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Mean Bean is an open source Java test library that tests equals and hashCode contract compliance, as well as JavaBean/POJO getter and setter methods.

Tiny Java utility to incrementally calculate Mean and Standard Deviation with a numerically stable algorithm. Contains a simple utility class to incrementally calculate moving average and moving standard deviation of a data series.

Mean Bean is an open source Java test library that tests equals and hashCode contract compliance, as well as JavaBean/POJO getter and setter methods.

"JSTUN" is a Java-based STUN (Simple Traversal of User Datagram Protocol (UDP) Through Network Address Translation (NAT)) implementation. STUN provides a mean for applications to discover the presence and type of firewalls or NATs between them and the public internet. Additionally, in presence of a NAT STUN can be used by applications to learn the public Internet Protocol (IP) address assigned to the NAT.

libssh is a C library that enables you to write a program that uses the SSH protocol. With it, you can remotely execute programs, transfer files, or use a secure and transparent tunnel for your remote programs. The SSH protocol is encrypted, ensures data integrity, and provides strong means of authenticating both the server of the client. The library hides a lot of technical details from the SSH protocol, but this does not mean that you should not try to know about and understand these details. This is the SSHAPI provider bridge for libssh, and uses JNA. Downloads and more information about libssh may be found at http://api.libssh.org/master/index.html. This library is deployed to SSHTools own Maven repository.

In order to get around the JBI Lifecycle Classpath limitation in the application server, a new JBI jar file is being created called esb_jbi_framework.jar. This jar file will contain a MANIFEST.MF file that contains Class-Path: entries that point towards the jbi_rt.jar (JBI Runtime) and jbi_tests.jar (Scaffolding Registry et al) . This way we dont have to combine the throwaway jbi_tests.jar with the main jbi_rt.jar later. We would simply have to dereference it from jbi_framework.jar. Likewise adding a new JAR to the same classpath would simply mean adding a new entry to the ClassPath: header field.

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.

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.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes: forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set. Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions. Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set. Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc. It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes. Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.