removed "snapshot" references

docs/adding_tabs_in_the_explorer.md

@@ -3,10 +3,10 @@
 # Description
 This article explains how to add extra tabs in the Explorer in order to add new functionality without the hassle of having to dig into the Explorer code oneself. With the new plugin-architecture of the Explorer it is fairly easy making your extensions available in the GUI.
 
-**Note:** This is also covered in chapter *Extending WEKA* of the WEKA manual in versions later than 3.6.1/3.7.0 or [snapshots](snapshots.md) of the stable-3.6/developer version later than 10/01/2010.
+**Note:** This is also covered in chapter *Extending WEKA* of the WEKA manual in versions later than 3.6.1/3.7.0 of the stable-3.6/developer version later than 10/01/2010.
 
 # Version
->3.5.5 or [Snapshot](snapshots.md)
+>3.5.5
 
 # Requirements
 Here is roughly what is required in order to add a new tab (the examples go into more detail):

docs/downloading_weka.md

@@ -1,8 +1,7 @@
 
 There are two versions of Weka: Weka 3.8 is the latest stable version
 and Weka 3.9 is the development version. New releases of these two versions
-are normally made once or twice a year. For the bleeding edge, it is
-also possible to download nightly snapshots of these two versions. 
+are normally made once or twice a year. 
 
 The stable version receives only bug fixes and feature upgrades that
 do not break compatibility with its earlier releases, while the
@@ -14,15 +13,6 @@ easy for the Weka community to add new functionality to Weka. The
 package management system requires an internet connection in order to
 download and install packages.
 
-# Snapshots
-              
-Every night, a snapshot of the [Git](git.md) repository with the Weka
-source code is taken, compiled, and put together in ZIP files. This
-happens for both the development branch of the software and the stable
-branch. Those who want the latest bug fixes before the next official
-release is made can download these
-[snapshots](https://ml.cms.waikato.ac.nz/weka/snapshots/weka_snapshots.html).
-
 # Stable version
 
 Weka 3.8 is the latest stable version of Weka. This branch of Weka

docs/faqs/how_do_i_perform_one_class_classification.md

@@ -1,4 +1,4 @@
 WEKA offers some rudimentary support for one-class classfication:
 
 * via the `weka.classifiers.functions.LibSVM` wrapper classifier (stable 3.6 and developer version). See the [LibSVM](../lib_svm.md) article for more information.
-* via the `weka.classifiers.meta.OneClassClassifier` meta-classifier (developer version >3.7.0 or [snapshot](../snapshots.md) later than 23/7/2009)
+* via the `weka.classifiers.meta.OneClassClassifier` meta-classifier (developer version >3.7.0)

docs/faqs/how_do_i_use_wekas_classes_in_my_own_code.md

@@ -5,7 +5,7 @@ It's not that hard to use WEKA classes in your own code, the following articles
 
 Further resources:
 
-* Check out the chapter *Using the API* in the Weka manual ([snapshots](../snapshots.md) later than 09/08/2009 and releases >3.6.1 and >3.7.0).
-* The [Weka Examples](../weka_examples.md) collection is a an [ANT](../ant.md) project that is available through [snapshots](../snapshots.md) and releases later than 09/08/2009, containing a lot of example classes.
+* Check out the chapter *Using the API* in the Weka manual (releases >3.6.1 and >3.7.0).
+* The [Weka Examples](../weka_examples.md) collection is a an [ANT](../ant.md) project that is available through releases later than 09/08/2009, containing a lot of example classes.
 
 **Note:** WEKA is open-source software under the [GNU General Public License](http://www.gnu.org/copyleft/gpl.html), which means that your code has to be licensed under the GPL as well.

docs/faqs/how_do_i_write_a_new_classifier_or_filter.md

@@ -5,4 +5,4 @@ Basically, a classifier needs to be derived from `weka.classifiers.Classifier` a
 
 If your scheme is outside the usual WEKA packages, you need to make WEKA aware of this package in order to be able to use it in the GUI as well. See [How do I add a new classifier, filter, kernel, etc?](how_do_i_add_a_new_classifier_filter_kernel_etc.md) for more information about this.
 
