def probvec(m, k, random_state=None, parallel=True):
 if (k == 1):
  return np.ones((m, k))
 random_state = check_random_state(random_state)
 r = random_state.random_sample(size=(m, (k - 1)))
 x = np.empty((m, k))
 if parallel:
  _probvec_parallel(r, x)
 else:
  _probvec_cpu(r, x)
 return x

def probvec ( m , k , random_state = None , parallel = True ) : if ( k == 1 ) : return np . ones ( ( m , k ) ) random_state = check_random_state ( random_state ) r = random_state . random_sample ( size = ( m , ( k - 1 ) ) ) x = np . empty ( ( m , k ) ) if parallel : _probvec_parallel ( r , x ) else : _probvec_cpu ( r , x ) return x
return m randomly sampled probability vectors of dimension k .