Package 'mlpack'

Description

An implementation of the AdaBoost.MH (Adaptive Boosting) algorithm for classification. This can be used to train an AdaBoost model on labeled data or use an existing AdaBoost model to predict the classes of new points.

Usage

adaboost(
  input_model = NA,
  iterations = NA,
  labels = NA,
  test = NA,
  tolerance = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE),
  weak_learner = NA
)

Arguments

Details

This program implements the AdaBoost (or Adaptive Boosting) algorithm. The variant of AdaBoost implemented here is AdaBoost.MH. It uses a weak learner, either decision stumps or perceptrons, and over many iterations, creates a strong learner that is a weighted ensemble of weak learners. It runs these iterations until a tolerance value is crossed for change in the value of the weighted training error.

For more information about the algorithm, see the paper "Improved Boosting Algorithms Using Confidence-Rated Predictions", by R.E. Schapire and Y. Singer.

This program allows training of an AdaBoost model, and then application of that model to a test dataset. To train a model, a dataset must be passed with the "training" option. Labels can be given with the "labels" option; if no labels are specified, the labels will be assumed to be the last column of the input dataset. Alternately, an AdaBoost model may be loaded with the "input_model" option.

Once a model is trained or loaded, it may be used to provide class predictions for a given test dataset. A test dataset may be specified with the "test" parameter. The predicted classes for each point in the test dataset are output to the "predictions" output parameter. The AdaBoost model itself is output to the "output_model" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to run AdaBoost on an input dataset "data" with labels
# "labels"and perceptrons as the weak learner type, storing the trained model
# in "model", one could use the following command: 

## Not run: 
output <- adaboost(training=data, labels=labels, weak_learner="perceptron")
model <- output$output_model

## End(Not run)

# Similarly, an already-trained model in "model" can be used to provide class
# predictions from test data "test_data" and store the output in
# "predictions" with the following command: 

## Not run: 
output <- adaboost(input_model=model, test=test_data)
predictions <- output$predictions

## End(Not run)

Description

An implementation of two strategies for furthest neighbor search. This can be used to compute the furthest neighbor of query point(s) from a set of points; furthest neighbor models can be saved and reused with future query point(s).

Usage

approx_kfn(
  algorithm = NA,
  calculate_error = FALSE,
  exact_distances = NA,
  input_model = NA,
  k = NA,
  num_projections = NA,
  num_tables = NA,
  query = NA,
  reference = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program implements two strategies for furthest neighbor search. These strategies are:

- The 'qdafn' algorithm from "Approximate Furthest Neighbor in High Dimensions" by R. Pagh, F. Silvestri, J. Sivertsen, and M. Skala, in Similarity Search and Applications 2015 (SISAP). - The 'DrusillaSelect' algorithm from "Fast approximate furthest neighbors with data-dependent candidate selection", by R.R. Curtin and A.B. Gardner, in Similarity Search and Applications 2016 (SISAP).

These two strategies give approximate results for the furthest neighbor search problem and can be used as fast replacements for other furthest neighbor techniques such as those found in the mlpack_kfn program. Note that typically, the 'ds' algorithm requires far fewer tables and projections than the 'qdafn' algorithm.

Specify a reference set (set to search in) with "reference", specify a query set with "query", and specify algorithm parameters with "num_tables" and "num_projections" (or don't and defaults will be used). The algorithm to be used (either 'ds'—the default—or 'qdafn') may be specified with "algorithm". Also specify the number of neighbors to search for with "k".

Note that for 'qdafn' in lower dimensions, "num_projections" may need to be set to a high value in order to return results for each query point.

If no query set is specified, the reference set will be used as the query set. The "output_model" output parameter may be used to store the built model, and an input model may be loaded instead of specifying a reference set with the "input_model" option.

Results for each query point can be stored with the "neighbors" and "distances" output parameters. Each row of these output matrices holds the k distances or neighbor indices for each query point.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to find the 5 approximate furthest neighbors with
# "reference_set" as the reference set and "query_set" as the query set using
# DrusillaSelect, storing the furthest neighbor indices to "neighbors" and
# the furthest neighbor distances to "distances", one could call

## Not run: 
output <- approx_kfn(query=query_set, reference=reference_set, k=5,
  algorithm="ds")
neighbors <- output$neighbors
distances <- output$distances

## End(Not run)

# and to perform approximate all-furthest-neighbors search with k=1 on the
# set "data" storing only the furthest neighbor distances to "distances", one
# could call

## Not run: 
output <- approx_kfn(reference=reference_set, k=1)
distances <- output$distances

## End(Not run)

# A trained model can be re-used.  If a model has been previously saved to
# "model", then we may find 3 approximate furthest neighbors on a query set
# "new_query_set" using that model and store the furthest neighbor indices
# into "neighbors" by calling

## Not run: 
output <- approx_kfn(input_model=model, query=new_query_set, k=3)
neighbors <- output$neighbors

## End(Not run)

Description

An implementation of the bayesian linear regression.

Usage

bayesian_linear_regression(
  center = FALSE,
  input = NA,
  input_model = NA,
  responses = NA,
  scale = FALSE,
  test = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

An implementation of the bayesian linear regression. This model is a probabilistic view and implementation of the linear regression. The final solution is obtained by computing a posterior distribution from gaussian likelihood and a zero mean gaussian isotropic prior distribution on the solution. Optimization is AUTOMATIC and does not require cross validation. The optimization is performed by maximization of the evidence function. Parameters are tuned during the maximization of the marginal likelihood. This procedure includes the Ockham's razor that penalizes over complex solutions.

This program is able to train a Bayesian linear regression model or load a model from file, output regression predictions for a test set, and save the trained model to a file.

To train a BayesianLinearRegression model, the "input" and "responses"parameters must be given. The "center"and "scale" parameters control the centering and the normalizing options. A trained model can be saved with the "output_model". If no training is desired at all, a model can be passed via the "input_model" parameter.

The program can also provide predictions for test data using either the trained model or the given input model. Test points can be specified with the "test" parameter. Predicted responses to the test points can be saved with the "predictions" output parameter. The corresponding standard deviation can be save by precising the "stds" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following command trains a model on the data "data" and
# responses "responses"with center set to true and scale set to false (so,
# Bayesian linear regression is being solved, and then the model is saved to
# "blr_model":

## Not run: 
output <- bayesian_linear_regression(input=data, responses=responses,
  center=1, scale=0)
blr_model <- output$output_model

## End(Not run)

# The following command uses the "blr_model" to provide predicted  responses
# for the data "test" and save those  responses to "test_predictions": 

## Not run: 
output <- bayesian_linear_regression(input_model=blr_model, test=test)
test_predictions <- output$predictions

## End(Not run)

# Because the estimator computes a predictive distribution instead of a
# simple point estimate, the "stds" parameter allows one to save the
# prediction uncertainties: 

## Not run: 
output <- bayesian_linear_regression(input_model=blr_model, test=test)
test_predictions <- output$predictions
stds <- output$stds

## End(Not run)

Description

An implementation of several collaborative filtering (CF) techniques for recommender systems. This can be used to train a new CF model, or use an existing CF model to compute recommendations.

Usage

cf(
  algorithm = NA,
  all_user_recommendations = FALSE,
  input_model = NA,
  interpolation = NA,
  iteration_only_termination = FALSE,
  max_iterations = NA,
  min_residue = NA,
  neighbor_search = NA,
  neighborhood = NA,
  normalization = NA,
  query = NA,
  rank = NA,
  recommendations = NA,
  seed = NA,
  test = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs collaborative filtering (CF) on the given dataset. Given a list of user, item and preferences (the "training" parameter), the program will perform a matrix decomposition and then can perform a series of actions related to collaborative filtering. Alternately, the program can load an existing saved CF model with the "input_model" parameter and then use that model to provide recommendations or predict values.

The input matrix should be a 3-dimensional matrix of ratings, where the first dimension is the user, the second dimension is the item, and the third dimension is that user's rating of that item. Both the users and items should be numeric indices, not names. The indices are assumed to start from 0.

A set of query users for which recommendations can be generated may be specified with the "query" parameter; alternately, recommendations may be generated for every user in the dataset by specifying the "all_user_recommendations" parameter. In addition, the number of recommendations per user to generate can be specified with the "recommendations" parameter, and the number of similar users (the size of the neighborhood) to be considered when generating recommendations can be specified with the "neighborhood" parameter.

For performing the matrix decomposition, the following optimization algorithms can be specified via the "algorithm" parameter:

- 'RegSVD' – Regularized SVD using a SGD optimizer - 'NMF' – Non-negative matrix factorization with alternating least squares update rules - 'BatchSVD' – SVD batch learning - 'SVDIncompleteIncremental' – SVD incomplete incremental learning - 'SVDCompleteIncremental' – SVD complete incremental learning - 'BiasSVD' – Bias SVD using a SGD optimizer - 'SVDPP' – SVD++ using a SGD optimizer - 'RandSVD' – RandomizedSVD learning - 'QSVD' – QuicSVD learning - 'BKSVD' – Block Krylov SVD learning

The following neighbor search algorithms can be specified via the "neighbor_search" parameter:

- 'cosine' – Cosine Search Algorithm - 'euclidean' – Euclidean Search Algorithm - 'pearson' – Pearson Search Algorithm

The following weight interpolation algorithms can be specified via the "interpolation" parameter:

- 'average' – Average Interpolation Algorithm - 'regression' – Regression Interpolation Algorithm - 'similarity' – Similarity Interpolation Algorithm

The following ranking normalization algorithms can be specified via the "normalization" parameter:

- 'none' – No Normalization - 'item_mean' – Item Mean Normalization - 'overall_mean' – Overall Mean Normalization - 'user_mean' – User Mean Normalization - 'z_score' – Z-Score Normalization

A trained model may be saved to with the "output_model" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# To train a CF model on a dataset "training_set" using NMF for decomposition
# and saving the trained model to "model", one could call: 

## Not run: 
output <- cf(training=training_set, algorithm="NMF")
model <- output$output_model

## End(Not run)

# Then, to use this model to generate recommendations for the list of users
# in the query set "users", storing 5 recommendations in "recommendations",
# one could call 

## Not run: 
output <- cf(input_model=model, query=users, recommendations=5)
recommendations <- output$output

## End(Not run)

Description

An implementation of DBSCAN clustering. Given a dataset, this can compute and return a clustering of that dataset.

Usage

dbscan(
  input,
  epsilon = NA,
  min_size = NA,
  naive = FALSE,
  selection_type = NA,
  single_mode = FALSE,
  tree_type = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program implements the DBSCAN algorithm for clustering using accelerated tree-based range search. The type of tree that is used may be parameterized, or brute-force range search may also be used.

The input dataset to be clustered may be specified with the "input" parameter; the radius of each range search may be specified with the "epsilon" parameters, and the minimum number of points in a cluster may be specified with the "min_size" parameter.

The "assignments" and "centroids" output parameters may be used to save the output of the clustering. "assignments" contains the cluster assignments of each point, and "centroids" contains the centroids of each cluster.

The range search may be controlled with the "tree_type", "single_mode", and "naive" parameters. "tree_type" can control the type of tree used for range search; this can take a variety of values: 'kd', 'r', 'r-star', 'x', 'hilbert-r', 'r-plus', 'r-plus-plus', 'cover', 'ball'. The "single_mode" parameter will force single-tree search (as opposed to the default dual-tree search), and '"naive" will force brute-force range search.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# An example usage to run DBSCAN on the dataset in "input" with a radius of
# 0.5 and a minimum cluster size of 5 is given below:

## Not run: 
dbscan(input=input, epsilon=0.5, min_size=5)

## End(Not run)

Description

An implementation of an ID3-style decision tree for classification, which supports categorical data. Given labeled data with numeric or categorical features, a decision tree can be trained and saved; or, an existing decision tree can be used for classification on new points.

