Every essay — page 11
What a bound is Counting The floors What the machine does Structures Two parameters The other axis When the algorithm flips a coin What the libraries do When it does not fit One pass, and no room The data that is not a number When the algorithm is a table The index that replaces the text What is taught wrongly Ladders Objects Search
When the algorithm is a table
A dynamic program's cost is settled before its input is touched, by how many distinct subproblems its recurrence has. The unit is the subproblem; the second number is how many must be held at once; and the methods worth knowing are the ones that compute more in order to keep less.
The ties a rounded matrix makes
Measure how far each optimal alignment is from a tie — the smallest change to any one cost that makes another alignment win — and it predicts which alignments a refitted substitution matrix will move. A resample of the same corpus moves 30 of the 63 test alignments that sit on a tie and 3 of the other 137. A matrix fitted to a different divergence moves alignments far from a tie as well, and the prediction weakens to a chance of 0.62. And a third of the alignments were on a tie only because the matrix was rounded to whole bits — fitted without rounding, 15 of 200 are, and every prediction improves.
The columns the candidates share
Three thousand tables against one query, and most of them begin the same way. Stored as a trie, the 2,424-word vocabulary has 7,710 distinct prefixes holding 17,239 letters, and a search that computes one column per prefix reads 61,449 cells against 156,714 — before it applies any bound at all. Apply the bound at a prefix instead of at a word and it reads 16,958, beating a list search that was told the answer in advance.
The bound a block can and cannot have
Knuth's condition turns an interval table's cubic fill into a quadratic one by bounding each cell's best split between its two neighbours'. A blocked fill cannot use it a cell at a time, and the two cells that bound a block lie outside the block — one to its left, one below it. The schedule has finished both for ten per cent of the blocks, the bound then removes eleven per cent of the splits, and it removes half a per cent of the cache misses, because the splits it skips are the ones already in the cache.
The lattice that decides the ties
Rounding a fitted substitution matrix to whole bits puts 63 of 200 alignments on a tie where the exact fit puts 15. Rounding to half bits — a finer grain, and the obvious repair — puts 79. What tracks the ties is not how fine the lattice is but how many of the six fitted costs it keeps apart: whole and half bits both leave three, an eighth of a bit leaves all six, and matches the exact fit exactly.
The data that is not a number
A string comparison costs characters, and how many depends on what the two strings share. A symbol stream repeats, and repetition is the only thing any compressor has ever used. Both are invisible to a counter that charges one for a comparison, and both change which algorithm wins.
The comparison that is not one comparison
Sorting 512 keys costs 3,955 comparisons whatever the keys are, and between 7,849 and 134,409 character examinations depending only on how much those keys have in common. The first number is the one every bound so far is stated in. The second is the one the machine pays, it grows without limit, and nothing here has ever counted it.
The shift the pattern already knows
On a text of 8,192 characters the naive scan examines 520,256 of them and Knuth–Morris–Pratt examines 16,321 — a factor of 32, and 16,321 is 99.6% of the 2n that no input can push it past. On ordinary random text the same two algorithms examine 20,862 and 20,833. Both measurements are of the same pair of algorithms and only one of them is the reason anybody uses the second.
The text that answers without reading it
Boyer–Moore–Horspool finds every occurrence of an eight-character pattern in a twenty-thousand-character text while examining 2,985 characters. Not 2,985 comparisons of eight characters each — 2,985 characters, 0.149 per character of text. It is a correct algorithm returning a complete answer about a text it has mostly not looked at, and the reason it can is a property of the alphabet rather than of the algorithm.
The model is the compressor
One stream of 32,768 symbols has an entropy of 3.886 bits per symbol, and 2.243, and 1.186, and 0.991, and 0.909. All five numbers are correct, all five are floors, and nothing about the data changed between them. The only thing that changed is how many preceding symbols the model was allowed to look at — which makes the entropy of a file a property of a decision rather than a property of a file.
The shift a set of patterns allows
Aho-Corasick reads every character of the text exactly once, whatever the number of patterns. Commentz-Walter reads backwards inside a window and steps over what it can, and on eight patterns of ten characters it looks at sixty-two per cent of a twenty-thousand-character text. The rule that does the skipping is not the one everybody implements.
The transform that emits nothing
The Burrows–Wheeler transform outputs exactly the characters it was given, in a different order. Its zeroth-order entropy is therefore identical to its input's, to fifteen decimal places, and by that measure it has done nothing at all. A Huffman coder handed the result spends 1.935 bits per symbol where the same coder on the same data spends 4.209, and the difference is entirely in the order.
A distance that is a path through a grid
How far apart two strings are is a shortest-path problem on a grid whose every edge is drawn by the recurrence — and finding a string in a text costs 4,988 character comparisons where measuring how far it is from one costs 96,000.
The filter that feeds the table
A self-index answers exact queries and nothing else. Approximate matching needs a table with twenty thousand columns in it. The pigeonhole joins them — cut the pattern into k+1 pieces and at least one occurs exactly, and the index that cannot answer the question decides where to ask it.
One pass for every pattern at once
Sixty-four patterns, one text, twenty thousand characters read. Running this site's KMP once per pattern reads 1,595,445 — and an implementation missing one kind of link finds fewer occurrences, reads exactly as much, and looks better on every counter.
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 row that starts at zero
The same 1,413 cells, filled by the same recurrence in the same order, answer 148 and 0. One line of initialisation decides which question the table was asked, and only one of the two answers is about whether the pattern is there.
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.
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 q-grams an error cannot destroy
A pattern of twenty-four characters holds twenty-one four-grams. Two errors can destroy at most eight of them, so any occurrence with two errors still shares thirteen — and a filter that keeps only the windows sharing thirteen proposes 104 of 3,977 and computes 16,744 table cells instead of 96,000.
The shift somebody published
The exact rules for shifting a multi-pattern window are a definition that quantifies over every pattern at every offset. The 1979 rules are two tables read off the trie's own failure links, they are computed in one pass, and on this pattern set they agree with the definition at every node.
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 errors the rest of the pattern needs
Read the pattern left to right in an index of the reversed text and count the points where the interval empties. That count is a lower bound on the errors any alignment of the prefix must contain, it costs 72 rank operations, and it removes 70% of a search tree.
The table the links already knew
The exact good-suffix rule costs 757,058 character comparisons to build from its definition at 128 patterns, and 3,824 from the trie's failure links. Same table, checked at every node — a factor of 198, and the definition was never the algorithm.
The search that starts in the middle
The same pattern, the same six occurrences, the same index — and 830 interval extensions, or 303, according to which end the search begins at. A pattern cut into three pieces has an error-free one, and only a search with two ends can start there.
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 shape of a real history's depth
Four essays here are about capping how far an extraction follows a chain of copies, and every number in them came from a generated collection. Here is the depth histogram of a real version history, which is a bell, and of twelve unrelated essays, which is nearly the same bell.
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 array is the length distribution
The document array holds each document once per character it contributed, so its symbol distribution is the collection's length distribution exactly. On equal-length documents its entropy is log d and no coding saves anything.