ornithos/MTDS.jl

This repository has been archived on 28/Nov/2021
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Created over 4 years ago
Updated almost 3 years ago

ornithos/MTDS.jl

ornithos

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Implementation of some examples of multi-task dynamical system (in Julia).

compare_df

Compare two dataframes

ProbLineSearch.jl

Probabilistic Line Searches for Stochastic Optimization (Mahsereci & Hennig, 2017) ported from MATLAB.

teachingnotes

Jupyter Notebook

SeqAdaptiveIS

Sequential adaptive importance sampling for static (and non-static) sequential problems.

pytorch-mtds-mocap

Implementation of Mocap application for MTDS

heroku-sqlgen

Heroku hosted SQL Generation for demo purposes

matlabParser

A file to parse called and assigned variables in MATLAB

AxUtil.jl

A collection of utilities I've developed while working with Julia

dynamicalSystems

Collection of dynamical system implementations

mocap-mtds

Multi-Task Dynamical Systems for Mocap Data

Jupyter Notebook

pySQLGen

Generating SQL for OMOP standard relational databases via Python

pyalexutil

Various utilities for python.

matlab-utils

Assortment of MATLAB utility scripts

heroku-decovid-temp

InferGMM.jl

Implementation of GMM and various inference methods using GMMs

lpsvm

(UCL) Project work for large scale SVM solver

mtds-dblpend

MTDS applied to video data of a double pendulum

Jupyter Notebook

stan-intro

Initial exploration of stan, including user defined functions

AxPlot.jl

Plotting utilities for Julia (PyPlot) spun out from my utils package.

athenstimeseries2018

Time series excercises for the ACM data science summer school - Athens 2018

Jupyter Notebook

pynamicalsystem

a library of (discrete time) dynamical system code written in python. In part a port of the dynamicalSystems MATLAB repo.

nectr

nectr: Non-parametric Exploratory Clustering with TURN-RES. This is an R package including an implementation of the non-parametric clustering method authored by Foss, 2002. It was designed for industry analysts who need a fast way to explore a low dimension (p<10) dataset which does not impose a priori assumptions on the structure of the data.