Numpy

numpy_linear_algebra3.py

## performing svd using svd() function.
## Returns full matrices by default.
U,V,S = lnp.svd(a = np.random.randn(9, 6))

## Printing the shapes of all matrices.
print(U.shape,V.shape,S.shape)

#[Output]:
#(9, 9) (6,) (6, 6)

## Eigen vectors
##1. eig()
##Compute the eigenvalues and right eigenvectors of a square array.In [11]:

## It is returning eigenvalues and eigenvectors.
w, v = lnp.eig(np.array([[1, -1], [1, 1]]))


## eigenvalues
print(w)

#[Output]:
#array([1.+1.j, 1.-1.j])

## eigenvectors
print(v)

#[Output]:
#array([[0.70710678+0.j        , 0.70710678-0.j        ],
#       [0.        -0.70710678j, 0.        +0.70710678j]])