Package org.hipparchus.stat.projection

Class PCA

java.lang.Object
org.hipparchus.stat.projection.PCA
public class PCA extends Object
Principal component analysis (PCA) is a statistical technique for reducing the dimensionality of a dataset. PCA can be thought of as a projection or scaling of the data to reduce the number of dimensions but done in a way that preserves as much information as possible.
Since:
3.0

  • Constructor Summary

    Constructors
    Constructor
    Description
    PCA(int numC)
    A default PCA will center but not scale.
    PCA(int numC, boolean scale, boolean biasCorrection)
    Create a PCA with the ability to adjust scaling parameters.

  • Method Summary

    Modifier and Type
    Method
    Description
    PCA
    fit(double[][] data)
    Fit our model to the data, ready for subsequence transforms.
    double[][]
    fitAndTransform(double[][] data)
    Fit our model to the data and then transform it to the reduced dimensions.
    double[]
    getCenter()
    Get by column center (or mean) of the fitted data.
    double[][]
    getComponents()
    Returns the principal components of our projection model.
    int
    getNumComponents()
    GEt number of components.
    double[]
    getVariance()
    Get principal component variances.
    boolean
    isBiasCorrection()
    Check whether scaling (correlation), if in use, adjusts for bias.
    boolean
    isScale()
    Check whether scaling (correlation) or no scaling (covariance) is used.
    double[][]
    transform(double[][] data)
    Transform the supplied data using our projection model.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • PCA

      public PCA(int numC, boolean scale, boolean biasCorrection)
      Create a PCA with the ability to adjust scaling parameters.
      Parameters:
      numC - the number of components
      scale - whether to also scale (correlation) rather than just center (covariance)
      biasCorrection - whether to adjust for bias when scaling

    • PCA

      public PCA(int numC)
      A default PCA will center but not scale.
      Parameters:
      numC - the number of components

  • Method Details

    • getNumComponents

      public int getNumComponents()
      GEt number of components.
      Returns:
      the number of components

    • isScale

      public boolean isScale()
      Check whether scaling (correlation) or no scaling (covariance) is used.
      Returns:
      whether scaling (correlation) or no scaling (covariance) is used

    • isBiasCorrection

      public boolean isBiasCorrection()
      Check whether scaling (correlation), if in use, adjusts for bias.
      Returns:
      whether scaling (correlation), if in use, adjusts for bias

    • getVariance

      public double[] getVariance()
      Get principal component variances.
      Returns:
      the principal component variances, ordered from largest to smallest, which are the eigenvalues of the covariance or correlation matrix of the fitted data

    • getCenter

      public double[] getCenter()
      Get by column center (or mean) of the fitted data.
      Returns:
      the by column center (or mean) of the fitted data

    • getComponents

      public double[][] getComponents()
      Returns the principal components of our projection model. These are the eigenvectors of our covariance/correlation matrix.
      Returns:
      the principal components

    • fitAndTransform

      public double[][] fitAndTransform(double[][] data)
      Fit our model to the data and then transform it to the reduced dimensions.
      Parameters:
      data - the input data
      Returns:
      the fitted data

    • transform

      public double[][] transform(double[][] data)
      Transform the supplied data using our projection model.
      Parameters:
      data - the input data
      Returns:
      the fitted data

    • fit

      public PCA fit(double[][] data)
      Fit our model to the data, ready for subsequence transforms.
      Parameters:
      data - the input data
      Returns:
      this