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A platform for discovery and communication between various parts of a distributed service or application. At its minimum, the platform is a library which can be used to serialize and deserialize application constructs into payloads which it can then send to and receive from remote endpoints. For now it supports TCP and HTTP communication protocols with WebSockets coming soon. It supports raw byte streams via TCP, as well as application/json via JAX-RS as serialization protocols. It offers plain or SSL/TLS communication for security of your data in transit. For now it is only available in Java, with bindings for other languages coming soon. The communication pattern is request-reply, with fail fast semantics, and with service consumers (clients) knowing beforehand the coordinates of the service providers (servers). The TCP communication protocol offers blocking/unblocking/asynchronous flavors whereas the rest are only blocking for now. Coming soon, the platform will provide a Discovery Service to facilitate the registration of the service providers (servers) and service consumers (clients). In this case, the callers don't need to resolve or even balance their calls towards various endpoints, since the Platform handles this tasks. In this case, the pub/sub communication pattern will also be available (coming soon) with at-most-once delivery guarantees. We intend to provide a Security Service for managing the keys/and certificates of various services as well as Metrics and Logging services (coming soon).

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.