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A bunch of little data generators to simulate multivariate dependencies.

A bunch of little data generators to simulate multivariate dependencies.

Correlation Detective is a fast and scalable family of algorithms for finding interesting multivariate correlations in vector datasets. Release notes: - 1.0.1: Solved issue with running multiple CD jobs sequentially.

Java implementation of Uncertainty-Aware Multidimensional Scaling (UAMDS). UAMDS is a dimensionality reduction algorithm for sets of multivariate normal distributions. (Related publication DOI: 10.1109/TVCG.2022.3209420)

Replaces missing numeric values using Expectation Maximization with a multivariate normal model. Described in " Schafer, J.L. Analysis of Incomplete Multivariate Data, New York: Chapman and Hall, 1997."

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.