Class PCA

JSAT/src/jsat/datatransform/PCA.java:35–277

Principle Component Analysis is a method that attempts to create a basis of the given space that maintains the variance in the data set while eliminating correlation of the variables. When a full basis is formed, the dimensionality will remain the same, but the data will be transformed to a new

 * @see ZeroMeanTransform
 */
public class PCA implements DataTransform
{

    private static final long serialVersionUID = 8736609877239941617L;
    /**
     * The transposed matrix of the Principal Components
     */
    private Matrix P;
    private int maxPCs;
    private double threshold;
    
    /**
     * Creates a new object for performing PCA that stops at 50 principal components. This may not be optimal for any particular dataset
     *
     */
    public PCA()
    {
        this(50);
    }

    /**
     * Performs PCA analysis using the given data set, so that transformations may be performed on future data points.  <br>
     * <br>
     * NOTE: The maximum number of PCs will be learned until a convergence threshold is meet. It is possible that the 
     * number of PCs computed will be equal to the number of dimensions, meaning no dimensionality reduction has 
     * occurred, but a transformation of the dimensions into a new space. 
     * 
     * @param dataSet the data set to learn from
     */
    public PCA(DataSet dataSet)
    {
        this(dataSet, Integer.MAX_VALUE);
    }
    
    /**
     * Performs PCA analysis using the given data set, so that transformations may be performed on future data points.  
     * 
     * @param dataSet the data set to learn from
     * @param maxPCs the maximum number of Principal Components to let the algorithm learn. The algorithm may stop
     * earlier if all the variance has been explained, or the convergence threshold has been met. 
     * Note, the computable maximum number of PCs is limited to the minimum of the number of samples and the
     * number of dimensions. 
     */
    public PCA(DataSet dataSet, int maxPCs)
    {
        this(dataSet, maxPCs, 1e-4);
    }
    
    /**
     * Creates a new object for performing PCA
     *
     * @param maxPCs the maximum number of Principal Components to let the
     * algorithm learn. The algorithm may stop earlier if all the variance has
     * been explained, or the convergence threshold has been met. Note, the
     * computable maximum number of PCs is limited to the minimum of the number
     * of samples and the number of dimensions.
     */
    public PCA(int maxPCs)