Usage

decision_tree(
  input_model = NA,
  labels = NA,
  maximum_depth = NA,
  minimum_gain_split = NA,
  minimum_leaf_size = NA,
  print_training_accuracy = FALSE,
  test = NA,
  test_labels = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE),
  weights = NA
)

Arguments

Details

Train and evaluate using a decision tree. Given a dataset containing numeric or categorical features, and associated labels for each point in the dataset, this program can train a decision tree on that data.

The training set and associated labels are specified with the "training" and "labels" parameters, respectively. The labels should be in the range '[0, num_classes - 1]'. Optionally, if "labels" is not specified, the labels are assumed to be the last dimension of the training dataset.

When a model is trained, the "output_model" output parameter may be used to save the trained model. A model may be loaded for predictions with the "input_model" parameter. The "input_model" parameter may not be specified when the "training" parameter is specified. The "minimum_leaf_size" parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The "minimum_gain_split" parameter specifies the minimum gain that is needed for the node to split. The "maximum_depth" parameter specifies the maximum depth of the tree. If "print_training_accuracy" is specified, the training accuracy will be printed.

Test data may be specified with the "test" parameter, and if performance numbers are desired for that test set, labels may be specified with the "test_labels" parameter. Predictions for each test point may be saved via the "predictions" output parameter. Class probabilities for each prediction may be saved with the "probabilities" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to train a decision tree with a minimum leaf size of 20 on the
# dataset contained in "data" with labels "labels", saving the output model
# to "tree" and printing the training error, one could call

## Not run: 
output <- decision_tree(training=data, labels=labels, minimum_leaf_size=20,
  minimum_gain_split=0.001, print_training_accuracy=TRUE)
tree <- output$output_model

## End(Not run)

# Then, to use that model to classify points in "test_set" and print the test
# error given the labels "test_labels" using that model, while saving the
# predictions for each point to "predictions", one could call 

## Not run: 
output <- decision_tree(input_model=tree, test=test_set,
  test_labels=test_labels)
predictions <- output$predictions

## End(Not run)

Description

An implementation of density estimation trees for the density estimation task. Density estimation trees can be trained or used to predict the density at locations given by query points.

Usage

det(
  folds = NA,
  input_model = NA,
  max_leaf_size = NA,
  min_leaf_size = NA,
  path_format = NA,
  skip_pruning = FALSE,
  test = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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 (specified by "training") using cross-validation (with number of folds specified with the "folds" parameter). This trained density estimation tree may then be saved with the "output_model" output parameter.

The variable importances (that is, the feature importance values for each dimension) may be saved with the "vi" output parameter, and the density estimates for each training point may be saved with the "training_set_estimates" output parameter.

Enabling path printing for each node outputs the path from the root node to a leaf for each entry in the test set, or training set (if a test set is not provided). Strings like 'LRLRLR' (indicating that traversal went to the left child, then the right child, then the left child, and so forth) will be output. If 'lr-id' or 'id-lr' are given as the "path_format" parameter, then the ID (tag) of every node along the path will be printed after or before the L or R character indicating the direction of traversal, respectively.

This program also can provide density estimates for a set of test points, specified in the "test" parameter. The density estimation tree used for this task will be the tree that was trained on the given training points, or a tree given as the parameter "input_model". The density estimates for the test points may be saved using the "test_set_estimates" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Description

An implementation of the Dual-Tree Boruvka algorithm for computing the Euclidean minimum spanning tree of a set of input points.

Usage

emst(
  input,
  leaf_size = NA,
  naive = FALSE,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program can compute the Euclidean minimum spanning tree of a set of input points using the dual-tree Boruvka algorithm.

The set to calculate the minimum spanning tree of is specified with the "input" parameter, and the output may be saved with the "output" output parameter.

The "leaf_size" parameter controls the leaf size of the kd-tree that is used to calculate the minimum spanning tree, and if the "naive" option is given, then brute-force search is used (this is typically much slower in low dimensions). The leaf size does not affect the results, but it may have some effect on the runtime of the algorithm.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the minimum spanning tree of the input dataset "data" can be
# calculated with a leaf size of 20 and stored as "spanning_tree" using the
# following command:

## Not run: 
output <- emst(input=data, leaf_size=20)
spanning_tree <- output$output

## End(Not run)

# The output matrix is a three-dimensional matrix, where each row indicates
# an edge.  The first dimension corresponds to the lesser index of the edge;
# the second dimension corresponds to the greater index of the edge; and the
# third column corresponds to the distance between the two points.

Description

An implementation of the single-tree and dual-tree fast max-kernel search (FastMKS) algorithm. Given a set of reference points and a set of query points, this can find the reference point with maximum kernel value for each query point; trained models can be reused for future queries.

Usage

fastmks(
  bandwidth = NA,
  base = NA,
  degree = NA,
  input_model = NA,
  k = NA,
  kernel = NA,
  naive = FALSE,
  offset = NA,
  query = NA,
  reference = NA,
  scale = NA,
  single = FALSE,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program will find the k maximum kernels of a set of points, using a query set and a reference set (which can optionally be the same set). More specifically, for each point in the query set, the k points in the reference set with maximum kernel evaluations are found. The kernel function used is specified with the "kernel" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following command will calculate, for each point in the
# query set "query", the five points in the reference set "reference" with
# maximum kernel evaluation using the linear kernel.  The kernel evaluations
# may be saved with the  "kernels" output parameter and the indices may be
# saved with the "indices" output parameter.

## Not run: 
output <- fastmks(k=5, reference=reference, query=query, kernel="linear")
indices <- output$indices
kernels <- output$kernels

## End(Not run)

# The output matrices are organized such that row i and column j in the
# indices matrix corresponds to the index of the point in the reference set
# that has j'th largest kernel evaluation with the point in the query set
# with index i.  Row i and column j in the kernels matrix corresponds to the
# kernel evaluation between those two points.
# 
# This program performs FastMKS using a cover tree.  The base used to build
# the cover tree can be specified with the "base" parameter.

Description

A sample generator for pre-trained GMMs. Given a pre-trained GMM, this can sample new points randomly from that distribution.

Usage

gmm_generate(
  input_model,
  samples,
  seed = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program is able to generate samples from a pre-trained GMM (use gmm_train to train a GMM). The pre-trained GMM must be specified with the "input_model" parameter. The number of samples to generate is specified by the "samples" parameter. Output samples may be saved with the "output" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# The following command can be used to generate 100 samples from the
# pre-trained GMM "gmm" and store those generated samples in "samples":

## Not run: 
output <- gmm_generate(input_model=gmm, samples=100)
samples <- output$output

## End(Not run)

Description

A probability calculator for GMMs. Given a pre-trained GMM and a set of points, this can compute the probability that each point is from the given GMM.

Usage

gmm_probability(
  input,
  input_model,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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" parameter, and the points are specified with the "input" parameter. The output probabilities may be saved via the "output" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# So, for example, to calculate the probabilities of each point in "points"
# coming from the pre-trained GMM "gmm", while storing those probabilities in
# "probs", the following command could be used:

## Not run: 
output <- gmm_probability(input_model=gmm, input=points)
probs <- output$output

## End(Not run)

Description

An implementation of the EM algorithm for training Gaussian mixture models (GMMs). Given a dataset, this can train a GMM for future use with other tools.

Usage

gmm_train(
  gaussians,
  input,
  diagonal_covariance = FALSE,
  input_model = NA,
  kmeans_max_iterations = NA,
  max_iterations = NA,
  no_force_positive = FALSE,
  noise = NA,
  percentage = NA,
  refined_start = FALSE,
  samplings = NA,
  seed = NA,
  tolerance = NA,
  trials = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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 saved and reused by other mlpack GMM tools.

The input data to train on must be specified with the "input" parameter, and the number of Gaussians in the model must be specified with the "gaussians" parameter. Optionally, many trials with different random initializations may be run, and the result with highest log-likelihood on the training data will be taken. The number of trials to run is specified with the "trials" parameter. By default, only one trial is run.

The tolerance for convergence and maximum number of iterations of the EM algorithm are specified with the "tolerance" and "max_iterations" parameters, respectively. The GMM may be initialized for training with another model, specified with the "input_model" parameter. Otherwise, the model is initialized by running k-means on the data. The k-means clustering initialization can be controlled with the "kmeans_max_iterations", "refined_start", "samplings", and "percentage" parameters. If "refined_start" is specified, then the Bradley-Fayyad refined start initialization will be used. This can often lead to better clustering results.

The 'diagonal_covariance' flag will cause the learned covariances to be diagonal matrices. This significantly simplifies the model itself and causes training to be faster, but restricts the ability to fit more complex GMMs.

If GMM training fails with an error indicating that a covariance matrix could not be inverted, make sure that the "no_force_positive" parameter is not specified. Alternately, adding a small amount of Gaussian noise (using the "noise" parameter) to the entire dataset may help prevent Gaussians with zero variance in a particular dimension, which is usually the cause of non-invertible covariance matrices.

The "no_force_positive" parameter, if set, will avoid the checks after each iteration of the EM algorithm which ensure that the covariance matrices are positive definite. Specifying the flag can cause faster runtime, but may also cause non-positive definite covariance matrices, which will cause the program to crash.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# As an example, to train a 6-Gaussian GMM on the data in "data" with a
# maximum of 100 iterations of EM and 3 trials, saving the trained GMM to
# "gmm", the following command can be used:

## Not run: 
output <- gmm_train(input=data, gaussians=6, trials=3)
gmm <- output$output_model

## End(Not run)

# To re-train that GMM on another set of data "data2", the following command
# may be used: 

## Not run: 
output <- gmm_train(input_model=gmm, input=data2, gaussians=6)
new_gmm <- output$output_model

## End(Not run)

Description

A utility to generate random sequences from a pre-trained Hidden Markov Model (HMM). The length of the desired sequence can be specified, and a random sequence of observations is returned.

Usage

hmm_generate(
  length,
  model,
  seed = NA,
  start_state = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This utility takes an already-trained HMM, specified as the "model" parameter, and generates a random observation sequence and hidden state sequence based on its parameters. The observation sequence may be saved with the "output" output parameter, and the internal state sequence may be saved with the "state" output parameter.

The state to start the sequence in may be specified with the "start_state" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to generate a sequence of length 150 from the HMM "hmm" and
# save the observation sequence to "observations" and the hidden state
# sequence to "states", the following command may be used: 

## Not run: 
output <- hmm_generate(model=hmm, length=150)
observations <- output$output
states <- output$state

## End(Not run)

Description

A utility for computing the log-likelihood of a sequence for Hidden Markov Models (HMMs). Given a pre-trained HMM and an observation sequence, this computes and returns the log-likelihood of that sequence being observed from that HMM.

Usage

hmm_loglik(input, input_model, verbose = getOption("mlpack.verbose", FALSE))

Arguments

Details

This utility takes an already-trained HMM, specified with the "input_model" parameter, and evaluates the log-likelihood of a sequence of observations, given with the "input" parameter. The computed log-likelihood is given as output.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to compute the log-likelihood of the sequence "seq" with the
# pre-trained HMM "hmm", the following command may be used: 

## Not run: 
hmm_loglik(input=seq, input_model=hmm)

## End(Not run)

Description

An implementation of training algorithms for Hidden Markov Models (HMMs). Given labeled or unlabeled data, an HMM can be trained for further use with other mlpack HMM tools.

Usage

hmm_train(
  input_file,
  batch = FALSE,
  gaussians = NA,
  input_model = NA,
  labels_file = NA,
  seed = NA,
  states = NA,
  tolerance = NA,
  type = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program allows a Hidden Markov Model to be trained on labeled or unlabeled data. It supports four types of HMMs: Discrete HMMs, Gaussian HMMs, GMM HMMs, or Diagonal GMM HMMs

Either one input sequence can be specified (with "input_file"), or, a file containing files in which input sequences can be found (when "input_file"and"batch" are used together). In addition, labels can be provided in the file specified by "labels_file", and if "batch" is used, the file given to "labels_file" should contain a list of files of labels corresponding to the sequences in the file given to "input_file".

