Search Results for "dataset"

The Mathematics Dataset, developed by Google DeepMind, is a synthetic dataset designed to evaluate and train machine learning models on mathematical reasoning and symbolic manipulation. It generates question-and-answer pairs across a wide range of mathematical topics typically found in school-level curricula, testing a model’s ability to reason about algebra, arithmetic, calculus, probability, and more.

DataPrep v0.2 is a Tkinter-based GUI application/tool designed to assist users in data preprocessing, multicollinearity removal, and feature selection for a wide range of applications in Cheminformatics, Bioinformatics, Data Analysis, Feature Selection, Molecular Modeling, Machine Learning, and Quantitative-structure-property relationship (QSPR) studies. It includes functionality to load, process, and save datasets with support for different preprocessing & multicollinearity removal...

...Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot.

...Directories under "Files" with descriptions: NESUG 2014: "A Fast, High-Precision Implementation of the Univariate One-Parameter Box-Cox Transformation Using the Golden Section Search in SAS/IML®" SESUG 2017: "Decomposing the R-squared of a Regression Using the Shapley value in SAS®" SESUG 2019: "Conditionally Executing Data Steps and Statements Based on the Presence of Variables in a SAS® Dataset" GASP 2020: "Online Winsorization of the Survey of Construction’s Price Estimates" My SESUG 2015 materials can also be found in my project "constrainingarrays" at https://sourceforge.net/projects/constrainingarrays/.

...This could be a common math symbol or greek letter, start/end signposts, or a number of the position either from the begining or after a reset. After all the information has been loaded for your dataset. Either passed over the command line, or as a text file. Lottie Vectors displays inside a figure that you can resize, rotate and turn 2D positions into a 3D picture.

Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.

Data Management for Matlab, MathCAD, and SciLab: - Automatically import/export data to/from Matlab/MathCAD/Scilab - Easily separate data and analysis - Batch processing of datasets - Data filtering - Easily manage metadata for all your data files - Built in Data Acquisition (DAQ) - Implements Microsoft Scientific Dataset so data files are stored in a standard format

FastPval is multiple stage p-value computing software that computes empirical p-values from a large set of permutated/resampled background data.

GMM-GMR is a light package of functions in C/C++ to compute Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR). It allows to encode any dataset in a GMM, and GMR can then be used to retrieve partial data by specifying the desired inputs.

PROJECT IS BEING DEVELOPED UNDER A DIFFERENT NAME: Please visit http://sourceforge.net/projects/modelbuilder/ Datamodeler gives scientists and engineers the possiblity to find any multivariate model for any dataset, how complex or large both may be.

GHAPACK is a suite of tools around the Generalized Hebbian Algorithm. The Generalized Hebbian Algorithm (GHA) is a neural-net-like algorithm that allows the eigen decomposition of a dataset to be "learned" from serially-presented data.