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Rooted by: Pi Estimate with Monte Carlo Algorithm
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  • Code 
    import random

def pi_estimate(n, seed=0):
    random.seed(seed)
    
    n_inside = sum(True for i in range(n) if random.random()**2 + random.random()**2 < 1)
    
    return 4 * (n_inside / n)
     Test Cases 
    # TODO: Replace examples and use TDD development by writing your own tests
# These are some of the methods available:
#   test.expect(boolean, [optional] message)
# test.assert_equals(100000, 3.14844)
# test.assert_equals(1000000, 3.14244)
test.assert_equals(pi_estimate(1000, seed=42), 3.128)
test.assert_equals(pi_estimate(1000000, seed=42), 3.140592)

# You can use Test.describe and Test.it to write BDD style test groupings
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  • Code
    • import random
    • def pi_estimate(n, seed=0):
    • random.seed(seed)
    • n_inside = 0
    • for i in range(n):
    • if random.random()**2 + random.random()**2 < 1:
    • n_inside += 1
    • n_inside = sum(True for i in range(n) if random.random()**2 + random.random()**2 < 1)
    • return 4 * (n_inside / n)