Repetition — where it appears
Named by 31 essays across 7 fields — each of them below, with the objects they name alongside it.
A floor under a run count
A structure whose size is a function of the number of runs in a transform must give a different bit string to every text with that many runs, so it needs at least the logarithm of how many such texts there are. That count is walked rather than estimated — all four thousand and ninety-six of them — and the representation everybody uses turns out to have five bits of slack.
The entropy that cannot see a copy
Two copies of a text have exactly the same symbol statistics as one, so every entropy on this site doubles when the second copy arrives and the second copy carries no information at all. The number of runs in the Burrows-Wheeler transform is 224 at two copies and 224 at thirty-two.
The index that stores the runs
A compressed self-index over thirty-two copies of a text is 30,557 bits, because its size follows an entropy that cannot see a copy. An index that stores the transform as its runs is 11,900 — and at a single copy it is the larger of the two, which is what makes the comparison a claim about repetition rather than about size.
The sampling that follows the runs
A run-length index over thirty-two copies of one text spends 11,286 bits on its suffix-array sampling and 5,605 on the transform it was built to compress. Sample at the run boundaries instead and the sampling is 10,942 bits that stop moving — two values per run, and a function that fills in everything between them.
The phrases a text copies from itself
Four thousand characters of English-like text hold 604 phrases in the greedy parse and 1,219 runs in the transform. Thirty-two copies of one text hold 156 phrases and 233 runs. Two measures of repetition, neither of them an entropy, and they do not agree about which text is the more repetitive.
What is still proportional to n
An index whose size is a function of the run count still has an n in it, and at thirty-two copies of a text the n is half of it. Everything that stores the transform grew by 45 per cent; the two arrays that answer "where" grew by eighteen times, and neither of them has anything to do with repetition.
An index with z in its size
Over thirty-two copies of one text, an index built on the parse is 8,892 bits, the r-index is 17,047 and the entropy-bounded index is 34,615. Over eight thousand characters of four-symbol text the same three are 7,844, 25,177 and 20,413, and the smallest of the three has changed places twice.
The occurrences that cross a boundary
One pattern, thirty-two copies of a text, thirty-two occurrences. The search finds one of them and produces the other thirty-one by arithmetic, and the count it finds is the same one at two copies, at eight and at thirty-two — the searching does not grow when the answer does.
The collection decides which index is small
Three compressed self-indexes over one text of five hundred characters measure 3,511, 7,285 and 10,974 bits. Repeat that text thirty-two times and the same three measure 34,615, 8,892 and 17,047 — the ordering has completely reversed, and nothing about any of the structures changed.
A parse that will not follow a long chain
A greedy self-referential parse bounds the copy depth by nothing at all — thirty-two copies of a text give a position costing twenty-two phrase follows. Restricting every phrase to sources no deeper than D holds it at D, and the whole question is what that costs.
The number that would choose a cap
A depth histogram is one linear pass — 3.16 operations a character over thirty-two thousand of them — and it says the whole text sits at a mean depth of 3.98 with a worst of ten. Nobody prints it, and every choice of cap in this collection was made without it.
The character that costs a chain
The index over thirty-two copies is 8,892 bits and does not grow. Producing one character of the text it indexes costs 17.89 phrase-follows on average and 38 in the worst case, against 2.38 and 7 at one copy — the size stopped growing and the price of reading it did not.
The term that came back
A phrase index is worth building because 8,192 characters parse into 156 phrases. Cap the copy depth at one and the same text parses into 7,351 — ninety per cent of the characters — and the structure is proportional to the text again.
The parse in one pass of the text
The same parse, phrase for phrase, from 1,324,336 character comparisons or from 25,420 transitions and suffix-link steps. One of those numbers grows with the text and the other grows with its square, and the difference is why every measurement about a depth cap here was taken on a few thousand characters.
