Java中的PCA实现 [英] PCA Implementation in Java

问题描述

我需要用Java实现PCA.我感兴趣的是找到有据可查，实用且易于使用的东西.有什么建议吗?

I need implementation of PCA in Java. I am interested in finding something that's well documented, practical and easy to use. Any recommendations?

推荐答案

现在有许多针对Java的主成分分析实现.

There are now a number of Principal Component Analysis implementations for Java.

1. Apache Spark:

2. ND4J: https://javadoc.io/doc/org.nd4j/nd4j-api/1.0.0-beta7/org/nd4j/linalg/Dimensionityreduction/PCA.html

   //Create points as NDArray instances
   List<INDArray> ndArrays = Arrays.asList(
           new NDArray(new float [] {-1.0F, -1.0F}),
           new NDArray(new float [] {-1.0F, 1.0F}),
           new NDArray(new float [] {1.0F, 1.0F}));
   
   //Create matrix of points (rows are observations; columns are features)
   INDArray matrix = new NDArray(ndArrays, new int [] {3,2});
   
   //Execute PCA - again to 2 dimensions
   INDArray factors = PCA.pca_factor(matrix, 2, false);
   

3. Apache Commons Math(单线程；无框架)

4. Apache Commons Math (single threaded; no framework)

   //create points in a double array
   double[][] pointsArray = new double[][] { 
       new double[] { -1.0, -1.0 }, 
       new double[] { -1.0, 1.0 },
       new double[] { 1.0, 1.0 } };
   
   //create real matrix
   RealMatrix realMatrix = MatrixUtils.createRealMatrix(pointsArray);
   
   //create covariance matrix of points, then find eigen vectors
   //see https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues
   
   Covariance covariance = new Covariance(realMatrix);
   RealMatrix covarianceMatrix = covariance.getCovarianceMatrix();
   EigenDecomposition ed = new EigenDecomposition(covarianceMatrix);
   

请注意，奇异值分解(也可用于查找主要成分)具有等效的实现.

Note, Singular Value Decomposition, which can also be used to find Principal Components, has equivalent implementations.

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