digital-garbageandClaude Sonnet 5.5
459daebbc2
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
"""Which words the user's prompts keep dwelling on, for choosing where a camera move aims.
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Each candidate target already gets a score from its own prompt (an owned part beats a bare
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object, objects of a verb beat doers). This adds a second: how many DIFFERENT prompts the word
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turned up in. "It keeps coming back, so it matters" is fused into the first score as a bonus.
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Silent until MIN_PROMPTS prompts have been seen: one prompt's words are not a habit. Kept per
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refinement key in `<key>.focus_memory.json` (keys are disposable; nothing is migrated). A prompt
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seen again counts nothing, so regenerating does not inflate a word.
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"""
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import json
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import math
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import os
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MIN_PROMPTS = 3
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FREQ_W = 0.6 # max bonus: reorders near-equals, never outranks an owned part
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MAX_HASHES = 300
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def _path(key):
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try:
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from .conditioning import refinement_state_path
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except ImportError:
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from conditioning import refinement_state_path
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return refinement_state_path(key, "focus_memory", prefix="refine_v2", extension="json")
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def _read(key):
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try:
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with open(_path(key), "r", encoding="utf-8") as f:
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data = json.load(f)
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return data if isinstance(data, dict) else {}
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except (FileNotFoundError, ValueError):
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return {}
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def prior(key):
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"""{lemma: bonus} for `key`, or {} while too few prompts have been seen."""
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if not key:
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return {}
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data = _read(key)
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seen = data.get("seen") or {}
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if int(data.get("prompts", 0)) < MIN_PROMPTS or not seen:
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return {}
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top = math.log1p(max(seen.values()))
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return {lemma: FREQ_W * math.log1p(n) / top for lemma, n in seen.items()}
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def observe(key, prompt_hash, lemmas):
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"""Count each of `lemmas` once for this prompt. -> prompts seen so far (0 = nothing kept)."""
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if not key or not lemmas:
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return 0
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data = _read(key)
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hashes = data.get("hashes") or []
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if prompt_hash in hashes:
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return int(data.get("prompts", 0))
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seen = data.get("seen") or {}
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for lemma in set(lemmas):
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seen[lemma] = seen.get(lemma, 0) + 1
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data.update(seen=seen, prompts=int(data.get("prompts", 0)) + 1,
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hashes=(hashes + [prompt_hash])[-MAX_HASHES:])
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path = _path(key)
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os.makedirs(os.path.dirname(path), exist_ok=True)
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tmp = path + ".tmp"
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with open(tmp, "w", encoding="utf-8") as f:
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json.dump(data, f)
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os.replace(tmp, path)
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return data["prompts"]
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