A walkthrough of implementing a Markov chain text generator in 20 lines of Python. The algorithm builds a lookup table mapping word pairs (bigrams) to lists of possible following words, then randomly walks the table to produce readable but nonsensical output. The post explains the algorithm step by step, shows example outputs generated from various classic texts (Alice in Wonderland, the King James Bible, War of the Worlds), and presents the full Python implementation using collections.defaultdict and random.choice. The author also highlights the elegant design choice of treating punctuation as part of words to naturally preserve sentence structure in output.
3 Impressions