Experimental results: schema #
The primitive values of Data/Experiments/<Paper>.lean, the per-paper modules that
scripts/gen_experiments.py generates from Data/Experiments/<Paper>.json. A paper's own coding
labels (its factors, response types, predictors) become generated enums, and each table it
prints becomes a generated structure with one row per printed line; this file holds only what
every paper shares.
This is data: it imports nothing from Linglib/ and states no theorems about theories.
A statistic is kept as printed, Decimal recording the digits so that 75 and 75.0 differ,
and a study reads it through Decimal.toRat. What a study concludes from the numbers (a
prototype, a preference, a significant difference) is a definition in the study, not a stored
label.
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- Data.Experiments.instReprDecimal = { reprPrec := Data.Experiments.instReprDecimal.repr }
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- d.instDecidableLtRatOfNatToRat = decidable_of_iff' (0 < d.mantissa) ⋯
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- d.instDecidableLtRatToRatOfNat = decidable_of_iff' (d.mantissa < 0) ⋯
The printed value in hundredths, exact when at most two places are printed: a proportion printed to two places, in percent.
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- d.hundredths = d.mantissa * 100 / 10 ^ d.exponent
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The printed value is the percentage 100 * k / n rounded to the printed places: the two
differ by at most half a unit in the last printed place.
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- d.RoundsPercent k n = (2 * |100 * 10 ^ d.exponent * ↑k - d.mantissa * ↑n| ≤ ↑n)
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Some count out of n rounds to the printed percentage.
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- d.AttainablePercent n = ∃ (k : Fin (n + 1)), d.RoundsPercent (↑k) n
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- d.instDecidableAttainablePercent n = { decide := decide (∃ k < n + 1, ∀ (h : k < n + 1), (fun (x : Fin (n + 1)) => d.RoundsPercent (↑x) n) ⟨k, h⟩), reflects_decide := ⋯ }
The sign of a binary feature in the [+F] / [-F] notation.
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- Data.Experiments.instDecidableEqSign x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
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- Data.Experiments.instReprSign = { reprPrec := Data.Experiments.instReprSign.repr }