The HMM is trained with the Baum-Welch algorithm if no labels are provided. The tolerance of the Baum-Welch algorithm can be set with the "tolerance"option. By default, the transition matrix is randomly initialized and the emission distributions are initialized to fit the extent of the data.

Optionally, a pre-created HMM model can be used as a guess for the transition matrix and emission probabilities; this is specifiable with "output_model".

Value

A list with several components:

Author(s)

mlpack developers

Description

A utility for computing the most probable hidden state sequence for Hidden Markov Models (HMMs). Given a pre-trained HMM and an observed sequence, this uses the Viterbi algorithm to compute and return the most probable hidden state sequence.

Usage

hmm_viterbi(input, input_model, verbose = getOption("mlpack.verbose", FALSE))

Arguments

Details

This utility takes an already-trained HMM, specified as "input_model", and evaluates the most probable hidden state sequence of a given sequence of observations (specified as '"input", using the Viterbi algorithm. The computed state sequence may be saved using the "output" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to predict the state sequence of the observations "obs" using
# the HMM "hmm", storing the predicted state sequence to "states", the
# following command could be used:

## Not run: 
output <- hmm_viterbi(input=obs, input_model=hmm)
states <- output$output

## End(Not run)

Description

An implementation of Hoeffding trees, a form of streaming decision tree for classification. Given labeled data, a Hoeffding tree can be trained and saved for later use, or a pre-trained Hoeffding tree can be used for predicting the classifications of new points.

Usage

hoeffding_tree(
  batch_mode = FALSE,
  bins = NA,
  confidence = NA,
  info_gain = FALSE,
  input_model = NA,
  labels = NA,
  max_samples = NA,
  min_samples = NA,
  numeric_split_strategy = NA,
  observations_before_binning = NA,
  passes = NA,
  test = NA,
  test_labels = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program implements Hoeffding trees, a form of streaming decision tree suited best for large (or streaming) datasets. This program supports both categorical and numeric data. Given an input dataset, this program is able to train the tree with numerous training options, and save the model to a file. The program is also able to use a trained model or a model from file in order to predict classes for a given test set.

The training file and associated labels are specified with the "training" and "labels" parameters, respectively. Optionally, if "labels" is not specified, the labels are assumed to be the last dimension of the training dataset.

The training may be performed in batch mode (like a typical decision tree algorithm) by specifying the "batch_mode" option, but this may not be the best option for large datasets.

When a model is trained, it may be saved via the "output_model" output parameter. A model may be loaded from file for further training or testing with the "input_model" parameter.

Test data may be specified with the "test" parameter, and if performance statistics are desired for that test set, labels may be specified with the "test_labels" parameter. Predictions for each test point may be saved with the "predictions" output parameter, and class probabilities for each prediction may be saved with the "probabilities" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to train a Hoeffding tree with confidence 0.99 with data
# "dataset", saving the trained tree to "tree", the following command may be
# used:

## Not run: 
output <- hoeffding_tree(training=dataset, confidence=0.99)
tree <- output$output_model

## End(Not run)

# Then, this tree may be used to make predictions on the test set "test_set",
# saving the predictions into "predictions" and the class probabilities into
# "class_probs" with the following command: 

## Not run: 
output <- hoeffding_tree(input_model=tree, test=test_set)
predictions <- output$predictions
class_probs <- output$probabilities

## End(Not run)

Description

A utility to load an image or set of images into a single dataset that can then be used by other mlpack methods and utilities. This can also unpack an image dataset into individual files, for instance after mlpack methods have been used.

Usage

image_converter(
  input,
  channels = NA,
  dataset = NA,
  height = NA,
  quality = NA,
  save = FALSE,
  verbose = getOption("mlpack.verbose", FALSE),
  width = NA
)

Arguments

Details

This utility takes an image or an array of images and loads them to a matrix. You can optionally specify the height "height" width "width" and channel "channels" of the images that needs to be loaded; otherwise, these parameters will be automatically detected from the image. There are other options too, that can be specified such as "quality".

You can also provide a dataset and save them as images using "dataset" and "save" as an parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

#  An example to load an image : 

## Not run: 
output <- image_converter(input=X, height=256, width=256, channels=3)
Y <- output$output

## End(Not run)

#  An example to save an image is :

## Not run: 
image_converter(input=X, height=256, width=256, channels=3, dataset=Y,
  save=TRUE)

## End(Not run)

Description

An implementation of kernel density estimation with dual-tree algorithms. Given a set of reference points and query points and a kernel function, this can estimate the density function at the location of each query point using trees; trees that are built can be saved for later use.

Usage

kde(
  abs_error = NA,
  algorithm = NA,
  bandwidth = NA,
  initial_sample_size = NA,
  input_model = NA,
  kernel = NA,
  mc_break_coef = NA,
  mc_entry_coef = NA,
  mc_probability = NA,
  monte_carlo = FALSE,
  query = NA,
  reference = NA,
  rel_error = NA,
  tree = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs a Kernel Density Estimation. KDE is a non-parametric way of estimating probability density function. For each query point the program will estimate its probability density by applying a kernel function to each reference point. The computational complexity of this is O(N^2) where there are N query points and N reference points, but this implementation will typically see better performance as it uses an approximate dual or single tree algorithm for acceleration.

Dual or single tree optimization avoids many barely relevant calculations (as kernel function values decrease with distance), so it is an approximate computation. You can specify the maximum relative error tolerance for each query value with "rel_error" as well as the maximum absolute error tolerance with the parameter "abs_error". This program runs using an Euclidean metric. Kernel function can be selected using the "kernel" option. You can also choose what which type of tree to use for the dual-tree algorithm with "tree". It is also possible to select whether to use dual-tree algorithm or single-tree algorithm using the "algorithm" option.

Monte Carlo estimations can be used to accelerate the KDE estimate when the Gaussian Kernel is used. This provides a probabilistic guarantee on the the error of the resulting KDE instead of an absolute guarantee.To enable Monte Carlo estimations, the "monte_carlo" flag can be used, and success probability can be set with the "mc_probability" option. It is possible to set the initial sample size for the Monte Carlo estimation using "initial_sample_size". This implementation will only consider a node, as a candidate for the Monte Carlo estimation, if its number of descendant nodes is bigger than the initial sample size. This can be controlled using a coefficient that will multiply the initial sample size and can be set using "mc_entry_coef". To avoid using the same amount of computations an exact approach would take, this program recurses the tree whenever a fraction of the amount of the node's descendant points have already been computed. This fraction is set using "mc_break_coef".

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following will run KDE using the data in "ref_data" for
# training and the data in "qu_data" as query data. It will apply an
# Epanechnikov kernel with a 0.2 bandwidth to each reference point and use a
# KD-Tree for the dual-tree optimization. The returned predictions will be
# within 5% of the real KDE value for each query point.

## Not run: 
output <- kde(reference=ref_data, query=qu_data, bandwidth=0.2,
  kernel="epanechnikov", tree="kd-tree", rel_error=0.05)
out_data <- output$predictions

## End(Not run)

# the predicted density estimations will be stored in "out_data".
# If no "query" is provided, then KDE will be computed on the "reference"
# dataset.
# It is possible to select either a reference dataset or an input model but
# not both at the same time. If an input model is selected and parameter
# values are not set (e.g. "bandwidth") then default parameter values will be
# used.
# 
# In addition to the last program call, it is also possible to activate Monte
# Carlo estimations if a Gaussian kernel is used. This can provide faster
# results, but the KDE will only have a probabilistic guarantee of meeting
# the desired error bound (instead of an absolute guarantee). The following
# example will run KDE using a Monte Carlo estimation when possible. The
# results will be within a 5% of the real KDE value with a 95% probability.
# Initial sample size for the Monte Carlo estimation will be 200 points and a
# node will be a candidate for the estimation only when it contains 700 (i.e.
# 3.5*200) points. If a node contains 700 points and 420 (i.e. 0.6*700) have
# already been sampled, then the algorithm will recurse instead of keep
# sampling.

## Not run: 
output <- kde(reference=ref_data, query=qu_data, bandwidth=0.2,
  kernel="gaussian", tree="kd-tree", rel_error=0.05, monte_carlo=,
  mc_probability=0.95, initial_sample_size=200, mc_entry_coef=3.5,
  mc_break_coef=0.6)
out_data <- output$predictions

## End(Not run)

Description

An implementation of Kernel Principal Components Analysis (KPCA). This can be used to perform nonlinear dimensionality reduction or preprocessing on a given dataset.

Usage

kernel_pca(
  input,
  kernel,
  bandwidth = NA,
  center = FALSE,
  degree = NA,
  kernel_scale = NA,
  new_dimensionality = NA,
  nystroem_method = FALSE,
  offset = NA,
  sampling = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs Kernel Principal Components Analysis (KPCA) on the specified dataset with the specified kernel. This will transform the data onto the kernel principal components, and optionally reduce the dimensionality by ignoring the kernel principal components with the smallest eigenvalues.

For the case where a linear kernel is used, this reduces to regular PCA.

The kernels that are supported are listed below:

* 'linear': the standard linear dot product (same as normal PCA): 'K(x, y) = x^T y'

* 'gaussian': a Gaussian kernel; requires bandwidth: 'K(x, y) = exp(-(|| x - y || ^ 2) / (2 * (bandwidth ^ 2)))'

* 'polynomial': polynomial kernel; requires offset and degree: 'K(x, y) = (x^T y + offset) ^ degree'

* 'hyptan': hyperbolic tangent kernel; requires scale and offset: 'K(x, y) = tanh(scale * (x^T y) + offset)'

* 'laplacian': Laplacian kernel; requires bandwidth: 'K(x, y) = exp(-(|| x - y ||) / bandwidth)'

* 'epanechnikov': Epanechnikov kernel; requires bandwidth: 'K(x, y) = max(0, 1 - || x - y ||^2 / bandwidth^2)'

* 'cosine': cosine distance: 'K(x, y) = 1 - (x^T y) / (|| x || * || y ||)'

The parameters for each of the kernels should be specified with the options "bandwidth", "kernel_scale", "offset", or "degree" (or a combination of those parameters).

Optionally, the Nystroem method ("Using the Nystroem method to speed up kernel machines", 2001) can be used to calculate the kernel matrix by specifying the "nystroem_method" parameter. This approach works by using a subset of the data as basis to reconstruct the kernel matrix; to specify the sampling scheme, the "sampling" parameter is used. The sampling scheme for the Nystroem method can be chosen from the following list: 'kmeans', 'random', 'ordered'.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following command will perform KPCA on the dataset "input"
# using the Gaussian kernel, and saving the transformed data to
# "transformed": 

## Not run: 
output <- kernel_pca(input=input, kernel="gaussian")
transformed <- output$output

## End(Not run)

Description

An implementation of k-furthest-neighbor search using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k furthest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use.

Usage

kfn(
  algorithm = NA,
  epsilon = NA,
  input_model = NA,
  k = NA,
  leaf_size = NA,
  percentage = NA,
  query = NA,
  random_basis = FALSE,
  reference = NA,
  seed = NA,
  tree_type = NA,
  true_distances = NA,
  true_neighbors = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program will calculate the k-furthest-neighbors of a set of points. You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following will calculate the 5 furthest neighbors of
# eachpoint in "input" and store the distances in "distances" and the
# neighbors in "neighbors": 

## Not run: 
output <- kfn(k=5, reference=input)
distances <- output$distances
neighbors <- output$neighbors

## End(Not run)

# The output files are organized such that row i and column j in the
# neighbors output matrix corresponds to the index of the point in the
# reference set which is the j'th furthest neighbor from the point in the
# query set with index i.  Row i and column j in the distances output file
# corresponds to the distance between those two points.

