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)
class TestBinomialClass(unittest.TestCase):
def setUp(self):
self.binomial = Binomial('numbers_binomial.txt')
def test_readdata(self):
self.assertEqual(self.binomial.data,\
[0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0], 'data not read in correctly')
def test_meancalculation(self):
mean = self.binomial.calculate_mean()
self.assertEqual(round(mean, 2), 8.00,
'calculated mean not as expected')
def test_stdevcalculation(self):
stdev = self.binomial.calculate_stdev()
self.assertEqual(
round(stdev, 2), 1.75, 'calculated standard deviation\
not as expected')
def test_pmf(self):
self.assertEqual(
round(self.binomial.pmf(5), 3), 0.054, 'propability \
mass function does not give the expected result ')
self.assertEqual(round(self.binomial.pmf(3), 3), 0.005)
def test_def(self):
self.assertEqual(str(self.binomial),
"""Number of trials {}, success propability for each trial {} """ .\
format(13, 0.62),'Parameters of the Binomial not as expected')
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)
class TestBinomialClass(unittest.TestCase):
def setUp(self):
self.binomial = Binomial(0.4, 20)
self.binomial.read_data_file('numbers_binomial.txt')
def test_initialization(self):
self.assertEqual(self.binomial.p, 0.4, 'p value incorrect')
self.assertEqual(self.binomial.n, 20, 'n value incorrect')
def test_readdata(self):
self.assertEqual(self.binomial.data,\
[0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0], 'data not read in correctly')
def test_calculatemean(self):
mean = self.binomial.calculate_mean()
self.assertEqual(mean, 8)
def test_calculatestdev(self):
stdev = self.binomial.calculate_stdev()
self.assertEqual(round(stdev, 2), 2.19)
def test_replace_stats_with_data(self):
p, n = self.binomial.replace_stats_with_data()
self.assertEqual(round(p, 3), .615)
self.assertEqual(n, 13)
def test_pdf(self):
self.assertEqual(round(self.binomial.pdf(5), 5), 0.07465)
self.assertEqual(round(self.binomial.pdf(3), 5), 0.01235)
self.binomial.replace_stats_with_data()
self.assertEqual(round(self.binomial.pdf(5), 5), 0.05439)
self.assertEqual(round(self.binomial.pdf(3), 5), 0.00472)
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)
class TestBinomail(unittest.TestCase):
def setUp(self):
self.distribution = Binomial(3, 5, 0.85)
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 test_mean(self):
mean = self.distribution.mean()
self.assertEqual(mean, 4.25)
def test_pmf(self):
pmf = self.distribution.pmf()
self.assertEqual(pmf, 0.13817812500000001)
def test_variance(self):
variance = self.distribution.variance()
self.assertEqual(variance, 0.63750000000000007)
def test_std(self):
std = self.distribution.std()
self.assertTrue(std, 0.79843597113356568)
def test_cdf(self):
cdf = self.distribution.cdf()
self.assertTrue(cdf, 0.16479000000000005)
def test_pmfs(self):
t_pmfs = np.array(
[7.59375000e-05, 2.15156250e-03, 2.43843750e-02, 1.38178125e-01])
pmfs = self.distribution.pmfs()
self.assertIsNotNone(pmfs)
numpy_testing.assert_allclose(t_pmfs, pmfs)
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()
