Problem With Interpolate

Howdy!

I am trying to use the reduction interpolate function, and I encounter the following:

    171     
    172     L_reg = G.L + reg_eps * sparse.eye(G.N)
--> 173     K_reg = getattr(G.mr, 'K_reg', kron_reduction(L_reg, keep_inds))
    174     green_kernel = getattr(G.mr, 'green_kernel',
    175                            filters.Filter(G, lambda x: 1. / (reg_eps + x)))

AttributeError: 'Graph' object has no attribute 'mr

The following is how I am using it; my graph is fully connected, and oneSignal and indeces are one full signal on the graph in np array format and the indices in np array format respectively.

pygsp.reduction.interpolate(graph, oneSignal, indeces)

Any suggestions?

[codenameAggie]

Howdy!

Thank you sooo much; this is exactly what I needed! I was also using the wrong version of pyGSP, as I was using the 0.5.1, using pip install. I changed to the github version and I have things figured out now.

Thanks again, Arash

[nperraud]

Hi Arash,

I think what the best for you would be to use some basic interpolator based on a smoothness assumption.

We have function that will just do the trick for you (in master but for the next release). Here is an example.

from pygsp import graphs, learning, filters
import matplotlib.pyplot as plt
import numpy as np

G = graphs.Sensor(seed=42)
G.estimate_lmax()

# Create a ground truth signal:
g = filters.Heat(G, 10)
signal = g.filter(np.random.randn(G.n_vertices))

# Construct a measurement signal from a binary mask:
rs = np.random.RandomState(42)
mask = rs.uniform(0, 1, G.n_vertices) > 0.5
measures = signal.copy()
measures[~mask] = np.nan

# Solve the classification problem by reconstructing the signal:
recovery = learning.regression_tikhonov(
    G, measures, mask, tau=0)

# Plot the results.
# Note that we recover the class with ``np.argmax(recovery, axis=1)``.
fig, ax = plt.subplots(1, 3, sharey=True, figsize=(10, 3))
G.plot_signal(signal, ax=ax[0], title='Ground truth')
G.plot_signal(measures, ax=ax[1], title='Measurements')
G.plot_signal(recovery, ax=ax[2], title='Recovered class')
fig.tight_layout()

taken from https://github.com/epfl-lts2/pygsp/blob/bf4e4374fe925c6d5944b1d6016b812b8f4d3915/pygsp/learning.py#L254

Here is what you should get:

Good luck