title: sklearn matrix factorization example
body: <p>I am using a code currently given at <a href="http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/" rel="noreferrer">http://www.quuxlabs.com/blog</a></p>
  <p>It gives good result. And I can clearly see what changes in the matrix has happened .</p>
  <p>Also I tried to use sklearn library at <a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html" rel="noreferrer">sklearn.decomposition.NMF</a>
  But results I got with the same input are not good enough. Maybe i am missing something.</p>
  <p>Here is my sample code -</p>
  <pre><code>from sklearn.decomposition import NMF , ProjectedGradientNMF
  R = [
       [5,3,0,1],
       [4,0,0,1],
       [1,1,0,5],
       [1,0,0,4],
       [0,1,5,4],
      ]
  R = numpy.array(R)
  nmf = NMF(beta=0.001, eta=0.0001, init='random', max_iter=2000,nls_max_iter=20000, random_state=0, sparseness=None,tol=0.001)
  nR = nmf.fit_transform(R)
  print nR
  print
  print nmf.reconstruction_err_
  print
  </code></pre>
  <p>It is not maintaining exiting/filled values in matrix as I can see using the code given in the blog.</p>
  <p>Can someone help me understand !</p>