) 4. Using Weka from the command line - Classifiers - Association rules - Filters 5. Database access 6. The Experiment package 7. Weka manual 8. Source code 9. Credits 10. Submission of code and bug reports 11. Copyright ---------------------------------------------------------------------- 1. Using one of the graphical user interfaces in Weka: ------------------------------------------------------ This assumes that the Weka archive that you have downloaded has been extracted into a directory containing this README and that you haven't used an automatic installer (e.g. the one provided for Windows). Weka 3.7 requires Java 1.6 or higher. Depending on your platform you may be able to just double-click on the weka.jar icon to run the graphical user interfaces for Weka. Otherwise, from a command-line (assuming you are in the directory containing weka.jar), type java -jar weka.jar or if you are using Windows use javaw -jar weka.jar Note: Using "-jar" overrides your CLASSPATH variable! If you need to use classes specified in the CLASSPATH, use the following command instead: java -classpath $CLASSPATH:weka.jar weka.gui.GUIChooser or if you are using Windows use javaw -classpath "%CLASSPATH%;weka.jar" weka.gui.GUIChooser This will start a graphical user interface (weka.gui.GUIChooser) from which you can select various interfaces, like the SimpleCLI interface or the more sophisticated Explorer, Experimenter, and Knowledge Flow interfaces. SimpleCLI just acts like a simple command shell. The Explorer is currently the main interface for data analysis using Weka. The Experimenter can be used to compare the performance of different learning algorithms across various datasets. The Knowledge Flow provides a component-based alternative to the Explorer interface. Example datasets that can be used with Weka are in the sub-directory called "data", which should be located in the same directory as this README file. The Weka user interfaces provide extensive built-in help facilities (tool tips, etc.). Documentation for the Explorer can be found in ExplorerGuide.pdf (also in the same directory as this README). You can also start the GUI "Main" class from within weka.jar: java -classpath weka.jar:$CLASSPATH weka.gui.Main or if you are using Windows use javaw -classpath weka.jar;$CLASSPATH weka.gui.Main ---------------------------------------------------------------------- 2. Weka packages and the package manager: ----------------------------------------- From Weka 3.7.2 many Weka algorithms and tools have been extracted from the main Weka distribution and encapsulated in separate downloadable packages. These can be obtained from the Weka project on Sourceforge and installed manually, or Weka's new built-in package manager can be used to take care of installing/removing packages. There is both a command line and GUI package manager that can be used to browse and install packages. The package manager takes care of resolving dependencies and checking for conflicts. The GUI package manager can be found in the "Tools" menu of the GUIChooser. Detailed information on how to use the package management system can be found in the Weka manual ($WEKAINSTALL/WekaManual.pdf). ---------------------------------------------------------------------- 3. The Weka data format (ARFF): ------------------------------- Datasets for WEKA should be formatted according to the ARFF format. (However, there are several converters included in WEKA that can convert other file formats to ARFF. The Weka Explorer will use these automatically if it doesn't recognize a given file as an ARFF file.) Examples of ARFF files can be found in the "data" subdirectory. What follows is a short description of the file format. A more complete description is available from the Weka web page. A dataset has to start with a declaration of its name: @relation name followed by a list of all the attributes in the dataset (including the class attribute). These declarations have the form @attribute attribute_name specification If an attribute is nominal, specification contains a list of the possible attribute values in curly brackets: @attribute nominal_attribute {first_value, second_value, third_value} If an attribute is numeric, specification is replaced by the keyword numeric: (Integer values are treated as real numbers in WEKA.) @attribute numeric_attribute numeric In addition to these two types of attributes, there also exists a string attribute type. This attribute provides the possibility to store a comment or ID field for each of the instances in a dataset: @attribute string_attribute string After the attribute declarations, the actual data is introduced by a @data tag, which is followed by a list of all the instances. The instances are listed in comma-separated format, with a question mark representing a missing value. Comments are lines starting with % and are ignored by Weka. ---------------------------------------------------------------------- 4. Using Weka from the command line: ------------------------------------ If you want to use Weka from your standard command-line interface (e.g. bash under Linux): a) Set WEKAINSTALL to be the directory which contains this README. b) Add $WEKAINSTALL/weka.jar to your CLASSPATH environment variable. c) Bookmark $WEKAINSTALL/doc/packages.html in your web browser. Alternatively you can try using the SimpleCLI user interface available from the GUI chooser discussed above. In the following, the names of files assume use of a unix command-line with environment variables. For other command-lines (including SimpleCLI) you should substitute the name of the directory where weka.jar lives for $WEKAINSTALL. If your platform uses something other character than / as the path separator, also make the appropriate substitutions. =========== Classifiers =========== Try: java weka.classifiers.trees.J48 -t $WEKAINSTALL/data/iris.arff This prints out a decision tree classifier for the iris dataset and ten-fold cross-validation estimates of its performance. If you don't pass any options to the classifier, WEKA will list all the available options. Try: java weka.classifiers.trees.J48 The options are divided into "general" options that apply to most classification schemes in WEKA, and scheme-specific options that only apply to the current scheme---in this case J48. WEKA has a common interface to all classification methods. Any class that implements a classifier can be used in the same way as J48 is used above. WEKA knows that a class implements a classifier if it extends the Classifier class in weka.classifiers. Almost all classes in weka.classifiers fall into this category. Try, for example: java weka.classifiers.bayes.NaiveBayes -t $WEKAINSTALL/data/labor.arff Here is a list of some of the classifiers currently implemented in weka.classifiers: a) Classifiers for categorical prediction: weka.classifiers.lazy.IBk: k-nearest neighbour learner weka.classifiers.trees.J48: C4.5 decision trees weka.classifiers.rules.PART: rule learner weka.classifiers.bayes.NaiveBayes: naive Bayes with/without kernels weka.classifiers.rules.OneR: Holte's OneR weka.classifiers.functions.SMO: support vector machines weka.classifiers.functions.Logistic: logistic regression weka.classifiers.meta.AdaBoostM1: AdaBoost weka.classifiers.meta.LogitBoost: logit boost weka.classifiers.trees.DecisionStump: decision stumps (for boosting) etc. b) Classifiers for numeric prediction: weka.classifiers.functions.LinearRegression: linear regression weka.classifiers.trees.M5P: model trees weka.classifiers.rules.M5Rules: model rules weka.classifiers.lazy.IBk: k-nearest neighbour learner weka.classifiers.lazy.LWL: locally weighted learning ================= Association rules ================= Next to classification schemes, there is some other useful stuff in WEKA. Association rules, for example, can be extracted using the Apriori algorithm. Try java weka.associations.Apriori -t $WEKAINSTALL/data/weather.nominal.arff ======= Filters ======= There are also a number of tools that allow you to manipulate a dataset. These tools are called filters in WEKA and can be found in weka.filters. weka.filters.unsupervised.attribute.Discretize: discretizes numeric data weka.filters.unsupervised.attribute.Remove: deletes/selects attributes etc. Try: java weka.filters.supervised.attribute.Discretize -i $WEKAINSTALL/data/iris.arff -c last ---------------------------------------------------------------------- 5. Database access: ------------------- In terms of database connectivity, you should be able to use any database with a Java JDBC driver. When using classes that access a database (e.g. the Explorer), you will probably want to create a properties file that specifies which JDBC drivers to use, where to find the database, and specify a mapping for the data types. This file should reside in your home directory or the current directory and be called "DatabaseUtils.props". An example is provided in weka/experiment (you need to expand weka.jar to be able to look a this file). Note that the settings in this file are used unless they are overidden by settings in the DatabaseUtils.props file in your home directory or the current directory (in that order). There are also example DatabaseUtils.props files for several common databases available (also in weka/experiment): * HSQLDB: DatabaseUtils.props.hsql * MS SQL Server 2000: DatabaseUtils.props.mssqlserver * MS SQL Server 2005 Express Edition: DatabaseUtils.props.mssqlserver2005 * MySQL: DatabaseUtils.props.mysql * ODBC: DatabaseUtils.props.odbc * Oracle: DatabaseUtils.props.oracle * PostgreSQL: DatabaseUtils.props.postgresql ---------------------------------------------------------------------- 6. The Experiment package: ----------------------------------------- There is support for running experiments that involve evaluating classifiers on repeated randomizations of datasets, over multiple datasets (you can do much more than this, besides). The classes for this reside in the