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LaukvikCSV is a powerful API for reading, writing and querying tabular data stored in the CSV format. In contrast to other API it lets you specify data types for each column using meta data. It automatically detects delimiters so you don't have to worry about delimiters being comma, tab, pipe, semicolon etc. Run powerful queries to filter your data easily with a fluid query language thats type safe. Export your tabular data to CSV, JSON, XML and HTML.

This project contains artifacts that provide utility functions for Cloud Bigtable client readers and writers in Google Cloud Dataflow. Dataflow includes a different version of the cloud bigtable client, resulting in a need to have a separation of concerns and an abstraction. More importantly, there is a need for shading and renaming of packages so that Dataflow nad bigtable-hbase-dataflow's two sets of shading (needed for a variety of reasons) don't clash with each other. This submodule does package renaming which is needed for bigtable-hbase-dataflow and alleviates some issues related to developing that module.

MASSIS is a framework that facilitates the simulation of scenarios with multiple agents (representing people, robots, sensors, etc.) in indoor environments (i.e., in a building). MASSIS provides support for designing spaces and specifying the behavior of the elements and agents in them It is possible to define a great diversity of behaviours, from a simple sensor to the decisions of a person. MASSIS has been designed to keep this flexibility withough hindering performance. The framework is capable of supporting thousands of agents, each one with an specific behavior.

Package for loading a directory containing MRI data in NIfTI format. The directory to be loaded must contain as many subdirectories as there are classes of MRI data. Each subdirectory name will be used as the class label for the corresponding .nii files in that subdirectory. (This is the same strategy as the one used by WEKA's TextDirectoryLoader.) Currently, the package only reads volume information for the first time slot from each .nii file. The readDoubleVol(short ttt) method from the Nifti1Dataset class (http://niftilib.sourceforge.net/java_api_html/Nifti1Dataset.html) is used to read the data for each volume into a sparse WEKA instance (with ttt=0). For an LxMxN volume (the dimensions must be the same for each .nii file in the directory!), the order of values in the generated instance is [(z_1, y_1, x_1), ..., (z_1, y_1, x_L), (z_1, y_2, x_1), ..., (z_1, y_M, x_L), (z_2, y_1, x_1), ..., (z_N, y_M, x_L)]. If the volume is an image, then only x and y coordinates are used.

This is the main aggregator for all implementation code shared between modules. Each module contains one or more service interfaces, exposed statically using "X_" prefixed classes. Core modules will define their API interfaces, their static accessor, and often a "plain-jre" default implementation which can be replaced by injection override. There must be zero external dependencies in any core modules. A low-performance, low-feature default is preferred over thick, dependency-ridden shared inherits.

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.