Python Markov-decision-processes Resources

DecisionTree A simple python implementation of Decision Tree, using Gini index. Usage: import DecisionTree node = DecisionTree.trainDecisionTree(lab

Gorrabot is a Gitlab bot made to automate checks and processes in the Faraday development. Features Check that the CHANGELOG is modified By default, m

Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th

Modular Gaussian Processes for Transfer Learning 🧩 Introduction This repository contains the implementation of our paper Modular Gaussian Processes f

Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks This is the master thesi

Parental Control Application LittleBrother Overview LittleBrother is a simple parental control application monitoring specific processes (read "games"

Class HiddenMarkovModel HiddenMarkovModel implements hidden Markov models with Gaussian mixtures as distributions on top of TensorFlow 2.0 Installatio

PyImpetus PyImpetus is a Markov Blanket based feature selection algorithm that selects a subset of features by considering their performance both indi

vmgp This is the repository of Vivek Myers and Nikhil Sardana for our CS 330 final project, Bayesian Meta-Learning Through Variational Gaussian Proces

QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently bu

BO-GP Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations. The BO-GP codes are developed using GPy and GPyOpt. The optimizer

Sparse Implicit Processes In this repository we include the two different versions of the SIP code developed for the article Sparse Implicit Processes

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Decision Tree Writer This package allows you to train a binary classification decision tree on a list of labeled dictionaries or class instances, and

Markov Chain Composer Using Markov Chain to represent relationships between words in song lyrics and then generating new lyrics.. ahem interpretive po

Hanasubot Hanasubot (Japanese 話すボット, talking bot) is a Python chatbot running on Telegram. The bot is based on Markov Chains so it can learn your word

Data Efficient Decision Making

Project Azua 0. Overview Many modern AI algorithms are known to be data-hungry, whereas human decision-making is much more efficient. The human can re

Soft-Decision-Tree Soft-Decision-Tree is the pytorch implementation of Distilling a Neural Network Into a Soft Decision Tree, paper recently published

Neural-Backed Decision Trees · Site · Paper · Blog · Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah

Markovflow Documentation | Tutorials | API reference | Slack What does Markovflow do? Markovflow is a Python library for time-series analysis via prob

DecisionTree 决策树分类与回归模型，以及可视化 DecisionTree ID3 C4.5 CART 分类 回归 决策树绘制 分类树 回归树 调参 剪枝 ID3 ID3决策树是最朴素的决策树分类器： 无剪枝 只支持离散属性 采用信息增益准则 在data.py中，我们记录了一个小的西瓜数据

Decentralized Reinforcement Learning This is the code complementing the paper Decentralized Reinforcment Learning: Global Decision-Making via Local Ec

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay This is the official implementation of our paper "Diversity-based Traje

This tool gives developers, researchers and companies the ability to analyze software packages of different programming languages that are being or will be used in their codes, providing information that allows them to know in advance if this library complies with processes. secure development, if currently supported, possible backdoors (malicious embedded code), typosquatting analysis, the history of versions and reported vulnerabilities (CVEs) of the package.

Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy

RawVSR This repo contains the official codes for our paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference Xiaoh

A python library to build Model Trees with Linear Models at the leaves.

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Due to a normalization bug the expert trajectories have lower performance than the rl_baseline_zoo reported experts. Please see the following link in

Decision Transformer Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas†, and Igor M

Decipher using Markov Chain Monte Carlo

A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs, supporting both control flow and dataflow execution paradigms as well as de-centralized CPU & GPU scheduling.

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

Matern Gaussian Processes on Graphs This repo provides an extension for gpflow with Matérn kernels, inducing variables and trainable models implemente

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Fast and Flexible Temporal Point Processes with Triangular Maps This repository includes a reference implementation of the algorithms described in "Fa

Neural Oblivious Decision Ensembles A supplementary code for anonymous ICLR 2020 submission. What does it do? It learns deep ensembles of oblivious di

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Algorithmic Trading This machine learning algorithm was built using Python 3 and scikit-learn with a Decision Tree Classifier. The program gathers sto

Socorro Socorro is a Mozilla-centric ingestion pipeline and analysis tools for crash reports using the Breakpad libraries. Support This is a Mozilla-s

Markov Decision Process A Markov decision process (MDP), by definition, is a sequential decision problem for a fully observable, stochastic environmen

The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. I

Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

hmmlearn hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and

PyTorch Implementation of Differentiable SDE Solvers This library provides stochastic differential equation (SDE) solvers with GPU support and efficie

Neural ODE Processes Official code for the paper Neural ODE Processes (ICLR 2021). Abstract Neural Ordinary Differential Equations (NODEs) use a neura

NColony Infrastructure for running "colonies" of processes. Hacking $ tox Should DTRT -- if it passes, it means unit tests are passing, and 100% cover

SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p

Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

hsmmlearn hsmmlearn is a library for unsupervised learning of hidden semi-Markov models with explicit durations. It is a port of the hsmm package for

Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes

TreeInterpreter Package for interpreting scikit-learn's decision tree and random forest predictions. Allows decomposing each prediction into bias and

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

Highly interpretable, sklearn-compatible classifier based on decision rules This is a scikit-learn compatible wrapper for the Bayesian Rule List class

Orange Data Mining Orange is a data mining and visualization toolbox for novice and expert alike. To explore data with Orange, one requires no program

MazeRL is an application oriented Deep Reinforcement Learning (RL) framework, addressing real-world decision problems. Our vision is to cover the complete development life cycle of RL applications ranging from simulation engineering up to agent development, training and deployment.

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

pyrasite Tools for injecting arbitrary code into running Python processes. homepage: http://pyrasite.com documentation: http://pyrasite.rtfd.org downl

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

MLJAR Automated Machine Learning Documentation: https://supervised.mljar.com/ Source Code: https://github.com/mljar/mljar-supervised Table of Contents

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

FukuML Simple machine learning library / 簡單易用的機器學習套件 Installation $ pip install FukuML Tutorial Lesson 1: Perceptron Binary Classification Learning Al

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

DISCLAIMER: This is not an official google project, this is just something I wrote while at Google. Pyringe What this is Pyringe is a python debugger