Search Results for "multiple linear regression"

Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming...

Gatling is a high-performance load testing tool built on the JVM that emphasizes realism, scalability, and developer ergonomics. Test scenarios are scripted in a concise Scala-based DSL, allowing you to model user journeys with think times, feeders (dynamic data), checks, and assertions all in code. Its asynchronous, non-blocking engine (backed by Netty) can drive very high concurrency from a single injector, reducing the need for large injector farms. Gatling supports HTTP out of the box as...

STK++ (http://www.stkpp.org) is a versatile, fast, reliable and elegant collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen-like API), regression, dimension reduction, etc. Some functionalities provided by the library are available in the R environment as R functions (http://cran.at.r-project.org/web/packages/rtkore/index.html). At a convenience, we propose the source packages on sourceforge. The library offers a dense set of (mostly) template classes in C++ and is suitable for projects ranging from small one-off projects to complete data mining application suites.

...FluentLenium is shipped with adapters for JUnit4, JUnit5, TestNG, Spock, Spring TestNG, Cucumber and Kotest, but it can also be used standalone. FluentLenium best integrates with AssertJ, but you can also choose to use the assertion framework you want. FluentLenium gives you multiple methods which help you write tests quicker. All those methods are tested daily by commercial regression test suites maintained by project developers.

...By using LAMA for their application, software developers benefit from higher productivity and stay up to date with the latest hardware innovations, both leading to shorter time-to-market. The framework supports multiple target platforms within a distributed heterogeneous environment. It offers optimized device code on the backend side, high scalability through latency hiding and asynchronous execution across multiple nodes. LAMA's modular and extensible software design supports the developer on several levels, regardless of whether writing his own portable code with the Heterogeneous Computing Development Kit or using prepared functionality from the Linear Algebra Package, the user always gains high productivity and maximum performance.