78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
from __future__ import annotations
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import random
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from typing import Any
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from .hypothesis import HypothesisAgentGenome
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FEATURE_POOL: tuple[str, ...] = ("open", "high", "low", "close", "volume")
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COGNITIVE_STYLES: tuple[str, ...] = (
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"physicist",
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"biologist",
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"historian",
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"meteorologist",
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"ecologist",
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"engineer",
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)
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def _clone_with(g: HypothesisAgentGenome, **overrides: Any) -> HypothesisAgentGenome:
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payload: dict[str, Any] = g.to_dict()
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payload.update(overrides)
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payload.pop("id", None)
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payload["parent_ids"] = [*g.parent_ids, g.id]
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payload["generation"] = g.generation + 1
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return HypothesisAgentGenome.from_dict(payload)
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def mutate_temperature(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
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delta = rng.choice([-0.1, 0.1])
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new_t = max(0.6, min(1.3, g.temperature + delta))
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return _clone_with(g, temperature=round(new_t, 4))
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def mutate_lookback(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
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delta = rng.choice([-50, 50])
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new_lb = max(50, min(500, g.lookback_window + delta))
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return _clone_with(g, lookback_window=new_lb)
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def mutate_feature_access(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
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current = set(g.feature_access)
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if len(current) == len(FEATURE_POOL):
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op = "remove"
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elif len(current) <= 1:
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op = "add"
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else:
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op = rng.choice(["add", "remove"])
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if op == "add":
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candidates = [f for f in FEATURE_POOL if f not in current]
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choice = rng.choice(candidates)
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new_set = current | {choice}
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else:
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choice = rng.choice(sorted(current))
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new_set = current - {choice}
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return _clone_with(g, feature_access=sorted(new_set))
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def mutate_cognitive_style(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
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candidates = [s for s in COGNITIVE_STYLES if s != g.cognitive_style]
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new_style = rng.choice(candidates)
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return _clone_with(g, cognitive_style=new_style)
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MUTATION_OPS = (
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mutate_temperature,
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mutate_lookback,
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mutate_feature_access,
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mutate_cognitive_style,
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)
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def random_mutate(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
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op = rng.choice(MUTATION_OPS)
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return op(g, rng)
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