run HouseHolderDecomposition.py sample.txt
--> ere sample.txt contains the input matrix mxn where m>=n and separted by ','
----Returns--- [U,B,V]
where is U: Left Householder reflection matrices product
         B: Upper Bidiagonal Matrix
         V: Right Householder reflection matrices product

run QRDecomposition.py sample.txt
---- Returns --- [Q ,R]

run NormalJacobi.py sample.txt
---- Returns ---- [U,sigma,V,iterations] 
iterations: no of iterations till the convergence 

run NormalJacobi.py sample.txt
This program optimized algorithm to find the SVD
It first Decomposes the input matrix in to Upper triangular matrix using the QR decomposition(which uses house holder reflection matrices)
----- Returns ---- null
----- Output CLI ---- 
    SVD of the matrix using the normal implementation
            and
    SVD of the QR(decomposed matrix)