def test_no_kwargs(self, dummy_dmap, dim):
mydmap = dummy_dmap
fig = viz.embedding_plot(mydmap,
dim=dim,
scatter_kwargs=None,
show=False)
assert (fig)
def test_colormap(self, dummy_dmap, cmap):
# This just tests if the code runs...
# Replace with something more stringent?
mydmap = dummy_dmap
scatter_kwargs = {'c': mydmap.dmap[:, 0], 'cmap': cmap}
fig = viz.embedding_plot(mydmap, scatter_kwargs=scatter_kwargs, show=False)
assert(fig)
def test_size(self, dummy_dmap, size):
mydmap = dummy_dmap
scatter_kwargs = {'s': size}
fig = viz.embedding_plot(mydmap, scatter_kwargs=scatter_kwargs, show=False)
SC = fig.axes[0].collections[0]
actual_sizes = SC.get_sizes()
assert(np.all(actual_sizes == size))
def test_fixed_coloring(self, dummy_dmap):
mydmap = dummy_dmap
scatter_kwargs = {'c': 'r'}
true_coloring = (1.0, 0., 0., 1)
fig = viz.embedding_plot(mydmap, scatter_kwargs=scatter_kwargs, show=False)
SC = fig.axes[0].collections[0]
assert(np.all(SC._facecolors[0] == true_coloring))
eps = 0.01
mydmap = dm.DiffusionMap(n_evecs=2, epsilon=eps, alpha=1.0, k=400)
mydmap.fit_transform(data)
#real_evals = np.array([2, 2, 2, 6])
#test_evals = -4./eps*(mydmap.evals - 1)
#eval_error = np.abs(test_evals-real_evals)/real_evals
#print(test_evals)
#print(eval_error)
from pydiffmap.visualization import embedding_plot, data_plot
embedding_plot(mydmap,
dim=2,
scatter_kwargs={
'c': mydmap.dmap[:, 0],
'cmap': 'Spectral'
})
plt.show()
data_plot(mydmap, dim=3, scatter_kwargs={'cmap': 'Spectral'})
plt.show()
northpole = np.argmax(mydmap.dmap[:, 0])
north = data[northpole, :]
phi_n = Phi[northpole]
theta_n = Theta[northpole]
R = np.array([[
np.sin(theta_n) * np.cos(phi_n),
np.sin(theta_n) * np.sin(phi_n), -np.cos(theta_n)
], [-np.sin(phi_n), np.cos(phi_n), 0],
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
mydmap = dm.DiffusionMap(n_evecs=2,
epsilon=.2,
alpha=0.5,
k=400,
metric='euclidean')
dmap = mydmap.fit_transform(X)
from pydiffmap.visualization import embedding_plot
embedding_plot(mydmap,
scatter_kwargs={
'c': X[:, 0],
's': 5,
'cmap': 'coolwarm'
})
plt.show()
from pydiffmap.visualization import data_plot
data_plot(mydmap, scatter_kwargs={'s': 5, 'cmap': 'coolwarm'})
plt.show()
V = DW
beta = 1
target_distribution = np.zeros(len(X))
for i in range(len(X)):
target_distribution[i] = np.exp(-beta * V(X[i]))
# initialize Diffusion map object.
neighbor_params = {'n_jobs': -1, 'algorithm': 'ball_tree'}
mydmap = dm.DiffusionMap(n_evecs=2,
k=200,
epsilon='bgh',
alpha=1.0,
neighbor_params=neighbor_params)
# fit to data and return the diffusion map.
dmap = mydmap.fit_transform(swiss_roll)
from pydiffmap.visualization import embedding_plot, data_plot
embedding_plot(mydmap,
scatter_kwargs={
'c': phi,
's': mydmap.q,
'cmap': 'Spectral'
})
data_plot(mydmap, dim=3, scatter_kwargs={'cmap': 'Spectral'})
plt.show()
print('Correlation between \phi and \psi_1')
print(np.corrcoef(dmap[:, 0], phi))
plt.figure(figsize=(16, 6))
ax = plt.subplot(121)
ax.scatter(phi, dmap[:, 0])
ax.set_title('First DC against $\phi$')
ax.set_xlabel(r'$\phi$')
ax.set_ylabel(r'$\psi_1$')
ax.axis('tight')
