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renamed markov_words to gibberish
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‎anagram/discussion.md‎

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@@ -129,7 +129,7 @@ True
129129

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## Selecting words to compare
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My first implementation of this program was quite naive and yet worked fine for find all other single words that were anagrams. Everything came crashing down when I attempted to find combinations. I suddenly realized the number of 2-word combinations I needed to check (that 55 *billion* I mentioned before). As it happened, I then rewatched "The Imitation Game" about Alan Turing and the creation of his machine ("Christopher") to crack the Enigma code which has a possible 150 million million possible states. He was unable to build a machine that could churn through that many possibilities in the 18 hours or so per day they had to find the right combination, so they had to find a way to cut down the number of combinations they attempted. Similarly, I realized I only needed to look at combinations of words whose lengths sum to the length of the given word; hency my decision to store `words` using the word length as the key and then as a `set` of words that length.
132+
My first implementation of this program was quite naive and yet worked fine for find all other single words that were anagrams. Everything came crashing down when I attempted to find combinations. I suddenly realized the number of 2-word combinations I needed to check (that 55 *billion* I mentioned before). As it happened, I then rewatched "The Imitation Game" about Alan Turing and the creation of his machine ("Christopher") to crack the Enigma code which has a possible 150 million million possible states. He was unable to build a machine that could churn through that many possibilities in the 18 hours or so per day they had to find the right combination, so they had to find a way to cut down the number of combinations they attempted. Similarly, I realized I only needed to look at combinations of words whose lengths sum to the length of the given word; hence my decision to store `words` using the word length as the key and then as a `set` of words that length.
133133

134134
Next I needed to find all combinations of numbers that add up to that number. Let's consider we are using "listen" as the `text`:
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@@ -216,7 +216,7 @@ Let's take the first combo:
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````
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>>> keys = key_combos[0]
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>>> keys
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(1, 3)
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(3, 3)
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````
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And pretend we have a very small `words` list:
@@ -270,4 +270,4 @@ tin les
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Some are repeated which is why I chose to create my `anagrams` holder as a `set` to make them unique.
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In the end, I look to see how many `anagrams` I found using `len(anagrams)`. If there are some, I report how many and what they are in `sorted` order; otherwise I let the user know that none were found.
273+
In the end, I look to see how many `anagrams` I found using `len(anagrams)`. If there are some, I report how many and what they are in `sorted` order; otherwise I let the user know that none were found.

‎bin/chapters.txt‎

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@@ -23,7 +23,7 @@ mommys_little_helper
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kentucky_friar
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mad_libs
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license_plates
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markov_words
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gibberish
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piggie
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soundex_rhymer
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substring

