def test_add(self):
binomial_one = Binomial(.4, 20)
binomial_two = Binomial(.4, 60)
binomial_sum = binomial_one + binomial_two
self.assertEqual(binomial_sum.p, .4)
self.assertEqual(binomial_sum.n, 80)
def __add__(self, other):
"""Function to add together two Binomial distributions with equal p
Args:
other (Binomial): Binomial instance
Returns:
Binomial: Binomial distribution
"""
try:
assert self.p == other.p, 'p values are not equal'
except AssertionError as error:
raise ValueError('p values must be equal')
# TODO: Define addition for two binomial distributions. Assume that the
# p values of the two distributions are the same. The formula for
# summing two binomial distributions with different p values is more complicated,
# so you are only expected to implement the case for two distributions with equal p.
# the try, except statement above will raise an exception if the p values are not equal
# Hint: You need to instantiate a new binomial object with the correct n, p,
# mean and standard deviation values. The __add__ method should return this
# new binomial object.
# When adding two binomial distributions, the p value remains the same
# The new n value is the sum of the n values of the two distributions.
size_new = self.n + other.n
return Binomial(size=size_new, prob=self.p)
def test_init_with_errors(self):
params = ((None, None, None), (1.2, None, None), (-1, None, None),
(-1.2, None, None), (3, None, None), (3, 1.2, None),
(3, -1, None), (3, 5, None), (3, 5, -2), (3, 5, -1.2),
('some', 5, 0.85), (5, 'string', 0.85), (5, 10,
'some'), (3, 5, 2))
for param in params:
with self.assertRaises(ValueError):
Binomial(*param)
def setUp(self):
self.binomial = Binomial(0.4, 20)
self.binomial.read_data_file('numbers_binomial.txt')
def setUp(self):
self.binomial = Binomial('numbers_binomial.txt')
def setUp(self):
self.distribution = Binomial(3, 5, 0.85)
from distributions import Binomial
binomial_one = Binomial()
binomial_one.read_data_file('numbers_binomial.txt')
binomial_one.replace_stats_with_data()
binomial_one.plot_bar()
binomial_one.plot_bar_pdf()
