sample_dclvm.Rd
A DCLVM with K clusters has edges generated as
E[Aij∣x,θ]∝θiθje−‖
where e_k is the kth basis vector of R^d, w_i \sim N(0, I_d),
and \{z_i\} \subset [K]^n. The proportionality constant is chosen such
that the overall network has expected average degree \lambda.
To calculate the scaling constant, we approximate E[e^{- \|x_i - x_j\|^2}]
for i \neq j by generating random npairs
\{z_i, z_j\} and average over them.
sample_dclvm(z, lambda, theta, npairs = NULL)
a vector of cluster labels
desired average degree of the network
degree parameter
number of pairs of \{z_i, z_j\}
Sample form a degree-corrected latent variable model with Gaussian kernel