Displaying 1 to 11 from 11 results

irlba - Fast truncated singular value decompositions

  •    R

Implicitly-restarted Lanczos methods for fast truncated singular value decomposition of sparse and dense matrices (also referred to as partial SVD). IRLBA stands for Augmented, Implicitly Restarted Lanczos Bidiagonalization Algorithm. The package provides the following functions (see help on each for details and examples).Help documentation for each function includes extensive documentation and examples. Also see the package vignette, vignette("irlba", package="irlba").

pca - Principal component analysis (PCA) in Ruby

  •    Ruby

Principal component analysis in Ruby. Uses GSL for calculations. PCA can be used to map data to a lower dimensional space while minimizing information loss. It's useful for data visualization, where you're limited to 2-D and 3-D plots.

jlearn - Machine Learning Library, written in J

  •    J

WIP Machine learning library, written in J. Various algorithm implementations, including MLPClassifiers, MLPRegressors, Mixture Models, K-Means, KNN, RBF-Network, Self-organizing Maps. Models can be serialized to text files, with a mixture of text and binary packing. The size of the serialized file depends on the size of the model, but will probably range from 10 MB and upwards for NN models (including convnets and rec-nets).

diffusion-map - Comparison of principal components analysis with diffusion maps on toy data sets and a molecular simulation trajectory

  •    Fortran

Personal code for principal component analysis and diffusion map examples. Specifically made to test the idea on some well-known types of data, but it wouldn't take much to modify the source for use with whatever data set or distance metric you desire. A library is compiled with the classes needed for the main program and the main program links to that. The main program requires json-fortran. LAPACK is required for the library to calculate the eigenvectors and eigenvalues of various matrices.

Microscope - ChIP-seq/RNA-seq analysis software suite for gene expression heatmaps

  •    R

We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network visualization. MicroScope is financially supported by the United States Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Program. This research was conducted with Government support under and awarded by DoD, Army Research Office (ARO), National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a.

Faces - Do you look like a Nobel Laureate :medal_military:, Physicist, Chemist, Mathematician, Actor or a Programmer? God gave you one face and you went on to get a peek into the mind of God

  •    Python

God gave you one face and you went on to get a peek into what the future holds for you. On a technical note, the application uses the robust techniques of Principal component analysis and eigenfaces to decrypt your future.

machine-learning-course - R code for the assignments of Coursera machine learning course

  •    R

This is the R version assignments of the online machine learning course (MOOC) on Coursera website by Prof. Andrew Ng. This repository provides the starter code to solve the assignment in R statistical software; the completed assignments are also available beside each exercise file.

Machine_Learning - Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks

  •    Jupyter

Esse repositório foi criado com a intenção de difundir o ensino de Machine Learning em português. Os algoritmos aqui implementados não são otimizados e foram implementados visando o fácil entendimento. Portanto, não devem ser utilizados para fins de pesquisa ou outros fins além dos especificados.

synthia - 📈 🐍 Multidimensional synthetic data generation in Python

  •    Python

Synthetic data need to preserve the statistical properties of real data in terms of their individual behavior and (inter-)dependences (Meyer et al. 2021). Copula and functional Principle Component Analysis (fPCA) are statistical models that allow these properties to be simulated (Joe 2014). As such, copula generated data have shown potential to improve the generalization of machine learning (ML) emulators (Meyer et al. 2021) or anonymize real-data datasets (Patki et al. 2016). Synthia is an open source Python package to model univariate and multivariate data, parameterize data using empirical and parametric methods, and manipulate marginal distributions. It is designed to enable scientists and practitioners to handle labelled multivariate data typical of computational sciences. For example, given some vertical profiles of atmospheric temperature, we can use Synthia to generate new but statistically similar profiles in just three lines of code (Table 1).

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