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分离算法

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Wang Zhiyu
2025-10-23 23:45:40 +08:00
parent 3fef587ff5
commit 809a6dbe75
9 changed files with 181 additions and 81 deletions
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91
src/heurams/kernel/particles/electron.py
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@@ -1,49 +1,40 @@
import heurams.services.timer as timer
from heurams.context import config_var
from heurams.kernel.algorithms import get_algorithm
class Electron:
"""电子: 记忆分析元数据及算法"""
algo = "SM-2"
def __init__(self, ident: str, algodata: dict = {}):
def __init__(self, ident: str, algodata: dict = {}, algo: str = "supermemo2"):
"""初始化电子对象 (记忆数据)
Args:
ident: 算法的唯一标识符, 用于区分不同的算法实例, 使用 algodata[ident] 获取
algodata: 算法数据字典, 包含算法的各项参数和设置
algo: 使用的算法模块标识
"""
self.algodata = algodata
self.ident = ident
self.algo = algo
algorithm_config = get_algorithm(self.algo)
if self.algo not in self.algodata.keys():
self.algodata[self.algo] = {}
if algodata[self.algo] == {}:
self._default_init()
if not self.algodata[self.algo]:
self._default_init(algorithm_config['defaults'])
def _default_init(self):
"""默认初始化包装
"""
defaults = {
'efactor': 2.5, # 易度系数, 越大越简单, 最大为5
'real_rept': 0, # (实际)重复次数
'rept': 0, # (有效)重复次数
'interval': 0, # 最佳间隔
'last_date': 0, # 上一次复习的时间戳
'next_date': 0, # 将要复习的时间戳
'is_activated': 0, # 激活状态
# *NOTE: 此处"时间戳"是以天为单位的整数, 即 UNIX 时间戳除以一天的秒数取整
'last_modify': timer.get_timestamp() # 最后修改时间戳(此处是UNIX时间戳)
}
self.algodata[self.algo] = defaults
def _default_init(self, defaults: dict):
"""默认初始化包装"""
self.algodata[self.algo] = defaults.copy()
def activate(self):
"""激活此电子
"""
"""激活此电子"""
self.algodata[self.algo]['is_activated'] = 1
self.algodata[self.algo]['last_modify'] = timer.get_timestamp()
def modify(self, var: str, value):
"""修改 algodata[algo] 中子字典数据
"""
"""修改 algodata[algo] 中子字典数据"""
if var in self.algodata[self.algo]:
self.algodata[self.algo][var] = value
self.algodata[self.algo]['last_modify'] = timer.get_timestamp()
@@ -51,52 +42,20 @@ class Electron:
print(f"警告: '{var}' 非已知元数据字段")
def revisor(self, quality: 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
"""算法迭代决策机制实现
Args:
quality (int): 记忆保留率量化参数
quality (int): 记忆保留率量化参数 (0-5)
is_new_activation (bool): 是否为初次激活
"""
print(f"REVISOR: {quality}, {is_new_activation}")
if quality == -1:
return -1
self.algodata[self.algo]['efactor'] = self.algodata[self.algo]['efactor'] + (
0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02)
)
self.algodata[self.algo]['efactor'] = max(1.3, self.algodata[self.algo]['efactor'])
if quality < 3:
# 若保留率低于 3，重置重复次数
self.algodata[self.algo]['rept'] = 0
self.algodata[self.algo]['interval'] = 0 # 设为0，以便下面重新计算 I(1)
else:
self.algodata[self.algo]['rept'] += 1
self.algodata[self.algo]['real_rept'] += 1
if is_new_activation: # 初次激活
self.algodata[self.algo]['rept'] = 0
self.algodata[self.algo]['efactor'] = 2.5
if self.algodata[self.algo]['rept'] == 0: # 刚被重置或初次激活后复习
self.algodata[self.algo]['interval'] = 1 # I(1)
elif self.algodata[self.algo]['rept'] == 1:
self.algodata[self.algo]['interval'] = 6 # I(2) 经验公式
else:
self.algodata[self.algo]['interval'] = round(
self.algodata[self.algo]['interval'] * self.algodata[self.algo]['efactor']
)
self.algodata[self.algo]['last_date'] = timer.get_daystamp()
self.algodata[self.algo]['next_date'] = timer.get_daystamp() + self.algodata[self.algo]['interval']
self.algodata[self.algo]['last_modify'] = timer.get_timestamp()
algorithm_config = get_algorithm(self.algo)
algorithm_config['revisor'](self.algodata, quality, is_new_activation)
def __str__(self):
return (
f"记忆单元预览 \n"
f"标识符: '{self.ident}' \n"
f"算法: {self.algo} \n"
f"易度系数: {self.algodata[self.algo]['efactor']:.2f} \n"
f"已经重复的次数: {self.algodata[self.algo]['rept']} \n"
f"下次间隔: {self.algodata[self.algo]['interval']} 天 \n"
@@ -126,12 +85,10 @@ class Electron:
self.algodata[self.algo]['last_modify'] = timer.get_timestamp()
def __len__(self):
"""仅返回当前算法的配置数量
"""
"""仅返回当前算法的配置数量"""
return len(self.algodata[self.algo])
@staticmethod
def placeholder():
"""生成一个电子占位符
"""
return Electron("电子对象样例内容", {})
"""生成一个电子占位符"""
return Electron("电子对象样例内容", {})