42 skills found · Page 1 of 2
rubysolo / Dentakumath and logic formula parser and evaluator
soonfx-engine / Core🎮 TypeScript game numeric engine for RPG & strategy games. Zero dependencies, type-safe formula parsing, battle system simulation, and expression evaluation. 基于 TypeScript 的游戏数值引擎，专为 RPG 和策略游戏设计。零依赖、类型安全的公式解析、战斗系统模拟和表达式计算。
LesterLyu / Fast Formula ParserParse and evaluate MS Excel formula in javascript.
cortex-js / Compute EngineAn engine for symbolic manipulation and numeric evaluation of math formulas expressed with MathJSON
LeoDingggg / AlphaEvalThe implementation of AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining(https://www.arxiv.org/abs/2508.13174).
bradbase / Xlcalculatorxlcalculator converts MS Excel formulas to Python and evaluates them.
PetroProtsyk / SSharpS# is a weakly-typed dynamic language and runtime infrastructure to make your applications extendable, customizable and highly flexible. It allows introducing expressions and large code blocks evaluation within your applications in the similar way Microsoft Office deals with VBScript, gives you possibilities providing rich formula evaluation capabilities like it can be seen in MS Excel and other office applications.
Blake-Madden / Tinyexpr PlusplusTiny recursive descent expression parser, compiler, and evaluation engine for math expressions and spreadsheet formulas in C++.
PSU3D0 / FormualizerEmbeddable spreadsheet engine — parse, evaluate & mutate Excel workbooks from Rust, Python, or the browser. Arrow-powered, 320+ functions.
denissimon / Formula ParserParsing and evaluating mathematical formulas given as strings.
GeorgDangl / Dangl.CalculatorC# library to evaluate formulas
dahomey-technologies / Dahomey.ExpressionEvaluatorEvaluate C# Formulas at Runtime
colinta / SublimeCalculateSelect a formula and evaluate it using python.
stomita / SformulaLibrary for parsing / evaluating Salesforce formula in JavaScript
cesarParra / ExpressionEvaluate formulas through Apex code.
KovtunV / NoStringEvaluatingFast low memory consuming mathematical evaluation without endless string parsing! Parses string formula once and uses its object sequence in each evaluation. Moreover, provides user defined functions and variables.
skschum / MFAssignRThe MFAssignR package was designed for multi-element molecular formula (MF) assignment of ultrahigh resolution mass spectrometry measurements. A number of tools for internal mass recalibration, MF assignment, signal-to-noise evaluation, and unambiguous formula selections are provided.
alexed1 / FormulasAndExpressionsLWCInclude apex support for formula evaluation, an lwc formula builder, and a flow-optimized Update Field Action
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).
omid / FormulaA parser and evaluator of spreadsheet-like formulas
