Download JAR files tagged by functions with all dependencies

A Functional Workflow Language. Cuneiform is a workflow specification language which makes it easy to integrate heterogeneous tools and libraries and exploit data parallelism. Users do not have to create heavy-weight wrappers for establised tools or to reimplement them. Instead, they apply their existing software to partitioned data. Using the Hi-WAY application master Cuneiform can be executed on Hadoop YARN which makes it suitable for large scale data analysis. Cuneiform comes in the form of a functional programming language with a Foreign Function Interface (FFI) that lets users create functions in any suitable scripting language and apply these functions in a uniform way. Data paralelism is expressed by applying map, cross-product, dot-product, or combinations of the aforementioned algorithmic skeletons to collections of black-box data.

Bond is a small library that can be used to spy values and mock functions during tests. Spying is a replacement for writing the assertEquals in your test, which are tedious to write and even more tedious to update whem your test setup or code inevitably changes. With Bond, you separate what is being verified, e.g., the variable named output, from what value it should have. This way you can quickly spy several variables, even have structured values such as lists or dictionaries, and these values are saved into an observation log that is saved for future reference. If the test observations are different you have the option to interact with a console or visual tool to see what has changed, and whether the reference set of observations need to be updated.

The Universal Java Matrix Package (UJMP) is an open source library for dense and sparse matrix computations and linear algebra in Java. In addition to the basic operations like matrix multiplication, matrix inverse or decomposition methods, it also supports visualization, JDBC import/export and many other useful functions such as mean, correlation, standard deviation, mutual information, or the replacement of missing values. It's a swiss army knife for data processing in Java, tailored to machine learning applications.

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

A filter that applies the LOF (Local Outlier Factor) algorithm to compute an outlier score for each instance in the data. Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable. For more information, see: Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.

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.

What is jparsec? Jparsec is a recursive-desent parser combinator framework written for Java. It constructs parsers in native Java language only. Why yet another parser framework? Jparsec stands out for its combinator nature. It is no parser generator like YACC or ANTLR. No extra grammar file is required. Grammar is written in native Java /C# language, which also means you can utilize all the utilities in the Java/.Net community to get your parser fancy. What does "jparsec" stand for? Jparsec is an implementation of Haskell Parsec on the Java platform. Feature highlights. operator precendence grammar. accurate error location and customizable error message. rich set of pre-defined reusable combinator functions. declarative API that resembles BNF. [email protected] [email protected]

The idea and motivation of the lindbergframework is to provide resources and simple solutions to problems based on the concept 'Plug and Use'. In the current version, the framework provides easy and simple solutions to the population of beans from SQL queries, abstraction of the complexities to access to stored procedures and stored functions , population of database cursors, transaction management, dependency injection, integration with Spring, JSF and Adobe Flex. The lindberg-persistence (LINP) solution, implements a important concept: separated repository of SQL commands. Among other resources.

This service implements the Indexer and Searcher interface of WebLab and connect to a remote SOLR engine in order to realize the functions. The connection is mandatory and thus the remote SOLR server should be started beforehand. Configuration of the SOLR server URL is found in src/main/webapp/WEB-INF/cxf-servlet.xml . See in particular "indexerServiceBean" and "searcherServiceBean". Moreover, the service implements several "Analyser" services that could be included in a complete search chain in order to: - enrich a ResultSet with metadata about Hits (see resultSetMetaEnricherServiceBean) - highlight snippet in ResultSet (see highlighterServiceBean) - provide facets related to the current results (see facetSuggestionServiceBean) - suggest spell correction of the original query (see spellSuggestionServiceBean)