<｜begin▁of▁sentence｜>### Instruction:
Given a set of 5 nodes and their corresponding latent positions in a 2-dimensional space, can you sample a binary random graph and a weighted random graph with Poisson-distributed edge weights using the Random Dot Product Graph (RDPG) model from the `graspologic` package, and then print the adjacency matrices for both graphs?
### Response:
import numpy as np
from graspologic.simulations import rdpg

# Seed the random number generator for reproducibility
np.random.seed(1)

# Generate random latent positions using 2-dimensional Dirichlet distribution.
X = np.random.dirichlet([1, 1], size=5)

# Sample a binary RDPG using sampled latent positions.
A = rdpg(X, loops=False)
print(A)

# Sample a weighted RDPG with Poisson(2) weight distribution
wt = np.random.poisson
wtargs = {'lam': 2}
A_weighted = rdpg(X, loops=False, wt=wt, wtargs=wtargs)
print(A_weighted)
<|EOT|>