Description

An implementation of several strategies for efficient k-means clustering. Given a dataset and a value of k, this computes and returns a k-means clustering on that data.

Usage

kmeans(
  clusters,
  input,
  algorithm = NA,
  allow_empty_clusters = FALSE,
  in_place = FALSE,
  initial_centroids = NA,
  kill_empty_clusters = FALSE,
  kmeans_plus_plus = FALSE,
  labels_only = FALSE,
  max_iterations = NA,
  percentage = NA,
  refined_start = FALSE,
  samplings = NA,
  seed = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs K-Means clustering on the given dataset. It can return the learned cluster assignments, and the centroids of the clusters. Empty clusters are not allowed by default; when a cluster becomes empty, the point furthest from the centroid of the cluster with maximum variance is taken to fill that cluster.

Optionally, the strategy to choose initial centroids can be specified. The k-means++ algorithm can be used to choose initial centroids with the "kmeans_plus_plus" parameter. The Bradley and Fayyad approach ("Refining initial points for k-means clustering", 1998) can be used to select initial points by specifying the "refined_start" parameter. This approach works by taking random samplings of the dataset; to specify the number of samplings, the "samplings" parameter is used, and to specify the percentage of the dataset to be used in each sample, the "percentage" parameter is used (it should be a value between 0.0 and 1.0).

There are several options available for the algorithm used for each Lloyd iteration, specified with the "algorithm" option. The standard O(kN) approach can be used ('naive'). Other options include the Pelleg-Moore tree-based algorithm ('pelleg-moore'), Elkan's triangle-inequality based algorithm ('elkan'), Hamerly's modification to Elkan's algorithm ('hamerly'), the dual-tree k-means algorithm ('dualtree'), and the dual-tree k-means algorithm using the cover tree ('dualtree-covertree').

The behavior for when an empty cluster is encountered can be modified with the "allow_empty_clusters" option. When this option is specified and there is a cluster owning no points at the end of an iteration, that cluster's centroid will simply remain in its position from the previous iteration. If the "kill_empty_clusters" option is specified, then when a cluster owns no points at the end of an iteration, the cluster centroid is simply filled with DBL_MAX, killing it and effectively reducing k for the rest of the computation. Note that the default option when neither empty cluster option is specified can be time-consuming to calculate; therefore, specifying either of these parameters will often accelerate runtime.

Initial clustering assignments may be specified using the "initial_centroids" parameter, and the maximum number of iterations may be specified with the "max_iterations" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# As an example, to use Hamerly's algorithm to perform k-means clustering
# with k=10 on the dataset "data", saving the centroids to "centroids" and
# the assignments for each point to "assignments", the following command
# could be used:

## Not run: 
output <- kmeans(input=data, clusters=10)
assignments <- output$output
centroids <- output$centroid

## End(Not run)

# To run k-means on that same dataset with initial centroids specified in
# "initial" with a maximum of 500 iterations, storing the output centroids in
# "final" the following command may be used:

## Not run: 
output <- kmeans(input=data, initial_centroids=initial, clusters=10,
  max_iterations=500)
final <- output$centroid

## End(Not run)

Description

An implementation of k-nearest-neighbor search using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k nearest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use.

Usage

knn(
  algorithm = NA,
  epsilon = NA,
  input_model = NA,
  k = NA,
  leaf_size = NA,
  query = NA,
  random_basis = FALSE,
  reference = NA,
  rho = NA,
  seed = NA,
  tau = NA,
  tree_type = NA,
  true_distances = NA,
  true_neighbors = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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 may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following command will calculate the 5 nearest neighbors
# of each point in "input" and store the distances in "distances" and the
# neighbors in "neighbors": 

## Not run: 
output <- knn(k=5, reference=input)
neighbors <- output$neighbors
distances <- output$distances

## End(Not run)

# The output is organized such that row i and column j in the neighbors
# output matrix corresponds to the index of the point in the reference set
# which is the j'th nearest neighbor from the point in the query set with
# index i.  Row j and column i in the distances output matrix corresponds to
# the distance between those two points.

Description

An implementation of rank-approximate k-nearest-neighbor search (kRANN) using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k nearest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use.

Usage

krann(
  alpha = NA,
  first_leaf_exact = FALSE,
  input_model = NA,
  k = NA,
  leaf_size = NA,
  naive = FALSE,
  query = NA,
  random_basis = FALSE,
  reference = NA,
  sample_at_leaves = FALSE,
  seed = NA,
  single_mode = FALSE,
  single_sample_limit = NA,
  tau = NA,
  tree_type = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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 just a reference set which will be used as both the reference and query set. You must specify the rank approximation (in success probability).

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following will return 5 neighbors from the top 0.1% of the
# data (with probability 0.95) for each point in "input" and store the
# distances in "distances" and the neighbors in "neighbors.csv":

## Not run: 
output <- krann(reference=input, k=5, tau=0.1)
distances <- output$distances
neighbors <- output$neighbors

## End(Not run)

# Note that tau must be set such that the number of points in the
# corresponding percentile of the data is greater than k.  Thus, if we choose
# tau = 0.1 with a dataset of 1000 points and k = 5, then we are attempting
# to choose 5 nearest neighbors out of the closest 1 point -- this is invalid
# and the program will terminate with an error message.
# 
# The output matrices are organized such that row i and column j in the
# neighbors output file corresponds to the index of the point in the
# reference set which is the i'th nearest neighbor from the point in the
# query set with index j.  Row i and column j in the distances output file
# corresponds to the distance between those two points.

Description

An implementation of Least Angle Regression (Stagewise/laSso), also known as LARS. This can train a LARS/LASSO/Elastic Net model and use that model or a pre-trained model to output regression predictions for a test set.

Usage

lars(
  input = NA,
  input_model = NA,
  lambda1 = NA,
  lambda2 = NA,
  no_intercept = FALSE,
  no_normalize = FALSE,
  responses = NA,
  test = NA,
  use_cholesky = FALSE,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

An implementation of LARS: Least Angle Regression (Stagewise/laSso). This is a stage-wise homotopy-based algorithm for L1-regularized linear regression (LASSO) and L1+L2-regularized linear regression (Elastic Net).

This program is able to train a LARS/LASSO/Elastic Net model or load a model from file, output regression predictions for a test set, and save the trained model to a file. The LARS algorithm is described in more detail below:

Let X be a matrix where each row is a point and each column is a dimension, and let y be a vector of targets.

The Elastic Net problem is to solve

min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 + 0.5 lambda_2 ||beta||_2^2

If lambda1 > 0 and lambda2 = 0, the problem is the LASSO. If lambda1 > 0 and lambda2 > 0, the problem is the Elastic Net. If lambda1 = 0 and lambda2 > 0, the problem is ridge regression. If lambda1 = 0 and lambda2 = 0, the problem is unregularized linear regression.

For efficiency reasons, it is not recommended to use this algorithm with "lambda1" = 0. In that case, use the 'linear_regression' program, which implements both unregularized linear regression and ridge regression.

To train a LARS/LASSO/Elastic Net model, the "input" and "responses" parameters must be given. The "lambda1", "lambda2", and "use_cholesky" parameters control the training options. A trained model can be saved with the "output_model". If no training is desired at all, a model can be passed via the "input_model" parameter.

The program can also provide predictions for test data using either the trained model or the given input model. Test points can be specified with the "test" parameter. Predicted responses to the test points can be saved with the "output_predictions" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following command trains a model on the data "data" and
# responses "responses" with lambda1 set to 0.4 and lambda2 set to 0 (so,
# LASSO is being solved), and then the model is saved to "lasso_model":

## Not run: 
output <- lars(input=data, responses=responses, lambda1=0.4, lambda2=0)
lasso_model <- output$output_model

## End(Not run)

# The following command uses the "lasso_model" to provide predicted responses
# for the data "test" and save those responses to "test_predictions": 

## Not run: 
output <- lars(input_model=lasso_model, test=test)
test_predictions <- output$output_predictions

## End(Not run)

Description

An implementation of simple linear regression and ridge regression using ordinary least squares. Given a dataset and responses, a model can be trained and saved for later use, or a pre-trained model can be used to output regression predictions for a test set.

Usage

linear_regression(
  input_model = NA,
  lambda = NA,
  test = NA,
  training = NA,
  training_responses = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

An implementation of simple linear regression and simple ridge regression using ordinary least squares. This solves the problem

y = X * b + e

where X (specified by "training") and y (specified either as the last column of the input matrix "training" or via the "training_responses" parameter) are known and b is the desired variable. If the covariance matrix (X'X) is not invertible, or if the solution is overdetermined, then specify a Tikhonov regularization constant (with "lambda") greater than 0, which will regularize the covariance matrix to make it invertible. The calculated b may be saved with the "output_predictions" output parameter.

Optionally, the calculated value of b is used to predict the responses for another matrix X' (specified by the "test" parameter):

y' = X' * b

and the predicted responses y' may be saved with the "output_predictions" output parameter. This type of regression is related to least-angle regression, which mlpack implements as the 'lars' program.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to run a linear regression on the dataset "X" with responses
# "y", saving the trained model to "lr_model", the following command could be
# used:

## Not run: 
output <- linear_regression(training=X, training_responses=y)
lr_model <- output$output_model

## End(Not run)

# Then, to use "lr_model" to predict responses for a test set "X_test",
# saving the predictions to "X_test_responses", the following command could
# be used:

## Not run: 
output <- linear_regression(input_model=lr_model, test=X_test)
X_test_responses <- output$output_predictions

## End(Not run)

Description

An implementation of linear SVM for multiclass classification. Given labeled data, a model can be trained and saved for future use; or, a pre-trained model can be used to classify new points.

Usage

linear_svm(
  delta = NA,
  epochs = NA,
  input_model = NA,
  labels = NA,
  lambda = NA,
  max_iterations = NA,
  no_intercept = FALSE,
  num_classes = NA,
  optimizer = NA,
  seed = NA,
  shuffle = FALSE,
  step_size = NA,
  test = NA,
  test_labels = NA,
  tolerance = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

An implementation of linear SVMs that uses either L-BFGS or parallel SGD (stochastic gradient descent) to train the model.

This program allows loading a linear SVM model (via the "input_model" parameter) or training a linear SVM model given training data (specified with the "training" parameter), or both those things at once. In addition, this program allows classification on a test dataset (specified with the "test" parameter) and the classification results may be saved with the "predictions" output parameter. The trained linear SVM model may be saved using the "output_model" output parameter.

The training data, if specified, may have class labels as its last dimension. Alternately, the "labels" parameter may be used to specify a separate vector of labels.

When a model is being trained, there are many options. L2 regularization (to prevent overfitting) can be specified with the "lambda" option, and the number of classes can be manually specified with the "num_classes"and if an intercept term is not desired in the model, the "no_intercept" parameter can be specified.Margin of difference between correct class and other classes can be specified with the "delta" option.The optimizer used to train the model can be specified with the "optimizer" parameter. Available options are 'psgd' (parallel stochastic gradient descent) and 'lbfgs' (the L-BFGS optimizer). There are also various parameters for the optimizer; the "max_iterations" parameter specifies the maximum number of allowed iterations, and the "tolerance" parameter specifies the tolerance for convergence. For the parallel SGD optimizer, the "step_size" parameter controls the step size taken at each iteration by the optimizer and the maximum number of epochs (specified with "epochs"). If the objective function for your data is oscillating between Inf and 0, the step size is probably too large. There are more parameters for the optimizers, but the C++ interface must be used to access these.

