nlp - Selected Machine Learning algorithms for basic natural language processing in Golang

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An implementation of selected machine learning algorithms for basic natural language processing in golang. The initial focus for this project is Latent Semantic Analysis to allow retrieval/searching, clustering and classification of text documents based upon semantic content.Built upon the gonum/gonum matrix library with some inspiration taken from Python's scikit-learn.

natural-language-processing nlp lsa latent-semantic-analysis feature-vector machine-learning machine-learning-algorithms svd singular-value-decomposition tf-idf feature-hash feature-extraction

prince - :crown: Python factor analysis library (PCA, CA, MCA, FAMD)

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Prince uses pandas to manipulate dataframes, as such it expects an initial dataframe to work with. In the following example, a Principal Component Analysis (PCA) is applied to the iris dataset. Under the hood Prince decomposes the dataframe into two eigenvector matrices and one eigenvalue array thanks to a Singular Value Decomposition (SVD). The eigenvectors can then be used to project the initial dataset onto lower dimensions.The first plot displays the rows in the initial dataset projected on to the two first right eigenvectors (the obtained projections are called principal coordinates). The ellipses are 90% confidence intervals.

pandas pca ca mca svd factor-analysis

irlba - Fast truncated singular value decompositions

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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").

svd pca principal-component-analysis singular-value-decomposition

RSpectra - R Interface to the Spectra Library for Large Scale Eigenvalue and SVD Problems

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RSpectra is an R interface to the Spectra library. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigen values, which is usually more efficient than eigen() if k << n. Symmetric matrices have real eigenvalues.

eigenvalues spectra svd