def test_two():
density1 = [0, 0, 0, 0, 0.1, 0.4, 0.2, 0.1, 0.1, 0.1, 0, 0, 0, 0, 0]
density2 = [0, 0, 0, 0.1, 0.15, 0.35, 0.15, 0.15, 0.1, 0, 0, 0, 0]
density = convolve_two(density1=density1, density2=density2, L=20)
mu_before = mean_of_density(density1, unit=1) + mean_of_density(density2,
unit=1)
mu_after = mean_of_density(density, unit=1)
assert abs(mu_after - mu_before) < 1e-4
def test_two_skew():
density1 = skew_normal_density(L=50, unit=1.0, scale=5.0, a=1.0)
density2 = skew_normal_density(L=50, unit=1.0, scale=5.0, loc=1.2, a=-1.0)
density = convolve_two(density1=density1, density2=density2, L=100)
mu_before = mean_of_density(density1, unit=1) + mean_of_density(density2,
unit=1)
mu_after = mean_of_density(density, unit=1)
assert abs(mu_after - mu_before) < 1e-4
def test_shift_pdf_integer():
cdf = example_cdf()
pdf = cdf_to_pdf(cdf)
x = 2
pdf_moved = fractional_shift(cdf=pdf, x=x)
mean_before = mean_of_density(pdf, unit=1)
mean_after = mean_of_density(pdf_moved, unit=1)
mean_diff = mean_after - mean_before
assert abs(mean_diff - x) < 1e-6
def test_many_skew():
densities = [
skew_normal_density(L=50, unit=1.0, scale=5.0, a=1.0)
for _ in range(10)
]
density = convolve_many(densities=densities, L=500, do_padding=True)
assert len(density) == 1001
mu_before = sum([mean_of_density(d, unit=1) for d in densities])
mu_after = mean_of_density(density, unit=1)
assert abs(mu_after - mu_before) < 1e-4
def test_many_many_normal():
densities = [
skew_normal_density(L=50, unit=1.0, scale=5.0, a=0.0)
for _ in range(100)
]
L = 5000
density = convolve_many(densities=densities, L=L)
assert len(density) == 2 * L + 1
mu_before = sum([mean_of_density(d, unit=1) for d in densities])
mu_after = mean_of_density(density, unit=1)
assert abs(mu_after - mu_before) < 1e-4
def test_die():
x = [
np.random.choice([-2.5, -1.5, -0.5, 0, 0.5, 1.5, 2.5])
for _ in range(10000)
]
unit = 1
d = density_from_samples(x=x, L=5, unit=0.5)
mu = mean_of_density(density=d, unit=unit)
print(mu)
return d
def mean_of_cdf(cdf):
pdf = cdf_to_pdf(cdf)
return mean_of_density(pdf, unit=1)
def demo():
skew = skew_normal_density(L=L, unit=unit, a=1.0)
print("mean is " + str(mean_of_density(skew, unit=unit)))
densitiesPlot([skew], unit=unit)
def test_samples():
x = np.random.randn(5000)
unit = 0.05
d = density_from_samples(x=x, L=501, unit=unit)
mu = mean_of_density(density=d, unit=unit)