Download JAR files tagged by value with all dependencies

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

HermiT is reasoner for ontologies written using the Web Ontology Language (OWL). Given an OWL file, HermiT can determine whether or not the ontology is consistent, identify subsumption relationships between classes, and much more. This is the maven build of HermiT and is designed for people who wish to use HermiT from within the OWL API. It is now versioned in the main HermiT version repository, although not officially supported by the HermiT developers. The version number of this package is a composite of the HermiT version and an value representing releases of this packaged version. So, 1.3.7.1 is the first release of the mavenized version of HermiT based on the 1.3.7 release of HermiT. This package includes the Jautomata library (http://jautomata.sourceforge.net/), and builds with it directly. This library appears to be no longer under active development, and so a "fork" seems appropriate. No development is intended or anticipated on this code base.

Reco4j is an open source project aims at developing a recommendation framework based on graph data sources. We choose graph databases for several reasons. They are NoSQL databases, so "schemaless". This means that it is possible to extend the basic data structure with intermediate information, i.e. similarity value between item and so on. Moreover, since every information is expressed with properties, nodes and relations, the recommendation process can be customized to work on every graph. Reco4j can be used on every graph where "user" and "item" is represented by node and the preferences are modelled as relationship between them. Current implementation leverage on Neo4j as first graph database integrated in our framework.

This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.

This project is a GUI utility for processing files. It allows selecting a set of source files and a pipeline of processes to apply onto those files. The applications shows in a nice-looking user interface where you can define profiles for your repetitive tasks. It provides pre-defined processors doing usual file manipulation tasks like: Copy, Head, Tail, Chunk, Search, Replace, Zip, Unzip... But the biggest value of this file processor tool is the ability to add easily custom file processors written in java.

HermiT is reasoner for ontologies written using the Web Ontology Language (OWL). Given an OWL file, HermiT can determine whether or not the ontology is consistent, identify subsumption relationships between classes, and much more. This is the maven build of HermiT and is designed for people who wish to use HermiT from within the OWL API. It is now versioned in the main HermiT version repository, although not officially supported by the HermiT developers. The version number of this package is a composite of the HermiT version and a value representing the OWLAPI release it is compatible with. Note that the group id for the upstream HermiT is com.hermit-reasoner, while this fork is released under net.sourceforge.owlapi. This fork exists to allow HermiT users to use newer OWLAPI versions than the ones supported by the original HermiT codebase. This package includes the Jautomata library (http://jautomata.sourceforge.net/), and builds with it directly. This library appears to be no longer under active development, and so a "fork" seems appropriate. No development is intended or anticipated on this code base.

This module defines the interfaces for Comparators in the TCS Alignment Toolbox. A Comparator has the purpose of defining the dissimilarity between elements in the input sequences of an Alignment. More specific information on Comparators can be found in the 'Comparator' interface. You can find a lot of helpful standard implementations of Comparators in the comparators-lib module. In the TCS Alignment Toolbox we require the output values of Comparators to lie in the range [0,1]. Many natural dissimilarities on value sets do not meet this criterion, such that additional normalization has to be applied. To that end this package also contains a Normalizer interface for functions that map real values from the range [0, infinity) to the range [0,1]. This package also provides a few convenience implementations of the Comparator interface to make the implementation of custom Comparators simpler, namely: SkipExtendedComparator, ParameterLessSkipExtendedComparator, ComparisonBasedSkipExtendedComparator, and ParameterLessComparisonBasedSkipExtendedComparator. Finally the TCS Alignment Toolbox also provides the means to learn parameters of Comparators. To enable that Comparators must implement the DerivableComparator interface to properly define the parameters that can be learned and the gradient of the dissimilarity with respect to these parameters. Gradients are stored using the Gradient interface as well as some convenience implementations of said interface, namely EmptyGradient, SingletonGradient, ArrayGradient and ListGradient.

HermiT is reasoner for ontologies written using the Web Ontology Language (OWL). Given an OWL file, HermiT can determine whether or not the ontology is consistent, identify subsumption relationships between classes, and much more. This is the maven build of HermiT and is designed for people who wish to use HermiT from within the OWL API. It is not officially supported by the HermiT development team, but was built initially for use with the Clojure-OWL library. It is built using the HermiT source tree without modification. There have been some additions to the test source tree to account for differences between the maven and ant environment; these are small and (hopefully) maintainable. The version number of this package is a composite of the HermiT version and an value representing releases of this packaged version. So, 1.3.7.1 is the first release of the mavenized version of HermiT based on the 1.3.7 release of HermiT. This package includes the Jautomata library (http://jautomata.sourceforge.net/), and builds with it directly. This library appears to be no longer under active development, and so a "fork" seems appropriate. No development is intended or anticipated on this code base.

Show color codes preview per line in a sidebar area of an editor. <h2>Disable / Enable</h2> Check/Uncheck View > Show Colors <h2>Supported color patterns</h2> <ul> <li>Hex color code (e.g. #ffffff, #000)</li> <li>Css rgb/rgba values (e.g. rgb(0,0,0), rgba(255, 255, 255, 0.8))</li> <li>Css hsl/hsla values (e.g. hsl(0, 100%, 50%), hsla(120, 100%, 50%, 0.5))</li> <li>Named colors (e.g. red, blue)</li> <li>Java Color class (e.g. Color.black, new Color(100, 100, 100))</li> </ul> <h2>Multiple colors</h2> <ul> <li>Show top two colors in a sidebar if there are multiple colors in a line.</li> <li>If you want to check all colors, please click a specific rectangle. They will be shown as a list.</li> </ul> <h2>Change a color using the color chooser</h2> <ul> <li>Click a colored rectangle</li> <li>Click a color value of a list</li> <li>Select a new color in the color chooser</li> <li>An old color value will be changed to new one with the same format</li> </ul> <h2>Generate color codes</h2><p>You can generate color codes via a code generator(<kbd>Alt</kbd> + <kbd>Ins</kbd>).</p> <ol> <li>Run a code generator(Alt + Ins)</li> <li>Choose <code>Color...</code></li> <li>Choose format you expect (e.g. <code>new Color(r, g, b)</code>)</li> <li>Choose a color</li> <li>Click the OK button</li> <li>A color code is generated at the caret position</li> </ol> <h2>Options</h2> Tools > Options > Miscellaneous > Color Codes Preview <h3>Regex for enabled mime-types for Hex and CSS colors</h3> Default value is `^text/(x-)?(css|less|sass|scss)$`. If you would like to disable/enable some mime-types, please change the default regex. This pattern is used when the plugin checks a mime-type. <h3>Named Colors</h3> This option is `false` by default. If you would like to show named colors, please check it. <h2>NOTE</h2> <ul> <li>If you would like to show colors of `Color.decode(<hex>)` e.g. `Color.decode(#000000)`, Please add `java` to "Regex for enabled mime-types" of Hex and CSS e.g. (`^text/(x-)?(css|less|sass|scss|java)$`)</li> <li>Colors may be shown if they are not color codes. e.g. "#feature" contains #fea. This plugin recognizes it as a hex color code.</li> <li>If you use the GTK Look and Feel, you cannot change an alpha value in the color chooser.</li> <li>Hsl or hsla color values may not be changed correctly when you use the color chooser. (There may be 1% errors.)</li> </ul>

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.