Java weka library download

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Email required Address never made public. Name required. Next Post ». Follow Following. Ian Ma. Sign me up. Already have a WordPress. Log in now. It creates a copy of the original classifier that you hand over to the crossValidateModel for each run of the cross-validation. In case you have a dedicated test set, you can train the classifier and then evaluate it on this test set.

In the following example, a J48 is instantiated, trained and then evaluated. Some statistics are printed to stdout :.

You can access these predictions via the predictions method of the Evaluation class. In case you have an unlabeled dataset that you want to classify with your newly trained classifier, you can use the following code snippet. A clusterer is built in much the same way as a classifier, but the buildClusterer Instances method instead of buildClassifier Instances.

The following code snippet shows how to build an EM clusterer with a maximum of iterations. Clusterers implementing the weka. UpdateableClusterer interface can be trained incrementally.

See the Javadoc for this interface to see which clusterers implement it. Cobweb :. For evaluating a clusterer, you can use the ClusterEvaluation class.

In this example, the number of clusters found is written to output:. Or, in the case of DensityBasedClusterer , you can cross-validate the clusterer Note: with MakeDensityBasedClusterer you can turn any clusterer into a density-based one :.

The only difference with regard to classification is the method name. Instead of classifyInstance Instance , it is now clusterInstance Instance. The method for obtaining the distribution is still the same, i. If your data contains a class attribute and you want to check how well the generated clusters fit the classes, you can perform a so-called classes to clusters evaluation.

The Weka Explorer offers this functionality, and it's quite easy to implement. These are the necessary steps complete source code: ClassesToClusters.

There is no real need to use the attribute selection classes directly in your own code, since there are already a meta-classifier and a filter available for applying attribute selection, but the low-level approach is still listed for the sake of completeness. The code listed below is taken from the AttributeSelectionTest. The following meta-classifier performs a preprocessing step of attribute selection before the data gets presented to the base classifier in the example here, this is J The filter approach is straightforward: after setting up the filter, one just filters the data through the filter and obtains the reduced dataset.

If neither the meta-classifier nor filter approach is suitable for your purposes, you can use the attribute selection classes themselves. Most machine learning schemes, like classifiers and clusterers, are susceptible to the ordering of the data. Using a different seed for randomizing the data will most likely produce a different result.

Random number generator, whereas the weka. Unless one runs fold cross-validation 10 times and averages the results, one will most likely get different results. Weka Wiki. Docs » Use weka in your java code. Attribute selection - removing irrelevant attributes from your data The following sections explain how to use them in your own code.

Instances; import weka. Option handling Weka schemes that implement the weka. Especially handy if the command line contains nested classes that have their own options, such as kernels for SMO: java OptionsToCode weka. PolyKernel -C -E 1. Filter A filter has two different properties: supervised or unsupervised either takes the class attribute into account or not attribute - or instance -based e. For example, if you want to remove the first attribute of a dataset, you need this filter weka.

Environment : Ubuntu Jaunty 9. This is going to be my most random post. Ran into a series of problems related to openssl and ftp using ruby1. Here are just some useful links. Problem: LoadError: no such file to load — openssl Solution: ruby-v. Setup and Configuration 1 Make sure you have Weka downloaded and Weka. Look for two files, DataUtils.

The code below shows two examples combined. One uses the configuration above to create instances directly from a query. The other is just a reference for general use of the database.

This code is taken pretty much line for line from weka. I wanted to visualize the instances I predicted incorrectly on a time-series plot. The first step, however, is to get it into a format that R is happy with. When finished, right-click the model name from the Result List. CSVSaver -i your.