Search Results for "pca"
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Showing 53 open source projects for "pca"
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PCA
binary encryption archiver
Petoron Crypto Archiver (PCA) Key features: AES-256-CFB encryption with unique IV per archive Argon2id key derivation (256MB RAM / time_cost=3 / parallelism=4) HMAC-SHA512 integrity and anti-tampering check No compression, no fingerprinting, no trace of original data Works with both single files and entire directories Fully CLI-based, portable, and open format (.pca) https://github.com/01alekseev/PCA -
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OBLITERATUS
OBLITERATE THE CHAINS THAT BIND YOU
...The toolkit provides a full pipeline for probing, analyzing, and modifying model behavior, including visualization tools that help researchers understand where and how refusal mechanisms are encoded. It supports multiple analytical methods such as PCA and SVD to locate these behavioral directions within model layers. -
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MultivariateStats.jl
A Julia package for multivariate statistics and data analysis
A Julia package for multivariate statistics and data analysis (e.g. dimensionality reduction). -
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A Matlab toolkit for all aspects of EEG/ERP analysis, especially PCA. If you run into a problem, please send me a note and I'll fix it. The tutorial pdf is in the documentation folder and the tutorial data can be downloaded from a link provided by the pdf. The tutorial file has full install instructions. For all platforms supported by Matlab.
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MatlabMachine
Machine learning algorithms
Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts. -
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ml.js
Machine learning tools in JavaScript
This library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often. We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find. -
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Linfa
A Rust machine learning framework
linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust. Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks. -
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ESPectre
Motion detection system based on Wi-Fi spectre analysis (CSI)
ESPectre is an open-source motion detection system that transforms ordinary Wi-Fi signals into an ambient sensor capable of detecting movement without using cameras, microphones, or additional radio hardware, making it a privacy-preserving and cost-effective alternative for presence sensing and smart home automation. At its core, it analyzes Wi-Fi Channel State Information (CSI) — detailed measurements of how Wi-Fi waves change as they propagate — to mathematically detect disturbances caused... -
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
...To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for ica (Independent Component Analysis) and pca (Principal Component Analysis) transformations, respectively, concatenated with the hot-bit encoding (ohe) of categorical features (catf) of a given data for rf (Random Forest) modeling. -
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awesome-single-cell
Community-curated list of software packages and data resources
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc. List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc. Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data. Find bimodal, unimodal, and multimodal features in your data. Ascend is an R package comprised of fast, streamlined analysis functions optimized to... -
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componentsOfData
Funciones que sirven para determinar las componentes principales.
Se tienen las funciones que determinan las componentes principales (PCA) e independientes (ICA). Se tienen también ejemplos de aplicación para cada caso (tutorial para PCA, transformada de Hotelling, y comparación entre PCA e ICA "problema del coctail"). -
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GromacsProSuite
Graphical User Interface for Gromacs
...Built-in monitoring tracks CPU, RAM, and disk usage to ensure stable performance during parallel processing. Beyond simulation execution, it includes advanced trajectory processing and analysis tools such as RMSD, RMSF, SASA, clustering, PCA, hydrogen-bond analysis, Ramachandran plots, and FEL mapping. With integrated visualization and plotting utilities, it offers a unified platform for researchers, educators, and students to perform complete MD workflows efficiently and reproducibly. Our Goal is to bring MD simulations to every biologist's toolkit. -
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olinkwrapper
The app provides user-friendly interface for performing olink analysis
This Shiny app provides a user-friendly interface for performing quick analysis of Olink data using OlinkAnalyze package. It allows for data upload, parameter customization, visualization of results, and exporting of analysis outputs. Using Docker docker run --rm -p 3838:3838 jd21/shinyolink:v1.2.1 Online webserver https://olink-wrapper.serve.scilifelab.se/ -
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proSeq
ProSeq is a GUI program to edit and analyse DNA polymorphism datasets
...It shows sequence alignments in a graphical window and allows the user to edit, manipulate and analyse sequences in various ways. ProSeq4 includes tools to analyse DNA polymorphism, scan for selection, do PCA and reconstruct phylogenies from sequence or SNP data. The program can also run coalescent simulations and generate and visualise phylogenies. The main changes in Proseq4 compared to Proseq3: - Multiplatform. Binaries available for Windows, Mac and Ubuntu. - It is now a 64-bit program, which can handle larger datasets (genome-scale from multiple individuals)... -
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SAGA - System for Automated Geoscientific Analyses - is a Geographic Information System (GIS) software with immense capabilities for geodata processing and analysis. SAGA is programmed in the object oriented C++ language and supports the implementation of new functions with a very effective Application Programming Interface (API). Functions are organised as modules in framework independent Module Libraries and can be accessed via SAGA’s Graphical User Interface (GUI) or various scripting...
