986 skills found · Page 14 of 33
asteroidprotocol / Asteroid ProtocolA reference implementation for inscribing arbitrary data on a Cosmos SDK blockchain
Maximax67 / CefrpyA Python module designed to facilitate linguistic data analysis aligned with the Common European Framework of Reference for Languages (CEFR).
ika-rwth-aachen / Omega FormatA Python library for reading, writing and visualizing the OMEGA Format, targeted towards storing reference and perception data in the automotive context on an object list basis with a focus on an urban use case.
OPCFoundation / UA CloudDashboardA cross-platform OPC UA cloud dashboard reference implementation leveraging MQTT. It runs in a Docker container and displays OPC UA PubSub telemetry data, read directly from an MQTT broker or Azure EventHub/IoT Hub. It supports both JSON and binary payloads as well as OPC UA Complex Types decoding.
Azure-Samples / Gaming In Editor TelemetryThis reference architecture focuses on the development phase and a small number of users, gathering data from gameplay sessions and displaying it directly within the game engine - Unreal Engine in this case. It provides the fastest response time so your development and QA teams don't have to wait to get results from testing sessions.
vkgnandhu177 / Bayesian Regression And Bitcoin# Bayesian-Regression-to-Predict-Bitcoin-Price-Variations Predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, we will have to familiarize ourself with a machine learning technique, Bayesian Regression, and implement this technique in Python. # Datasets We have the datasets in the data folder. The original raw data can be found here: http://api.bitcoincharts.com/v1/csv/. The datasets from this site have three attributes: (1) time in epoch, (2) price in USD per bitcoin, and (3) bitcoin amount in a transaction (buy/sell). However, only the first two attributes are relevant to this project. To make the data to have evenly space records, we took all the records within a 20 second window and replaced it by a single record as the average of all the transaction prices in that window. Not every 20 second window had a record; therefore those missing entries were filled using the prices of the previous 20 observations and assuming a Gaussian distribution. The raw data that has been cleaned is given in the file dataset.csv Finally, as discussed in the paper, the data was divided into a total of 9 different datasets. The whole dataset is partitioned into three equally sized (50 price variations in each) subsets: train1, train2, and test. The train sets are used for training a linear model, while the test set is for evaluation of the model. There are three csv files associated with each subset of data: *_90.csv, *_180.csv, and *_360.csv. In _90.csv, for example, each line represents a vector of length 90 where the elements are 30 minute worth of bitcoin price variations (since we have 20 second intervals) and a price variation in the 91st column. Similarly, the *_180.csv represents 60 minutes of prices and *_360.csv represents 120 minutes of prices. # Project Requirements We are expected to implement the Bayesian Regression model to predict the future price variation of bitcoin as described in the reference paper. The main parts to focus on are Equation 6 and the Predicting Price Change section. # Logic in bitcoin.py 1. Compute the price variations (Δp1, Δp2, and Δp3) for train2 using train1 as input to the Bayesian Regression equation (Equations 6). Make sure to use the similarity metric (Equation 9) in place of the Euclidean distance in Bayesian Regression (Equation 6). 2. Compute the linear regression parameters (w0, w1, w2, w3) by finding the best linear fit (Equation 8). Here you will need to use the ols function of statsmodels.formula.api. Your model should be fit using Δp1, Δp2, and Δp3 as the covariates. Note: the bitcoin order book data was not available, so you do not have to worry about the rw4 term. 3. Use the linear regression model computed in Step 2 and Bayesian Regression estimates, to predict the price variations for the test dataset. Bayesian Regression estimates for test dataset are computed in the same way as they are computed for train2 dataset – using train1 as an input. 4. Once the price variations are predicted, compute the mean squared error (MSE) for the test dataset (the test dataset has 50 vectors => 50 predictions).