Optionally, the model can be used to predict the labels for another matrix of data points, if "test" is specified. The "test" parameter can be specified without the "training" parameter, so long as an existing linear SVM model is given with the "input_model" parameter. The output predictions from the linear SVM model may be saved with the "predictions" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# As an example, to train a LinaerSVM on the data '"data"' with labels
# '"labels"' with L2 regularization of 0.1, saving the model to
# '"lsvm_model"', the following command may be used:

## Not run: 
output <- linear_svm(training=data, labels=labels, lambda=0.1, delta=1,
  num_classes=0)
lsvm_model <- output$output_model

## End(Not run)

# Then, to use that model to predict classes for the dataset '"test"',
# storing the output predictions in '"predictions"', the following command
# may be used: 

## Not run: 
output <- linear_svm(input_model=lsvm_model, test=test)
predictions <- output$predictions

## End(Not run)

Description

An implementation of Large Margin Nearest Neighbors (LMNN), a distance learning technique. Given a labeled dataset, this learns a transformation of the data that improves k-nearest-neighbor performance; this can be useful as a preprocessing step.

Usage

lmnn(
  input,
  batch_size = NA,
  center = FALSE,
  distance = NA,
  k = NA,
  labels = NA,
  linear_scan = FALSE,
  max_iterations = NA,
  normalize = FALSE,
  optimizer = NA,
  passes = NA,
  print_accuracy = FALSE,
  rank = NA,
  regularization = NA,
  seed = NA,
  step_size = NA,
  tolerance = NA,
  update_interval = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program implements Large Margin Nearest Neighbors, a distance learning technique. The method seeks to improve k-nearest-neighbor classification on a dataset. The method employes the strategy of reducing distance between similar labeled data points (a.k.a target neighbors) and increasing distance between differently labeled points (a.k.a impostors) using standard optimization techniques over the gradient of the distance between data points.

To work, this algorithm needs labeled data. It can be given as the last row of the input dataset (specified with "input"), or alternatively as a separate matrix (specified with "labels"). Additionally, a starting point for optimization (specified with "distance"can be given, having (r x d) dimensionality. Here r should satisfy 1 <= r <= d, Consequently a Low-Rank matrix will be optimized. Alternatively, Low-Rank distance can be learned by specifying the "rank"parameter (A Low-Rank matrix with uniformly distributed values will be used as initial learning point).

The program also requires number of targets neighbors to work with ( specified with "k"), A regularization parameter can also be passed, It acts as a trade of between the pulling and pushing terms (specified with "regularization"), In addition, this implementation of LMNN includes a parameter to decide the interval after which impostors must be re-calculated (specified with "update_interval").

Output can either be the learned distance matrix (specified with "output"), or the transformed dataset (specified with "transformed_data"), or both. Additionally mean-centered dataset (specified with "centered_data") can be accessed given mean-centering (specified with "center") is performed on the dataset. Accuracy on initial dataset and final transformed dataset can be printed by specifying the "print_accuracy"parameter.

This implementation of LMNN uses AdaGrad, BigBatch_SGD, stochastic gradient descent, mini-batch stochastic gradient descent, or the L_BFGS optimizer.

AdaGrad, specified by the value 'adagrad' for the parameter "optimizer", uses maximum of past squared gradients. It primarily on six parameters: the step size (specified with "step_size"), the batch size (specified with "batch_size"), the maximum number of passes (specified with "passes"). Inaddition, a normalized starting point can be used by specifying the "normalize" parameter.

BigBatch_SGD, specified by the value 'bbsgd' for the parameter "optimizer", depends primarily on four parameters: the step size (specified with "step_size"), the batch size (specified with "batch_size"), the maximum number of passes (specified with "passes"). In addition, a normalized starting point can be used by specifying the "normalize" parameter.

Stochastic gradient descent, specified by the value 'sgd' for the parameter "optimizer", depends primarily on three parameters: the step size (specified with "step_size"), the batch size (specified with "batch_size"), and the maximum number of passes (specified with "passes"). In addition, a normalized starting point can be used by specifying the "normalize" parameter. Furthermore, mean-centering can be performed on the dataset by specifying the "center"parameter.

The L-BFGS optimizer, specified by the value 'lbfgs' for the parameter "optimizer", uses a back-tracking line search algorithm to minimize a function. The following parameters are used by L-BFGS: "max_iterations", "tolerance"(the optimization is terminated when the gradient norm is below this value). For more details on the L-BFGS optimizer, consult either the mlpack L-BFGS documentation (in lbfgs.hpp) or the vast set of published literature on L-BFGS. In addition, a normalized starting point can be used by specifying the "normalize" parameter.

By default, the AMSGrad optimizer is used.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# Example - Let's say we want to learn distance on iris dataset with number
# of targets as 3 using BigBatch_SGD optimizer. A simple call for the same
# will look like: 

## Not run: 
output <- lmnn(input=iris, labels=iris_labels, k=3, optimizer="bbsgd")
output <- output$output

## End(Not run)

# Another program call making use of update interval & regularization
# parameter with dataset having labels as last column can be made as: 

## Not run: 
output <- lmnn(input=letter_recognition, k=5, update_interval=10,
  regularization=0.4)
output <- output$output

## End(Not run)

Description

An implementation of Local Coordinate Coding (LCC), a data transformation technique. Given input data, this transforms each point to be expressed as a linear combination of a few points in the dataset; once an LCC model is trained, it can be used to transform points later also.

Usage

local_coordinate_coding(
  atoms = NA,
  initial_dictionary = NA,
  input_model = NA,
  lambda = NA,
  max_iterations = NA,
  normalize = FALSE,
  seed = NA,
  test = NA,
  tolerance = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

An implementation of Local Coordinate Coding (LCC), which codes data that approximately lives on a manifold using a variation of l1-norm regularized sparse coding. Given a dense data matrix X with n points and d dimensions, LCC seeks to find a dense dictionary matrix D with k atoms in d dimensions, and a coding matrix Z with n points in k dimensions. Because of the regularization method used, the atoms in D should lie close to the manifold on which the data points lie.

The original data matrix X can then be reconstructed as D * Z. Therefore, this program finds a representation of each point in X as a sparse linear combination of atoms in the dictionary D.

The coding is found with an algorithm which alternates between a dictionary step, which updates the dictionary D, and a coding step, which updates the coding matrix Z.

To run this program, the input matrix X must be specified (with -i), along with the number of atoms in the dictionary (-k). An initial dictionary may also be specified with the "initial_dictionary" parameter. The l1-norm regularization parameter is specified with the "lambda" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to run LCC on the dataset "data" using 200 atoms and an
# l1-regularization parameter of 0.1, saving the dictionary "dictionary" and
# the codes into "codes", use

## Not run: 
output <- local_coordinate_coding(training=data, atoms=200, lambda=0.1)
dict <- output$dictionary
codes <- output$codes

## End(Not run)

# The maximum number of iterations may be specified with the "max_iterations"
# parameter. Optionally, the input data matrix X can be normalized before
# coding with the "normalize" parameter.
# 
# An LCC model may be saved using the "output_model" output parameter.  Then,
# to encode new points from the dataset "points" with the previously saved
# model "lcc_model", saving the new codes to "new_codes", the following
# command can be used:

## Not run: 
output <- local_coordinate_coding(input_model=lcc_model, test=points)
new_codes <- output$codes

## End(Not run)

Description

An implementation of L2-regularized logistic regression for two-class classification. Given labeled data, a model can be trained and saved for future use; or, a pre-trained model can be used to classify new points.

Usage

logistic_regression(
  batch_size = NA,
  decision_boundary = NA,
  input_model = NA,
  labels = NA,
  lambda = NA,
  max_iterations = NA,
  optimizer = NA,
  print_training_accuracy = FALSE,
  step_size = NA,
  test = NA,
  tolerance = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

An implementation of L2-regularized logistic regression using either the L-BFGS optimizer or SGD (stochastic gradient descent). This solves the regression problem

y = (1 / 1 + e^-(X * b)).

In this setting, y corresponds to class labels and X corresponds to data.

This program allows loading a logistic regression model (via the "input_model" parameter) or training a logistic regression model given training data (specified with the "training" parameter), or both those things at once. In addition, this program allows classification on a test dataset (specified with the "test" parameter) and the classification results may be saved with the "predictions" output parameter. The trained logistic regression model may be saved using the "output_model" output parameter.

The training data, if specified, may have class labels as its last dimension. Alternately, the "labels" parameter may be used to specify a separate matrix of labels.

When a model is being trained, there are many options. L2 regularization (to prevent overfitting) can be specified with the "lambda" option, and the optimizer used to train the model can be specified with the "optimizer" parameter. Available options are 'sgd' (stochastic gradient descent) and 'lbfgs' (the L-BFGS optimizer). There are also various parameters for the optimizer; the "max_iterations" parameter specifies the maximum number of allowed iterations, and the "tolerance" parameter specifies the tolerance for convergence. For the SGD optimizer, the "step_size" parameter controls the step size taken at each iteration by the optimizer. The batch size for SGD is controlled with the "batch_size" parameter. If the objective function for your data is oscillating between Inf and 0, the step size is probably too large. There are more parameters for the optimizers, but the C++ interface must be used to access these.

For SGD, an iteration refers to a single point. So to take a single pass over the dataset with SGD, "max_iterations" should be set to the number of points in the dataset.

Optionally, the model can be used to predict the responses for another matrix of data points, if "test" is specified. The "test" parameter can be specified without the "training" parameter, so long as an existing logistic regression model is given with the "input_model" parameter. The output predictions from the logistic regression model may be saved with the "predictions" parameter.

This implementation of logistic regression does not support the general multi-class case but instead only the two-class case. Any labels must be either 0 or 1. For more classes, see the softmax regression implementation.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# As an example, to train a logistic regression model on the data '"data"'
# with labels '"labels"' with L2 regularization of 0.1, saving the model to
# '"lr_model"', the following command may be used:

## Not run: 
output <- logistic_regression(training=data, labels=labels, lambda=0.1,
  print_training_accuracy=TRUE)
lr_model <- output$output_model

## End(Not run)

# Then, to use that model to predict classes for the dataset '"test"',
# storing the output predictions in '"predictions"', the following command
# may be used: 

## Not run: 
output <- logistic_regression(input_model=lr_model, test=test)
predictions <- output$predictions

## End(Not run)

Description

An implementation of approximate k-nearest-neighbor search with locality-sensitive hashing (LSH). Given a set of reference points and a set of query points, this will compute the k approximate nearest neighbors of each query point in the reference set; models can be saved for future use.

Usage

lsh(
  bucket_size = NA,
  hash_width = NA,
  input_model = NA,
  k = NA,
  num_probes = NA,
  projections = NA,
  query = NA,
  reference = NA,
  second_hash_size = NA,
  seed = NA,
  tables = NA,
  true_neighbors = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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 points and query points, or just a reference set which will be used as both the reference and query set.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, the following will return 5 neighbors from the data for each
# point in "input" and store the distances in "distances" and the neighbors
# in "neighbors":

## Not run: 
output <- lsh(k=5, reference=input)
distances <- output$distances
neighbors <- output$neighbors

## End(Not run)

# The output is organized such that row i and column j in the neighbors
# output corresponds to the index of the point in the reference set which is
# the j'th nearest neighbor from the point in the query set with index i. 
# Row j and column i in the distances output file corresponds to the distance
# between those two points.
# 
# Because this is approximate-nearest-neighbors search, results may be
# different from run to run.  Thus, the "seed" parameter can be specified to
# set the random seed.
# 
# This program also has many other parameters to control its functionality;
# see the parameter-specific documentation for more information.

Description

A fast implementation of mean-shift clustering using dual-tree range search. Given a dataset, this uses the mean shift algorithm to produce and return a clustering of the data.

Usage

mean_shift(
  input,
  force_convergence = FALSE,
  in_place = FALSE,
  labels_only = FALSE,
  max_iterations = NA,
  radius = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs mean shift clustering on the given dataset, storing the learned cluster assignments either as a column of labels in the input dataset or separately.

The input dataset should be specified with the "input" parameter, and the radius used for search can be specified with the "radius" parameter. The maximum number of iterations before algorithm termination is controlled with the "max_iterations" parameter.

