Commit 2591ae4193244ddc572a6a36d368f5324554b17a - NaNoGenLab

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levenshtein-equidistant/README.md less more

0	0	levenshtein-equidistant
1	1	=======================
2	2
3		Requirements
4		------------
	3	Hypothesis
	4	----------
	5
	6	I expect that if I carry out this experiment, I will discover that I was
	7	not quite sure what I was expecting.
	8
	9	Apparatus
	10	---------
5	11
6	12	* Python 2.7.6 (probably works with older versions too)
7	13	* [py-editdist](http://www.mindrot.org/projects/py-editdist/)
8	14	* Two or three input texts
9	15
10		Basic Strategy
11		--------------
	16	Method
	17	------
12	18
13	19	* Input two reference texts. Then either input a "seed text", or
14	20	generate a random string of characters and use that as the seed text.

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narrative-makefile/README.md less more

0	0	narrative-makefile
1	1	==================
2	2
3		Requirements
4		------------
	3	Hypothesis
	4	----------
	5
	6	We can generate a story using only `make`.
	7
	8	Apparatus
	9	---------
5	10
6	11	* GNU Make (other makes might work too)
7	12
8		Basic Strategy
9		--------------
	13	Method
	14	------
10	15
11	16	* Each point in the narrative is a goal, and may require other goals to
12	17	be met, before it can be met.
13	18	* Each goal outputs some text as a side-effect.
14	19
15		Sample Output
16		-------------
	20	Observations
	21	------------
17	22
18	23	$ make
19	24	Once upon a time there was a brave knight.

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poetic-inventory/README.md less more

0	0	poetic-inventory
1	1	================
2	2
3		Requirements
4		------------
	3	Hypothesis
	4	----------
	5
	6	We hypothesize that it is possible to extract phrases from public-domain novels
	7	and alphabetize them. Well, I suppose we also hypothesize that, with enough
	8	input texts, the final result may look like an interesting inventory of things.
	9
	10	Apparatus
	11	---------
5	12
6	13	* Python 2.7.6 (probably works with older versions too)
7	14	* The `gutenberg.py` module from [gutenizer](https://github.com/okfn/gutenizer/)
8	15	* A bunch of Project Gutenberg texts in plain text format
9	16
10		Basic Strategy
11		--------------
	17	Method
	18	------
12	19
13	20	* Find all phrases which are neither "beginners" nor "enders" in the sense
14	21	of [join-at-pivot](../join-at-pivot/).

17	24	* Dump out those phrases, capitalizing the first word and replacing each
18	25	semicolon with a full stop and a paragraph break.
19	26
20		Sample Output
21		-------------
	27	Observations
	28	------------
	29
	30	(The list of input texts that produced this TBA.)
22	31
23	32	> A 5.9, a Believer, a Bible, a Biscay lady on her way to Seville, a Biscayan, a Blackbird, a Bonanza King, a Bricklayer passed by, a British aÃ«roplane, a Carabineer* happened along, a Castilian, a Castilian, a Cervantes Saavedra, a Cheese Committee to consist of seven members of the Exchange, a Cheese Inspector and also a Deputy Inspector, a Chicago millionaire, a Chinaman, a Chinese body-servant, a Christian, a Colonial trader, a Crow, a Curious Thing.
24	33	>

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pyramidal-reduction/README.md less more

0	0	pyramidal-reduction
1	1	===================
2	2
3		Requirements
4		------------
	3	Hypothesis
	4	----------
	5
	6	Can we transform a text by building rewriting rules for it on-the-fly?
	7
	8	Apparatus
	9	---------
5	10
6	11	* Python 2.7.6 (probably works with older versions too)
7	12	* An input sentence
8	13
9		Basic Strategy
10		--------------
	14	Method
	15	------
11	16
12	17	* For each pair of symbols in the input sentence, use a rule to reduce
13	18	them to a single symbol. This results in a sentence which is one

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reluctance-generator/README.md less more

0	0	reluctance-generator
1	1	====================
2	2
3		Requirements
4		------------
	3	Hypothesis
	4	----------
	5
	6	We hypothesize that it is possible to make a computer sound reluctant.
	7
	8	Apparatus
	9	---------
5	10
6	11	* Python 2.7.6 (probably works with older versions too)
7	12
8		Basic Strategy
9		--------------
	13	Method
	14	------
10	15
11	16	* Use a "recursive descent generator" to generate statements suggesting
12	17	reluctance. This is like a recursive descent parser, but run "in reverse."

17	22	it will not mind at all creating sentences after which a linguist would
18	23	be inclined to put an asterisk.
19	24
20		Sample Output
21		-------------
	25	Observations
	26	------------
22	27
23	28	$ ./reluctance-generator.py
24	29	I don't think .

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shanty-generator/README.md less more

0	0	shanty-generator
1	1	================
2	2
3		Requirements
4		------------
	3	Hypothesis
	4	----------
	5
	6	We hypothesize that we can get a computer to generate sea shanties, or at
	7	least texts which are somewhat shanty-like.
	8
	9	Apparatus
	10	---------
5	11
6	12	* Python 2.7.6 (probably works with older versions too)
7	13	* Some input text
8	14
9		Basic Strategy
10		--------------
	15	Method
	16	------
11	17
12	18	* Pick one of a number of templates.
13	19	* The template will require _n_ words. Consume _n_ words from the input.
14	20	* Replace the variables in the template with the words.
15	21	* Repeat until no more (or not enough more) words available in the input.
16	22
17		Notes
18		-----
	23	It should be noted that the method described here is by no means specific
	24	to shanties.
19	25
20		The procedure above is not specific, by any means, to shanties. But that
21		is what we will try to generate here.
22
23		Sample Output
24		-------------
	26	Observations
	27	------------
25	28
26	29	$ ./shanty-generator.py It was the best of times it was the worst of times
27	30	It, was-was,