def poisson_pmf(k, the_lambda):
"""
Gives the value of the Poisson probability mass function
for the given k (k can be a whole, nonnrgative nuber)
and the specified parameter of the Poisson distribution-
the_lambda must be > 0.
"""
if the_lambda <= 0:
raise IncorrectInputError()
A = math.pow(the_lambda, k)
B = math.exp(-the_lambda)
C = functions.factorial(k)
return A*B/C
def poisson_cdf(k, the_lambda):
"""
Gives the value of the Poisson cumulative density function
for the given k (k can be a whole, nonnrgative nuber)
and the specified parameter of the Poisson distribution-
the_lambda must be > 0.
"""
if the_lambda <= 0:
raise IncorrectInputError()
A = math.exp(-the_lambda)
index = functions.floor_function(k)
summation = 0
for i in range(index+1):
summation += math.pow(the_lambda, i)/functions.factorial(i)
return A*summation
def test_input(self):
string = "asd"
invalid_float = 7.15
invalid_int = -5
with self.assertRaises(functions.IncorrectInputError):
functions.factorial(string)
with self.assertRaises(functions.IncorrectInputError):
functions.factorial(invalid_float)
with self.assertRaises(functions.IncorrectInputError):
functions.factorial(invalid_int)
def test_results(self):
valid_float = 7.0
valid_int = 4
self.assertEqual(5040, functions.factorial(valid_float))
self.assertEqual(24, functions.factorial(valid_int))
def test_zero(self):
zero = 0
self.assertEqual(1, functions.factorial(zero))