The output labels may be saved with the "output" output parameter and the centroids of each cluster may be saved with the "centroid" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to run mean shift clustering on the dataset "data" and store
# the centroids to "centroids", the following command may be used: 

## Not run: 
output <- mean_shift(input=data)
centroids <- output$centroid

## End(Not run)

Description

mlpack is a fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack provides these algorithms as simple command-line programs, C++ classes and bindings for : Python, Julia, Go and R which can then be integrated into larger-scale machine learning solutions.

Author(s)

Maintainer: Ryan Curtin [email protected] [contributor, copyright holder]

Authors:

• Yashwant Singh Parihar [email protected] [contributor, copyright holder]

• Dirk Eddelbuettel [email protected] [contributor, copyright holder]

• James Balamuta [email protected] [contributor, copyright holder]

Other contributors:

• Bill March [email protected] [contributor, copyright holder]

• Dongryeol Lee [email protected] [contributor, copyright holder]

• Nishant Mehta [email protected] [contributor, copyright holder]

• Parikshit Ram [email protected] [contributor, copyright holder]

• James Cline [email protected] [contributor, copyright holder]

• Sterling Peet [email protected] [contributor, copyright holder]

• Matthew Amidon [email protected] [contributor, copyright holder]

• Neil Slagle [email protected] [contributor, copyright holder]

• Ajinkya Kale [email protected] [contributor, copyright holder]

• Vlad Grantcharov [email protected] [contributor, copyright holder]

• Noah Kauffman [email protected] [contributor, copyright holder]

• Rajendran Mohan [email protected] [contributor, copyright holder]

• Trironk Kiatkungwanglai [email protected] [contributor, copyright holder]

• Patrick Mason [email protected] [contributor, copyright holder]

• Marcus Edel [email protected] [contributor, copyright holder]

• Mudit Raj Gupta [email protected] [contributor, copyright holder]

• Sumedh Ghaisas [email protected] [contributor, copyright holder]

• Michael Fox [email protected] [contributor, copyright holder]

• Siddharth Agrawal [email protected] [contributor, copyright holder]

• Saheb Motiani [email protected] [contributor, copyright holder]

• Yash Vadalia [email protected] [contributor, copyright holder]

• Abhishek Laddha [email protected] [contributor, copyright holder]

• Vahab Akbarzadeh [email protected] [contributor, copyright holder]

• Andrew Wells [email protected] [contributor, copyright holder]

• Zhihao Lou [email protected] [contributor, copyright holder]

• Udit Saxena [email protected] [contributor, copyright holder]

• Stephen Tu [email protected] [contributor, copyright holder]

• Jaskaran Singh [email protected] [contributor, copyright holder]

• Hritik Jain [email protected] [contributor, copyright holder]

• Vladimir Glazachev [email protected] [contributor, copyright holder]

• QiaoAn Chen [email protected] [contributor, copyright holder]

• Janzen Brewer [email protected] [contributor, copyright holder]

• Trung Dinh [email protected] [contributor, copyright holder]

• Tham Ngap Wei [email protected] [contributor, copyright holder]

• Grzegorz Krajewski [email protected] [contributor, copyright holder]

• Joseph Mariadassou [email protected] [contributor, copyright holder]

• Pavel Zhigulin [email protected] [contributor, copyright holder]

• Andy Fang [email protected] [contributor, copyright holder]

• Barak Pearlmutter [email protected] [contributor, copyright holder]

• Ivari Horm [email protected] [contributor, copyright holder]

• Dhawal Arora [email protected] [contributor, copyright holder]

• Alexander Leinoff [email protected] [contributor, copyright holder]

• Palash Ahuja [email protected] [contributor, copyright holder]

• Yannis Mentekidis [email protected] [contributor, copyright holder]

• Ranjan Mondal [email protected] [contributor, copyright holder]

• Mikhail Lozhnikov [email protected] [contributor, copyright holder]

• Marcos Pividori [email protected] [contributor, copyright holder]

• Keon Kim [email protected] [contributor, copyright holder]

• Nilay Jain [email protected] [contributor, copyright holder]

• Peter Lehner [email protected] [contributor, copyright holder]

• Anuraj Kanodia [email protected] [contributor, copyright holder]

• Ivan Georgiev [email protected] [contributor, copyright holder]

• Shikhar Bhardwaj [email protected] [contributor, copyright holder]

• Yashu Seth [email protected] [contributor, copyright holder]

• Mike Izbicki [email protected] [contributor, copyright holder]

• Sudhanshu Ranjan [email protected] [contributor, copyright holder]

• Piyush Jaiswal [email protected] [contributor, copyright holder]

• Dinesh Raj [email protected] [contributor, copyright holder]

• Vivek Pal [email protected] [contributor, copyright holder]

• Prasanna Patil [email protected] [contributor, copyright holder]

• Lakshya Agrawal [email protected] [contributor, copyright holder]

• Praveen Ch [email protected] [contributor, copyright holder]

• Kirill Mishchenko [email protected] [contributor, copyright holder]

• Abhinav Moudgil [email protected] [contributor, copyright holder]

• Thyrix Yang [email protected] [contributor, copyright holder]

• Sagar B Hathwar [email protected] [contributor, copyright holder]

• Nishanth Hegde [email protected] [contributor, copyright holder]

• Parminder Singh [email protected] [contributor, copyright holder]

• CodeAi [email protected] [contributor, copyright holder]

• Franciszek Stokowacki [email protected] [contributor, copyright holder]

• Samikshya Chand [email protected] [contributor, copyright holder]

• N Rajiv Vaidyanathan [email protected] [contributor, copyright holder]

• Kartik Nighania [email protected] [contributor, copyright holder]

• Eugene Freyman [email protected] [contributor, copyright holder]

• Manish Kumar [email protected] [contributor, copyright holder]

• Haritha Sreedharan Nair [email protected] [contributor, copyright holder]

• Sourabh Varshney [email protected] [contributor, copyright holder]

• Projyal Dev [email protected] [contributor, copyright holder]

• Nikhil Goel [email protected] [contributor, copyright holder]

• Shikhar Jaiswal [email protected] [contributor, copyright holder]

• B Kartheek Reddy [email protected] [contributor, copyright holder]

• Atharva Khandait [email protected] [contributor, copyright holder]

• Wenhao Huang [email protected] [contributor, copyright holder]

• Roberto Hueso [email protected] [contributor, copyright holder]

• Prabhat Sharma [email protected] [contributor, copyright holder]

• Tan Jun An [email protected] [contributor, copyright holder]

• Moksh Jain [email protected] [contributor, copyright holder]

• Manthan-R-Sheth [email protected] [contributor, copyright holder]

• Namrata Mukhija [email protected] [contributor, copyright holder]

• Conrad Sanderson [contributor, copyright holder]

• Thanasis Mattas [email protected] [contributor, copyright holder]

• Shashank Shekhar [email protected] [contributor, copyright holder]

• Yasmine Dumouchel [email protected] [contributor, copyright holder]

• German Lancioni [contributor, copyright holder]

• Arash Abghari [email protected] [contributor, copyright holder]

• Ayush Chamoli [contributor, copyright holder]

• Tommi Laivamaa [email protected] [contributor, copyright holder]

• Kim SangYeon [email protected] [contributor, copyright holder]

• Niteya Shah [email protected] [contributor, copyright holder]

• Toshal Agrawal [email protected] [contributor, copyright holder]

• Dan Timson [contributor, copyright holder]

• Miguel Canteras [email protected] [contributor, copyright holder]

• Bishwa Karki [email protected] [contributor, copyright holder]

• Mehul Kumar Nirala [email protected] [contributor, copyright holder]

• Heet Sankesara [email protected] [contributor, copyright holder]

• Jeffin Sam [email protected] [contributor, copyright holder]

• Vikas S Shetty [email protected] [contributor, copyright holder]

• Khizir Siddiqui [email protected] [contributor, copyright holder]

• Tejasvi Tomar [email protected] [contributor, copyright holder]

• Jai Agarwal [email protected] [contributor, copyright holder]

• Ziyang Jiang [email protected] [contributor, copyright holder]

• Rohit Kartik [email protected] [contributor, copyright holder]

• Aditya Viki [email protected] [contributor, copyright holder]

• Kartik Dutt [email protected] [contributor, copyright holder]

• Suryoday Basak [email protected] [contributor, copyright holder]

• Sriram S K [email protected] [contributor, copyright holder]

• Manoranjan Kumar Bharti ( Nakul Bharti ) [email protected] [contributor, copyright holder]

• Saraansh Tandon [email protected] [contributor, copyright holder]

• Gaurav Singh [email protected] [contributor, copyright holder]

• Lakshya Ojha [email protected] [contributor, copyright holder]

• Bisakh Mondal [email protected] [contributor, copyright holder]

• Benson Muite [email protected] [contributor, copyright holder]

• Sarthak Bhardwaj [email protected] [contributor, copyright holder]

• Aakash Kaushik [email protected] [contributor, copyright holder]

• Anush Kini [email protected] [contributor, copyright holder]

• Nippun Sharma [email protected] [contributor, copyright holder]

• Rishabh Garg [email protected] [contributor, copyright holder]

• Sudhakar Brar [email protected] [contributor, copyright holder]

• Alex Nguyen [email protected] [contributor, copyright holder]

• Gaurav Ghati [email protected] [contributor, copyright holder]

• Anmolpreet Singh [email protected] [contributor, copyright holder]

• Anjishnu Mukherjee [email protected] [contributor, copyright holder]

• Omar Shrit [email protected] [contributor, copyright holder]

• Tru Hoang [email protected] [contributor, copyright holder]

• Mark Fischinger [email protected] [contributor, copyright holder]

• Muhammad Fawwaz Mayda [email protected] [contributor, copyright holder]

• Roshan Nrusing Swain [email protected] [contributor, copyright holder]

• Suvarsha Chennareddy [email protected] [contributor, copyright holder]

• Shubham Agrawal [email protected] [contributor, copyright holder]

• James Joseph Balamuta [email protected] [contributor, copyright holder]

• Sri Madhan M [email protected] [contributor, copyright holder]

• Zhuojin Liu [email protected] [contributor, copyright holder]

• Richèl Bilderbeek [email protected] [contributor, copyright holder]

• Chetan Pandey [email protected] [contributor, copyright holder]

• Nikolay Apanasov [email protected] [contributor, copyright holder]

See Also

Useful links:

• https://www.mlpack.org/doc/mlpack-git/r_documentation.html

• https://github.com/mlpack/mlpack

• Report bugs at https://github.com/mlpack/mlpack/issues

Description

An implementation of the Naive Bayes Classifier, used for classification. Given labeled data, an NBC model can be trained and saved, or, a pre-trained model can be used for classification.

Usage

nbc(
  incremental_variance = FALSE,
  input_model = NA,
  labels = NA,
  test = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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 model to classify the points in a given test set.

The training set is specified with the "training" parameter. Labels may be either the last row of the training set, or alternately the "labels" parameter may be specified to pass a separate matrix of labels.

If training is not desired, a pre-existing model may be loaded with the "input_model" parameter.

The "incremental_variance" parameter can be used to force the training to use an incremental algorithm for calculating variance. This is slower, but can help avoid loss of precision in some cases.

If classifying a test set is desired, the test set may be specified with the "test" parameter, and the classifications may be saved with the "predictions"predictions parameter. If saving the trained model is desired, this may be done with the "output_model" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to train a Naive Bayes classifier on the dataset "data" with
# labels "labels" and save the model to "nbc_model", the following command
# may be used:

## Not run: 
output <- nbc(training=data, labels=labels)
nbc_model <- output$output_model

## End(Not run)

# Then, to use "nbc_model" to predict the classes of the dataset "test_set"
# and save the predicted classes to "predictions", the following command may
# be used:

## Not run: 
output <- nbc(input_model=nbc_model, test=test_set)
predictions <- output$predictions

## End(Not run)

Description

An implementation of neighborhood components analysis, a distance learning technique that can be used for preprocessing. Given a labeled dataset, this uses NCA, which seeks to improve the k-nearest-neighbor classification, and returns the learned distance metric.

