分离算法

src/heurams/kernel/algorithms/__init__.py

@@ -0,0 +1,33 @@
+from typing import Dict, Any, Callable, TypedDict
+
+class AlgorithmConfig(TypedDict):
+    algo_name: str
+    defaults: Dict[str, Any]
+    revisor: Callable[[dict, int, bool], None]
+
+_algorithms: Dict[str, AlgorithmConfig] = {}
+
+def register_algorithm(algo_name: str, defaults: Dict[str, Any], revisor: Callable):
+    _algorithms[algo_name] = {
+        'algo_name': algo_name,
+        'defaults': defaults,
+        'revisor': revisor
+    }
+
+def get_algorithm(algo_name: str) -> AlgorithmConfig:
+    if algo_name not in _algorithms:
+        raise ValueError(f"算法 {algo_name}' 未找到, 可用值: {list_algorithms()}")
+    return _algorithms[algo_name]
+
+def list_algorithms() -> list[str]:
+    return list(_algorithms.keys())
+
+# 导入注册
+from . import supermemo2
+register_algorithm(
+    algo_name=supermemo2.algo_name,
+    defaults=supermemo2.defaults,
+    revisor=supermemo2.revisor
+)
+
+__all__ = ['get_algorithm', 'list_algorithms', 'register_algorithm', 'AlgorithmConfig']

src/heurams/kernel/algorithms/supermemo2.py

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+import heurams.services.timer as timer
+from typing import TypedDict
+
+algo_name = "supermemo2"
+
+class AlgodataDict(TypedDict):
+    efactor: float
+    real_rept: int  
+    rept: int
+    interval: int
+    last_date: int
+    next_date: int
+    is_activated: int
+    last_modify: float
+
+defaults = {
+    'efactor': 2.5,
+    'real_rept': 0,
+    'rept': 0,
+    'interval': 0,
+    'last_date': 0,
+    'next_date': 0,
+    'is_activated': 0,
+    'last_modify': timer.get_timestamp()
+}
+
+def revisor(algodata: dict, feedback: int = 5, is_new_activation: bool = False):
+    """SM-2 算法迭代决策机制实现
+        根据 quality(0 ~ 5) 进行参数迭代最佳间隔
+        quality 由主程序评估
+
+        Args:
+            quality (int): 记忆保留率量化参数
+    """
+    if feedback == -1:
+        return
+
+    algodata[algo_name]['efactor'] = algodata[algo_name]['efactor'] + (
+        0.1 - (5 - feedback) * (0.08 + (5 - feedback) * 0.02)
+    )
+    algodata[algo_name]['efactor'] = max(1.3, algodata[algo_name]['efactor'])
+
+    if feedback < 3:
+        algodata[algo_name]['rept'] = 0
+        algodata[algo_name]['interval'] = 0
+    else:
+        algodata[algo_name]['rept'] += 1
+
+    algodata[algo_name]['real_rept'] += 1
+
+    if is_new_activation:
+        algodata[algo_name]['rept'] = 0
+        algodata[algo_name]['efactor'] = 2.5
+
+    if algodata[algo_name]['rept'] == 0:
+        algodata[algo_name]['interval'] = 1
+    elif algodata[algo_name]['rept'] == 1:
+        algodata[algo_name]['interval'] = 6
+    else:
+        algodata[algo_name]['interval'] = round(
+            algodata[algo_name]['interval'] * algodata[algo_name]['efactor']
+        )
+
+    algodata[algo_name]['last_date'] = timer.get_daystamp()
+    algodata[algo_name]['next_date'] = timer.get_daystamp() + algodata[algo_name]['interval']
+    algodata[algo_name]['last_modify'] = timer.get_timestamp()