Created
October 12, 2017 18:48
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n_fittest
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| elif self.mode == 'n_fittest': | |
| if r[i][x] < self.disturbance_threshold: | |
| p[x] = np.random.normal(dev, mean) | |
| else: | |
| leader_rate = np.random.uniform(0.0, 1.0) | |
| update = np.average(self.population[n_fittest]) - best_neighbour[x] | |
| p[x] = best_neighbour[x] + leader_rate * update |
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