若干善后改进
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# 贡献指南
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## 使用 Nuitka 静态编译
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运行
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```bash
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nuitka --clang --jobs=6 --standalone --onefile main.py
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```
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24
README.md
24
README.md
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## 概述
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"潜进" (HeurAMS, 中文含义: 启发式辅助记忆软件) 是为习题册, 古诗词, 及其他问答/记忆/理解型题目设计的记忆辅助软件, 提供优化记忆方案
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"潜进" (HeurAMS, 中文含义: 启发式辅助记忆软件) 是为习题册, 古诗词, 及其他问答/记忆/理解型知识设计的辅助记忆软件, 提供动态规划的优化记忆方案
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## 技术集成与特性
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@@ -13,10 +12,10 @@
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- 采用经实证的 SM-2 间隔迭代算法, 此算法亦用作 Anki 闪卡记忆软件的默认闪卡调度器
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> 计划: 将添加 FSRS 算法 (Anki 的新可选闪卡调度器) 与一种 SM-15 变体算法作为后续替代
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> 参考 https://github.com/slaypni/SM-15
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> 为什么使用 SM-15 的变体?
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> SM-2 后续算法仅有论文, 无具体方程, 故使用一种基于 SM-15 描述实现的变体算法
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- 动态优化每首诗词的记忆间隔时间表
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- 实时跟踪记忆曲线,优化长期记忆保留率与稳定性
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> 使用 SM-15 的变体:
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> SM-2 后续算法并非完全开放, 故使用一种基于 SM-15 描述实现的变体算法
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- 动态规划每个记忆单元的记忆间隔时间表
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- 动态跟踪记忆反馈数据,优化长期记忆保留率与稳定性
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### 学习进程优化
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- 逐字解析:支持逐字详细释义解析
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@@ -30,8 +29,11 @@
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- 简洁直观的复习流程设计
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## 屏幕截图
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> 由于使用 Textual 框架的矢量截图, 部分字体可能无法正确显示属浏览器造成的正常现象, 不会影响实际终端模拟器显示, 不会影响 Web 部署界面显示
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## 技术架构
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> 有关技术与实现的细节, 请参阅 CONTRIBUTING.md
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@@ -63,9 +65,3 @@ graph TD
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- 平台支持:Windows / macOS / Linux / Android (需要 Termux 或 Linux) (终端或浏览器)
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- 网络连接:可预缓存语音文件, 需联网使用大模型服务功能
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## 使用 Nuitka 静态编译
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运行
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```bash
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nuitka --clang --jobs=6 --standalone --onefile main.py
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```
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@@ -1,12 +1,14 @@
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# [调试] 将更改保存到文件
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save = 1
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# [调试] 覆写时间
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time_override = -1
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# [调试] 一键通过
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quick_pass = 0
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# 对于每个项目的新记忆核子数量
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tasked_number = 8
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# 竖屏适配
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# 竖屏适配 (未完成)
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mobile_mode = 1
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9
main.py
9
main.py
@@ -117,11 +117,6 @@ class MemScreen(Screen):
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def refresh_ui(self):
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self.call_later(self.recompose)
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def report(self, quality):
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print(f"reported: {quality}")
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assessment = self.reactor.report(self.reactor.current_atom, quality)
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return assessment
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def action_play_voice(self):
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def play():
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cache_dir = pathlib.Path(f"./cache/voice/")
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@@ -140,7 +135,7 @@ class MemScreen(Screen):
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threading.Thread(target=play).start()
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def action_quick_pass(self):
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self.report(5)
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self.reactor.report(self.reactor.current_atom, 5)
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self._forward_judge(0)
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def action_toggle_dark(self):
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self.app.action_toggle_dark()
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@@ -177,7 +172,7 @@ class PreparationScreen(Screen):
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classes="start-button",
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)
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yield Static(f"\n全文如下:\n")
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yield Static(self._get_full_content(), classes="full")
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yield Static(self._get_full_content().replace("/", ""), classes="full")
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yield Footer()
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def _get_full_content(self):
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@@ -48,6 +48,7 @@ class Electron:
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Args:
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quality (int): 记忆保留率量化参数
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"""
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print(f"REVISOR: {quality}, {is_new_activation}")
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if quality == -1:
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return -1
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29
reactor.py
29
reactor.py
@@ -32,7 +32,6 @@ class Reactor():
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"""反应堆对象, 处理和分配一次文件记忆流程的资源与策略"""
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def __init__(self, nucleon_file: pt.NucleonUnion, electron_file: pt.ElectronUnion, screen, tasked_num):
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# 导入原子对象
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self.reported = set()
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self.nucleon_file = nucleon_file
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self.electron_file = electron_file
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self.tasked_num = tasked_num
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@@ -41,6 +40,7 @@ class Reactor():
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counter = self.tasked_num
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self.screen = screen
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self.electron_dict = electron_file.electrons_dict
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self.quality_dict = {}
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def electron_dict_get_fallback(key) -> pt.Electron:
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value = self.electron_dict.get(key)
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# 如果值不存在,则设置默认值
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@@ -71,7 +71,6 @@ class Reactor():
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self.procession: list
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self.failed: list
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self.round_title: str
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self.reported: set
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self.current_atom: typing.Tuple[pt.Electron, pt.Nucleon, dict]
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self.round_set = 0
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self.current_atom = pt.Atom.placeholder()
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@@ -127,23 +126,25 @@ class Reactor():
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return 0
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def save(self):
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self._deploy_report()
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print("Progress saved")
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# self.nucleon_file.save()
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self.electron_file.save()
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def _deploy_report(self):
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"部署所有 _report"
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for e, q in self.quality_dict.items():
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if q == -1:
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e.revisor(5, True)
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continue
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e.revisor(q)
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def report(self, atom, quality):
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"""
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0: 初次激活/通过
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1: 不通过
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"""
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"向反应器和最低质量记录汇报"
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if atom in self.atoms_new:
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atom[0].revisor(quality, True)
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return 0
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if atom[0] not in self.reported:
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atom[0].revisor(quality)
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self.reported.add(atom[0])
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self.quality_dict[atom[0]] = -1
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print(self.quality_dict)
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return
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self.quality_dict[atom[0]] = min(quality, self.quality_dict.get(atom[0], 5))
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if quality <= 3:
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self.failed.append(atom)
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return 1
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else:
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return 0
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print(self.quality_dict)
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Reference in New Issue
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