<｜begin▁of▁sentence｜>### Instruction:
Given a set of labeled graph samples for two distinct classes (10 samples per class), each with 5 vertices, and a constraint of a signal-subgraph with at most 5 edges and incident to exactly 3 vertices, can you use SignalSubgraph in graspologic to estimate the signal-subgraph and the contingency matrices for these samples?
### Response:
import numpy as np
from graspologic.subgraph import SignalSubgraph

# Generate synthetic data for two classes
np.random.seed(0)
class1 = np.random.rand(5, 5, 10)
class2 = np.random.rand(5, 5, 10)

# Upper triangle matrices for undirected graphs
for i in range(10):
    class1[:, :, i] = np.triu(class1[:, :, i], 1)
    class2[:, :, i] = np.triu(class2[:, :, i], 1)

# Combine the data
graphs = np.concatenate((class1, class2), axis=2)

# Create labels for the two classes
labels = np.array([0] * 10 + [1] * 10)

# Initialize the Signal Subgraph estimator with constraints
sse = SignalSubgraph(constraints=(5, 3))

# Fit the estimator to the synthetic data
sse.fit(graphs, labels)

# Get the signal subgraph
sigsub = sse.sigsub_
contmat = sse.contmat_
mask = sse.mask_

# Print the results
print('Signal subgraph:', sigsub)
print('Contingency matrices:', contmat)
print('Mask of signal subgraph in adjacency matrix:', mask)
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