Concept

Fitting — where it appears

Choosing among candidate growth classes by how well each matches counts measured across several orders of magnitude. The residual is reported beside the winner, because a class awarded without one is read off the loops rather than measured.

Named by 7 essays across 5 fields — each of them below, with the objects they name alongside it.

1,00010,00010³10⁴bits · runs12481632characters in the collection · copies aboven·H₃, bitsr, runs in thetransformEnglish-like · base 512 · divergence 0n·H₃ x15.8 · r x1.00

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.

text · Repeat
stay 0.9, 400 pairsacgtacgt0525505225045240stay 0.5, 400 pairsacgtacgt0212202112022120cost of aligning the row letter with the column letterpseudocount 1

The matrix a corpus wrote

A substitution matrix is not a property of an alphabet. Fit one to four hundred pairs of sequences that rarely change and the dearest substitution costs five; fit the same model to four hundred pairs that often change and it costs two. Two hundred test pairs aligned under each matrix give different alignments in 115 cases — and a matrix fitted to eight pairs of the first kind moves 79 of them, from sampling alone.

tables · Cost
10³10⁴10⁵10⁶passes over the datapeak bits of stateone pass, exact: 1,048,576 bitsradix, uniformsample, uniformradix, paretosample, paretoevery point exact · 32,768 values320 bits at best

What a second pass buys

Exact selection of a median from thirty-two thousand values needs the whole stream in one pass — a million bits — and eleven thousand in two. By nine passes it is three hundred and twenty. The state falls as n to the power one over p, which is a law with an exponent worth fitting, and on skewed data the deterministic rule misses it by three orders of magnitude.

space · Pass
rounded to whole bitsunroundeda resample, 400 near pairs0.860.9340 pairs at stay 0.90.820.928 pairs at stay 0.90.710.88400 pairs at stay 0.70.680.86400 pairs at stay 0.50.620.790.5: no prediction200 test pairs, 16 directionsdashed: a coin flip

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.

tables · Cost
0.111010010³10³10⁴floor, in countsarrivals in the shard, nround-robin — n^1.02hashed — fit refusedresidual 2.7%slope 5.2 → 1.19k = 32 · 40,000 arrivalsthe table holds 1.33 of a hashed shard's keys and 0.01 of the stream's

A floor with two variables in it

Under round-robin a Space-Saving summary's floor is 0.0203·n^1.018 over a hundred-and-twenty-eight-fold range of shard size, worst residual 2.7%. Under hashing the same measurement has no exponent at all — the local slope runs from n^5.17 to n^1.19 — and a least-squares line through it reports n^1.73 at a 441% residual.

floors · Floor
100.0010.010.1fraction of the text proposedlog_4 n = 7.101234,56,7seed length, characters · errors allowed abovepattern 24 · text 20,000 · four symbolsrings: the closed form

The filter that proposes everything

Seed-and-extend saves two thousand times the work at zero errors and costs more than doing nothing at four. Between them the selectivity falls through the floor, and where it falls is set by two numbers that can be computed before the filter is run — one of which does not contain the length of the text at all.

wrong · Distance
alignments on a tiedistinct costs · predictionwhole bits633 of 6 · 0.86half bits793 of 6 · 0.91quarter bits414 of 6 · 0.93a grain of 0.2235 of 6 · 0.89eighth bits156 of 6 · 0.93a grain of 0.05205 of 6 · 0.95unrounded156 of 6 · 0.93200 test pairs, 16 directionsbar: alignments within 0.01 of a tie

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.

tables · Cost

Named alongside it

The objects these essays reach for when they reach for this one.

Honest limitMeasurementAlignmentCorpusCost modelEstimatorParameter choiceSubstitution matrixAlphabetEdit distanceOptimalityRounding

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