Usage

nca(
  input,
  armijo_constant = NA,
  batch_size = NA,
  labels = NA,
  linear_scan = FALSE,
  max_iterations = NA,
  max_line_search_trials = NA,
  max_step = NA,
  min_step = NA,
  normalize = FALSE,
  num_basis = NA,
  optimizer = NA,
  seed = NA,
  step_size = NA,
  tolerance = NA,
  verbose = getOption("mlpack.verbose", FALSE),
  wolfe = NA
)

Arguments

Details

This program implements Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique. The method seeks to improve k-nearest-neighbor classification on a dataset by scaling the dimensions. The method is nonparametric, and does not require a value of k. It works by using stochastic ("soft") neighbor assignments and using optimization techniques over the gradient of the accuracy of the neighbor assignments.

To work, this algorithm needs labeled data. It can be given as the last row of the input dataset (specified with "input"), or alternatively as a separate matrix (specified with "labels").

This implementation of NCA uses stochastic gradient descent, mini-batch stochastic gradient descent, or the L_BFGS optimizer. These optimizers do not guarantee global convergence for a nonconvex objective function (NCA's objective function is nonconvex), so the final results could depend on the random seed or other optimizer parameters.

Stochastic gradient descent, specified by the value 'sgd' for the parameter "optimizer", depends primarily on three parameters: the step size (specified with "step_size"), the batch size (specified with "batch_size"), and the maximum number of iterations (specified with "max_iterations"). In addition, a normalized starting point can be used by specifying the "normalize" parameter, which is necessary if many warnings of the form 'Denominator of p_i is 0!' are given. Tuning the step size can be a tedious affair. In general, the step size is too large if the objective is not mostly uniformly decreasing, or if zero-valued denominator warnings are being issued. The step size is too small if the objective is changing very slowly. Setting the termination condition can be done easily once a good step size parameter is found; either increase the maximum iterations to a large number and allow SGD to find a minimum, or set the maximum iterations to 0 (allowing infinite iterations) and set the tolerance (specified by "tolerance") to define the maximum allowed difference between objectives for SGD to terminate. Be careful—setting the tolerance instead of the maximum iterations can take a very long time and may actually never converge due to the properties of the SGD optimizer. Note that a single iteration of SGD refers to a single point, so to take a single pass over the dataset, set the value of the "max_iterations" parameter equal to the number of points in the dataset.

The L-BFGS optimizer, specified by the value 'lbfgs' for the parameter "optimizer", uses a back-tracking line search algorithm to minimize a function. The following parameters are used by L-BFGS: "num_basis" (specifies the number of memory points used by L-BFGS), "max_iterations", "armijo_constant", "wolfe", "tolerance" (the optimization is terminated when the gradient norm is below this value), "max_line_search_trials", "min_step", and "max_step" (which both refer to the line search routine). For more details on the L-BFGS optimizer, consult either the mlpack L-BFGS documentation (in lbfgs.hpp) or the vast set of published literature on L-BFGS.

By default, the SGD optimizer is used.

Value

A list with several components:

Author(s)

mlpack developers

Description

An implementation of non-negative matrix factorization. This can be used to decompose an input dataset into two low-rank non-negative components.

Usage

nmf(
  input,
  rank,
  initial_h = NA,
  initial_w = NA,
  max_iterations = NA,
  min_residue = NA,
  seed = NA,
  update_rules = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs non-negative matrix factorization on the given dataset, storing the resulting decomposed matrices in the specified files. For an input dataset V, NMF decomposes V into two matrices W and H such that

V = W * H

where all elements in W and H are non-negative. If V is of size (n x m), then W will be of size (n x r) and H will be of size (r x m), where r is the rank of the factorization (specified by the "rank" parameter).

Optionally, the desired update rules for each NMF iteration can be chosen from the following list:

- multdist: multiplicative distance-based update rules (Lee and Seung 1999) - multdiv: multiplicative divergence-based update rules (Lee and Seung 1999) - als: alternating least squares update rules (Paatero and Tapper 1994)

The maximum number of iterations is specified with "max_iterations", and the minimum residue required for algorithm termination is specified with the "min_residue" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to run NMF on the input matrix "V" using the 'multdist' update
# rules with a rank-10 decomposition and storing the decomposed matrices into
# "W" and "H", the following command could be used: 

## Not run: 
output <- nmf(input=V, rank=10, update_rules="multdist")
W <- output$w
H <- output$h

## End(Not run)

Description

An implementation of several strategies for principal components analysis (PCA), a common preprocessing step. Given a dataset and a desired new dimensionality, this can reduce the dimensionality of the data using the linear transformation determined by PCA.

Usage

pca(
  input,
  decomposition_method = NA,
  new_dimensionality = NA,
  scale = FALSE,
  var_to_retain = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs principal components analysis on the given dataset using the exact, randomized, randomized block Krylov, or QUIC SVD method. It will transform the data onto its principal components, optionally performing dimensionality reduction by ignoring the principal components with the smallest eigenvalues.

Use the "input" parameter to specify the dataset to perform PCA on. A desired new dimensionality can be specified with the "new_dimensionality" parameter, or the desired variance to retain can be specified with the "var_to_retain" parameter. If desired, the dataset can be scaled before running PCA with the "scale" parameter.

Multiple different decomposition techniques can be used. The method to use can be specified with the "decomposition_method" parameter, and it may take the values 'exact', 'randomized', or 'quic'.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to reduce the dimensionality of the matrix "data" to 5
# dimensions using randomized SVD for the decomposition, storing the output
# matrix to "data_mod", the following command can be used:

## Not run: 
output <- pca(input=data, new_dimensionality=5,
  decomposition_method="randomized")
data_mod <- output$output

## End(Not run)

Description

An implementation of a perceptron—a single level neural network–=for classification. Given labeled data, a perceptron can be trained and saved for future use; or, a pre-trained perceptron can be used for classification on new points.

Usage

perceptron(
  input_model = NA,
  labels = NA,
  max_iterations = NA,
  test = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program implements a perceptron, which is a single level neural network. The perceptron makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The perceptron learning rule is able to converge, given enough iterations (specified using the "max_iterations" parameter), if the data supplied is linearly separable. The perceptron is parameterized by a matrix of weight vectors that denote the numerical weights of the neural network.

This program allows loading a perceptron from a model (via the "input_model" parameter) or training a perceptron given training data (via the "training" parameter), or both those things at once. In addition, this program allows classification on a test dataset (via the "test" parameter) and the classification results on the test set may be saved with the "predictions" output parameter. The perceptron model may be saved with the "output_model" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# The training data given with the "training" option may have class labels as
# its last dimension (so, if the training data is in CSV format, labels
# should be the last column).  Alternately, the "labels" parameter may be
# used to specify a separate matrix of labels.
# 
# All these options make it easy to train a perceptron, and then re-use that
# perceptron for later classification.  The invocation below trains a
# perceptron on "training_data" with labels "training_labels", and saves the
# model to "perceptron_model".

## Not run: 
output <- perceptron(training=training_data, labels=training_labels)
perceptron_model <- output$output_model

## End(Not run)

# Then, this model can be re-used for classification on the test data
# "test_data".  The example below does precisely that, saving the predicted
# classes to "predictions".

## Not run: 
output <- perceptron(input_model=perceptron_model, test=test_data)
predictions <- output$predictions

## End(Not run)

# Note that all of the options may be specified at once: predictions may be
# calculated right after training a model, and model training can occur even
# if an existing perceptron model is passed with the "input_model" parameter.
#  However, note that the number of classes and the dimensionality of all
# data must match.  So you cannot pass a perceptron model trained on 2
# classes and then re-train with a 4-class dataset.  Similarly, attempting
# classification on a 3-dimensional dataset with a perceptron that has been
# trained on 8 dimensions will cause an error.

Description

A utility to binarize a dataset. Given a dataset, this utility converts each value in the desired dimension(s) to 0 or 1; this can be a useful preprocessing step.

Usage

preprocess_binarize(
  input,
  dimension = NA,
  threshold = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This utility takes a dataset and binarizes the variables into either 0 or 1 given threshold. User can apply binarization on a dimension or the whole dataset. The dimension to apply binarization to can be specified using the "dimension" parameter; if left unspecified, every dimension will be binarized. The threshold for binarization can also be specified with the "threshold" parameter; the default threshold is 0.0.

The binarized matrix may be saved with the "output" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, if we want to set all variables greater than 5 in the dataset
# "X" to 1 and variables less than or equal to 5.0 to 0, and save the result
# to "Y", we could run

## Not run: 
output <- preprocess_binarize(input=X, threshold=5)
Y <- output$output

## End(Not run)

# But if we want to apply this to only the first (0th) dimension of "X",  we
# could instead run

## Not run: 
output <- preprocess_binarize(input=X, threshold=5, dimension=0)
Y <- output$output

## End(Not run)

Description

A utility for printing descriptive statistics about a dataset. This prints a number of details about a dataset in a tabular format.

Usage

preprocess_describe(
  input,
  dimension = NA,
  population = FALSE,
  precision = NA,
  row_major = FALSE,
  verbose = getOption("mlpack.verbose", FALSE),
  width = NA
)

Arguments

Details

This utility takes a dataset and prints out the descriptive statistics of the data. Descriptive statistics is the discipline of quantitatively describing the main features of a collection of information, or the quantitative description itself. The program does not modify the original file, but instead prints out the statistics to the console. The printed result will look like a table.

Optionally, width and precision of the output can be adjusted by a user using the "width" and "precision" parameters. A user can also select a specific dimension to analyze if there are too many dimensions. The "population" parameter can be specified when the dataset should be considered as a population. Otherwise, the dataset will be considered as a sample.

Author(s)

mlpack developers

Examples

# So, a simple example where we want to print out statistical facts about the
# dataset "X" using the default settings, we could run 

## Not run: 
preprocess_describe(input=X, verbose=TRUE)

## End(Not run)

# If we want to customize the width to 10 and precision to 5 and consider the
# dataset as a population, we could run

## Not run: 
preprocess_describe(input=X, width=10, precision=5, verbose=TRUE)

## End(Not run)

Description

A utility to do one-hot encoding on features of dataset.

Usage

preprocess_one_hot_encoding(
  input,
  dimensions = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This utility takes a dataset and a vector of indices and does one-hot encoding of the respective features at those indices. Indices represent the IDs of the dimensions to be one-hot encoded.

If no dimensions are specified with "dimensions", then all categorical-type dimensions will be one-hot encoded. Otherwise, only the dimensions given in "dimensions" will be one-hot encoded.

The output matrix with encoded features may be saved with the "output" parameters.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# So, a simple example where we want to encode 1st and 3rd feature from
# dataset "X" into "X_output" would be

## Not run: 
output <- preprocess_one_hot_encoding(input=X, dimensions=1, dimensions=3)
X_ouput <- output$output

## End(Not run)

Description

A utility to perform feature scaling on datasets using one of sixtechniques. Both scaling and inverse scaling are supported, andscalers can be saved and then applied to other datasets.

Usage

preprocess_scale(
  input,
  epsilon = NA,
  input_model = NA,
  inverse_scaling = FALSE,
  max_value = NA,
  min_value = NA,
  scaler_method = NA,
  seed = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This utility takes a dataset and performs feature scaling using one of the six scaler methods namely: 'max_abs_scaler', 'mean_normalization', 'min_max_scaler' ,'standard_scaler', 'pca_whitening' and 'zca_whitening'. The function takes a matrix as "input" and a scaling method type which you can specify using "scaler_method" parameter; the default is standard scaler, and outputs a matrix with scaled feature.

The output scaled feature matrix may be saved with the "output" output parameters.

