Stream

A python library to create multi-page Streamlit applications with ease

The Hydralit package is a wrapping and template project to combine multiple independant (or somewhat dependant) Streamlit applications into a multi-page application. Currently the project implements a host application HydraApp and each child application simply needs to be a class deriving from the HydraHeadApp class and implement a single, simple method, run(). When converting existing applications, you can effectively put all the existing code inside the run() method and create a wrapper class deriving from HydraHeadApp. Then you create the […]

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Interpreter

Neovim integration for Google Keep built using gkeepapi

Neovim integration for Google Keep, built using gkeepapi Requirements Neovim 0.5 Python 3.6+ A patched font (optional. Used for icons) Table of Contents Installation gkeep supports all the usual plugin managers Packer require(‘packer’).startup(function() use {‘stevearc/gkeep.nvim’, run = ‘:UpdateRemotePlugins’} end) Paq require “paq” { {‘stevearc/gkeep.nvim’, run = vim.fn[‘remote#host#UpdateRemotePlugins’]}; } vim-plug

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Simulation

Monopoly simulator with python

Original creator: Games Computer Play config.py allows dynamic variable loadingIt is more versitle than .env files example: log=True # disables override logs if False (default True) showMap=True # overrides the showmap=False option in the main file Powershell:get-content log.txt -wait -tail 30 Bash:tail log.txt -f Copyright Copyright (C) 2021 gamescomputersplay and nopeless All code is licensed under GPL-3.0-or-later. That is, the GNU General Public License, either version 3, or (at your option) any later version. The

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Google

Gcp-doctor: a command-line diagnostics tool for GCP customers

gcp-doctor is a command-line diagnostics tool for GCP customers. It finds and helps to fix common issues in Google Cloud Platform projects. It is used to test projects against a wide range of best-practices and frequent mistakes, based on the troubleshooting experience of the Google Cloud Support team. gcp-doctor is open-source and contributions are welcome! Note that this is not an officially supported Google product, but a community effort. The Google Cloud Support team maintains this code and we do […]

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Algorithms

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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Generator

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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Generator

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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