PCA

java.lang.Object
- jhplot.math.pca.PCA

```
public final class PCA
extends Object
```
The class responsible mainly for preparing the PCA transformation parameters based on training data and executing the actual transformation on test data.

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class PCA.TransformationType
Type of the possible data transformation.

Nested Classes
Modifier and Type	Class and Description
`static class`	`PCA.TransformationType` Type of the possible data transformation.

Constructor Summary

Constructors
Constructor and Description
`PCA(Matrix data)` Create the PCA transformation.
`PCA(Matrix data, boolean center)` Create the PCA transformation.
`PCA(Matrix data, CovarianceMatrixEVDCalculator evdCalc)` Create the PCA transformation.
`PCA(Matrix data, CovarianceMatrixEVDCalculator evdCalc, boolean center)` Create the PCA transformation

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`boolean`	`belongsToGeneratedSubspace(Matrix pt)` Check if given point lies in PCA-generated subspace.
`static Matrix`	`calculateCovarianceMatrix(Matrix data)` Function for JUnit testing purposes only
`double`	`getEigenvalue(int dimNo)` Get selected eigenvalue
`Matrix`	`getEigenvectorsMatrix()`
`int`	`getInputDimsNo()` Get number of dimensions of the input vectors
`int`	`getOutputDimsNo()` Get number of dimensions of the output vectors
`Matrix`	`transform(Matrix data, PCA.TransformationType type)` Execute selected transformation on given data.

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - PCA
```
public PCA(Matrix data)
```
    Create the PCA transformation. Use the popular SVD method for internal calculations
    
    Parameters:
    
    data - data matrix used to compute the PCA transformation. Rows of the matrix are the instances/samples, columns are dimensions. It is assumed that the matrix is already centered.
  - PCA
```
public PCA(Matrix data,
           boolean center)
```
    Create the PCA transformation. Use the popular SVD method for internal calculations
    
    Parameters:
    
    data - data matrix used to compute the PCA transformation. Rows of the matrix are the instances/samples, columns are dimensions.
    
    center - should the data matrix be centered before doing the calculations?
  - PCA
```
public PCA(Matrix data,
           CovarianceMatrixEVDCalculator evdCalc)
```
    Create the PCA transformation.
    
    Parameters:
    
    data - data matrix used to compute the PCA transformation. Rows of the matrix are the instances/samples, columns are dimensions. It is assumed that the matrix is already centered.
    
    evdCalc - method of computing eigenvalue decomposition of data's covariance matrix
  - PCA
```
public PCA(Matrix data,
           CovarianceMatrixEVDCalculator evdCalc,
           boolean center)
```
    Create the PCA transformation
    
    Parameters:
    
    data - data matrix used to compute the PCA transformation. Rows of the matrix are the instances/samples, columns are dimensions.
    
    evdCalc - method of computing eigenvalue decomposition of data's covariance matrix
    
    center - should the data matrix be centered before doing the calculations?
- Method Detail
  - getEigenvectorsMatrix
```
public Matrix getEigenvectorsMatrix()
```
    Returns:
    
    matrix where eigenvectors are placed in columns
  - getEigenvalue
```
public double getEigenvalue(int dimNo)
```
    Get selected eigenvalue
    
    Parameters:
    
    dimNo - dimension number corresponding to given eigenvalue
  - getInputDimsNo
```
public int getInputDimsNo()
```
    Get number of dimensions of the input vectors
  - getOutputDimsNo
```
public int getOutputDimsNo()
```
    Get number of dimensions of the output vectors
  - transform
```
public Matrix transform(Matrix data,
                        PCA.TransformationType type)
```
    Execute selected transformation on given data.
    
    Parameters:
    
    data - data to transform. Rows of the matrix are the instances/samples, columns are dimensions. If the original PCA data matrix was set to be centered, this matrix will also be centered using the same parameters.
    
    type - transformation to apply
    
    Returns:
    
    transformed data
  - belongsToGeneratedSubspace
```
public boolean belongsToGeneratedSubspace(Matrix pt)
```
    Check if given point lies in PCA-generated subspace. If it does not, it means that the point doesn't belong to the transformation domain i.e. it is an outlier.
    
    Parameters:
    
    pt - point. If the original PCA data matrix was set to be centered, this point will also be centered using the same parameters.
    
    Returns:
    
    true iff the point lies on all principal axes
  - calculateCovarianceMatrix
```
public static Matrix calculateCovarianceMatrix(Matrix data)
```
    Function for JUnit testing purposes only

Class PCA

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

PCA

PCA

PCA

PCA

Method Detail

getEigenvectorsMatrix

getEigenvalue

getInputDimsNo

getOutputDimsNo

transform

belongsToGeneratedSubspace

calculateCovarianceMatrix