这个版本的计算包追求计算速度，而GGanalysis包有着更多计算功能。 GGanalysisLite包通过卷积计算分布列，通过FFT和快速幂加速卷积计算。 测试玩家得到的排名值rank的数学意义是：与抽了同样数量五星的其他玩家相比，测试玩家花费的抽数大于等于比例rank的玩家。rank的值在(0,1]上，值越高说明玩家越不幸运，达到1则说明达到了保底系统的极限。 安装方法 打开终端输入以下指令，安装完成后下载文件可以删除。 git clone https://github.com/OneBST/GGanalysisLite.git cd GGanalysisLite pip install . 卷积计算方法 采用的模型见我的B站专栏。通过卷积计算分布列。 卷积采用scipy的signal.convolve()函数，由其自动根据规模选择使用FFT计算或是直接卷积。 当计算物品数量上升，获取分布列需要多次卷积时，朴素FFT计算复杂度接近，故采用快速幂进行加速，复杂度降为。 用例 使用GenshinPlayer类添加玩家，初始化其抽卡情况，随后使用类函数输出rank

Numerical Analysis toolkit centred around PDEs, for demonstration and understanding purposes not production Use procedure: Initialise a new instance of the PDE class, all calculations and variables will be managed by this object Inputs: tlim – time to solve up to dx – x spacing, by default a uniform grid is used xlims – np.array([lower, upper]) bounds    

This is the official source code for the Paper: Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data based on Pytorch Framework. Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horizontally partitioned local dataset setting. This paper will study the unsupervised FL under the vertically partitioned dataset setting. Server-Clients Architecture Figure 1.: Server-Clients Architecture Master Branch

VADER-Sentiment-Analysis VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. It is fully open-sourced under the [MIT License] (we sincerely appreciate all attributions and readily accept most contributions, but please don’t hold us liable). Features and Updates Many thanks to George Berry, Ewan Klein, Pierpaolo Pantone for key contributions to make VADER better. The new updates includes capabilities regarding: Refactoring for Python 3 compatibility, […]

Developed a website to analyze and generate report of students based on the curriculum that represents student’s academic performance. We have developed the system such that, it will automatically parse data onto the database from excel file, which will in return reduce time consumption of analysis of data. After completing the template, proceed to the “Process data” functionality using the left navigational pane. This function will guide you through processing the prepared dataset, and allow you to update any mislabelled […]

Currently in rewriting phase. Package for uncertainty analysis in Python.It was developed while I was doing my Bachelor in physics.The functionality is based on the lab courses. Warnings The function exec is used within some functionalities.This can cause harm if exploited. General introduction The main classes of the package are Data, Table and Formula.The package holds functionality for: Error/Uncertainty propagation in equations Create tables and calculate different types of averages including arithmetic, geometric, modus, … Normal and simplified gauss propagation […]

ERQA – a full-reference quality metric designed to analyze how good image and video restoration methods (SR, deblurring, denoising, etc) are restoring real details. It is part of MSU Video Super Resolution Benchmark project. Quick start Run pip install erqa and run it from command line or directly from Python code. Command line python -m erqa /path/to/target.png /path/to/gt.png Python code    

This repository is related to the analysis of crystals containing dislocations by X-ray diffraction. It is part of a project conducted during a research internship at the laboratory of material and structural sciences of the École Nationale Supérieure des Mines de Saint-Étienne. Three python packages have been developed to conduct line profile analyses based on simulation results: lpa.input (line profile analysis input generator) lpa.xrd (line profile analysis x-ray diffraction simulation program) lpa.output (line profile analysis output analyzer) The package lpa.output […]

Business Intelligence for Modern Data Teams Evidence enables analysts to deliver a polished business intelligence system using SQL and markdown. It offers a modern development workflow for analysts and a polished user experience for decision-makers. An Evidence project is a collection of markdown documents. Whenever you write SQL queries in those documents, Evidence runs that SQL against your data warehouse. You can reference the results of those queries directly in your markdown document’s text, or you can use them to […]

thunder Thunder is an ecosystem of tools for the analysis of image and time series data in Python. It provides data structures and algorithms for loading, processing, and analyzing these data, and can be useful in a variety of domains, including neuroscience, medical imaging, video processing, and geospatial and climate analysis. It can be used locally, but also supports large-scale analysis through the distributed computing engine spark. All data structures and analyses in Thunder are designed to run identically and […]