Download JAR files tagged by matrix with all dependencies

Matrix is an APM (Application Performance Manage) used in Wechat to monitor, locate and analyse performance problems. It is a plugin style, non-invasive solution and is currently available on iOS, macOS and Android.

Matrix is an APM (Application Performance Manage) used in Wechat to monitor, locate and analyse performance problems. It is a plugin style, non-invasive solution and is currently available on iOS, macOS and Android.

A comprehensive collection of matrix data structures, linear solvers, least squares methods, eigenvalue, and singular value decompositions. Forked from: https://github.com/fommil/matrix-toolkits-java and added support for eigenvalue computation of general matrices

Matrix data structures, linear solvers, least squares methods, eigenvalue, and singular value decompositions. For larger random dense matrices (above ~ 350 x 350) matrix-matrix multiplication C = A.B is about 50% faster than MTJ.

Open Jazari Library for Java (OJL4J), formerly known as Open Cezeri Library is a Java based Matrix, Vectorization, Image Processing, Machine Learning (Weka + DL4J), Data Visualization and Analysis DSL (Domain Specific Language) which enables developers finish the code with almost single line of code.

This module provides a very basic support for the parallel computing of tasks (Engine class) and entries of a matrix (MatrixEngine). This is basically just a wrapper around the java standard functionality for parallel computing (mainly the Standard Thread Pool and the Future interface). Additional functionality is provided by the ProgressReporter interface, which can be used as a hook to provide information on the current state of the parallel computing task to other modules or the user.

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.)

This package provides two meta attribute selection evaluators - one for performing cost-sensitive attribute evaluation (CostSensitiveAttributeEval) and a second for performing cost-sensitive subset evaluation (CostSensitiveSubsetEval). Both methods take a cost matrix and a base evaluator. If the base evaluator can handle instance weights, then the training data is weighted according to the cost matrix, otherwise the training data is sampled according to the cost matrix.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.