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Using std. dev. of sampled mean for comparison

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This commit is contained in:
Patrick Shriwise 2022-06-26 01:10:53 -04:00
parent 3e61076715
commit e9eaf2edef
1 changed files with 25 additions and 8 deletions
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33
tests/unit_tests/test_stats.py
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@ -36,8 +36,10 @@ def test_discrete():
# sample discrete distribution
n_samples = 1_000_000
samples = d3.sample(n_samples, seed=100)
# check that the mean of the samples is close to the true mean
assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
# check that the mean of the samples is within 3 std. dev.
# of the expected mean
std_dev = samples.std() / np.sqrt(n_samples)
assert np.abs(exp_mean - samples.mean()) < 3*std_dev
def test_merge_discrete():
@ -81,7 +83,10 @@ def test_uniform():
exp_mean = 0.5 * (a + b)
n_samples = 1_000_000
samples = d.sample(n_samples, seed=100)
assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
# check that the mean of the samples is within 3 std. dev.
# of the expected mean
std_dev = samples.std() / np.sqrt(n_samples)
assert np.abs(exp_mean - samples.mean()) < 3*std_dev
def test_powerlaw():
@ -100,7 +105,10 @@ def test_powerlaw():
# sample power law distribution
n_samples = 1_000_000
samples = d.sample(n_samples, seed=100)
assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
# check that the mean of the samples is within 3 std. dev.
# of the expected mean
std_dev = samples.std() / np.sqrt(n_samples)
assert np.abs(exp_mean - samples.mean()) < 3*std_dev
def test_maxwell():
@ -117,13 +125,19 @@ def test_maxwell():
# sample maxwell distribution
n_samples = 1_000_000
samples = d.sample(n_samples, seed=100)
assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
# check that the mean of the samples is within 3 std. dev.
# of the expected mean
std_dev = samples.std() / np.sqrt(n_samples)
assert np.abs(exp_mean - samples.mean()) < 3*std_dev
# A second sample with a different seed
samples_2 = d.sample(n_samples, seed=200)
assert samples_2.mean() == pytest.approx(exp_mean, rel=1e-02)
assert samples_2.mean() != samples.mean()
# check that the mean of the samples is within 3 std. dev.
# of the expected mean
std_dev = samples_2.std() / np.sqrt(n_samples)
assert np.abs(exp_mean - samples_2.mean()) < 3*std_dev
assert samples_2.mean() != samples.mean()
def test_watt():
@ -145,7 +159,10 @@ def test_watt():
# sample Watt distribution
n_samples = 1_000_000
samples = d.sample(n_samples, seed=100)
assert samples.mean() == pytest.approx(exp_mean, rel=1e-03)
# check that the mean of the samples is within 3 std. dev.
# of the expected mean
std_dev = samples.std() / np.sqrt(n_samples)
assert np.abs(exp_mean - samples.mean()) < 3*std_dev
def test_tabular():