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A python script to test strategies for the for the riddle described on TED-ed's youtube channel: www.youtube.com/watch?v=dh4nEuhZBgg
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""" | |
A python script to test strategies for the for the riddle described on TED-ed's | |
youtube channel: www.youtube.com/watch?v=dh4nEuhZBgg | |
Your interstellar police squad has tracked a group of criminals to a cluster of | |
seven planets. Now you must apprehend them before their reinforcements arrive. | |
Of course, the fugitives won’t just stay put – they’ll try to dodge you by | |
moving from planet to planet. Can you devise a sequence for searching the | |
planets that’s guaranteed to catch them in ten warps or less? | |
Edwin F. Meyer shows how. | |
Planets are arranged like so: | |
1 7 | |
2 6 | |
3 | |
4 | |
5 | |
""" | |
import numpy as np | |
# Describe the possible transitions at each turn for each planet | |
PLANETS = { | |
1: [2], | |
2: [1, 3], | |
3: [2, 4, 6], | |
4: [3, 5], | |
5: [4], | |
6: [3, 7], | |
7: [6] | |
} | |
def test(strategy, n=10000): | |
""" | |
Test a given strategy's chance of catching the rogues by Monte-Carlo. | |
""" | |
if len(strategy) != 10: | |
raise Exception('Must have 10 planets') | |
outcomes = [] | |
for run in range(n): | |
rebel_position = np.random.choice(list(PLANETS)) | |
for turn, planet in enumerate(strategy): | |
police_position = planet | |
rebel_position = np.random.choice(list(PLANETS[rebel_position])) | |
if police_position == rebel_position: | |
outcomes.append({ | |
'run': run, | |
'caught': True, | |
'turn': turn | |
}) | |
break | |
else: | |
outcomes.append({ | |
'run': run, | |
'caught': False, | |
'turn': None | |
}) | |
catches = sum((o['caught']) for o in outcomes) | |
return catches, outcomes | |
if __name__ == '__main__': | |
strategy = [2, 3, 4, 3, 6, 2, 3, 4, 3, 6] # This is the winning strategy | |
catches, outcomes = test(strategy) | |
print('Caught rebels {:0.2f} percent of the time'.format( | |
100 * catches / len(outcomes) | |
)) |
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