def test_partition_05(self):
np.random.seed(42)
matrix = np.random.uniform(-1, 1, 9).reshape(3, 3)
ss = KernelActiveSubspaces()
ss.evects = matrix
with self.assertRaises(ValueError):
ss.partition(dim=4)
def test_partition_02(self):
np.random.seed(42)
matrix = np.random.uniform(-1, 1, 9).reshape(3, 3)
ss = KernelActiveSubspaces()
ss.evects = matrix
ss.partition(dim=2)
np.testing.assert_array_almost_equal(matrix[:, 2:], ss.W2)
def test_plot_sufficient_summary_02(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 1, 4)
inputs = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = KernelActiveSubspaces()
ss.compute(inputs=inputs,
gradients=gradients,
weights=weights,
method='exact',
nboot=49,
n_features=8,
feature_map=None)
ss.partition(3)
with self.assertRaises(ValueError):
ss.plot_sufficient_summary(10, 10)
def test_plot_sufficient_summary_03(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 1, 4)
inputs = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = KernelActiveSubspaces()
ss.compute(inputs=inputs,
gradients=gradients,
weights=weights,
method='exact',
nboot=49,
n_features=8,
feature_map=None)
ss.partition(2)
with assert_plot_figures_added():
ss.plot_sufficient_summary(
np.random.uniform(-1, 1, 100).reshape(25, 4),
np.random.uniform(-1, 1, 25).reshape(-1, 1))
def test_forward_01(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 30).reshape(15, 1, 2)
inputs = np.random.uniform(-1, 1, 30).reshape(15, 2)
weights = np.ones((15, 1)) / 15
ss = KernelActiveSubspaces()
ss.compute(inputs=inputs,
gradients=gradients,
weights=weights,
method='exact',
nboot=49,
n_features=4,
feature_map=None)
ss.partition(2)
active = ss.forward(np.random.uniform(-1, 1, 4).reshape(2, 2))[0]
true_active = np.array([[1.34199032, 0.02509303],
[1.55021982, -0.29461026]])
np.testing.assert_array_almost_equal(true_active, active)
def test_forward_02(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 30).reshape(15, 1, 2)
inputs = np.random.uniform(-1, 1, 30).reshape(15, 2)
weights = np.ones((15, 1)) / 15
ss = KernelActiveSubspaces()
ss.compute(inputs=inputs,
gradients=gradients,
weights=weights,
method='exact',
nboot=49,
n_features=4,
feature_map=None)
ss.partition(2)
inactive = ss.forward(np.random.uniform(-1, 1, 4).reshape(2, 2))[1]
print(inactive)
true_inactive = np.array([[-0.47449407, 0.51271165],
[-0.27475082, 0.36433068]])
np.testing.assert_array_almost_equal(true_inactive, inactive)
