print '' print 'Completed states: %s' % len (states) print 'Percent of all states: %s' % (1.0 * len (states) / StateManager.dif_states) print '' # train neural network nn = NeuralNetwork () spm = SpeciesManager (nn) nn.train (bm) states, rewards = sm.get_complete_pairs () print states [40] print rewards [40] # insert pairs for i in range (len (states)): spm.insert (states [i], rewards [i]) scores = []
