mirror of
https://github.com/deunix-educ/aventuriers-des-mots.git
synced 2026-10-13 02:11:58 +02:00
Denis Defolie
2fe455f5d1
scripts/correction/: AnswerValidator (multiple choice, sentence correction via tap-the-faulty-word + retype) and DictationAnalyzer build the CorrectionResult structure from cahier des charges §6. Dictation analysis aligns words with WordAligner (Wagner-Fischer edit distance) before any character comparison, so an omitted or added word doesn't cascade into false errors on every following word. TextNormalizer handles non-breaking spaces, typographic apostrophes, and decomposed-vs-composed French accented characters via an explicit table (GDScript has no built-in NFC/NFD normalization). Error classification follows the data annotations only (rule_id present -> grammatical, absent -> unclassified), never a runtime linguistic guess. scripts/pedagogy/: DifficultyManager (per-skill difficulty from recent results), AttemptFlow (3-attempt graduated aid: hint, then explanation, then reveal, matching exercise-lifecycle.md's second gap), and ReviewScheduler (the five mastery states from §9, with session-counted spaced review intervals [1, 3, 6, 12] rather than wall-clock dates, since a family device's clock isn't trustworthy). All of it is pure: no scene tree, no clock or RNG read directly, every effectful input (session number, attempt outcome) is a parameter. 65 headless tests across both layers, including the word-omission-doesn't-cascade regression and the full decouverte -> acquise -> a_reviser -> acquise path. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
68 lines
1.9 KiB
GDScript
68 lines
1.9 KiB
GDScript
class_name TextNormalizer
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extends RefCounted
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## Normalisation de texte avant comparaison dans les dictees : espaces (dont
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## insecables), apostrophes typographiques, et sequences Unicode decomposees
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## des caracteres francais ramenees a leur forme composee. GDScript n'expose
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## pas de normalisation NFC/NFD native : table explicite ici, comme prevu par
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## les "Points durs" du prompt de lancement.
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const NBSP := " "
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const NARROW_NBSP := " "
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const TYPOGRAPHIC_APOSTROPHE := "’"
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## Sequences decomposees (lettre de base + marque combinante) -> caractere
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## compose. Couvre les caracteres francais courants : accents, cedille, trema.
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const DECOMPOSED_TO_COMPOSED := {
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"é": "é",
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"è": "è",
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"ê": "ê",
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"ë": "ë",
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"É": "É",
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"È": "È",
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"Ê": "Ê",
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"Ë": "Ë",
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"à": "à",
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"â": "â",
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"À": "À",
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"Â": "Â",
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"ù": "ù",
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"û": "û",
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"ü": "ü",
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"Ù": "Ù",
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"Û": "Û",
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"Ü": "Ü",
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"î": "î",
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"ï": "ï",
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"Î": "Î",
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"Ï": "Ï",
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"ô": "ô",
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"ö": "ö",
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"Ô": "Ô",
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"Ö": "Ö",
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"ç": "ç",
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"Ç": "Ç",
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}
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static func normalize(text: String) -> String:
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var result := text
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result = result.replace(NBSP, " ")
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result = result.replace(NARROW_NBSP, " ")
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result = result.replace(TYPOGRAPHIC_APOSTROPHE, "'")
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for decomposed: String in DECOMPOSED_TO_COMPOSED:
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result = result.replace(decomposed, DECOMPOSED_TO_COMPOSED[decomposed])
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while result.find(" ") != -1:
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result = result.replace(" ", " ")
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return result.strip_edges()
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static func split_words(text: String) -> Array[String]:
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var normalized := normalize(text)
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var words: Array[String] = []
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if normalized.is_empty():
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return words
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var parts: PackedStringArray = normalized.split(" ", false)
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for part in parts:
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words.append(part)
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return words
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