AiLearning - AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

•    Python

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AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

fp-growth apriori mahchine-leaning naivebayes svm adaboost kmeans svd pca logistic regression recommendedsystem sklearn scikit-learn nlp deeplearning dnn lstm rnn

gorse - A High Performance Recommender System Package based on Collaborative Filtering for Go

•    Go

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More examples could be found in the example folder. All models are tested by 5-fold cross validation on a PC with Intel(R) Core(TM) i5-4590 CPU (3.30GHz) and 16.0GB RAM. All scores are the best scores achieved by gorse yet.

recommender-system svd svdplusplus knn slope-one co-clustering nmf machine-learning recommender bpr collaborative-filtering data-mining machinelearning avx2

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

•    Go

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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)

•    Python

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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

•    R

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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

•    C++

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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

h2o4gpu - H2Oai GPU Edition

•    Python

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H2O4GPU is a collection of GPU solvers by H2Oai with APIs in Python and R. The Python API builds upon the easy-to-use scikit-learn API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn (i.e. import h2o4gpu as sklearn) with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algorithms and falls back to CPU algorithms when the GPU algorithm does not support an important existing scikit-learn class option. The R package is a wrapper around the H2O4GPU Python package, and the interface follows standard R conventions for modeling. Daal library added for CPU, currently supported only x86_64 architecture.

gpu glm cuda c-plus-plus cpu lasso elastic-net svd pca rstats r machine-learning

ISLE - This repository provides code for SVD and Importance sampling-based algorithms for large scale topic modeling

•    C++

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We built this project on Ubuntu 16.04LTS with gcc 5.4. Other linux versions with gcc 5+ could also work. This should generate two executables ISLETrain and ISLEInfer in the <ISLE_ROOT> directory.

topic-modeling spectral-clustering svd randomized-algorithm sampling-methods linear-algebra unsupervised-learning

dslite2svd - Converter of register descriptions from the TI DSLite format to CMSIS SVD format

•    Rust

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TI does not publish SVD files for many of their newer CPUs, including the TM4C series. However, they do publish some sort of debug-related files in Energia that are basically a not-invented-here variant of SVD. This repository contains a converter, dslite2svd. The up-to-date SVD files will be placed in the svd directory. For convenience, they are already provided in this repository.

texas-instruments svd tm4c tiva-c-series

GenericSVD.jl - Singular Value Decomposition for generic number types

•    Julia

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Implements Singular Value Decomposition for generic number types, such as BigFloat, Complex{BigFloat} or Quaternions. It internally overloads several Base functions such that existing methods (svd, svdfact and svdvals) should work directly. It uses a Golub-Kahan 2-stage algorithm of bidiagonalization with Householder reflections, followed by an implicit QR with shift.

julia math svd

svd - Python code implementing the power method for Singular Value Decomposition

•    Python

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An implementation of the greedy algorithm for SVD, using the power method for the 1-dimensional case. Run the following to set up all the requirements needed to run the code in this repository.

svd algorithm linear-algebra programming optimization

matrix - Matrix manipulation and computation library

•    Javascript

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Matrix manipulation and computation library.

matrix ml machine-learning decomposition svd singular value evd eigenvalue lu qr cholesky data mining datamining machine learning