weka.experiment package. The basic architecture is that a ResultProducer (which generates results on some randomization of a dataset) sends results to a ResultListener (which is responsible for stating whether it already has the result, and otherwise storing results). Example ResultListeners include: weka.experiment.CSVResultListener: outputs results as comma-separated-value files. weka.experiment.InstancesResultListener: converts results into a set of Instances. weka.experiment.DatabaseResultListener: sends results to a database via JDBC. Example ResultProducers include: weka.experiment.RandomSplitResultProducer: train/test on a % split weka.experiment.CrossValidationResultProducer: n-fold cross-validation weka.experiment.AveragingResultProducer: averages results from another ResultPoducer weka.experiment.DatabaseResultProducer: acts as a cache for results, storing them in a database. The RandomSplitResultProducer and CrossValidationResultProducer make use of a SplitEvaluator to obtain actual results for a particular split, provided are ClassifierSplitEvaluator (for nominal classification) and RegressionSplitEvaluator (for numeric classification). Each of these uses a Classifier for actual results generation. So, you might have a DatabaseResultListener, that is sent results from an AveragingResultProducer, which produces averages over the n results produced for each run of an n-fold CrossValidationResultProducer, which in turn is doing nominal classification through a ClassifierSplitEvaluator, which uses OneR for prediction. Whew. But you can combine these things together to do pretty much whatever you want. You might want to write a LearningRateResultProducer that splits a dataset into increasing numbers of training instances. To run a simple experiment from the command line, try: java weka.experiment.Experiment -r -T datasets/UCI/iris.arff \ -D weka.experiment.InstancesResultListener \ -P weka.experiment.RandomSplitResultProducer -- \ -W weka.experiment.ClassifierSplitEvaluator -- \ -W weka.classifiers.rules.OneR (Try "java weka.experiment.Experiment -h" to find out what these options mean) If you have your results as a set of instances, you can perform paired t-tests using weka.experiment.PairedTTester (use the -h option to find out what options it needs). However, all this is much easier if you use the Experimenter GUI. ---------------------------------------------------------------------- 7. Weka Manual: ------------------ A comprehensive manual covering Weka's graphical and command-line user interfaces. $WEKAINSTALL/WekaManual.pdf ---------------------------------------------------------------------- 8. Source code: --------------- The source code for WEKA is in $WEKAINSTALL/weka-src.jar. To expand it, use the jar utility that's in every Java distribution (or any file archiver that can handle ZIP files). ---------------------------------------------------------------------- 9. Credits: ------------ Refer to the web page for a list of contributors: http://www.cs.waikato.ac.nz/~ml/weka/ ---------------------------------------------------------------------- 10. Call for code and bug reports: --------------------------------- If you have implemented a learning scheme, filter, application, visualization tool, etc., using the WEKA classes, and you would like to make it available to the community, then create a Weka "package" and submit your package's "Description.props" file to us. We will check the package to make sure that it works as advertised and doesn't contain any "nasties" and then add your Description.props to the central package meta data repository hosted on Sourceforge. Hosting downloadable packages is the responsibility of the contributer. The conditions for new classifiers (schemes in general) are that, firstly, they have to be published in the proceedings of a renowned conference (e.g., ICML) or as an article of respected journal (e.g., Machine Learning) and, secondly, that they outperform other standard schemes (e.g., J48/C4.5). More information on contributing to Weka and how to create a Weka package can be found in the Weka manual ($WEKAINSTALL/WekaManual.pdf). If you find any bugs, send a bug report to the wekalist mailing list. 1) For core Weka components, i.e. everything in the main weka.jar file (not including packages), send a bug report to the wekalist mailing list. 2) For packages, check the package description (either online at Sourceforge or by using the package management system) and contact the maintainer of the package directly. ----------------------------------------------------------------------- 11. Copyright: -------------- The core WEKA system is distributed under the GNU public license. Please read the file COPYING. Packages may be distributed under various licenses - check the description of the package in question for license details. ----------------------------------------------------------------------- $Revision: 1.13 $

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：