Search Results for "principal component analysis (pca)"
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Showing 28 open source projects for "principal component analysis (pca)"
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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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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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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
...It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. 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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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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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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proSeq
ProSeq is a GUI program to edit and analyse DNA polymorphism datasets
.... - TreeViewer can show multiple phylogenies in densiTree-like style - Principal components analysis Disclamer: No warranty of any kind is provided - use at your own risk -
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Jacobi-Static
STATIC/EX - simple multivariate statistics calculator
The STATIC/EX includes three applications: static.exe - a simple multivariate statistics calculator; spector.exe - file and online data viewer; tester.exe - an IPC client to develop and debug operations with embedded STATIC/EX IPC server. This toolkit can be used for: • user's training (who are interested in multivariate statistics) • scientific and other data researching. -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Raman Tool Set
Analysis of Raman spectra and spectroscopy data.
Raman Tool Set has been developed as a user-friendly free software for processing and 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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SVPhylA
SVPhylA: Sequence Vectorization for Phylogenetic Analyses
SVPhylA is a python tool for the calculation of several alignment-free distances for phylogenetics analysis from the most popular alignment-free approaches. 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). ... -
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TOFSIMS
R/Bioconductor toolkit for mass spectrometry data
...The package allows transformation of spectra into 2D image structures (mass images), with operations such as binning, scaling, subsetting, and visual rendering. For data exploration and dimensionality reduction, it includes multivariate methods common in the ToF-SIMS community: PCA (Principal Component Analysis), MCR (Multivariate Curve Resolution), MAF (Maximum Autocorrelation Factors), and MNF (Minimum Noise Fraction). It also interoperates with Bioconductor’s imaging stack (e.g. EBImage) so users can apply segmentation and image analysis operations on mass images. -
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A Matlab software routine to perform Principal Component Analysis using Covariance, Correlation or Comedian as the criterion. Though, initially developed for experiments related to fretting wear but can be effectively used to interpret experimental data from any field. The attached files contain source code as well as a sample MATLAB (.mat) data file of 13 variables. It could be replaced to the data file of your choice.
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Face recognition PCA
Face recognition using Principal component Analysis Algorithm
A Face recognition Dynamic Link Library using Principal component Analysis Algorithm -
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Zika-RNAseq-Pipeline
An open RNA-Seq data analysis pipeline tutorial
...The pipeline uses state-of-the-art tools and can run on various platforms with minimal configuration overhead. The pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression. -
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RDA Toolbox
MATLAB tool for analysis of Raman spectra.
Provides principal component analysis, discriminant analysis, peak analysis, and clustering tools powered by MATLAB in an easy-to-use interface for the interpretation of Raman spectroscopy data. -
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...The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
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# ASPI Kit - Adaptive Identification and Control Software This software is under development by the ASPICC group. Its purpose is to allow users to learn and experiment the use of Neural Networks (NN's) and related Computational Intelligence algorithms on their own data. Users can upload their own data and experiment with various algorithms of various setups to see how the algorithms performs on their data. Users can also investigate and familiarize themselves with the Python code of...
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This project provides a Fortran90 library and a python module for singular spectrum analyses such as PCA/EOF or MSSA. It is intended for people interested, for example, in analysing climate or financial variability.
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NGSbinningpytools
Python tools for binning NGS scaffolds to various genomes.
Next generation sequencing has opened new avenues for sequencing prokaryotes which were difficult or either impossible to culture. Now, it is possible to sequence all organisms present in a metagenomic community. However, a critical problem is to determine the host organism for each genomic fragment present in the dataset. This project provides the computational tools for assigning the organismal identity for each genomic scaffold in the given dataset. For more detailed documentation... -
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principal component analysis
This program demonstrates the application of principal component analy
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ImageComponentParser
Matlab tool for automated analysis of Voltage and Calcium Imaging Data
The ImageComoponentParser is a matlab tool for automated analysis of Voltage and Calcium Imaging Data. The main functionality is automated extraction of component signals using PCA and ICA. The GUI enables the user to alter the settings, browse the components, manually remove artifacts, and visualize functional activity. -
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EMGU Face Recognition
Using EMGU to perform Principle Component Analysis (PCA)
...Face Recognition has always been a popular subject for image processing and this article builds upon the good work by Sergio Andrés Gutiérrez Rojas and his original article (codeproject). The reason that face recognition is so popular is not only it’s real world application but also the common use of principle component analysis (PCA). PCA is an ideal method for recognising statistical patterns in data. The popularity of face recognition is the fact a user can apply a method easily and see if it is working without needing to know to much about how the process is working. -
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dPCA
dPCA is a data exploration technique using metadata
Demixed Principal Component Analysis (dPCA) is a new data exploration technique. Just like Principal Component Analysis (PCA), dPCA searches for a subspace that captures a high amount of information about a data set. However, often your data points have labels like time, stimulus presented, reward achieved, etc. In contrast to PCA, that completely ignores these labels, dPCA benefits from the labels and tries to find components that capture variance due to only a small subsets of the labels. ... -
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imDEV
Tools for multivariate data visualization, exploration and analysis.
You may also like some newer work: http://createdatasol.com/ Project imDEV is an application of RExcel, which seamlessly integrates Excel and R for tasks focused on multivariate data visualization, exploration, and analysis. Interactive modules for dimensional reduction (imPCA), prediction (imPLS), feature selection, analysis of correlation (imCorrelations) and generation of networked structures (imGraph) provide an integrated environment for systems level analysis of multivariate data.
