leobispo/Hive-JSON-Serde
Read - Write JSON SerDe for Apache Hive.
Developer Profile
leobispo
Discovered public repositories for leobispo in the GitHub catalog.
leobispo/angular-strap
Bootstrap directives for Angular
leobispo/mk_parser
Public repository.
leobispo/rtbkit
RTBkit: a real-time bidder framework
leobispo/jml
Jeremy's Machine Learning Library
leobispo/soa
Repository for service oriented architecture code and components.
leobispo/leveldb
Mirror of Google's leveldb project
leobispo/tinyxml2
TinyXML2 is a simple, small, efficient, C++ XML parser that can be easily integrating into other programs.
leobispo/google-url
Google's URL parsing library
leobispo/zip
Public repository.
leobispo/hunT
A C++ implementation of the HunT application
leobispo/lockfree
C++ Lock free structure Implementations
leobispo/sort
Sort Algorightms in C
leobispo/tinyhttp
An implementation of a HTTP Server Framework written in java. I started this project, because I couldn't find a Tiny HTTP Server framework suitable for my necessities, such as Session, Cookies, HTTP/HTTPS, Asynchronous IO, etc.
leobispo/exercises
Public repository.
leobispo/Stock
Public repository.
leobispo/nsfp
NSFP Charite Project
leobispo/spyne
This is a simple, easily extendible rpc library that supports multiple protocols and provides several useful tools for creating, publishing and consuming online services in python.
leobispo/findgenepattern
Public repository.
leobispo/som
SOM - Self organizing Map is a Swing application that implements the Self organizing map algorithm. Self-organizing map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce low-dimensional representation of the training samples while preserving the topological properties of the input space. Self-Organizing Map showing US Congress voting patterns visualized in Synapse Self-Organizing Map showing US Congress voting patterns visualized in Synapse This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map. Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. It is a competitive process, also called vector quantization. Mapping automatically classifies a new input vector.
leobispo/kermit
A full implementation of kermit protocol over UDP.