simple artificial intelligence utilities

Related tags

Deep Learningsimpleai
Overview

Simple AI

Project home: http://github.com/simpleai-team/simpleai

This lib implements many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach", from Stuart Russel and Peter Norvig. We strongly recommend you to read the book, or at least the introductory chapters and the ones related to the components you want to use, because we won't explain the algorithms here.

This implementation takes some of the ideas from the Norvig's implementation (the aima-python lib), but it's made with a more "pythonic" approach, and more emphasis on creating a stable, modern, and maintainable version. We are testing the majority of the lib, it's available via pip install, has a standard repo and lib architecture, well documented, respects the python pep8 guidelines, provides only working code (no placeholders for future things), etc. Even the internal code is written with readability in mind, not only the external API.

At this moment, the implementation includes:

  • Search
    • Traditional search algorithms (not informed and informed)
    • Local Search algorithms
    • Constraint Satisfaction Problems algorithms
    • Interactive execution viewers for search algorithms (web-based and terminal-based)
  • Machine Learning
    • Statistical Classification

Installation

Just get it:

pip install simpleai

And if you want to use the interactive search viewers, also install:

pip install pydot flask

You will need to have pip installed on your system. On linux install the python-pip package, on windows follow this. Also, if you are on linux and not working with a virtualenv, remember to use sudo for both commands (sudo pip install ...).

Examples

Simple AI allows you to define problems and look for the solution with different strategies. Another samples are in the samples directory, but here is an easy one.

This problem tries to create the string "HELLO WORLD" using the A* algorithm:

from simpleai.search import SearchProblem, astar

GOAL = 'HELLO WORLD'


class HelloProblem(SearchProblem):
    def actions(self, state):
        if len(state) < len(GOAL):
            return list(' ABCDEFGHIJKLMNOPQRSTUVWXYZ')
        else:
            return []

    def result(self, state, action):
        return state + action

    def is_goal(self, state):
        return state == GOAL

    def heuristic(self, state):
        # how far are we from the goal?
        wrong = sum([1 if state[i] != GOAL[i] else 0
                    for i in range(len(state))])
        missing = len(GOAL) - len(state)
        return wrong + missing

problem = HelloProblem(initial_state='')
result = astar(problem)

print(result.state)
print(result.path())

More detailed documentation

You can read the docs online here. Or for offline access, you can clone the project code repository and read them from the docs folder.

Help and discussion

Join us at the Simple AI google group.

Authors

  • Many people you can find on the contributors section.
  • Special acknowledgements to Machinalis for the time provided to work on this project. Machinalis also works on some other very interesting projects, like Quepy and more.
KIDA: Knowledge Inheritance in Data Aggregation

KIDA: Knowledge Inheritance in Data Aggregation This project releases our 1st place solution on NeurIPS2021 ML4CO Dual Task. Slide and model weights a

24 Sep 08, 2022
Neural Koopman Lyapunov Control

Neural-Koopman-Lyapunov-Control Code for our paper: Neural Koopman Lyapunov Control Requirements dReal4: v4.19.02.1 PyTorch: 1.2.0 The learning framew

Vrushabh Zinage 6 Dec 24, 2022
Data and extra materials for the food safety publications classifier

Data and extra materials for the food safety publications classifier The subdirectories contain detailed descriptions of their contents in the README.

1 Jan 20, 2022
Fast, general, and tested differentiable structured prediction in PyTorch

Fast, general, and tested differentiable structured prediction in PyTorch

HNLP 1.1k Dec 16, 2022
CLIP + VQGAN / PixelDraw

clipit Yet Another VQGAN-CLIP Codebase This started as a fork of @nerdyrodent's VQGAN-CLIP code which was based on the notebooks of @RiversWithWings a

dribnet 276 Dec 12, 2022
3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

3rd Place Solution of Traffic4Cast 2021 Core Challenge This is the code for our solution to the NeurIPS 2021 Traffic4Cast Core Challenge. Paper Our so

7 Jul 25, 2022
YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

Introduction Yolov5-face is a real-time,high accuracy face detection. Performance Single Scale Inference on VGA resolution(max side is equal to 640 an

DeepCam Shenzhen 1.4k Jan 07, 2023
A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Pedro Savarese 35 Jul 29, 2022
A PyTorch Library for Accelerating 3D Deep Learning Research

Kaolin: A Pytorch Library for Accelerating 3D Deep Learning Research Overview NVIDIA Kaolin library provides a PyTorch API for working with a variety

NVIDIA GameWorks 3.5k Jan 07, 2023
Official Pytorch implementation of Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR 2022)

The Official Implementation of CLIB (Continual Learning for i-Blurry) Online Continual Learning on Class Incremental Blurry Task Configuration with An

NAVER AI 34 Oct 26, 2022
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

NeRF-pytorch NeRF (Neural Radiance Fields) is a method that achieves state-of-the-art results for synthesizing novel views of complex scenes. Here are

Yen-Chen Lin 3.2k Jan 08, 2023
Text-to-Image generation

Generate vivid Images for Any (Chinese) text CogView is a pretrained (4B-param) transformer for text-to-image generation in general domain. Read our p

THUDM 1.3k Dec 29, 2022
Conceptual 12M is a dataset containing (image-URL, caption) pairs collected for vision-and-language pre-training.

Conceptual 12M We introduce the Conceptual 12M (CC12M), a dataset with ~12 million image-text pairs meant to be used for vision-and-language pre-train

Google Research Datasets 226 Dec 07, 2022
OOD Generalization and Detection (ACL 2020)

Pretrained Transformers Improve Out-of-Distribution Robustness How does pretraining affect out-of-distribution robustness? We create an OOD benchmark

littleRound 57 Jan 09, 2023
Object-Centric Learning with Slot Attention

Slot Attention This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055). Requirement

Untitled AI 72 Jan 02, 2023
Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

NBFNet: Neural Bellman-Ford Networks This is the official codebase of the paper Neural Bellman-Ford Networks: A General Graph Neural Network Framework

MilaGraph 136 Dec 21, 2022
GLIP: Grounded Language-Image Pre-training

GLIP: Grounded Language-Image Pre-training Updates 12/06/2021: GLIP paper on arxiv https://arxiv.org/abs/2112.03857. Code and Model are under internal

Microsoft 862 Jan 01, 2023
Tutorials and implementations for "Self-normalizing networks"

Self-Normalizing Networks Tutorials and implementations for "Self-normalizing networks"(SNNs) as suggested by Klambauer et al. (arXiv pre-print). Vers

Institute of Bioinformatics, Johannes Kepler University Linz 1.6k Jan 07, 2023
Style-based Neural Drum Synthesis with GAN inversion

Style-based Drum Synthesis with GAN Inversion Demo TensorFlow implementation of a style-based version of the adversarial drum synth (ADS) from the pap

Sound and Music Analysis (SoMA) Group 29 Nov 19, 2022
automatic color-grading

color-matcher Description color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, painting

hahnec 168 Jan 05, 2023