Command-line executables for mlpack (machine learning library)

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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)...

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

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...