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. If you do not find the classifier you want , you might have to install the Weka package that includes it. For a step-by-step description on how to install new packages, have a look at this tutorial. The classifier uses by the default all the user traces to train.
By clicking on this option, we filter first the classes in order to provide a balanced distribution of the samples. This implies that the less numerous classes will duplicate some of their samples and the more populated classes will lose some of their samples for the sake of even distribution. This option is strongly recommended if we want to give the same importance to all classes.
An alternative is to use the Weka CostSensitiveClassifier and set a corresponding cost matrix. We can save the features as a stack of images by clicking on this button. It will use the last feature configuration that is available. This slider sets the opacity of the resulting overlay image.
Depending on the image contrast of our input images, we might be interested on adjusting this value. For a complete step-by-step description on how to compare classifiers for image segmentation using the Weka Explorer, have a look at the Trainable Weka Segmentation - How to compare classifiers tutorial.
On the right side of the GUI, we have one button and one list of traces for each of the classes defined by the user two by default: class 1 and class 2. By left-clicking on one of the Add to […] buttons, the current selection ROI gets added to the class defined by that button. Notice the text of the button can be modified in the Settings dialog as described before.
By right-clicking on any of those buttons, a new dialog will be displayed to change the color associated to that class and therefore the overlay and result lookup table. Below every Add to […] button there is its corresponding list of added traces empty by default.
By left-clicking on one of the traces, it will be hightlighted in the main image. On the contrary, double clicking on it will remove the trace from the list. Trainable Weka Segmentation is completely compatible with the popular ImageJ macro language.
Each of the buttons in the GUI are macro-recordable and their commands can be reproduced later from a simple macro file. Format: addTrace class index, slice number. Format: applyClassifier input directory, input image or stack, show results flag, store results flag, probability maps flag, store folder. Format: changeClassName class index, class new name. Format: setClassifier classifier class, classifier options.
The plugin GUI is independent from the plugin methods. The methods are implemented in a separate file in a library-style fashion, so they can be called from any other Fiji plugin without having to interact with the GUI. This facilitates its integration with other plugins and allows easy scripting. For examples on how to use the plugin methods from scripts, have a look at the Trainable Weka Segmentation scripting page.
As a pixel classifier , the Trainable Weka Segmentation presents a wide range of applications such as Edge detection , semantic segmentation, or Object detection and localization. All of them at the distance of a few clicks on the plugin GUI and sometimes in combination with other Fiji tools or plugins.
To see who is using Trainable Weka Segmentation and its multiple applications, you can have a look at these publications. If you already have an existing installation of weka using Java 1. Since the 3. If you absolutely need to reuse an old model, you can transform it to the new version thanks to a model migrator tool provided by the Weka developers.
For more information, check this post in the ImageJ forum. For all questions, suggestions, bug reports and problems related to the Trainable Weka Segmentation plugin or library, please use the ImageJ forum and make sure to check previous posts that might have been done covering the same topic. Please note that Trainable Weka Segmentation is based on a publication.
If you use it successfully for your research please be so kind to cite our work:. Page history Edit this page How do I edit this website? Original MediaWiki page. Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats 1, commits. Failed to load latest commit information. View code. WekaDeeplearning4j WekaDeeplearning4j gives users the ability to train and test deep learning models from within the Weka environment.
Alternatively, the latest release on GitHub provides the zip file of the package that allow easy installation via the commandline: java -cp weka.
About Weka package for the Deeplearning4j java library deeplearning. Releases 41 WekaDeeplearning4j - v1. Sep 24, Packages 0 No packages published.
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