-**Note:** This is also covered in chapter *Extending WEKA* of the WEKA manual in versions later than 3.6.1/3.7.0 or [snapshots](../snapshots.md) of the stable-3.6/developer version later than 10/01/2010. Furthermore, this chapter also covers clusterers, attribute selection algorithms and associators.
+**Note:** This is also covered in chapter *Extending WEKA* of the WEKA manual in versions later than 3.6.1/3.7.0. Furthermore, this chapter also covers clusterers, attribute selection algorithms and associators.

docs/faqs/latest_bugfixes.md

@@ -1 +1 @@
-The article [How to get the latest bugfixes](../get_latest_bugfixes.md) explains it in detail (it's basically either obtaining the source code from [git](../git.md) and compiling it yourself or getting a snapshot from the download section of the [WEKA homepage](http://cs.waikato.ac.nz/ml/weka/)).
+The article [How to get the latest bugfixes](../get_latest_bugfixes.md) explains it in detail (it's basically either obtaining the source code from [git](../git.md) and compiling it yourself.

docs/faqs/pluggable_evaluation_metrics.md

@@ -1,4 +1,4 @@
-WEKA 3.7.8 (and nightly snapshots of the developer branch of WEKA from 15-11-2012) has a mechanism to allow new classification and regression evaluation metrics to be added as plugins. The new metrics will be output, along with WEKA's standard set of evaluation metrics, in the output generated on the command line, in the Explorer's Classify panel and by the Knowledge Flow's ClassifierPerformanceEvaluator component. Furthermore, new plugin metrics are also available for analysis in the Experimenter.
+WEKA 3.7.8 has a mechanism to allow new classification and regression evaluation metrics to be added as plugins. The new metrics will be output, along with WEKA's standard set of evaluation metrics, in the output generated on the command line, in the Explorer's Classify panel and by the Knowledge Flow's ClassifierPerformanceEvaluator component. Furthermore, new plugin metrics are also available for analysis in the Experimenter.
 
 Previously, adding a new evaluation metric involved editing and recompiling the monolithic weka.classifiers.Evaluation class - a shudder-worthy undertaking at the best of times. With the new plugin mechanism it is easy to add a new metric and deploy it via the package management system. The "Additional configuration files" section of [How are packages structured for the package management system?](../packages/structure.md) details how to tell the PluginManager class about your new plugin evaluation metric.
 

docs/generating_source_code_from_weka_classes.md

@@ -1,6 +1,6 @@
 
 
-Some of the schemes in Weka can generate Java source code that represents their current internal state. At the moment these are classifiers (book and developer version) and filters ([snapshot](snapshots.md) or >3.5.6). The generated code can be used within Weka as normal classifier/filter, since this code will be derived from the same superclass (`weka.classifiers.Classifier` or `weka.filters.Filter`) as the generating code.
+Some of the schemes in Weka can generate Java source code that represents their current internal state. At the moment these are classifiers (book and developer version) and filters (>3.5.6). The generated code can be used within Weka as normal classifier/filter, since this code will be derived from the same superclass (`weka.classifiers.Classifier` or `weka.filters.Filter`) as the generating code.
 
 **Note:** The commands listed here are for a Linux/Unix bash (the backslash tells the shell that the command isn't finished yet and continues on the next line). In case of Windows or the SimpleCLI, just remove the backslashes and put everything on one line.
 
@@ -27,7 +27,7 @@ The following command calls the generated classifier with a training set (traini
 **Note:** the Explorer can output source code as well, you only have to check the *Output source code* option in the *More options* dialog.
 
 # Filters
-With versions of Weka later than 3.5.6 or a [snapshot](snapshots.md) of the developer version, one can now also turn filters into source code. The process is basically the same as with classifiers outlined above. All filters that implement the `weka.filters.Sourcable` interface can be turned into Java code (again, check out the Javadoc for this interface, to see the filters implementing it).
+With versions of Weka later than 3.5.6 of the developer version, one can now also turn filters into source code. The process is basically the same as with classifiers outlined above. All filters that implement the `weka.filters.Sourcable` interface can be turned into Java code (again, check out the Javadoc for this interface, to see the filters implementing it).
 
 The following command turns an initialized ReplaceMissingValues filter into source code:
 

docs/get_latest_bugfixes.md

@@ -1,13 +1,8 @@
 Weka is actively developed, that means that bugs are fixed and new functionality is added (only to the developer version) all the time. Every now and then (about every 6-12 months), when there was a sufficiently large number of improvements or fixes, a release is made and uploaded to [Sourceforget.net](http://sourceforge.net/projects/weka/).
 
-If you don't want to wait that long, you have two options:
+If you don't want to wait that long, you can get the latest source code from **Git** and compile it yourself. 
 
-1. Get the latest source code from **Subversion** and compile it yourself. See the following articles for more information
-
-    1. [obtaining the source code from Subversion](subversion.md), either book or developer version
-    2. [compiling the source code](compiling_weka.md)
-
-2. Download a **snapshot** from the download section of the [Weka homepage](http://cs.waikato.ac.nz/~ml/weka/). Snapshots for book and developer version are generated automatically every night, based on the current Subversion code. The [ZIP files](http://en.wikipedia.org/wiki/ZIP_%28file_format%29) have the same content as a release, i.e., compiled classes (= weka.jar), source code (= weka-src.jar), Javadoc and other documentation.
-
-  ***Note:*** compare the timestamp of the [Weka Mailing List](mailing_list.md) post that reports a bugfix with the one of the snapshot to make sure the bugfix is already included in the snapshot.
+See the following articles for more information:
 
+1. [obtaining the source code from Git](git.md), either book or developer version
+2. [compiling the source code](compiling_weka.md)

docs/making_predictions.md

@@ -42,7 +42,7 @@ In this case, taken directly from a test dataset where all class attributes were
 * Using the `-classifications` option instead of `-p ...` you can also use different output formats, like [CSV](https://weka.sourceforge.io/doc.stable-3-8/weka/classifiers/evaluation/output/prediction/CSV.html): `-classifications "weka.classifiers.evaluation.output.prediction.CSV -p ..."` (the `-p` option takes the indices of the additional attributes to output).
 