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NeuroSuite
Klusters, NeuroScope and NDManager
...NDManager (Neurophysiological Data Manager) is a simple graphical application designed to help neurophysiologists manage their experimental recording parameters (e.g., number of channels and sampling rate of the acquisition system) and process their data (data filtering, spike extraction, PCA, etc.) -
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Streamline Analyst
AI agent that streamlines the entire process of data analysis
Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates all the tasks such as data cleaning, preprocessing, and even complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. -
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MLP Network Configurator
interactive tool designed to help users understand how neural network
... 💡 Key Features - Data Handling: Load, normalize, and validate datasets (JSON format). - Model Configuration: Define hidden layers, activation functions, loss and optimizer types. - Training: Visualize loss curves, predictions, PCA-based class separation. - Testing: Accuracy calculation, error graphs, confusion matrix, misclassified sample montage. - Camera Dataset Builder: Capture and label images live via webcam. - Model Insight: Animated weight & bias evolution, interactive heatmaps. - Save & Load: Export models and configurations for reuse and sharing... -
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...Contract Details: - Ethereum Mainnet - Contract: 0xCe675D637bB5E3aDAFDC4d1e475a35D75F6D95bc - Total supply: 20,000,000 PTRN - Treasury: 0x31187017dC243d4D46126bB5a160Bb98FFeef39b Petoron is an entire cryptography ecosystem developed to give people real tools for privacy and data protection: - 🔐 PTBC — Petoron Time Burn Cipher (time-based self-destructing encryption) - 📜 PSC — Petoron Seal Contracts (tamper-proof sealed data) - 📦 PCA — Petoron Crypto Archiver (secure archiving) - 🧹 PLD — Petoron Local Destroyer (irreversible deletion) - ⚛️ PQS — Petoron Quantum Standard (quantum-resistant encryption) - 💬 P2P Messenger — fully encrypted peer-to-peer communication https://github.com/01alekseev/-PTRN--Petoron--token
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Artifact GeoMorph Toolbox 3D 3.1
A toolbox for 3DGM shape analysis of archaeological artifacts
The Artifact Geomorph Toolbox 3D software is designed to provide the archaeologist interested in artifact shape variability with a toolbox to allow the acquisition, analysis and results exploration of homologous 3D landmark-based geometric morphometric data. As such, the toolbox contains an automated item and semi-landmarks positioning procedure and the fundamental statistical analyses and procedures to allow the processing and analysis of the data. It is designed to be easy to use and... -
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Raman Tool Set
Analysis of Raman spectra and spectroscopy data.
...RamanToolSet offers the following functions for processing: - scaling spectra - offset spectra - cut spectra - averaging spectra - smoothing spectra - normalizing spectra - background subtraction - polynomial subtraction The following analytical tools have been implemented: - Principal Component Analysis (PCA) - Clustering analysis - Extended Multiplicative Signal Correction (EMSC) analysis The following mapping tools have been implemented: - Producing single spectra files from Renishaw, Witec and Horiba mapping files (.txt) - Mapping the single spectra so imported from Renishaw, Witec or Horiba files - PCA is performed over mapping spectra and PCA-Scores are mapped - ICA is performed over mapping spectra and ICA-coefficients are mapped - Clustering analysis is performed over mapping spectra and Clusters are mapped -
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Xmaldi
Viewing collections of MALDI spectra and perform PCA and DFA analyses
Xmaldi is an application for viewing collections of MALDI spectra and perform PCA and DFA analyses on them (Python 3 version). For more information, you can have a look at the README.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/xmaldi3/code/ci/default/tree/README.md -
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SVPhylA
SVPhylA: Sequence Vectorization for Phylogenetic Analyses
...Such alignment-free methods basically encode DNA and protein sequences (fasta files) into numerical vectors allowing the calculation of alignment-free distances which may be combined into a consensus/compromise matrix by using algorithms like DISTATIS based on Multidimensional Scaling (MSD), Lineal Principal Component Analysis (PCA) and PCA-Kernel (non-lineal). In addition, genetic distances derived can be either combined between them or with the alignment-free distances. So far, SVPhylA contain a module to compare tree topologies by using different distance measures as a validation procedure. The statistical validation (bootstrap and jacknife) of the alignment-free trees is being developed SVPhylA is mostly designed to interact to MEGA (MOLECULAR EVOLUTIONARY GENETICS ANALYSIS). -
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MothurR
This program mainly produces R scripts using Mothur result files.
The program generates R scripts to draws line/bar graphs using Mothur diversity and classification files, to calculate Shannon indexes, to perform PCA, Mann-Whitney U test, and pMANOVA test. -
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GSMLBook
Recipes for basic machine learning algorithms using sklearn in jupyter
...Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests; neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. Appendices provide a review of probability and linear algebra. While some mathematical foundation is provided, it is not essential for understanding the implementations. The target audience is advanced community college and university students.
