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snoopy0328
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
from calculateHW2P5 import simulate_culprit, simulate_crumbs, combination, print_probabilities, analytic_probabilities, print_analytic_probabilities
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import numpy as np
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if __name__ == "__main__":
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#N = 100000 #used while testing
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NSize = [1000, 10000, 100000]
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#Priors for each suspect
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pSuperman = 0.5
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pBatman = 0.5
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#Likelihoods of crumbs on each location
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supermanProb = np.array([0.3, 0.7, 0.2])
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batmanProb = np.array([0.4, 0.6, 0.3])
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#Simulate
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'''
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culprit = simulate_culprit(N, pSuperman)
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crumbResults = simulate_crumbs(N, supermanProb, batmanProb, culprit)
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print("Simulation:")
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print_probabilities(combination(crumbResults, culprit), N)
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'''
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print("Simulation:")
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for N in NSize:
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print(f"N = {N}")
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# Simulate culprit and crumbs
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culprit = simulate_culprit(N, pSuperman)
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crumbResults = simulate_crumbs(N, supermanProb, batmanProb, culprit)
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# Count combinations and print probabilities
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print("Simulated Probabilities:")
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print_probabilities(combination(crumbResults, culprit), N)
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print("\n")
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#Analytic
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print("Analytic:")
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print_analytic_probabilities(analytic_probabilities(pSuperman, pBatman, supermanProb, batmanProb))
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'''
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As the value of N increases, the simulated probabilities get closer to the analytic probabilities.
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'''
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