 ## Filters
-The [AddClassification](https://weka.sourceforge.io/doc.stable-3-8/weka/filters/supervised/attribute/AddClassification.html) filter (package `weka.filters.supervised.attribute`) can either train a classifier on the input data and transform this or load a serialized model to transform the input data (even though the filter was introduced in 3.5.4, due to a bug in the commandline option handling, it is recommended to download a version >3.5.5 or a snapshot from the Weka homepage).
+The [AddClassification](https://weka.sourceforge.io/doc.stable-3-8/weka/filters/supervised/attribute/AddClassification.html) filter (package `weka.filters.supervised.attribute`) can either train a classifier on the input data and transform this or load a serialized model to transform the input data (even though the filter was introduced in 3.5.4, due to a bug in the commandline option handling, it is recommended to download a version >3.5.5 from the Weka homepage).
 This filter can add the classification, class distribution and the error per row as extra attributes to the dataset.
 
 * training the classifier, e.g., J48, on the input data and replacing the class values with the ones of the trained classifier:
@@ -91,7 +91,7 @@ Example output for the *anneal* UCI dataset:
     20          ?        3:3      +   0      0     *1      0      0      0    
     ...
 ```
-**Note:** The developer version (>3.5.6 or [snapshot](snapshots.md)) can also output additional attributes like the commandline with the `-p` option. In the *More options...* dialog you can specify those attribute indices with *Output additional attributes*, e.g., *first* or *1-7*. In contrast to the commandline, this output also works for cross-validation.
+**Note:** The developer version (>3.5.6) can also output additional attributes like the commandline with the `-p` option. In the *More options...* dialog you can specify those attribute indices with *Output additional attributes*, e.g., *first* or *1-7*. In contrast to the commandline, this output also works for cross-validation.
 
 ## KnowledgeFlow
 ## Using the PredictionAppender

docs/optimizing_parameters.md

@@ -77,7 +77,7 @@ weka.classifiers.functions.LibSVM
 # GridSearch 
 `weka.classifiers.meta.GridSearch` is a meta-classifier for exploring 2 parameters, hence the *grid* in the name. If one turns the log on, the classifier will create output suitable for [gnuplot](http://www.gnuplot.info/), i.e., sections of the log will contain script and data sections. Instead of just using a classifier, one can specify a base classifier **and** a filter, which both of them can be optimized (one parameter each). In contrast to `CVParameterSelection`, `GridSearch` is not limited to first-level parameters of the base classifier, since it's using [Java Beans](http://java.sun.com/docs/books/tutorial/javabeans/) [Introspection](http://java.sun.com/docs/books/tutorial/javabeans/introspection/) and one can specify *paths* to the properties one wants to optimize. A *property* here is the string of the parameter displayed in the GenericObjectEditor (generated though Introspection), e.g., `bagSizePercent` or `classifier` of `weka.classifiers.meta.Bagging`.
 
-Due to some important bugfixes, one should obtain a version of Weka >3.5.6 or a [snapshot](snapshots.md) later than 11 Sept 2007.
+Due to some important bugfixes, one should obtain a version of Weka >3.5.6 later than 11 Sept 2007.
 
 For each of the two axes, X and Y, one can specify the following parameters:
 

docs/packages/manager.md

@@ -23,7 +23,7 @@ Options:
 
 ---
 
-Weka 3.7.8 and snapshot builds of the developer version of Weka after September 24 2012 now offer a completely "offline" mode that involves no attempts to connect to the internet. This mode can be used to install package zip files that the user already has on the file system, and to browse already installed packages. This mode can be accessed from the command line package manager by specifying the "-offline" option. Alternatively, the property weka.packageManager.offline=true can be provided to the Java virtual machine on the command line or in a properties file (see the section on properties below).
+Weka 3.7.8 now offers a completely "offline" mode that involves no attempts to connect to the internet. This mode can be used to install package zip files that the user already has on the file system, and to browse already installed packages. This mode can be accessed from the command line package manager by specifying the "-offline" option. Alternatively, the property weka.packageManager.offline=true can be provided to the Java virtual machine on the command line or in a properties file (see the section on properties below).
 
 ----
 
@@ -192,7 +192,7 @@ This can either be set when starting Weka from the command line with the `-D` fl
 
 
 # Package manager property file
-As mentioned in the previous section, an alternative package meta data repository can be specified by placing an entry in the PackageRepository.props file in `$WEKA_HOME/props`. From Weka 3.7.8 (and snapshot builds after 24 September 2012), the package manager also looks for properties placed in `$WEKA_HOME/props/PackageManager.props`. The current set of properties that can be set are:
+As mentioned in the previous section, an alternative package meta data repository can be specified by placing an entry in the PackageRepository.props file in `$WEKA_HOME/props`. From Weka 3.7.8, the package manager also looks for properties placed in `$WEKA_HOME/props/PackageManager.props`. The current set of properties that can be set are:
 
