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and select vfml-master/weka directory from the downloaded jar. Open Eclipse and select File > Import.Download Eclipse IDE For Java Developers (the version appropriate for your platform).
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#Cosmic view deep down code#
We suggest viewing and editing the code using the Eclipse IDE. Choose Classifier > weka/classifiers/trees/VFDT.Choose Filter > weka/filters/unsupervised/attribute/ReplaceMissingValues then click Apply.
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Note that both classifiers currently only support nominal attributes and do not support missing values.įollow these steps as a quick way to get started running VFML using the default Weka data sets: There should be two new classifiers available under weka/classifiers/trees: VFDT and CVFDT (CVFDT is an extension which support adapting to concept drift). Once Weka has been launched with the JVFML jar on the Java classpath, it can be used like any other Weka classifier. To give Weka 2GB of memory (for example) add the following option to the command above `-Xmx2g`. Note: To work with large data sets, the Java heap space allocated to Weka may need to be increased. Note: Linux users should use : instead of when typing the previous command into the terminal. Java -classpath weka.jar vfml-weka-1.0.0.jar
#Cosmic view deep down install#
Download Weka 3.6 from the project's Downloads page and install it.A slightly modified version of Domingos and Hulten's original C code which compiles under Ubuntu 12.04 LTS is packaged with JVFML. However, on Ubuntu 12.04 LTS (and possibly other modern Linux distros), the project does not build as-is. Their original source code can be downloaded from the VMFL Sourceforge repository.
#Cosmic view deep down software#
This software contains an implementation of VFML as well as a number of other stream classifiers.ĭomingos and Hulten also have an implementation of VFML in C. The developers of Weka have also developed a streaming machine learning toolkit called Moa. Although using Weka eliminates a major advantage of VFDT (its ability to process streaming data sets one data instance at a time without ever loading the entire data set into memory), the Weka implementation is potentially a useful tool for experimenting with the algorithm. JVFML is designed to interface with Weka. The Hoeffding Bound is used to decide when enough data instances have been processed to split a tree node and be confident that a traditional batch learner with all the data available would have made the same decision. JVFML is a Java implementation of Hulten and Domingos' Very Fast Decision Tree (VFDT) algorithm for building decision trees from streaming data using a statistical result known as the Hoeffding Bound. "VFML - A toolkit for mining high-speed time-changing data streams". Pedro Domingos and Geoff Hulten developed VFML in 2003 to experiment with applying machine learning techniques to situations where the scale of streaming data being learned from makes traditional techniques impractical. This project is maintained by ulmangt Very Fast Machine Learning Toolkit