A corpus that was not generated
Every collection in five strands has been copies of a generated text with a fraction of its characters replaced — three numbers, one dial. Here is one that was not — twelve essays, eight source modules, ten revisions of one file — measured beside the model of it.
A million characters of the same thing
Every measurement this collection has published about real text was taken on twenty-four thousand characters, because the phrase count was quadratic. It is linear now, so here is the same corpus at forty times the size — and what forty times does to its own numbers.
The half of a fall that is the logarithm
Phrases per character on a real collection of essays fall by a factor of 2.34 as it grows. A shuffle of the same characters falls by 1.70. Nearly three quarters of the movement is arithmetic, and no definition of the measure says so.
A sampling that costs more than the array
On four-symbol text the transform has 0.75 runs a character, so a sampling of two suffix-array values per run is one and a half values per position — 271,565 bits against the 131,088 that keeping every value costs. The structure built to remove a term proportional to the text is twice the thing it replaced.
The measure that cannot see the alphabet
Take a Fibonacci word of 4,181 characters and transform it with a before b — six runs. Transform the same word with b before a — nineteen. The parse gives eighteen phrases either way, and the gap between the two run counts grows with every word in the family.
The cell nobody filled
Every structure here was measured either on one long repetitive text or on prose cut into short documents. The collection that is both is a two-by-two with one empty corner, and what is in it is not the product of its margins.
What repetition is worth once the logarithm is gone
On prose, nearly three quarters of the fall in phrases per character with size is arithmetic that any text pays. On a collection built of copies it is a fifth, and what is left is a factor of three that is genuinely the arrangement.
The cap that binds on one text and not another
A periodic text looks like the one made of chains and pays 1.11 times the phrases for a cap of four. A text that repeats itself pays 2.96. The guess is backwards, and the reason is that depth measures nesting rather than repetition.
The occurrences a join invents
Eight documents run together hold forty-nine eight-character windows that span a join, twenty-three of which occur in no document at all. Every index built over the concatenation reports them, and five essays of this collection paid that cost silently.
What a quadratic construction was setting
A depth cap of one costs 90% of an 8,192-character text in phrases, and 86% of a 65,536-character one. The ladder's conclusions hold at thirty-two times the size — and the number the ladder could not reach, the deepest chain a real collection produces, turns out to be fourteen.
The dial that has no setting
The generator has one parameter. The real version history's run count asks it for 2.3% and its phrase count asks for 3.2% — and the reason is not that the dial is badly calibrated. Real edits average 7.3 characters a block and generated ones average 1.04.
The index that does not notice
Three compressed indexes over the same characters. One is flat at six and a half bits a character however many copies the collection holds; the other two fall by factors of five and six. At one copy the two that fall are the largest of the three.
A boundary that costs nothing
A separator occurs nowhere else in the collection, so it must break a phrase that would have spanned it. Cutting a repetitive text into a hundred and twenty-eight documents raises its run count by two per cent, and cutting prose raises it by ten.
The price of a boundary is what precedes it
A separator sorts before everything, so its rows sit at the top of the suffix array and hold the documents' last characters. What a document boundary costs the transform is the entropy of the character in front of it, and nothing else.
Two currencies for one separator
Giving every document its own boundary marker costs a fifth of the packed text and three per cent of the run count. Both numbers are right, they are about the same change, and which one a collection pays depends on a structure nobody named.
One copy per document is one occurrence per document
The output-sensitive listing apparatus wins when a pattern occurs far more often than it occurs in documents. A collection of versions was supposed to be that case, and it is the one collection where the two numbers are equal by construction.
A collection is a construction
The same characters, arranged as one copy per document or cut across the copies, give a different run count, a different boundary cost and a different answer about which structure to build. Which one a benchmark used is usually not recorded.
Named alongside it
The objects these essays reach for when they reach for this one.
MeasurementSelf-indexIndex sizeTrade offPhraseCompressibilityLempel ziv parseRun-lengthCorpusDocument collectionPhrase countAlphabet