Snmf factorization method does not maintain consistency of sparseness across runs if version='l'

[haesemeyer]

The Snmf factorization method Snmf.factorize() does not handle transposes correctly across multiple runs.
In order to enforce sparseness on the left factor the method transposes self.V (lines 175-176) , fits the model and then back-transposes V and swaps W and H (lines 224-226). However, the initial transpose is done outside run loop which starts in line 178. Therefore, sparseness is not enforced on the same factor across runs, with odd runs being correct and even runs operating on the orignal self.V (self.V.T.T). Symptomatic for this, if nruns is even, W and H will be returned with the wrong dimensionality (i.e. swapped and transposed).
This should be easily fixable by moving lines 175-176 of snmf.py below line 178 to ensure a re-transpose of self.V before each new fit.
#36