Hi professor Peixoto, I am very sorry to bother, but I have been trying to use the network inference method with LV model and I cannot make it work. I would really appreciate any help.

- Graph-tool version 2.77_1
- Python version 3.12.5
- Operating system: macOS Sonoma 14.5

**Code**

```
import graph_tool.all as gt
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.sparse
# Generate samples
g = gt.collection.data["karate"].copy()
E = g.num_edges()
N = g.num_vertices()
s = g.new_vp("double", vals= 10+ 10 * np.random.random(g.num_vertices()))
r = g.new_vp("double", val= + 20)
w = g.new_ep("double", vals=np.random.normal(0., .4, g.num_edges()))
for v in g.vertices():
e = g.add_edge(v,v) #adds if not already present in adiacency matrix
w[e] = - 1. #resets diagonal to -1
A = gt.adjacency(g, weight= w) #sparse
A_dense = A.toarray()
# Check linear stability
eigenvalues = np.linalg.eigvals(A_dense)
re_eigenvalues = np.real(eigenvalues)
print(f"leading eigenval = {max(re_eigenvalues)}")
sigma_ = 10.
ts = np.linspace(0, 1.5 , 1000)
state = gt.LVState(g, s=s, r=r, w=w, sigma= sigma_)
ss = []
for t in ts:
## RK not suited for stochastic ODEs, use Euler instead
ret = state.solve_euler(t, dt = 0.001)
ss.append(state.get_state().fa.copy())
ss = np.array(ss).T
#Reconstruction
state = gt.LVBlockState(ss)
for _ in range(20):
ret = state.mcmc_sweep(niter=10, verbose = True)
delta = abs(ret[0]) #entropy difference
print(delta)
```

The problem is that mcmc_sweep() does not return anything. In fact, output is:

```
theta_mcmc_sweep:
theta_multiflip_mcmc_sweep:
```

and then it gets stuck. Instead, when setting stochastic noise to zero, the output for this code:

```
for _ in range(20):
ret = state.mcmc_sweep(niter=10, verbose = False)
delta = abs(ret[0]) #entropy difference
print(delta)
u = state.get_graph()
print(f"True graph. Number of nodes {g.num_vertices()}, number of edges= {g.num_edges()}")
print(f"Reconstructed graph. Number of nodes {u.num_vertices()}, number of edges= {u.num_edges()}")
w_r = state.get_x()
print(gt.similarity(g, u, w, w_r))
```

is the following:

```
0.03653399749265418
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
True graph. Number of nodes 34, number of edges= 112
Reconstructed graph. Number of nodes 34, number of edges= 0
1.2037611980179927e-16
```