def test_plot_eigenvectors_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(dim=2,
n_features=8,
method='exact',
n_boot=5)
ss.fit(inputs=inputs, gradients=gradients, weights=weights)
with assert_plot_figures_added():
ss.plot_eigenvectors(n_evects=2,
figsize=(7, 7),
title='Eigenvectors')
def test_plot_eigenvectors_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(dim=2,
n_features=5,
method='exact',
n_boot=5)
ss.fit(inputs=inputs, gradients=gradients, weights=weights)
with assert_plot_figures_added():
ss.plot_eigenvectors(n_evects=2,
figsize=(5, 8),
labels=[r'$x$', r'$y$', 'q', r'$r$', r'$z$'])
def test_plot_eigenvectors_01(self):
ss = KernelActiveSubspaces(dim=2)
with self.assertRaises(TypeError):
ss.plot_eigenvectors(figsize=(7, 7), title='Eigenvalues')
