An open-source Python project series where beginners can contribute and practice coding
A collection of easy Python small projects to help you improve your programming skills. Table Of Contents
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Sequential Model-based Algorithm Configuration
Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). However, the reimplementation slightly differs from the original SMAC. For comparisons against the original SMAC, we refer to a stable release of SMAC (v2) in Java which can be found here. The documentation can be found here. Status for master branch: Status for the development branch SMAC is a tool for algorithm configuration to optimize the parameters of arbitrary algorithms across […]
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Genetic feature selection module for scikit-learn
Genetic feature selection module for scikit-learn Genetic algorithms mimic the process of natural selection to search for optimal values of a function. Installation The easiest way to install sklearn-genetic is using pip pip install sklearn-genetic or conda conda install -c conda-forge sklearn-genetic Requirements Python >= 2.7 scikit-learn >= 0.20.3 DEAP >= 1.0.2 Example from __future__ import print_function import numpy as np from sklearn import datasets, linear_model from genetic_selection import GeneticSelectionCV def main(): iris = datasets.load_iris() # Some
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A strongly-typed genetic programming framework for Python
“If an army of monkeys were strumming on typewriters they might write all the books in the British Museum.” monkeys is a framework designed to make genetic programming in Python accessible, quick, flexible, and fun. Get started here. What is genetic programming? Genetic programming algorithms are a class of evolutionary algorithms in which solutions to a problem are represented as executable tree structures – programs. In order to use genetic programming in order to solve a problem, two things must […]
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Supervised domain-agnostic prediction framework for probabilistic modelling
A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points. The package offers a variety of features and specifically allows for the implementation of probabilistic prediction strategies in the supervised contexts comparison of frequentist and Bayesian prediction methods strategy optimization through hyperparamter tuning and ensemble methods (e.g. bagging) workflow automation List of developers and contributors Documentation The full documentation is available here. Installation Installation is easy using Python’s package manager $ […]
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Build a Personal Diary With Django and Python
A diary is a personal safe space. With the help of Django, you can create a diary on your own computer without storing data in anyone else’s cloud. By following along with the project below, you’ll see how quickly you can build a functioning web app in Django without any external dependencies. In this tutorial, you’ll learn how to: Set up a Django project Work with the standard SQLite database Make use of the Django admin site Create models and […]
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slimIPL: Language-Model-Free Iterative Pseudo-Labeling
Abstract Recent results in end-to-end automatic speech recognition have demonstrated the efficacy of pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Classification (CTC) and Sequence-to-Sequence (seq2seq) losses. Iterative Pseudo-Labeling (IPL), which continuously trains a single model using pseudo-labels iteratively re-generated as the model learns, has been shown to further improve performance in ASR. We improve upon the IPL algorithm: as the model learns, we propose to iteratively re-generate transcriptions with hard labels (the most probable tokens), that is, without […]
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Fast, Simple API for fetching Apple Firmwares
Fast, Simple API for fetching Apple Firmwares. The API server is closed due to some reasons. Wait for v2 releases. Features Fetching Signed IPSWs Fetching Signed OTAs Fetching not only iDevices but also AirPods, Beats, and Keyboard Accessories Example Request curl -X GET “loyalapi.ml/v1/restore?device=iPhone12,1” [ { “build_id”: “18D61”, “docs_url”: “http://updates-http.cdn-apple.com/2021WinterFCS/documentation/071-15446/2D646674-5AE5-454D-94C9-EBA3E76437D7/iPhoneiTunesUpdateReadMe.ipd”, “sha1”: “45186bb240128a11a26a2342a6efeb87cfd1c029”, “url”: “http://updates-http.cdn-apple.com/2021WinterFCS/fullrestores/071-12530/598B4392-EF03-4F6C-962A-3A8CC9BA9DAC/iPhone11,8,iPhone12,1_14.4.1_18D61_Restore.ipsw”, “version”: “14.4.1” }, { “build_id”: “18D61”, “docs_url”: “http://updates-http.cdn-apple.com/2021WinterFCS/documentation/071-15446/2D646674-5AE5-454D-94C9-EBA3E76437D7/iPhoneiTunesUpdateReadMe.ipd”, “sha1”: “45186bb240128a11a26a2342a6efeb87cfd1c029”, “url”: “http://updates-http.cdn-apple.com/2021WinterFCS/fullrestores/071-12530/598B4392-EF03-4F6C-962A-3A8CC9BA9DAC/iPhone11,8,iPhone12,1_14.4.1_18D61_Restore.ipsw”, “version”: “14.4.1” } ] Documentation You can read documentation on here. Build and run […]
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Mitum-currency / mitum-data-blocksign python SDK
‘mitum-py-util’ will introduce the usage of mitum-currency and mitum-data-blocksign for python. This project is being totally copied to ProtoconNet/mitum-py-util. Installation Recommended requirements for ‘mitum-py-util’ is, $ python –version Python 3.9.2 $ git clone https://github.com/ProtoconNet/mitum-py-util.git $ cd mitum-py-util $ python setup.py install If setup.py doesn’t work properly, please just install necessary packages with requirements.txt before running setup.py. $ cd mitum-py-util $ pip install -r requirements.txt Generate New Operation Operations ‘mitum-py-util’ provides three operations of ‘mitum-currency’, ‘Create-Accounts’ creates an account corresponding to […]
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Simulation module for PyGran: a DEM toolkit for rapid quantitative analysis of granular/powder systems
PyGranSim is part of the PyGran project, an open-source toolkit primarily designed for DEM simulation & analysis. In addition to performing basic and custom post-processing, PyGran enables running DEM simulation with the PyGranSim module. For more info on PyGran, see here. If your find PyGran useful in your research, please consider citing the following paper: @article{aam2019pygran, title={PyGran: An object-oriented library for DEM simulation and analysis}, author={Abi-Mansour, Andrew}, journal={SoftwareX}, volume={9}, pages={168–174}, year={2019}, publisher={Elsevier}, doi={10.1016/j.softx.2019.01.016} } Quick Installation PyGranSim is typically installed […]
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