The model to scale features can be saved using "output_model" and later can be loaded back using"input_model".

Value

A list with several components:

Author(s)

mlpack developers

Examples

# So, a simple example where we want to scale the dataset "X" into "X_scaled"
# with  standard_scaler as scaler_method, we could run 

## Not run: 
output <- preprocess_scale(input=X, scaler_method="standard_scaler")
X_scaled <- output$output

## End(Not run)

# A simple example where we want to whiten the dataset "X" into "X_whitened"
# with  PCA as whitening_method and use 0.01 as regularization parameter, we
# could run 

## Not run: 
output <- preprocess_scale(input=X, scaler_method="pca_whitening",
  epsilon=0.01)
X_scaled <- output$output

## End(Not run)

# You can also retransform the scaled dataset back using"inverse_scaling". An
# example to rescale : "X_scaled" into "X"using the saved model "input_model"
# is:

## Not run: 
output <- preprocess_scale(input=X_scaled, inverse_scaling=TRUE,
  input_model=saved)
X <- output$output

## End(Not run)

# Another simple example where we want to scale the dataset "X" into
# "X_scaled" with  min_max_scaler as scaler method, where scaling range is 1
# to 3 instead of default 0 to 1. We could run 

## Not run: 
output <- preprocess_scale(input=X, scaler_method="min_max_scaler",
  min_value=1, max_value=3)
X_scaled <- output$output

## End(Not run)

Description

A utility to split data into a training and testing dataset. This can also split labels according to the same split.

Usage

preprocess_split(
  input,
  input_labels = NA,
  no_shuffle = FALSE,
  seed = NA,
  stratify_data = FALSE,
  test_ratio = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This utility takes a dataset and optionally labels and splits them into a training set and a test set. Before the split, the points in the dataset are randomly reordered. The percentage of the dataset to be used as the test set can be specified with the "test_ratio" parameter; the default is 0.2 (20

The output training and test matrices may be saved with the "training" and "test" output parameters.

Optionally, labels can also be split along with the data by specifying the "input_labels" parameter. Splitting labels works the same way as splitting the data. The output training and test labels may be saved with the "training_labels" and "test_labels" output parameters, respectively.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# So, a simple example where we want to split the dataset "X" into "X_train"
# and "X_test" with 60% of the data in the training set and 40% of the
# dataset in the test set, we could run 

## Not run: 
output <- preprocess_split(input=X, test_ratio=0.4)
X_train <- output$training
X_test <- output$test

## End(Not run)

# Also by default the dataset is shuffled and split; you can provide the
# "no_shuffle" option to avoid shuffling the data; an example to avoid
# shuffling of data is:

## Not run: 
output <- preprocess_split(input=X, test_ratio=0.4, no_shuffle=TRUE)
X_train <- output$training
X_test <- output$test

## End(Not run)

# If we had a dataset "X" and associated labels "y", and we wanted to split
# these into "X_train", "y_train", "X_test", and "y_test", with 30% of the
# data in the test set, we could run

## Not run: 
output <- preprocess_split(input=X, input_labels=y, test_ratio=0.3)
X_train <- output$training
y_train <- output$training_labels
X_test <- output$test
y_test <- output$test_labels

## End(Not run)
# To maintain the ratio of each class in the train and test sets,
# the"stratify_data" option can be used.

## Not run: 
output <- preprocess_split(input=X, test_ratio=0.4, stratify_data=TRUE)
X_train <- output$training
X_test <- output$test

## End(Not run)

Description

An implementation of RADICAL, a method for independent component analysis (ICA). Given a dataset, this can decompose the dataset into an unmixing matrix and an independent component matrix; this can be useful for preprocessing.

Usage

radical(
  input,
  angles = NA,
  noise_std_dev = NA,
  objective = FALSE,
  replicates = NA,
  seed = NA,
  sweeps = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

An implementation of RADICAL, a method for independent component analysis (ICA). Assuming that we have an input matrix X, the goal is to find a square unmixing matrix W such that Y = W * X and the dimensions of Y are independent components. If the algorithm is running particularly slowly, try reducing the number of replicates.

The input matrix to perform ICA on should be specified with the "input" parameter. The output matrix Y may be saved with the "output_ic" output parameter, and the output unmixing matrix W may be saved with the "output_unmixing" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to perform ICA on the matrix "X" with 40 replicates, saving
# the independent components to "ic", the following command may be used: 

## Not run: 
output <- radical(input=X, replicates=40)
ic <- output$output_ic

## End(Not run)

Description

An implementation of the standard random forest algorithm by Leo Breiman for classification. Given labeled data, a random forest can be trained and saved for future use; or, a pre-trained random forest can be used for classification.

Usage

random_forest(
  input_model = NA,
  labels = NA,
  maximum_depth = NA,
  minimum_gain_split = NA,
  minimum_leaf_size = NA,
  num_trees = NA,
  print_training_accuracy = FALSE,
  seed = NA,
  subspace_dim = NA,
  test = NA,
  test_labels = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE),
  warm_start = FALSE
)

Arguments

Details

This program is an implementation of the standard random forest classification algorithm by Leo Breiman. A random forest can be trained and saved for later use, or a random forest may be loaded and predictions or class probabilities for points may be generated.

The training set and associated labels are specified with the "training" and "labels" parameters, respectively. The labels should be in the range '[0, num_classes - 1]'. Optionally, if "labels" is not specified, the labels are assumed to be the last dimension of the training dataset.

When a model is trained, the "output_model" output parameter may be used to save the trained model. A model may be loaded for predictions with the "input_model"parameter. The "input_model" parameter may not be specified when the "training" parameter is specified. The "minimum_leaf_size" parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The "num_trees" controls the number of trees in the random forest. The "minimum_gain_split" parameter controls the minimum required gain for a decision tree node to split. Larger values will force higher-confidence splits. The "maximum_depth" parameter specifies the maximum depth of the tree. The "subspace_dim" parameter is used to control the number of random dimensions chosen for an individual node's split. If "print_training_accuracy" is specified, the calculated accuracy on the training set will be printed.

Test data may be specified with the "test" parameter, and if performance measures are desired for that test set, labels for the test points may be specified with the "test_labels" parameter. Predictions for each test point may be saved via the "predictions"output parameter. Class probabilities for each prediction may be saved with the "probabilities" output parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to train a random forest with a minimum leaf size of 20 using
# 10 trees on the dataset contained in "data"with labels "labels", saving the
# output random forest to "rf_model" and printing the training error, one
# could call

## Not run: 
output <- random_forest(training=data, labels=labels, minimum_leaf_size=20,
  num_trees=10, print_training_accuracy=TRUE)
rf_model <- output$output_model

## End(Not run)

# Then, to use that model to classify points in "test_set" and print the test
# error given the labels "test_labels" using that model, while saving the
# predictions for each point to "predictions", one could call 

## Not run: 
output <- random_forest(input_model=rf_model, test=test_set,
  test_labels=test_labels)
predictions <- output$predictions

## End(Not run)

Description

Serialize/Unserialize an mlpack model.

Usage

Serialize(model, filename)

Unserialize(filename)

Arguments

Value

For Unserialize, Output model_ptr.

Description

An implementation of softmax regression for classification, which is a multiclass generalization of logistic regression. Given labeled data, a softmax regression model can be trained and saved for future use, or, a pre-trained softmax regression model can be used for classification of new points.

Usage

softmax_regression(
  input_model = NA,
  labels = NA,
  lambda = NA,
  max_iterations = NA,
  no_intercept = FALSE,
  number_of_classes = NA,
  test = NA,
  test_labels = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

This program performs softmax regression, a generalization of logistic regression to the multiclass case, and has support for L2 regularization. The program is able to train a model, load an existing model, and give predictions (and optionally their accuracy) for test data.

Training a softmax regression model is done by giving a file of training points with the "training" parameter and their corresponding labels with the "labels" parameter. The number of classes can be manually specified with the "number_of_classes" parameter, and the maximum number of iterations of the L-BFGS optimizer can be specified with the "max_iterations" parameter. The L2 regularization constant can be specified with the "lambda" parameter and if an intercept term is not desired in the model, the "no_intercept" parameter can be specified.

The trained model can be saved with the "output_model" output parameter. If training is not desired, but only testing is, a model can be loaded with the "input_model" parameter. At the current time, a loaded model cannot be trained further, so specifying both "input_model" and "training" is not allowed.

The program is also able to evaluate a model on test data. A test dataset can be specified with the "test" parameter. Class predictions can be saved with the "predictions" output parameter. If labels are specified for the test data with the "test_labels" parameter, then the program will print the accuracy of the predictions on the given test set and its corresponding labels.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# For example, to train a softmax regression model on the data "dataset" with
# labels "labels" with a maximum of 1000 iterations for training, saving the
# trained model to "sr_model", the following command can be used: 

## Not run: 
output <- softmax_regression(training=dataset, labels=labels)
sr_model <- output$output_model

## End(Not run)

# Then, to use "sr_model" to classify the test points in "test_points",
# saving the output predictions to "predictions", the following command can
# be used:

## Not run: 
output <- softmax_regression(input_model=sr_model, test=test_points)
predictions <- output$predictions

## End(Not run)

Description

An implementation of Sparse Coding with Dictionary Learning. Given a dataset, this will decompose the dataset into a sparse combination of a few dictionary elements, where the dictionary is learned during computation; a dictionary can be reused for future sparse coding of new points.

Usage

sparse_coding(
  atoms = NA,
  initial_dictionary = NA,
  input_model = NA,
  lambda1 = NA,
  lambda2 = NA,
  max_iterations = NA,
  newton_tolerance = NA,
  normalize = FALSE,
  objective_tolerance = NA,
  seed = NA,
  test = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

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 regularizer on the codes (the Elastic Net). Given a dense data matrix X with d dimensions and n points, sparse coding seeks to find a dense dictionary matrix D with k atoms in d dimensions, and a sparse coding matrix Z with n points in k dimensions.

The original data matrix X can then be reconstructed as Z * D. Therefore, this program finds a representation of each point in X as a sparse linear combination of atoms in the dictionary D.

The sparse coding is found with an algorithm which alternates between a dictionary step, which updates the dictionary D, and a sparse coding step, which updates the sparse coding matrix.

Once a dictionary D is found, the sparse coding model may be used to encode other matrices, and saved for future usage.

To run this program, either an input matrix or an already-saved sparse coding model must be specified. An input matrix may be specified with the "training" option, along with the number of atoms in the dictionary (specified with the "atoms" parameter). It is also possible to specify an initial dictionary for the optimization, with the "initial_dictionary" parameter. An input model may be specified with the "input_model" parameter.

Value

A list with several components:

Author(s)

mlpack developers

Examples

# As an example, to build a sparse coding model on the dataset "data" using
# 200 atoms and an l1-regularization parameter of 0.1, saving the model into
# "model", use 

## Not run: 
output <- sparse_coding(training=data, atoms=200, lambda1=0.1)
model <- output$output_model

## End(Not run)

# Then, this model could be used to encode a new matrix, "otherdata", and
# save the output codes to "codes": 

## Not run: 
output <- sparse_coding(input_model=model, test=otherdata)
codes <- output$codes

## End(Not run)

Description

A simple program to test R binding functionality.

Usage

test_r_binding(
  double_in,
  int_in,
  string_in,
  build_model = FALSE,
  col_in = NA,
  flag1 = FALSE,
  flag2 = FALSE,
  matrix_and_info_in = NA,
  matrix_in = NA,
  model_in = NA,
  row_in = NA,
  str_vector_in = NA,
  tmatrix_in = NA,
  ucol_in = NA,
  umatrix_in = NA,
  urow_in = NA,
  vector_in = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

Details

A simple program to test R binding functionality. You can build mlpack with the BUILD_TESTS option set to off, and this binding will no longer be built.

Value

A list with several components:

Author(s)

mlpack developers