Java jama matrix problem?

Be careful here, JAMA supports SVD primarily for full rank matrices, and if you read the "readme" you'll notice that the behavior is not necessarily correct for rank deficient (m.

Be careful here, JAMA supports SVD primarily for full rank matrices, and if you read the "readme" you'll notice that the behavior is not necessarily correct for rank deficient (m = n, the singular value decomposition is an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and an n-by-n orthogonal matrix V so that A = U*S*V'. But this doesn't hold in this case, because m=4 and n=5 means mMin(m+1,n)) will create a matrix with assumed rows of m, here 4 (which is correct) and assumed columns n, here Math. Min(4+1,5)=5 (which is incorrect).

So: when you go print the matrix and the print routine calls getColumnDimension, the U matrix returns 5, which is greater than the actual backing array dimension. In short, switching to the line I pasted above will detect the dimensions of U, and as such return a valid result regardless of the rank.

Read the wiki article on SVD. The following code is representative of the example in Section 2. Import Jama.

Matrix; import Jama. SingularValueDecomposition; public class JAMATest { static public void printMatrix(Matrix m){ double d = m.getArray(); for(int row = 0; row > A = 1.0000, 0.0000, 0.0000, 0.0000, 2.0000; 0, 0, 3, 0, 0; 0, 0, 0, 0, 0; 0, 4, 0, 0, 0; >> A A = 1 0 0 0 2 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 >> U, S, V = svd(A); >> U U = 0 0 1 0 0 1 0 0 0 0 0 -1 1 0 0 0 >> S S = 4.0000 0 0 0 0 0 3.0000 0 0 0 0 0 2.2361 0 0 0 0 0 0 0 >> V V = 0 0 0.4472 0 -0.8944 1.0000 0 0 0 0 0 1.0000 0 0 0 0 0 0 1.0000 0 0 0 0.8944 0 0.4472 With regards to your first question, the following code produces no error: import Jama. Matrix; public class JAMATest { /** * @param args */ public static void main(String args) { double vals = {{1.

,1. ,0},{1. ,0.

,1. },{1. ,3.

,4. },{6. ,4.

,8. }}; Matrix A = new Matrix(vals); } } So something else you're doing must be causing it to have an exception. Try using my printMatrix method in place of whatever you are using and see if it helps.

I am sorry, but I couldn't understand your answer. Do you think the output I wrote in my question is correct? How much V and S has 3 rows?

Can you please explain? I am sorry I am not math expert :( – user238384 Jan 18 '10 at 18:05 modifications to my previous program above. I included my printMatrix function.

Note that I got a similar exception as you did when I tried using m.getRowDimension() and m. GetColumnDimension() as they don't appear to be accurate for the svd.getU() method. Converting the matrix to a double seems to do the trick.

– vicatcu Jan 18 '10 at 18:31 On a separate note, the SVD of a matrix is not unique. See this quote from the wikipedia article: "It should also be noted that this particular singular value decomposition is not unique. " When you multiply U * S * V you will get the original Matrix back.

The dimensions of a matrix sort of "join" when you multiply. So our original matrix was 4 x 5 = 4 x 4 * 4 x 5 * 5 x 5, and you're left with the outer two dimensions (4 ..x.. 5). It seems in you have to recognize that only the first four rows of Sigma are the actual matrix based on these properties.

– vicatcu Jan 18 '10 at 18:36 @agazerboy, As you can see from the Matlab and Java output, columns 3 and 4 of U in the Matlab output differ in sign from the Java output -- this is acceptable, U forms an orthornormal basis, and as such any column may be negated, and this is acceptable. – Mark Elliot Jan 18 '10 at 19:31.

The dimensions of U, S and V do not need to be the same dimensions as A. U will have the same number of rows and V^T will have the same number of columns. That is sufficient to recreate A by the rules of matrix multiplication.

The other dimension (columns of U, rows of V^T and rows/columns of S) will be the "rank" of A (in your example 3). This is, roughly speaking, the dimensionality of your data...how many axes are needed to uniquely represent a column or row in A. It will be at most min(rows, cols) but can often be much less.

That is ok.

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