perform varimax rotation in python using numpy

Question

I am working on principal component analysis of a matrix. I have already found the component matrix shown below

A = np.array([[-0.73465832 -0.24819766 -0.3204505         


        

Answer 1

Wikipedia has an example in python here!

Lifting the example and tailoring it for numpy:

from numpy import eye, asarray, dot, sum, diag
from numpy.linalg import svd
def varimax(Phi, gamma = 1.0, q = 20, tol = 1e-6):
    p,k = Phi.shape
    R = eye(k)
    d=0
    for i in xrange(q):
        d_old = d
        Lambda = dot(Phi, R)
        u,s,vh = svd(dot(Phi.T,asarray(Lambda)**3 - (gamma/p) * dot(Lambda, diag(diag(dot(Lambda.T,Lambda))))))
        R = dot(u,vh)
        d = sum(s)
        if d_old!=0 and d/d_old < 1 + tol: break
    return dot(Phi, R)

Answer 2

You can find a lot of examples with Python. Here is an example I found for Python using only numpy, on Wikipedia:

def varimax(Phi, gamma = 1, q = 20, tol = 1e-6):
    from numpy import eye, asarray, dot, sum, diag
    from numpy.linalg import svd
    p,k = Phi.shape
    R = eye(k)
    d=0
    for i in xrange(q):
        d_old = d
        Lambda = dot(Phi, R)
        u,s,vh = svd(dot(Phi.T,asarray(Lambda)**3 - (gamma/p) * dot(Lambda, diag(diag(dot(Lambda.T,Lambda))))))
        R = dot(u,vh)
        d = sum(s)
        if d/d_old < tol: break
    return dot(Phi, R)

Answer 3

I've looked up solutions for doing factor analysis in python on stack-overflow so many times, that I recently made my own package, fa-kit. Even though this is an old post, I'm throwing up this link in case there's anybody else in the future that gets here via google.