And what exactly are the results you are getting?
On Fri, Feb 14, 2014 at 6:07 PM, Patrick Meyer <meyer...@gmail.com> wrote:
> Hi,
>
>
>
> I am using the SingularValueDecomposition class with a matrix but it gives
> me a different result than R. My knowledge of SVD is limited, so any advice
> is welcomed.
>
>
>
> Here's the method in Java
>
>
>
> public void svdTest(){
>
>
>
> double[][] x = {
>
> {1.0, -0.053071807862720116, 0.04236086650321309},
>
> {0.05307180786272012, 1.0, 0.0058054424137053435},
>
> {-0.04236086650321309, -0.005805442413705342, 1.0}
>
> };
>
>
>
> RealMatrix X = new Array2DRowRealMatrix(x);
>
>
>
> SingularValueDecomposition svd = new SingularValueDecomposition(X);
>
>
>
> RealMatrix U = svd.getU();
>
> for(int i=0;i<U.getRowDimension();i++){
>
> for(int j=0;j<U.getColumnDimension();j++){
>
> System.out.print(U.getEntry(i,j) + " ");
>
> }
>
> System.out.println();
>
> }
>
>
>
> System.out.println();
>
> System.out.println();
>
> RealMatrix V = svd.getV();
>
> for(int i=0;i<V.getRowDimension();i++){
>
> for(int j=0;j<V.getColumnDimension();j++){
>
> System.out.print(V.getEntry(i,j) + " ");
>
> }
>
> System.out.println();
>
> }
>
>
>
>
>
> }
>
>
>
>
>
> And here's the function in R.
>
>
>
> x<-matrix(c(
>
> 1.0, -0.053071807862720116, 0.04236086650321309,
>
> 0.05307180786272012, 1.0, 0.0058054424137053435,
>
> -0.04236086650321309, -0.005805442413705342, 1.0),
>
> nrow=3, byrow=TRUE)
>
> svd(x)
>
>
>
> Does anyone know why I am getting different results for U and V? I am using
> commons math 3.1.
>
>
>
> Thanks,
>
> Patrick
>
>
>
>
>
>
>
>