nima0011 / Nima0011# Contributing to this repository <!-- omit in toc --> ## Getting started <!-- omit in toc --> Before you begin: - This site is powered by Node.js. Check to see if you're on the [version of node we support](contributing/development.md). - Have you read the [code of conduct](CODE_OF_CONDUCT.md)? - Check out the [existing issues](https://github.com/github/docs/issues) & see if we [accept contributions](#types-of-contributions-memo) for your type of issue. ### Use the 'make a contribution' button  Navigating a new codebase can be challenging, so we're making that a little easier. As you're using docs.github.com, you may come across an article that you want to make an update to. You can click on the **make a contribution** button right on that article, which will take you to the file in this repo where you'll make your changes. Before you make your changes, check to see if an [issue exists](https://github.com/github/docs/issues/) already for the change you want to make. ### Don't see your issue? Open one If you spot something new, open an issue using a [template](https://github.com/github/docs/issues/new/choose). We'll use the issue to have a conversation about the problem you want to fix. ### Ready to make a change? Fork the repo Fork using GitHub Desktop: - [Getting started with GitHub Desktop](https://docs.github.com/en/desktop/installing-and-configuring-github-desktop/getting-started-with-github-desktop) will guide you through setting up Desktop. - Once Desktop is set up, you can use it to [fork the repo](https://docs.github.com/en/desktop/contributing-and-collaborating-using-github-desktop/cloning-and-forking-repositories-from-github-desktop)! Fork using the command line: - [Fork the repo](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo#fork-an-example-repository) so that you can make your changes without affecting the original project until you're ready to merge them. Fork with [GitHub Codespaces](https://github.com/features/codespaces): - [Fork, edit, and preview](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace) using [GitHub Codespaces](https://github.com/features/codespaces) without having to install and run the project locally. ### Make your update: Make your changes to the file(s) you'd like to update. Here are some tips and tricks for [using the docs codebase](#working-in-the-githubdocs-repository). - Are you making changes to the application code? You'll need **Node.js v14** to run the site locally. See [contributing/development.md](contributing/development.md). - Are you contributing to markdown? We use [GitHub Markdown](contributing/content-markup-reference.md). ### Open a pull request When you're done making changes and you'd like to propose them for review, use the [pull request template](#pull-request-template) to open your PR (pull request). ### Submit your PR & get it reviewed - Once you submit your PR, others from the Docs community will review it with you. The first thing you're going to want to do is a [self review](#self-review). - After that, we may have questions, check back on your PR to keep up with the conversation. - Did you have an issue, like a merge conflict? Check out our [git tutorial](https://lab.github.com/githubtraining/managing-merge-conflicts) on how to resolve merge conflicts and other issues. ### Your PR is merged! Congratulations! The whole GitHub community thanks you. :sparkles: Once your PR is merged, you will be proudly listed as a contributor in the [contributor chart](https://github.com/github/docs/graphs/contributors). ### Keep contributing as you use GitHub Docs Now that you're a part of the GitHub Docs community, you can keep participating in many ways. **Learn more about contributing:** - [Types of contributions :memo:](#types-of-contributions-memo) - [:mega: Discussions](#mega-discussions) - [:beetle: Issues](#beetle-issues) - [:hammer_and_wrench: Pull requests](#hammer_and_wrench-pull-requests) - [:question: Support](#question-support) - [:earth_asia: Translations](#earth_asia-translations) - [:balance_scale: Site Policy](#balance_scale-site-policy) - [Starting with an issue](#starting-with-an-issue) - [Labels](#labels) - [Opening a pull request](#opening-a-pull-request) - [Working in the github/docs repository](#working-in-the-githubdocs-repository) - [Reviewing](#reviewing) - [Self review](#self-review) - [Pull request template](#pull-request-template) - [Suggested changes](#suggested-changes) - [Windows](#windows) ## Types of contributions :memo: You can contribute to the GitHub Docs content and site in several ways. This repo is a place to discuss and collaborate on docs.github.com! Our small, but mighty :muscle: docs team is maintaining this repo, to preserve our bandwidth, off topic conversations will be closed. ### :mega: Discussions Discussions are where we have conversations. If you'd like help troubleshooting a docs PR you're working on, have a great new idea, or want to share something amazing you've learned in our docs, join us in [discussions](https://github.com/github/docs/discussions). ### :beetle: Issues [Issues](https://docs.github.com/en/github/managing-your-work-on-github/about-issues) are used to track tasks that contributors can help with. If an issue has a triage label, we haven't reviewed it yet and you shouldn't begin work on it. If you've found something in the