def test_pi_simple(self):
# I'm not considering the multi alpha case. Just one alpha can be used.
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average, alpha=0.1, n_samples=100)
assert_array_almost_equal(results, [0.3148352, 1.10458334])
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average, alpha=0.2, n_samples=100)
assert_array_almost_equal(results, [0.35975001, 1.02974178])
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average, alpha=0.8, n_samples=100)
assert_array_almost_equal(results, [0.68154479, 0.84423589])
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average, alpha=0.9, n_samples=100)
assert_array_almost_equal(results, [0.71450871, 0.77017119])
def test_pi_multi_2dout_multialpha(self):
# I'm not considering the multi option of bootstrap so only 1d arrays
# are valid and this test should fail as is so i modified the original
# test to make it usable
np.random.seed(1234567890)
results = bootstrap_ci(np.vstack((self.x, self.y)).T,
lambda a: stats.linregress(a)[0],
alpha=0.1,
n_samples=2000)
assert_array_almost_equal(results, [-0.375, 0.90243902])
np.random.seed(1234567890)
results = bootstrap_ci(np.vstack((self.x, self.y)).T,
lambda a: stats.linregress(a)[1],
alpha=0.1,
n_samples=2000)
assert_array_almost_equal(results, [0.22727273, 3.95121951])
def test_pi_multi_2dout_multialpha(self):
# I'm not considering the multi option of bootstrap so only 1d arrays
# are valid and this test should fail as is so i modified the original
# test to make it usable
np.random.seed(1234567890)
results = bootstrap_ci(np.vstack((self.x, self.y)).T,
lambda a: stats.linregress(a)[0],
alpha = 0.1,
n_samples = 2000)
assert_array_almost_equal(results, [-0.375, 0.90243902])
np.random.seed(1234567890)
results = bootstrap_ci(np.vstack((self.x, self.y)).T,
lambda a: stats.linregress(a)[1],
alpha = 0.1,
n_samples = 2000)
assert_array_almost_equal(results, [0.22727273, 3.95121951])
def test_pi_simple(self, alpha, res):
# I'm not considering the multi alpha case. Just one alpha can be used.
np.random.seed(1234567890)
results = bootstrap_ci(self.data,
np.average,
alpha=alpha,
n_samples=100)
assert_array_almost_equal(results, res)
def test_pi_simple(self):
# I'm not considering the multi alpha case. Just one alpha can be used.
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average,
alpha = 0.1, n_samples = 100)
assert_array_almost_equal(results, [0.3148352 , 1.10458334])
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average,
alpha = 0.2, n_samples = 100)
assert_array_almost_equal(results, [0.35975001, 1.02974178])
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average,
alpha = 0.8, n_samples = 100)
assert_array_almost_equal(results, [0.68154479, 0.84423589])
np.random.seed(1234567890)
results = bootstrap_ci(self.data, np.average,
alpha = 0.9, n_samples = 100)
assert_array_almost_equal(results, [0.71450871, 0.77017119])
