def test_bootstrap_ranges(self):
np.random.seed(42)
X = np.random.normal(size=(50, 3))
f = np.random.normal(size=(50, 1))
df = np.random.normal(size=(50, 3))
weights = np.ones((50, 1)) / 50
e, W = ss.active_subspace(df, weights)
ssmethod = lambda X, f, df, weights: ss.active_subspace(df, weights)
d = ss._bootstrap_ranges(e,
W,
None,
None,
df,
weights,
ssmethod,
nboot=10)
e, W = ss.ols_subspace(X, f, weights)
ssmethod = lambda X, f, df, weights: ss.ols_subspace(X, f, weights)
d = ss._bootstrap_ranges(e, W, X, f, None, weights, ssmethod, nboot=10)
e, W = ss.qphd_subspace(X, f, weights)
ssmethod = lambda X, f, df, weights: ss.qphd_subspace(X, f, weights)
d = ss._bootstrap_ranges(e, W, X, f, None, weights, ssmethod, nboot=10)
e, W = ss.opg_subspace(X, f, weights)
ssmethod = lambda X, f, df, weights: ss.opg_subspace(X, f, weights)
d = ss._bootstrap_ranges(e, W, X, f, None, weights, ssmethod, nboot=10)
def test_ladle_partition(self):
np.random.seed(42)
df = np.random.normal(size=(10,3))
weights = np.ones((10,1)) / 10
e, W = ss.active_subspace(df, weights)
ssmethod = lambda X, f, df, weights: ss.active_subspace(df, weights)
e_br, sub_br, li_F = ss._bootstrap_ranges(e, W, None, None, df, weights, ssmethod, nboot=10)
d = ss.ladle_partition(e, li_F)
def test_ladle_partition(self):
np.random.seed(42)
df = np.random.normal(size=(10,3))
weights = np.ones((10,1)) / 10
e, W = ss.active_subspace(df, weights)
ssmethod = lambda X, f, df, weights: ss.active_subspace(df, weights)
e_br, sub_br, li_F = ss._bootstrap_ranges(e, W, None, None, df, weights, ssmethod, nboot=10)
d = ss.ladle_partition(e, li_F)
def test_errbnd_partition(self):
np.random.seed(42)
df = np.random.normal(size=(10,3))
weights = np.ones((10,1)) / 10
e, W = ss.active_subspace(df, weights)
ssmethod = lambda X, f, df, weights: ss.active_subspace(df, weights)
e_br, sub_br, li_F = ss._bootstrap_ranges(e, W, None, None, df, weights, ssmethod, nboot=10)
sub_err = sub_br[:,1].reshape((2, 1))
d = ss.errbnd_partition(e, sub_err)
def test_errbnd_partition(self):
np.random.seed(42)
df = np.random.normal(size=(10,3))
weights = np.ones((10,1)) / 10
e, W = ss.active_subspace(df, weights)
ssmethod = lambda X, f, df, weights: ss.active_subspace(df, weights)
e_br, sub_br, li_F = ss._bootstrap_ranges(e, W, None, None, df, weights, ssmethod, nboot=10)
sub_err = sub_br[:,1].reshape((2, 1))
d = ss.errbnd_partition(e, sub_err)
def test_bootstrap_ranges(self):
np.random.seed(42)
X = np.random.normal(size=(50,3))
f = np.random.normal(size=(50,1))
df = np.random.normal(size=(50,3))
weights = np.ones((50,1)) / 50
e, W = ss.active_subspace(df, weights)
ssmethod = lambda X, f, df, weights: ss.active_subspace(df, weights)
d = ss._bootstrap_ranges(e, W, None, None, df, weights, ssmethod, nboot=10)
e, W = ss.ols_subspace(X, f, weights)
ssmethod = lambda X, f, df, weights: ss.ols_subspace(X, f, weights)
d = ss._bootstrap_ranges(e, W, X, f, None, weights, ssmethod, nboot=10)
e, W = ss.qphd_subspace(X, f, weights)
ssmethod = lambda X, f, df, weights: ss.qphd_subspace(X, f, weights)
d = ss._bootstrap_ranges(e, W, X, f, None, weights, ssmethod, nboot=10)
e, W = ss.opg_subspace(X, f, weights)
ssmethod = lambda X, f, df, weights: ss.opg_subspace(X, f, weights)
d = ss._bootstrap_ranges(e, W, X, f, None, weights, ssmethod, nboot=10)