Warnings in snmf.py and performance

[jspark1105]

I'm seeing warning

nimfa/methods/factorization/snmf.py:610: RuntimeWarning: invalid value encountered in power
  np.mat(2 ** np.array(list(range(l_var - 1, -1, -1)))), p_set)

I see this happens when l_var is 64 exceeding int64 range. Shouldn't we use floating point here like

np.mat(2 ** np.array(list(range(l_var - 1, -1, -1)))).astype(np.float64)

I'm also seeing most of time is spent on flatten() function that can be hoisted out like
from

for i in range(len(i_f)):
    alpha[i_f[i], j_f[i]] = t_d.todense().flatten()[0, i]

to

t_d_flattened = t_d.todense().flatten()
for i in range(len(i_f)):
    alpha[i_f[i], j_f[i]] = t_d_flattened[0, i]