Determine if a number is perfect, abundant, or deficient based on Nicomachus' (60 - 120 CE) classification scheme for natural numbers.
The Greek mathematician Nicomachus devised a classification scheme for natural numbers, identifying each as belonging uniquely to the categories of perfect, abundant, or deficient based on their aliquot sum. The aliquot sum is defined as the sum of the factors of a number not including the number itself. For example, the aliquot sum of 15 is (1 + 3 + 5) = 9
Implement a way to determine whether a given number is perfect. Depending on your language track, you may also need to implement a way to determine whether a given number is abundant or deficient.
Sometimes it is necessary to raise an exception. When you do this, you should include a meaningful error message to indicate what the source of the error is. This makes your code more readable and helps significantly with debugging. Not every exercise will require you to raise an exception, but for those that do, the tests will only pass if you include a message.
To raise a message with an exception, just write it as an argument to the exception type. For example, instead of
raise Exception, you should write:
raise Exception("Meaningful message indicating the source of the error")
To run the tests, run the appropriate command below (why they are different):
Alternatively, you can tell Python to run the pytest module (allowing the same command to be used regardless of Python version):
python -m pytest perfect_numbers_test.py
-v: enable verbose output
-x: stop running tests on first failure
--ff: run failures from previous test before running other test cases
For other options, see
python -m pytest -h
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Taken from Chapter 2 of Functional Thinking by Neal Ford. http://shop.oreilly.com/product/0636920029687.do
It's possible to submit an incomplete solution so you can see how others have completed the exercise.
import unittest from perfect_numbers import classify # Tests adapted from `problem-specifications//canonical-data.json` @ v1.1.0 class PerfectNumbersTest(unittest.TestCase): def test_smallest_perfect_number(self): self.assertIs(classify(6), "perfect") def test_medium_perfect_number(self): self.assertIs(classify(28), "perfect") def test_large_perfect_number(self): self.assertIs(classify(33550336), "perfect") class AbundantNumbersTest(unittest.TestCase): def test_smallest_abundant_number(self): self.assertIs(classify(12), "abundant") def test_medium_abundant_number(self): self.assertIs(classify(30), "abundant") def test_large_abundant_number(self): self.assertIs(classify(33550335), "abundant") class DeficientNumbersTest(unittest.TestCase): def test_smallest_prime_deficient_number(self): self.assertIs(classify(2), "deficient") def test_smallest_nonprime_deficient_number(self): self.assertIs(classify(4), "deficient") def test_medium_deficient_number(self): self.assertIs(classify(32), "deficient") def test_large_deficient_number(self): self.assertIs(classify(33550337), "deficient") def test_edge_case(self): self.assertIs(classify(1), "deficient") class InvalidInputsTest(unittest.TestCase): def test_zero(self): with self.assertRaisesWithMessage(ValueError): classify(0) def test_negative(self): with self.assertRaisesWithMessage(ValueError): classify(-1) # Utility functions def setUp(self): try: self.assertRaisesRegex except AttributeError: self.assertRaisesRegex = self.assertRaisesRegexp def assertRaisesWithMessage(self, exception): return self.assertRaisesRegex(exception, r".+") if __name__ == '__main__': unittest.main()
import math def is_perfect(number): return sum(factors(number)) == number def factors(n): return set(reduce(list.__add__, pairs_of_factors(n))) - set([n]) def pairs_of_factors(n): return [[i, n / i] for i in xrange(1, int(math.sqrt(n)) + 1) if n % i == 0]
A huge amount can be learned from reading other people’s code. This is why we wanted to give exercism users the option of making their solutions public.
Here are some questions to help you reflect on this solution and learn the most from it.