def test_partition_05(self):
np.random.seed(42)
matrix = np.random.uniform(-1, 1, 9).reshape(3, 3)
ss = ActiveSubspaces(dim=4)
ss.evects = matrix
with self.assertRaises(ValueError):
ss._partition()
def test_partition_02(self):
np.random.seed(42)
matrix = np.random.uniform(-1, 1, 9).reshape(3, 3)
ss = ActiveSubspaces(dim=2)
ss.evects = matrix
ss._partition()
np.testing.assert_array_almost_equal(matrix[:, 2:], ss.W2)
#AS
ss = ActiveSubspaces(dim=1)
ss.fit(inputs=x, outputs=f, gradients=df)
ss.plot_eigenvalues()
ss.plot_eigenvectors()
ss.plot_sufficient_summary(inputs=x, outputs=f)
## Active Subspaces with vectorial outputs
#process data
x, f, df = x_, fa_, dfa_.reshape(n_samples, d, input_dim)
print("data", x.shape, f.shape, df.shape)
#vectorial AS
vss = ActiveSubspaces(dim=5, n_boot=10)
vss.fit(inputs=x, outputs=f, gradients=df, metric=metric)
np.save("data/modes_AS", vss.W1)
vss.dim = 1
vss._partition()
vss.plot_eigenvalues()
vss.plot_eigenvectors()
components = [100 + j * 100 for j in range(16)]
for i in components:
img = mpimg.imread("data/component_{}.png".format(i))
plt.imshow(img)
plt.axis('off')
vss.plot_sufficient_summary(inputs=x,
outputs=f[:, i],
title="dof {}".format(i),
figsize=(4, 4))
