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| import numpy as np | |
| n_samples = 500 | |
| mean_1 = 15 | |
| std_dev_1 = 5 | |
| mean_2 = -20 | |
| std_dev_2 = 3 | |
| X = np.concatenate([np.random.normal(mean_1, std_dev_1, n_samples / 2), | |
| np.random.normal(mean_2, std_dev_2, n_samples / 2)], axis=0) | |
| Z = (X - np.mean(X)) / np.std(X) |
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