معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

CoBaIR is a Python library for Context Based Intention Recognition

ویژگی	مقدار
سیستم عامل	-
نام فایل	CoBaIR-3.0.0
نام	CoBaIR
نسخه کتابخانه	3.0.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Adrian Lubitz
ایمیل نویسنده	Adrian.Lubitz@dfki.de
آدرس صفحه اصلی	https://github.com/dfki-ric/CoBaIR
آدرس اینترنتی	https://pypi.org/project/CoBaIR/
مجوز	BSD 3-Clause

# CoBaIR CoBaIR is a python library for **Co**ntext **Ba**sed **I**ntention **R**ecognition. It provides the means to infer an intention from given context. An intention is a binary value e.g. `repair pipe` that can either be present or not. Only one intention can be present at a time. Context on the otherhand can have multiple discrete instantiations e.g. `weather:sunny|cloudy|raining`. If context values are continuous, discretizer functions can be used to create discrete values. From the infered intention in a HRI scenario the robot can perform corresponding actions to help the human with a specific task. ## Publications For a more in-depth explanation consult the following papers: - [Concept Paper](https://www.dfki.de/fileadmin/user_upload/import/12351_lubitz_kimmi_cobabir_2022_-_Adrian_Lubitz.pdf) ## Install ```bash pip install CoBaIR ``` You can install the library from your local copy after cloning this repo with pip using `pip install .` or istall the the `develop` branch with `pip install git+https://github.com/dfki-ric/CoBaIR.git@develop` ### Known Issues On some Linux Distros there seems to be a problem with a shared library. [This Solutions](https://stackoverflow.com/questions/71010343/cannot-load-swrast-and-iris-drivers-in-fedora-35/72200748#72200748) suggests to `export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 ` which works on Ubuntu 22.04. ## Use the Graphical User Interface To make the configuration of a scenario easier we provide a Graphical User Interface(GUI). The GUI can be started with ```bash python start_configurator.py ``` if you want to start the GUI with a loaded config use ```bash python start_configurator.py -f config.yml ``` ## Documentation The Documentation can be accessed on https://dfki-ric.github.io/CoBaIR/ ## Bayesian Approach In the bayesian approach CoBaIR uses a two-layer Bayesian Net of the following structure. ![two-layer Bayesian Net](docs/images/2layerbayesian.svg) ## Config Format Configs will be saved in yml files. For convenience the is a configurator which can be started with ```bash python start_configurator.py ``` ### Bayesian Approach The configuration file for a two layer bayesian net for context based intention recognition follows the given format: ```yaml # List of contexts. Contexts can have different discrete instantiations. # Number of instantiations must be larger than 1. # For all discrete instantiations a prior probability must be given(sum for one context must be 1) contexts: context 1: instantiation 1 : float . instantiation m_1 : float context n: instantiation 1 : float . instantiation m_n : float # List of intentions. Intentions are always binary(either present or not) # For every intention the context variables and their influence on the intention is given # [very high, high, medium, low, very low, no] => [5, 4, 3, 2, 1, 0] intentions: intention 1: context 1: instantiation 1: int # one out of [5, 4, 3, 2, 1, 0] . instantiation m_1: int # one out of [5, 4, 3, 2, 1, 0] context n: instantiation 1: int # one out of [5, 4, 3, 2, 1, 0] . instantiation m_n: int # one out of [5, 4, 3, 2, 1, 0] intention p: context 1: instantiation 1: int # one out of [5, 4, 3, 2, 1, 0] . instantiation m_1: int # one out of [5, 4, 3, 2, 1, 0] context n: instantiation 1: int # one out of [5, 4, 3, 2, 1, 0] . instantiation m_n: int # one out of [5, 4, 3, 2, 1, 0] # decision_threshold is a float value between 0 and 1 which decides # when an intention should be considered in inference. # Probability must be greater than decision_threshold. decision_threshold: float ``` # How to contribute If you find any Bugs or want to contribute/suggest a new feature you can create a Merge Request / Pull Request or contact me directly via adrian.lubitz@dfki.de ## Run tests Tests are implemented with [pytest](https://docs.pytest.org/en/7.1.x/). To install test dependencies you need to run ```bash pip install -r requirements/test_requirements.txt ``` Then you can run ```bash python -m pytest tests/ ``` You can as well see the test report for a specific commit in gitlab under [pipeline->Tests](hhttps://git.hb.dfki.de/kimmi_sf/implementation/CoBaIR/-/pipelines/39889/test_report) ### Coverage If you want to see coverage for the tests you can run ```bash coverage run -m pytest tests/ ``` Use ```bash coverage report ``` or ```bash coverage html ``` You can as well see the coverage for a specific job in gitlab under [jobs](https://git.hb.dfki.de/kimmi_sf/implementation/CoBaIR/-/jobs) To show results of the coverage analysis. ## Build docu Documentation is implemented with the [material theme](https://squidfunk.github.io/mkdocs-material/) for [mkdocs](https://www.mkdocs.org/). ### Dependencies Install all dependencies for building the docu with ```bash pip install -r requirements/doc_requirements.txt ``` ### Build Build the docu with ```bash mkdocs build ``` The documentation will be in the `site` folder. # Authors Adrian Lubitz & Arunima Gopikrishnan ## Funding CoBaIR is currently developed in the [Robotics Group](https://robotik.dfki-bremen.de/de/ueber-uns/universitaet-bremen-arbeitsgruppe-robotik.html) of the [University of Bremen](https://www.uni-bremen.de/), together with the [Robotics Innovation Center](https://robotik.dfki-bremen.de/en/startpage.html) of the **German Research Center for Artificial Intelligence** (DFKI) in **Bremen**. CoBaIR has been funded by the German Federal Ministry for Economic Affairs and Energy and the [German Aerospace Center](https://www.dlr.de/DE/Home/home_node.html) (DLR). CoBaIR been used and/or developed in the [KiMMI-SF](https://robotik.dfki-bremen.de/en/research/projects/kimmi-sf/) project. <p align="center"> <img src="https://raw.githubusercontent.com/oarriaga/altamira-data/master/images/funding_partners.png" width="1200"> </p>

نیازمندی

مقدار	نام
==0.1.20	pgmpy
==0.7.10	bnlearn
==5.3.1	pyyaml
==5.15.9	PyQt5
==0.13.2	pyqtgraph

زبان مورد نیاز

مقدار	نام
>=3.8	Python

نحوه نصب

نصب پکیج whl CoBaIR-3.0.0:

pip install CoBaIR-3.0.0.whl

نصب پکیج tar.gz CoBaIR-3.0.0:

pip install CoBaIR-3.0.0.tar.gz