Shannon Entropy Average information content: $$ H(X) = -\sum_i p(x_i) \log_2 p(x_i) $$ Units: bits (if log base 2), nats (if natural log) 1import numpy as np 2 3def entropy(probabilities): 4 """Calculate Shannon entropy""" 5 p = np.array(probabilities) 6 p = p[p > 0] # Remove zeros 7 return …

Hardware random number generation using /dev/random, /dev/urandom, and hardware RNG sources. Linux Random Devices /dev/random vs /dev/urandom 1# /dev/random - Blocks when entropy pool is depleted 2# Use for: Long-term cryptographic keys 3 4# /dev/urandom - Never blocks, uses CSPRNG when entropy low 5# Use for: Most …

Foundational concepts and mathematical tools of information theory, including entropy, compression, and channel capacity.