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

# mlpack\_pca\_PCA(3)

This class implements principal components analysis (PCA).

## NAME

mlpack::pca::PCA -

This class implements principal components analysis (**PCA**).

## SYNOPSIS

### Public Member Functions

**PCA** (const bool **scaleData**=false)\
 *Create the* **PCA** *object, specifying if the data should be scaled in each dimension by standard deviation when* **PCA** *is performed.*  void **Apply** (const arma::mat &data, arma::mat &transformedData, arma::vec &eigval, arma::mat &eigvec) const \
 *Apply Principal Component Analysis to the provided data set.*  void **Apply** (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) const \
 *Apply Principal Component Analysis to the provided data set.*  double **Apply** (arma::mat &data, const size\_t newDimension) const \
 *Use* **PCA** *for dimensionality reduction on the given dataset.*  double **Apply** (arma::mat &data, const int newDimension) const \
 *This overload is here to make sure int gets casted right to size\_t.*  double **Apply** (arma::mat &data, const double varRetained) const \
 *Use* **PCA** *for dimensionality reduction on the given dataset.*  bool **ScaleData** () const \
 *Get whether or not this* **PCA** *object will scale (by standard deviation) the data when* **PCA** *is performed.*  bool & **ScaleData** ()\
 *Modify whether or not this* **PCA** *object will scale (by standard deviation) the data when* **PCA** *is performed.*  std::string **ToString** () const \

### Private Attributes

bool **scaleData**\
 *Whether or not the data will be scaled by standard deviation when* **PCA** *is performed.*

## Detailed Description

This class implements principal components analysis (**PCA**).

This is a common, widely-used technique that is often used for either dimensionality reduction or transforming data into a better basis. Further information on **PCA** can be found in almost any statistics or machine learning textbook, and all over the internet.

Definition at line 38 of file pca.hpp.

## Constructor & Destructor Documentation

### mlpack::pca::PCA::PCA (const boolscaleData = `false`)

Create the **PCA** object, specifying if the data should be scaled in each dimension by standard deviation when **PCA** is performed.

**Parameters:**

*scaleData* Whether or not to scale the data.

## Member Function Documentation

### void mlpack::pca::PCA::Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigval, arma::mat &eigvec) const

Apply Principal Component Analysis to the provided data set. It is safe to pass the same matrix reference for both data and transformedData.

**Parameters:**

*data* Data matrix. \
 *transformedData* Matrix to put results of **PCA** into. \
 *eigval* Vector to put eigenvalues into. \
 *eigvec* Matrix to put eigenvectors (loadings) into.

Referenced by Apply().

### void mlpack::pca::PCA::Apply (const arma::mat &data, arma::mat &transformedData, arma::vec &eigVal) const

Apply Principal Component Analysis to the provided data set. It is safe to pass the same matrix reference for both data and transformedData.

**Parameters:**

*data* Data matrix. \
 *transformedData* Matrix to store results of **PCA** in. \
 *eigval* Vector to put eigenvalues into.

### double mlpack::pca::PCA::Apply (arma::mat &data, const size\_tnewDimension) const

Use **PCA** for dimensionality reduction on the given dataset. This will save the newDimension largest principal components of the data and remove the rest. The parameter returned is the amount of variance of the data that is retained; this is a value between 0 and 1. For instance, a value of 0.9 indicates that 90% of the variance present in the data was retained.

**Parameters:**

*data* Data matrix. \
 *newDimension* New dimension of the data.

**Returns:**

Amount of the variance of the data retained (between 0 and 1).

### double mlpack::pca::PCA::Apply (arma::mat &data, const intnewDimension) const` [inline]`

This overload is here to make sure int gets casted right to size\_t.

Definition at line 89 of file pca.hpp.

References Apply().

### double mlpack::pca::PCA::Apply (arma::mat &data, const doublevarRetained) const

Use **PCA** for dimensionality reduction on the given dataset. This will save as many dimensions as necessary to retain at least the given amount of variance (specified by parameter varRetained). The amount should be between 0 and 1; if the amount is 0, then only 1 dimension will be retained. If the amount is 1, then all dimensions will be retained.

The method returns the actual amount of variance retained, which will always be greater than or equal to the varRetained parameter.

**Parameters:**

*data* Data matrix. \
 *varRetained* Lower bound on amount of variance to retain; should be between 0 and 1.

**Returns:**

Actual amount of variance retained (between 0 and 1).

### bool mlpack::pca::PCA::ScaleData () const` [inline]`

Get whether or not this **PCA** object will scale (by standard deviation) the data when **PCA** is performed.

Definition at line 113 of file pca.hpp.

References scaleData.

### bool& mlpack::pca::PCA::ScaleData ()` [inline]`

Modify whether or not this **PCA** object will scale (by standard deviation) the data when **PCA** is performed.

Definition at line 116 of file pca.hpp.

References scaleData.

### std::string mlpack::pca::PCA::ToString () const

## Member Data Documentation

### bool mlpack::pca::PCA::scaleData` [private]`

Whether or not the data will be scaled by standard deviation when **PCA** is performed.

Definition at line 124 of file pca.hpp.

Referenced by ScaleData().

## Author

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