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.
pytorch implementation of Attention is all you need

A Pytorch Implementation of the Transformer: Attention Is All You Need Our implementation is largely based on Tensorflow implementation Requirements N

230 Dec 07, 2022
An Official Repo of CVPR '20 "MSeg: A Composite Dataset for Multi-Domain Segmentation"

This is the code for the paper: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation (CVPR 2020, Official Repo) [CVPR PDF] [Journal PDF] J

226 Nov 05, 2022
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
My implementation of DeepMind's Perceiver

DeepMind Perceiver (in PyTorch) Disclaimer: This is not official and I'm not affiliated with DeepMind. My implementation of the Perceiver: General Per

Louis Arge 55 Dec 12, 2022
Self-Regulated Learning for Egocentric Video Activity Anticipation

Self-Regulated Learning for Egocentric Video Activity Anticipation Introduction This is a Pytorch implementation of the model described in our paper:

qzhb 13 Sep 23, 2022
[CVPR-2021] UnrealPerson: An adaptive pipeline for costless person re-identification

UnrealPerson: An Adaptive Pipeline for Costless Person Re-identification In our paper (arxiv), we propose a novel pipeline, UnrealPerson, that decreas

ZhangTianyu 70 Oct 10, 2022
Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch

Triangle Multiplicative Module - Pytorch Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or c

Phil Wang 22 Oct 28, 2022
U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI

U-Net for brain segmentation U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI based on a deep learning segmentation alg

562 Jan 02, 2023
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP Abstract: We introduce a method that allows to automatically se

Daniil Pakhomov 134 Dec 19, 2022
[NeurIPS 2021] "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators"

G-PATE This is the official code base for our NeurIPS 2021 paper: "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of T

AI Secure 14 Oct 12, 2022
Supervised Contrastive Learning for Product Matching

Contrastive Product Matching This repository contains the code and data download links to reproduce the experiments of the paper "Supervised Contrasti

Web-based Systems Group @ University of Mannheim 18 Dec 10, 2022
This repository contains a toolkit for collecting, labeling and tracking object keypoints

This repository contains a toolkit for collecting, labeling and tracking object keypoints. Object keypoints are semantic points in an object's coordinate frame.

ETHZ ASL 13 Dec 12, 2022
The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont

Ren Yurui 111 Dec 10, 2022
Material related to the Principles of Cloud Computing course.

CloudComputingCourse Material related to the Principles of Cloud Computing course. This repository comprises material that I use to teach my Principle

Aniruddha Gokhale 15 Dec 02, 2022
duralava is a neural network which can simulate a lava lamp in an infinite loop.

duralava duralava is a neural network which can simulate a lava lamp in an infinite loop. Example This is not a real lava lamp but a "fake" one genera

Maximilian Bachl 87 Dec 20, 2022
Interpolation-based reduced-order models

Interpolation-reduced-order-models Interpolation-based reduced-order models High-fidelity computational fluid dynamics (CFD) solutions are time consum

Donovan Blais 1 Jan 10, 2022
The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
This is the official implementation of Elaborative Rehearsal for Zero-shot Action Recognition (ICCV2021)

Elaborative Rehearsal for Zero-shot Action Recognition This is an official implementation of: Shizhe Chen and Dong Huang, Elaborative Rehearsal for Ze

DeLightCMU 26 Sep 24, 2022
Code for "Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis

HAABSAStar Code for "Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis". This project builds on the code from https://gith

1 Sep 14, 2020
Mememoji - A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

a project built with deep convolutional neural network and ❤️ Table of Contents Motivation The Database The Model 3.1 Input Layer 3.2 Convolutional La

Jostine Ho 761 Dec 05, 2022