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 maximum flexibility for
expert users. mlpack outperforms competing machine learning libraries by large
margins. This package provides the command-line executables which run mlpack
methods and related documentation.
mlpack_adaboost This program implements the AdaBoost (or Adaptive Boosting) algorithm. The variant of AdaBoost implemented here is AdaBoost.MH. It uses a weak learner, either... mlpack_allkfn This program will calculate the all k-furthest-neighbors of a set of points. You may specify a separate set of reference points and query points, or just a... mlpack_allknn This program will calculate the k-nearest-neighbors of a set of points using kd-trees or cover trees (cover tree support is experimental and may be slow). You... mlpack_allkrann This program will calculate the k rank-approximate-nearest-neighbors of a set of points. You may specify a separate set of reference points and query points, or... mlpack_cf This program performs collaborative filtering (CF) on the given dataset. Given a list of user, item and preferences (--training_file) the program will perform a... mlpack_decision_stump This program implements a decision stump, which is a single-level decision tree. The decision stump will split on one dimension of the input data, and will... mlpack_det This program performs a number of functions related to Density Estimation Trees. The optimal Density Estimation Tree (DET) can be trained on a set of data... mlpack_emst This program can compute the Euclidean minimum spanning tree of a set of input points using the dual-tree Boruvka algorithm. The output is saved in a... mlpack_fastmks This program will find the k maximum kernel of a set of points, using a query set and a reference set (which can optionally be the same set). More specifically... mlpack_gmm_generate This program is able to generate samples from a pre-trained GMM (use gmm_train to train a GMM). It loads a GMM from the file specified with --input_model_file... mlpack_gmm_probability This program calculates the probability that given points came from a given GMM (that is, P(X | gmm)). The GMM is specified with the --input_model_file option... mlpack_gmm_train This program takes a parametric estimate of a Gaussian mixture model (GMM) using the EM algorithm to find the maximum likelihood estimate. The model may be... mlpack_hmm_generate This utility takes an already-trained HMM (--model_file) and generates a random observation sequence and hidden state sequence based on its parameters, saving... mlpack_hmm_loglik This utility takes an already-trained HMM (--model_file) and evaluates the log-likelihood of a given sequence of observations (--input_file). The computed... mlpack_hmm_train This program allows a Hidden Markov Model to be trained on labeled or unlabeled data. It support three types of HMMs: discrete HMMs, Gaussian HMMs, or GMM HMMs... mlpack_hmm_viterbi This utility takes an already-trained HMM (--model_file) and evaluates the most probably hidden state sequence of a given sequence of observations... mlpack_hoeffding_tree This program implements Hoeffding trees, a form of streaming decision tree suited best for large (or streaming) datasets. This program supports both categorical... mlpack_kernel_pca This program performs Kernel Principal Components Analysis (KPCA) on the specified dataset with the specified kernel. This will transform the data onto the... mlpack_kmeans This program performs K-Means clustering on the given dataset, storing the learned cluster assignments either as a column of labels in the file containing the... mlpack_lars An implementation of LARS: Least Angle Regression (Stagewise/laSso). This is a stage-wise homotopy-based algorithm for L1-regularized linear regression (LASSO)... mlpack_linear_regression An implementation of simple linear regression and simple ridge regression using ordinary least squares. This solves the problem mlpack_local_coordinate_coding An implementation of Local Coordinate Coding (LCC), which codes data that approximately lives on a manifold using a variation of l1-norm regularized sparse... mlpack_logistic_regression An implementation of L2-regularized logistic regression using either the L-BFGS optimizer or SGD (stochastic gradient descent). This solves the regression... mlpack_lsh This program will calculate the k approximate-nearest-neighbors of a set of points using locality-sensitive hashing. You may specify a separate set of reference... mlpack_mean_shift This program performs mean shift clustering on the given dataset, storing the learned cluster assignments either as a column of labels in the file containing... mlpack_nbc This program trains the Naive Bayes classifier on the given labeled training set, or loads a model from the given model file, and then may use that trained... mlpack_nca This program implements Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique. The method seeks... mlpack_nmf This program performs non-negative matrix factorization on the given dataset, storing the resulting decomposed matrices in the specified files. For an input... mlpack_pca This program performs principal components analysis on the given dataset. It will transform the data onto its principal components, optionally performing... mlpack_perceptron This program implements a perceptron, which is a single level neural network. The perceptron makes its predictions based on a linear predictor function... mlpack_radical An implementation of RADICAL, a method for independentcomponent analysis (ICA). Assuming that we have an input matrix X, thegoal is to find a square unmixing... mlpack_range_search This program implements range search with a Euclidean distance metric. For a given query point, a given range, and a given set of reference points, the program... mlpack_softmax_regression This program performs softmax regression, a generalization of logistic regression to the multiclass case, and has support for L2 regularization. The program is... mlpack_sparse_coding An implementation of Sparse Coding with Dictionary Learning, which achieves sparsity via an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm...