def test_numpy():
# Generate 10,000 uniformly distributed random numbers with numpy.
num_list = np.random.uniform(0, 1, 10000)
# Test that your mean is a proper numerical match to the numpy one.
obs = mean(num_list)
exp = np.mean(num_list)
np.testing.assert_almost_equal(obs, exp)
def test_complex():
# given that complex numbers are an unordered field
# the arithmetic mean of complex numbers is meaningless
num_list = [2 + 3j, 3 + 4j, -32 - 2j]
obs = mean(num_list)
exp = NotImplemented
assert obs == exp
def test_numpy():
# testing with numpy
import numpy as np
num_list = np.random.uniform(0, 10, 10000)
obs = mean(num_list)
exp = np.mean(num_list)
np.testing.assert_almost_equal(obs, exp, decimal=10)
def test_uniform():
# test the mean of 10,000 uniforms
import numpy as np
np.random.seed(259)
unif_list = np.random.uniform(size=10000)
obs = mean(unif_list)
exp = np.mean(unif_list)
assert np.isclose(obs, exp)
def test_ints():
num_list = [1, 2, 3, 4, 5]
obs = mean(num_list)
exp = 3
assert obs == exp
def test_mean():
assert mean([1]) == 1
def test_rand():
x = np.random.uniform(size=10000)
# npt.assert_almost_equal(mean(x), np.mean(x))
npt.assert_allclose(mean(x), np.mean(x), rtol=1e-12)
def test_long():
big = 100000000
obs = mean(range(1, big))
exp = big / 2.0
assert obs == exp
def test_double():
# This one will fail in Python 2
num_list = [1, 2, 3, 4]
obs = mean(num_list)
exp = 2.5
assert obs == exp
def test_zero():
num_list = [0, 2, 4, 6]
obs = mean(num_list)
exp = 3
assert obs == exp
def test_mean_random():
data = np.random.rand(10000)
npm = np.mean(data)
mea = mean(data)
assert np.isclose(npm,mea)
def test_npmean():
np_list = np.random.uniform(size=10_000)
obs = mean(np_list)
exp = np.mean(np_list)
np.testing.assert_allclose(obs, exp, rtol=1e-12)
def test_np_mean():
uni = np.random.uniform(size=10_000)
npt.assert_allclose(mean(uni), np.mean(uni))
def test_numpy():
import numpy as np
nums = np.random.uniform(0, 1, 10000)
np.testing.assert_allclose(np.mean(nums), mean(nums), atol=1e-07)
def test_mean():
array = np.random.uniform(0, 100, 10000)
assert np.mean(array) == mean(array)
def test_numpy():
"Test against numpy's mean with a reasonably large array"
npr = np.random.uniform(size=10_000)
npm = npr.mean()
mym = mean(npr)
npt.assert_allclose(mym, npm, rtol=1e-12)
