---
name: "mlpack"
section: "3"
platform: "linux"
package: "mlpack-doc"
version: "1.0.10-1"
release: "Debian 8 (Jessie)"
platforms: ["linux"]
url: "https://new.carta.tech/mlpack"
---

# mlpack(3)

Linear algebra utility functions, generally performed on matrices or vectors.

## NAME

mlpack -

Linear algebra utility functions, generally performed on matrices or vectors.

## SYNOPSIS

### Namespaces

 **amf**\
  **bound**\
  **cf**\
 *Collaborative filtering.*   **data**\
 *Functions to load and save matrices.*   **decision\_stump**\
  **det**\
 *Density Estimation Trees.*   **distribution**\
 *Probability distributions.*   **emst**\
 *Euclidean Minimum Spanning Trees.*   **fastmks**\
 *Fast max-kernel search.*   **gmm**\
 *Gaussian Mixture Models.*   **hmm**\
 *Hidden Markov Models.*   **kernel**\
 *Kernel functions.*   **kmeans**\
 *K-Means clustering.*   **kpca**\
  **lcc**\
  **math**\
 *Miscellaneous math routines.*   **metric**\
  **mvu**\
  **naive\_bayes**\
 *The Naive Bayes Classifier.*   **nca**\
 *Neighborhood Components Analysis.*   **neighbor**\
 *Neighbor-search routines.*   **nn**\
  **optimization**\
  **pca**\
  **perceptron**\
  **radical**\
  **range**\
 *Range-search routines.*   **regression**\
 *Regression methods.*   **sparse\_coding**\
  **svd**\
  **tree**\
 *Trees and tree-building procedures.*   **util**\

### Classes

class **CLI**\
 *Parses the command line for parameters and holds user-specified parameters.*  class **Log**\
 *Provides a convenient way to give formatted output.*  struct **ParamData**\
 *Aids in the extensibility of* **CLI** *by focusing potential changes into one structure.*  class **Timer**\
 *The timer class provides a way for MLPACK methods to be timed.*  class **Timers**\

## Detailed Description

Linear algebra utility functions, generally performed on matrices or vectors.

This class is used to update the weightVectors matrix according to the simple update rule as discussed by Rosenblatt:

if a vector x has been incorrectly classified by a weight w, then w = w - x and w'= w'+ x

where w' is the weight vector which correctly classifies x.

## Author

Generated automatically by Doxygen for MLPACK from the source code.