‎book.md‎

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@@ -4277,9 +4277,9 @@ In creating all the possible plates from your regular expression, you are making
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\newpage
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# Chapter 26: Markov Chains for Words
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# Chapter 26: Gibberish Generator
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Write a Python program called `markov.py` that uses the Markov chain algorithm to generate new words from a set of training files. The program should take one or more positional arguments which are files that you read, word-by-word, and note the options of letters after a given `-k|--kmer_size` (default `2`) grouping of letters. E.g., in the word "alabama" with `k=1`, the frequency table will look like:
4282+
Write a Python program called `gibberish.py` that uses the Markov chain algorithm to generate new words from a set of training files. The program should take one or more positional arguments which are files that you read, word-by-word, and note the options of letters after a given `-k|--kmer_size` (default `2`) grouping of letters. E.g., in the word "alabama" with `k=1`, the frequency table will look like:
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42844284
````
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a = l, b, m
@@ -4300,11 +4300,11 @@ Chose the best words and create definitions for them:
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* urchenev: fungal growth found under cobblestones
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````
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$ ./markov.py
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usage: markov.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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markov.py: error: the following arguments are required: FILE
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$ ./markov.py -h
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usage: markov.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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$ ./gibberish.py
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usage: gibberish.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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gibberish.py: error: the following arguments are required: FILE
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$ ./gibberish.py -h
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usage: gibberish.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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Markov chain for characters/words
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Max word length (default: 12)
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-s int, --seed int Random seed (default: None)
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-d, --debug Debug to ".log" (default: False)
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$ ./markov.py /usr/share/dict/words -s 1
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$ ./gibberish.py /usr/share/dict/words -s 1
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1: oveli
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2: uming
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3: uylatiteda
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8: eyhopy
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9: auretrab
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10: ozogralach
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$ ./markov.py ../inputs/const.txt -s 2 -k 3
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$ ./gibberish.py ../inputs/const.txt -s 2 -k 3
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1: romot
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2: leasonsusp
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3: gdoned
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8: nmentyone
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9: effereof
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10: eipts
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$ ./markov.py -k 2 ../inputs/1945-boys.txt
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$ ./gibberish.py -k 2 ../inputs/1945-boys.txt
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1: baronaler
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2: lip
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3: oselli
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## Selecting words to compare
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My first implementation of this program was quite naive and yet worked fine for find all other single words that were anagrams. Everything came crashing down when I attempted to find combinations. I suddenly realized the number of 2-word combinations I needed to check (that 55 *billion* I mentioned before). As it happened, I then rewatched "The Imitation Game" about Alan Turing and the creation of his machine ("Christopher") to crack the Enigma code which has a possible 150 million million possible states. He was unable to build a machine that could churn through that many possibilities in the 18 hours or so per day they had to find the right combination, so they had to find a way to cut down the number of combinations they attempted. Similarly, I realized I only needed to look at combinations of words whose lengths sum to the length of the given word; hency my decision to store `words` using the word length as the key and then as a `set` of words that length.
5889+
My first implementation of this program was quite naive and yet worked fine for find all other single words that were anagrams. Everything came crashing down when I attempted to find combinations. I suddenly realized the number of 2-word combinations I needed to check (that 55 *billion* I mentioned before). As it happened, I then rewatched "The Imitation Game" about Alan Turing and the creation of his machine ("Christopher") to crack the Enigma code which has a possible 150 million million possible states. He was unable to build a machine that could churn through that many possibilities in the 18 hours or so per day they had to find the right combination, so they had to find a way to cut down the number of combinations they attempted. Similarly, I realized I only needed to look at combinations of words whose lengths sum to the length of the given word; hence my decision to store `words` using the word length as the key and then as a `set` of words that length.
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58915891
Next I needed to find all combinations of numbers that add up to that number. Let's consider we are using "listen" as the `text`:
58925892

@@ -5973,7 +5973,7 @@ Let's take the first combo:
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````
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>>> keys = key_combos[0]
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>>> keys
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(1, 3)
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(3, 3)
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````
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59795979
And pretend we have a very small `words` list:
@@ -6028,6 +6028,7 @@ tin les
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Some are repeated which is why I chose to create my `anagrams` holder as a `set` to make them unique.
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In the end, I look to see how many `anagrams` I found using `len(anagrams)`. If there are some, I report how many and what they are in `sorted` order; otherwise I let the user know that none were found.
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\newpage
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# Chapter 34: Hangman

‎gibberish/.log‎

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markov_words/Makefile renamed to gibberish/Makefile

File renamed without changes.

markov_words/README.md renamed to gibberish/README.md

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# Markov Chains for Words
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# Gibberish Generator
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3-
Write a Python program called `markov.py` that uses the Markov chain algorithm to generate new words from a set of training files. The program should take one or more positional arguments which are files that you read, word-by-word, and note the options of letters after a given `-k|--kmer_size` (default `2`) grouping of letters. E.g., in the word "alabama" with `k=1`, the frequency table will look like:
3+
Write a Python program called `gibberish.py` that uses the Markov chain algorithm to generate new words from a set of training files. The program should take one or more positional arguments which are files that you read, word-by-word, and note the options of letters after a given `-k|--kmer_size` (default `2`) grouping of letters. E.g., in the word "alabama" with `k=1`, the frequency table will look like:
44

55
````
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a = l, b, m
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* urchenev: fungal growth found under cobblestones
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````
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$ ./markov.py
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usage: markov.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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markov.py: error: the following arguments are required: FILE
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$ ./markov.py -h
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usage: markov.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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$ ./gibberish.py
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usage: gibberish.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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gibberish.py: error: the following arguments are required: FILE
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$ ./gibberish.py -h
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usage: gibberish.py [-h] [-n int] [-k int] [-m int] [-s int] [-d] FILE [FILE ...]
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Markov chain for characters/words
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Max word length (default: 12)
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-s int, --seed int Random seed (default: None)
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-d, --debug Debug to ".log" (default: False)
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$ ./markov.py /usr/share/dict/words -s 1
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$ ./gibberish.py /usr/share/dict/words -s 1
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1: oveli
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2: uming
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3: uylatiteda
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8: eyhopy
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9: auretrab
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10: ozogralach
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$ ./markov.py ../inputs/const.txt -s 2 -k 3
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$ ./gibberish.py ../inputs/const.txt -s 2 -k 3
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1: romot
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2: leasonsusp
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3: gdoned
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8: nmentyone
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9: effereof
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10: eipts
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$ ./markov.py -k 2 ../inputs/1945-boys.txt
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$ ./gibberish.py -k 2 ../inputs/1945-boys.txt
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1: baronaler
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2: lip
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3: oselli

markov_words/solution.py renamed to gibberish/solution.py

File renamed without changes.

markov_words/test.py renamed to gibberish/test.py

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#!/usr/bin/env python3
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"""tests for markov.py"""
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"""tests for gibberish.py"""
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import os
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import re
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import random
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import string
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from subprocess import getstatusoutput, getoutput
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prg = './markov.py'
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prg = './gibberish.py'
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const = '../inputs/const.txt'
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dickinson = '../inputs/dickinson.txt'
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‎playful_python.pdf‎

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