def test_seed_float(self):
"""
Test if seed change affects output
"""
random.seed(1)
data_num = 10
distribution = 'uniform'
min_value = 0
max_value = 1
got1 = gen_float(data_num, distribution, min_value, max_value, 0, 0)
random.seed(2)
got2 = gen_float(data_num, distribution, min_value, max_value, 0, 0)
random.seed(1)
got3 = gen_float(data_num, distribution, min_value, max_value, 0, 0)
self.assertTrue(got1 == got3 and got1 != got2)
def test_num_float(self):
"""
Test number of generated floats
"""
data_num = 10
distribution = 'uniform'
min_value = 0
max_value = 1
got = gen_float(data_num, distribution, min_value, max_value, 0, 0)
result = got.count('\n')
self.assertEqual(result, data_num)
def test_random(self):
"""
Test if generated floats are random
"""
random.seed(0)
data_num = 10
distribution = 'uniform'
min_value = 0
max_value = 1
got = [
gen_float(data_num, distribution, min_value, max_value, 0, 0)
for _ in range(1000)
]
self.assertTrue(len(set(got)) == len(got))
def test_uniform(self):
"""
Test uniform distribution
Value N should be [a; b]
"""
data_num = 10000
distribution = 'uniform'
a = 0
b = 1
got = gen_float(data_num, distribution, a, b, 0, 0).split('\n')
del got[-1]
float_arr = [float(x) for x in got]
flag = False
for i in float_arr:
if i < a or i > b:
flag = True
self.assertFalse(flag)
def test_normal(self):
"""
Test normal distribution
Value should be (x-3σ; x+3σ) - three-sigma rule (99.7%)
"""
random.seed(0)
data_num = 100000
distribution = 'normal'
mean = 5
std = 0.5
got = gen_float(data_num, distribution, 0, 0, mean, std).split('\n')
del got[-1]
float_arr = [float(x) for x in got]
counter = 0
for i in float_arr:
if i < mean - 3 * std or i > mean + 3 * std:
counter += 1
self.assertTrue(counter <= data_num * 0.3 / 100)