def test_iid_unequal_equiv():
rs = RandomState(0)
x = rs.standard_normal(500)
rs1 = RandomState(0)
bs1 = IIDBootstrap(x, random_state=rs1)
rs2 = RandomState(0)
bs2 = IndependentSamplesBootstrap(x, random_state=rs2)
v1 = bs1.var(np.mean)
v2 = bs2.var(np.mean)
assert_allclose(v1, v2)
def test_iid_unequal_equiv():
rs = RandomState(0)
x = rs.randn(500)
rs1 = RandomState(0)
bs1 = IIDBootstrap(x, random_state=rs1)
rs2 = RandomState(0)
bs2 = IndependentSamplesBootstrap(x, random_state=rs2)
v1 = bs1.var(np.mean)
v2 = bs2.var(np.mean)
assert_allclose(v1, v2)
def test_iid_unequal_equiv():
rs = RandomState(0)
x = rs.standard_normal(500)
rs1 = RandomState(0)
bs1 = IIDBootstrap(x, random_state=rs1)
rs2 = RandomState(0)
bs2 = IndependentSamplesBootstrap(x, random_state=rs2)
v1 = bs1.var(np.mean)
v2 = bs2.var(np.mean)
assert_allclose(v1, v2)
assert isinstance(bs2.index, tuple)
assert isinstance(bs2.index[0], list)
assert isinstance(bs2.index[0][0], np.ndarray)
assert bs2.index[0][0].shape == x.shape
def test_cov(bs_setup):
bs = IIDBootstrap(bs_setup.x)
num_bootstrap = 10
cov = bs.cov(func=bs_setup.func, reps=num_bootstrap, recenter=False)
bs.reset()
results = np.zeros((num_bootstrap, 2))
count = 0
for data, _ in bs.bootstrap(num_bootstrap):
results[count] = data[0].mean(axis=0)
count += 1
errors = results - bs_setup.x.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
bs.reset()
cov = bs.cov(func=bs_setup.func, recenter=True, reps=num_bootstrap)
errors = results - results.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
bs = IIDBootstrap(bs_setup.x_df)
cov = bs.cov(func=bs_setup.func, reps=num_bootstrap, recenter=False)
bs.reset()
var = bs.var(func=bs_setup.func, reps=num_bootstrap, recenter=False)
bs.reset()
results = np.zeros((num_bootstrap, 2))
count = 0
for data, _ in bs.bootstrap(num_bootstrap):
results[count] = data[0].mean(axis=0)
count += 1
errors = results - bs_setup.x.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
assert_allclose(var, np.diag(direct_cov))
bs.reset()
cov = bs.cov(func=bs_setup.func, recenter=True, reps=num_bootstrap)
errors = results - results.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
def test_cov(self):
bs = IIDBootstrap(self.x)
num_bootstrap = 10
cov = bs.cov(func=self.func, reps=num_bootstrap, recenter=False)
bs.reset()
results = np.zeros((num_bootstrap, 2))
count = 0
for data, _ in bs.bootstrap(num_bootstrap):
results[count] = data[0].mean(axis=0)
count += 1
errors = results - self.x.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
bs.reset()
cov = bs.cov(func=self.func, recenter=True, reps=num_bootstrap)
errors = results - results.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
bs = IIDBootstrap(self.x_df)
cov = bs.cov(func=self.func, reps=num_bootstrap, recenter=False)
bs.reset()
var = bs.var(func=self.func, reps=num_bootstrap, recenter=False)
bs.reset()
results = np.zeros((num_bootstrap, 2))
count = 0
for data, _ in bs.bootstrap(num_bootstrap):
results[count] = data[0].mean(axis=0)
count += 1
errors = results - self.x.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
assert_allclose(var, np.diag(direct_cov))
bs.reset()
cov = bs.cov(func=self.func, recenter=True, reps=num_bootstrap)
errors = results - results.mean(axis=0)
direct_cov = errors.T.dot(errors) / num_bootstrap
assert_allclose(cov, direct_cov)
