mloss | Projects authored by david cole.

revrand 1.0.0

by dsteinberg - January 29, 2017, 04:33:54 CET [ ] 47578 views, 12399 downloads, 0 subscriptions

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About: A library of scalable Bayesian generalised linear models with fancy features

Changes:

1.0 release!
Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
Random state can be set for all random objects
Numerous small improvements to make revrand production ready
Final report
Documentation improvements

Authors: Daniel Steinberg, Louis Tiao, Alistair Reid, Lachlan Mccalman, David Cole, Simon Ocallaghan
License: Apache 2.0
Programming Language: Python

Operating System: Platform Independent
Data Formats: Numpy
Tags: Stochastic Gradient Descent, Large Scale Learning, Nonparametric Bayes, Nonlinear Regression, Gaussian Processes, Generalized Linear Models, Spark, Fast Food, Random Features