A step in the simulation of random feature spread on a network guided by NPM

simulation_step.py

import numpy as np

# We're given an n by n distance matrix *D* with transfer
# probabilities for a given pair of nodes (for any feature),
# a feature matrix *M*, and a dropout probability p_d.

# We convert the transfer probabilities to no-transfer probabilities
# and take their logs
L = np.log(1 - D)

# No-transfer probabilities after considering all node pairs
LogNoTransfer = np.matmul(L, M)
NoTransfer = np.exp(LogNoTransfer)
Transfer = 1 - NoTransfer

# Update M
for i in range(M.shape[0]):
    for j in range(M.shape[1]):
        M[i,j] = max(M[i,j], np.random.binomial(1, Transfer[i,j]))
        
# Apply dropout
for i in range(M.shape[0]):
    for j in range(M.shape[1]):
        M[i,j] = min(M[i,j], M[i,j] * np.random.binomial(1, 1 - p_d))