content or the website that should be updated, search open issues to see if someone else has reported the same thing. If it's something new, open an issue using a [template](https://github.com/github/docs/issues/new/choose). We'll use the issue to have a conversation about the problem you want to fix. ### :hammer_and_wrench: Pull requests A [pull request](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests) is a way to suggest changes in our repository. When we merge those changes, they should be deployed to the live site within 24 hours. :earth_africa: To learn more about opening a pull request in this repo, see [Opening a pull request](#opening-a-pull-request) below. ### :question: Support We are a small team working hard to keep up with the documentation demands of a continuously changing product. Unfortunately, we just can't help with support questions in this repository. If you are experiencing a problem with GitHub, unrelated to our documentation, please [contact GitHub Support directly](https://support.github.com/contact). Any issues, discussions, or pull requests opened here requesting support will be given information about how to contact GitHub Support, then closed and locked. If you're having trouble with your GitHub account, contact [Support](https://support.github.com/contact). ### :earth_asia: Translations This website is internationalized and available in multiple languages. The source content in this repository is written in English. We integrate with an external localization platform called [Crowdin](https://crowdin.com) and work with professional translators to localize the English content. **We do not currently accept contributions for translated content**, but we hope to in the future. ### :balance_scale: Site Policy GitHub's site policies are published on docs.github.com, too! If you find a typo in the site policy section, you can open a pull request to fix it. For anything else, see [the CONTRIBUTING guide in the site-policy repo](https://github.com/github/site-policy/blob/main/CONTRIBUTING.md). ## Starting with an issue You can browse existing issues to find something that needs help! ### Labels Labels can help you find an issue you'd like to help with. - The [`help wanted` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) is for problems or updates that anyone in the community can start working on. - The [`good first issue` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) is for problems or updates we think are ideal for beginners. - The [`content` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3Acontent) is for problems or updates in the content on docs.github.com. These will usually require some knowledge of Markdown. - The [`engineering` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3Aengineering) is for problems or updates in the docs.github.com website. These will usually require some knowledge of JavaScript/Node.js or YAML to fix. ## Opening a pull request You can use the GitHub user interface :pencil2: for some small changes, like fixing a typo or updating a readme. You can also fork the repo and then clone it locally, to view changes and run your tests on your machine. ## Working in the github/docs repository Here's some information that might be helpful while working on a Docs PR: - [Development](/contributing/development.md) - This short guide describes how to get this app running on your local machine. - [Content markup reference](/contributing/content-markup-reference.md) - All of our content is written in GitHub-flavored Markdown, with some additional enhancements. - [Content style guide for GitHub Docs](/contributing/content-style-guide.md) - This guide covers GitHub-specific information about how we style our content and images. It also links to the resources we use for general style guidelines. - [Reusables](/data/reusables/README.md) - We use reusables to help us keep content up to date. Instead of writing the same long string of information in several articles, we create a reusable, then call it from the individual articles. - [Variables](/data/variables/README.md) - We use variables the same way we use reusables. Variables are for short strings of reusable text. - [Liquid](/contributing/liquid-helpers.md) - We use liquid helpers to create different versions of our content. - [Scripts](/script/README.md) - The scripts directory is the home for all of the scripts you can run locally. - [Tests](/tests/README.md) - We use tests to ensure content will render correctly on the site. Tests run automatically in your PR, and sometimes it's also helpful to run them locally. ## Reviewing We (usually the docs team, but sometimes GitHub product managers, engineers, or supportocats too!) review every single PR. The purpose of reviews is to create the best content we can for people who use GitHub. :yellow_heart: Reviews are always respectful, acknowledging that everyone did the best possible job with the knowledge they had at the time. :yellow_heart: Reviews discuss content, not the person who created it. :yellow_heart: Reviews are constructive and start conversation around feedback. ### Self review You should always review your own PR first. For content changes, make sure that you: - [ ] Confirm that the changes address every part of the content design plan from your issue (if there are differences, explain them). - [ ] Review the content for technical accuracy. - [ ] Review the entire pull request using the [localization checklist](contributing/localization-checklist.md). - [ ] Copy-edit the changes for grammar, spelling, and adherence to the style guide. - [ ] Check new or updated Liquid statements to confirm that versioning is correct. - [ ] Check that all of your changes render correctly in staging. Remember, that lists and tables can be tricky. - [ ] If there are any failing checks in your PR, troubleshoot them until they're all passing. ### Pull request template When you open a pull request, you must fill out the "Ready for review" template before we can review your PR. This template helps reviewers understand your changes and the purpose of your pull request. ### Suggested changes We may ask for changes to be made before a PR can be merged, either using [suggested changes](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/incorporating-feedback-in-your-pull-request) or pull request comments. You can apply suggested changes directly through the UI. You can make any other changes in your fork, then commit them to your branch. As you update your PR and apply changes, mark each conversation as [resolved](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/commenting-on-a-pull-request#resolving-conversations). ## Windows This site can be developed on Windows, however a few potential gotchas need to be kept in mind: 1. Regular Expressions: Windows uses `\r\n` for line endings, while Unix based systems use `\n`. Therefore when working on Regular Expressions, use `\r?\n` instead of `\n` in order to support both environments. The Node.js [`os.EOL`](https://nodejs.org/api/os.html#os_os_eol) property can be used to get an OS-specific end-of-line marker. 1. Paths: Windows systems use `\` for the path separator, which would be returned by `path.join` and others. You could use `path.posix`, `path.posix.join` etc and the [slash](https://ghub.io/slash) module, if you need forward slashes - like for constructing URLs - or ensure your code works with either. 1. Bash: Not every Windows developer has a terminal that fully supports Bash, so it's generally preferred to write [scripts](/script) in JavaScript instead of Bash.
sketch-hq / Sketch Reference FilesA store of automatically generated Sketch file JSON organised by document version and Sketch feature
powered-by-wq / Vera📑⚙️ Python/Django reference implementation of the ERAV data model
natacourby / Disease Ontologies For Knowledge GraphsSoftware "Disease_ontologies_for_knowledge_graphs" - a knowledge base solution that uses Grakn core and disease ontologies cross-references to allow easy switch between ontology hierarchies for data integration purpose.
pigulla / Json StrictifySafely serialize a value to JSON without unintended loss of data or going into an infinite loop due to circular references.
XCZchaos / Python Implementation Of Motion Imagination ClassificationThis is just a simple code of how to process EEG data when I learn motor imagination, just for reference.At the same time, it also contains the record of my learning of motor imagination
rwbrockhoff / ChartjsdemoInteractive data visualization tutorial - React, Chart.js. Featured in Medium article with 2k+ claps. Popular reference for Chart.js implementation.
BeelGroup / GIANT The 1 Billion Annotated Synthetic Bibliographic Reference String DatasetA script to generate tagged XML Citationstrings for citation parsing
mbjoseph / BbrR package to scrape data from basketball-reference.com
nerc-comet / VelmapVELMAP is a matlab code that solves for surface velocity and strain rate fields by joint inversion of InSAR and GNSS data. It can also be used for referencing to GNSS reference frames.
XamlBrewer / UWP IEXCloud SampleA WinUI reference app in UWP accessing IEXCloud data
GabrielPastorello / BRScraperPython package for Basketball Reference scraping and easy access to basketball data, including NBA, G League and international leagues
0PeterAdel / DEPI Data AnalysisDEPI Data Analysis Materials A repository of resources for the DEPI Data Analysis program, including PDFs, recorded video lectures, and assignments. This repository is designed to support learning by providing comprehensive study materials, practical exercises, and useful references in data analysis.
intel / Reference PE And Measurements DB For WiFi Time Based Scalable LocationThe repository includes a reference Matlab code for a mobile WiFi client receiver, Kalman-Filter-based positioning engine (PE) and a database of time-delay measurements of an indoor environment. The database includes both real-measured data, and simulated time-delay data, as well as ground-truth client position information, which can be used for validation and performance analysis. The PE code is designed to process the database data format and produce estimates of the mobile WiFi receiver position within the indoor venue. The database may be used for the development, validation and performance analysis of additional positioning engines. This kind of WiFi client PE enables an unlimited number of clients to estimate the position and navigate within the Wi-Fi network coverage area. Hence, it enables so-called "Wi-Fi Scalable Location". A *.pdf whitepaper is included in the repository and explains the functionality and usage of the code and database.
