code: public static INDArray pca(INDArray A, double variance, boolean normalize) {
          INDArray factor = pca_factor(A, variance, normalize);
          return A.mmul(factor);
      }
docs: Calculates pca reduced value of a matrix, for a given variance. A larger variance (99%)
  will result in a higher order feature set.
  
  The returned matrix is a projection of A onto principal components
  
  @see pca(INDArray, int, boolean)
  
  @param A the array of features, rows are results, columns are features - will be changed
  @param variance the amount of variance to preserve as a float 0 - 1
  @param normalize whether to normalize (set features to have zero mean)
  @return the matrix representing  a reduced feature set