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| import numpy as np | |
| max_values = list(map(int, input().split())) | |
| dice_counts = np.arange(1, 6) | |
| prior_probs = np.ones(len(dice_counts)) / len(dice_counts) | |
| def likelihood(max_val, dice_count): | |
| return (max_val / 100.0) ** dice_count - ((max_val - 1) / 100.0) ** dice_count | |
| for max_val in max_values: | |
| likelihoods = np.array( | |
| [likelihood(max_val, dice_count) for dice_count in dice_counts] | |
| ) | |
| prior_probs *= likelihoods | |
| prior_probs /= prior_probs.sum() | |
| estimated_dice_count = dice_counts[np.argmax(prior_probs)] | |
| print(estimated_dice_count) |
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| import numpy as np | |
| import random | |
| def predict(max_values): | |
| dice_counts = np.arange(1, 6) | |
| prior_probs = np.ones(len(dice_counts)) / len(dice_counts) | |
| def likelihood(max_val, dice_count): | |
| return (max_val / 100.0) ** dice_count - ((max_val - 1) / 100.0) ** dice_count | |
| for max_val in max_values: | |
| likelihoods = np.array( | |
| [likelihood(max_val, dice_count) for dice_count in dice_counts] | |
| ) | |
| prior_probs *= likelihoods | |
| prior_probs /= prior_probs.sum() | |
| estimated_dice_count = dice_counts[np.argmax(prior_probs)] | |
| return estimated_dice_count | |
| problems = [] | |
| for i in range(100000): | |
| dice_count = random.randint(1, 5) | |
| dice_max_values = [] | |
| for j in range(10): | |
| dice_values = [random.randint(1, 100) for _ in range(dice_count)] | |
| dice_max_values.append(max(dice_values)) | |
| problems.append((dice_max_values, dice_count)) | |
| success_count = 0 | |
| for max_values, answer in problems: | |
| if predict(max_values) == answer: | |
| success_count += 1 | |
| print(success_count / len(problems)) # 0.56361 |
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