 ```
 weka.core.wekaPackageRepositoryURL=http://some.mirror.somewhere

docs/packages/structure.md

@@ -211,7 +211,7 @@ Another example is the kfGroovy package. This package adds a plugin component to
 weka.gui.beans.KnowledgeFlow.Plugins=org.pentaho.dm.kf.GroovyComponent
 ```
 
-The new pluggable evaluation metrics for classification/regression (from Weka 3.7.8 and nightly developer snapshots from 15-11-2012) are managed by the PluginManager class. To tell PluginManager that your package provides a new evaluation metric you need to provide a "PluginManager.props" file in the package's top level directory. For example, a hypothetical bobsMetric package might declare a new "Area under Bob curve" metric like so:
+The new pluggable evaluation metrics for classification/regression (from Weka 3.7.8) are managed by the PluginManager class. To tell PluginManager that your package provides a new evaluation metric you need to provide a "PluginManager.props" file in the package's top level directory. For example, a hypothetical bobsMetric package might declare a new "Area under Bob curve" metric like so:
 
 ```
 # Specify a new plugin Evaluation metric

docs/plotting_multiple_roc_curves.md

@@ -14,9 +14,9 @@ This is the basic setup (based on a Wekalist post):
  ---thresholdData---> ModelPerformanceChart
 ```
 
-![Snapshot](img/Knowledgeflow_breast-cancer_roc-curve_setup.jpg)
+![Screenshot](img/Knowledgeflow_breast-cancer_roc-curve_setup.jpg)
 
-![Snapshot](img/Knowledgeflow_breast-cancer_roc-curve.jpg)
+![Screenshot](img/Knowledgeflow_breast-cancer_roc-curve.jpg)
 
 This setup can be easily extended to host several classifiers, which illustrates the [Plotting_multiple_roc.kfml](files/Plotting_multiple_roc.kfml) example, containing `J48` and `RandomForest` as classifiers.
 

docs/text_categorization_with_weka.md

@@ -77,7 +77,7 @@ And with the [MultiFilter](https://weka.sourceforge.io/doc.stable-3-8/weka/filte
 
 The [StringToWordVector](https://weka.sourceforge.io/doc.stable-3-8/weka/filters/unsupervised/attribute/StringToWordVector.html) filter can also work with a different stopword list than the built-in one (based on the Rainbow system). One can use the `-stopwords` option to load the external stopwords file. The format for such a stopword file is one stopword per line, lines starting with '#' are interpreted as comments and ignored.
 
-**Note:** There was a bug in Weka 3.5.6 (which introduced the support of external stopwords lists), which ignored the external stopwords list. Later versions or [snapshot](snapshots.md)s from 21/07/2007 on will work correctly.
+**Note:** There was a bug in Weka 3.5.6 (which introduced the support of external stopwords lists), which ignored the external stopwords list. Later versions from 21/07/2007 on will work correctly.
 
 # UTF-8
 

docs/using_cluster_algorithms.md

@@ -135,7 +135,7 @@ And some example output:
 
 ## ClassificationViaClustering
 
-A new meta-classifier, `weka.classifiers.meta.ClassificationViaClustering`, got introduced in the developer version (>3.5.6 or [snapshot](snapshots.md)), which mimics the *clusters to classes* functionality of the `weka.core.ClusterEvaluation` class. A user defined cluster algorithm is built with the training data presented to the meta-classifier (after the class attribute got removed, of course) and then the mapping between classes and clusters is determined. This mapping is then used for predicting class labels of unseen instances.
+A new meta-classifier, `weka.classifiers.meta.ClassificationViaClustering`, got introduced in the developer version (>3.5.6), which mimics the *clusters to classes* functionality of the `weka.core.ClusterEvaluation` class. A user defined cluster algorithm is built with the training data presented to the meta-classifier (after the class attribute got removed, of course) and then the mapping between classes and clusters is determined. This mapping is then used for predicting class labels of unseen instances.
 
 Here's an example for the UCI [dataset](datasets.md) *balance-scale*: