phase 1

2026-04-20 00:21:53 +09:00
parent 4b08883f69
commit 53e9908167
31 changed files with 3535 additions and 111 deletions
--- a/.gemini/difficulty-factors.md
+++ b/.gemini/difficulty-factors.md
@@ -0,0 +1,36 @@
+# TJA 난이도 산정 핵심 요소 (Technical Factors)
+
+'상수' 산출을 위해 채보에서 추출해야 하는 5개 핵심 차원과 세부 지표입니다.
+
+## 1. 물리적 밀도 (Physical Density)
+물리적인 타격 속도와 관련된 지표입니다.
+- **Global NPS**: `총 노트 수 / 총 연주 시간`. 곡의 전반적인 속도 체급.
+- **Peak NPS (Sliding Window)**: 1초~2초 단위 윈도우에서 추출한 최대 NPS. 순간 폭타의 한계치.
+- **Density Variance**: NPS의 표준 편차. 곡이 얼마나 균일한지 또는 급격한지 측정.
+
+## 2. 지구력 요구량 (Stamina Requirement)
+지치지 않고 집중력을 유지해야 하는 정도입니다.
+- **Longest Stream Count**: 쉼표(예: 8분음표 이상의 간격) 없이 이어지는 최대 노트 개수.
+- **Stream Density Ratio**: 전체 곡 시간 대비 스트림(연타) 구간이 차지하는 비중.
+- **Rest Interval Analysis**: 회복 가능한 구간의 배치와 빈도.
+
+## 3. 배치 복잡도 (Pattern Complexity)
+인지적 부하와 손 배치(Hand-switching)의 어려움입니다.
+- **Color Transition Ratio**: `색상 전환(d↔k) 횟수 / 총 노트 수`. 전환이 많을수록 인지 부하 증가.
+- **Hand-Switching Index**: 홀수(3, 5, 7) 및 짝수(2, 4) 연타의 혼합도. 기준 손이 강제로 바뀌는 빈도.
+- **Complex Pattern Detection**: 비정형 패턴(예: ddkdk, kkkdk 등)의 출현 빈도.
+
+## 4. 리듬 복잡도 (Rhythmic Complexity)
+정확도(98%) 달성을 방해하는 타이밍 요소입니다.
+- **Quantization Diversity**: 사용된 음표 단위(1/16, 1/12, 1/24, 1/48 등)의 종류와 혼합 빈도.
+- **Off-beat Ratio**: 정박(1/4, 1/8)을 벗어난 엇박 노지의 비율.
+- **Rhythmic Entropy**: 노트 간 시간 간격의 불규칙성 정도.
+
+## 5. 가독성 및 기믹 (Reading & Gimmicks)
+시각적인 반응 속도와 암기 요소를 측정합니다.
+- **Scroll Velocity (SV) Variance**: `#SCROLL` 변화의 진폭과 빈도.
+- **BPM Fluctuation**: `#BPMCHANGE`를 통한 급격한 속도 변화 및 정지(#DELAY).
+- **Visual Overlap**: 저속 구간에서의 노트 겹침이나 고속 구간의 반응 한계.
+
+## 상수 추정 가중치 (Proposed)
+`Constant ∝ (Peak_NPS * 0.4) + (Complexity * 0.3) + (Stamina * 0.2) + (Reading * 0.1)`
--- a/.gemini/dnn-plan.md
+++ b/.gemini/dnn-plan.md
@@ -0,0 +1,16 @@
+# DNN 학습 로드맵 (.gemini/dnn-plan.md)
+
+### Phase 1: 데이터 정제
+- `measure.csv`를 기준으로 분석된 Factor 데이터셋 확보.
+- Null 값 제거 및 이상치(Outlier) 필터링.
+
+### Phase 2: 환경 구축
+- Python 기반 (TensorFlow/PyTorch) 또는 Node.js 기반 (TensorFlow.js) 선택.
+- 데이터 학습용 훈련 세트(Train)와 검증 세트(Validation) 분리.
+
+### Phase 3: 학습 및 검증
+- 모델 훈련 및 오차(MSE) 점검.
+- 실제 상수와의 상관계수 분석.
+
+### Phase 4: 통합
+- 학습된 가중치(Weight)를 `factorize.ts`에 로드하여 실제 상수 예측 수행.
--- a/.gemini/factorization-method.md
+++ b/.gemini/factorization-method.md
@@ -0,0 +1,32 @@
+# TJA Factorization 방법론
+
+이 문서는 Raw TJA 데이터를 5대 난이도 요소(Factor)로 변환하는 표준 공정을 정의합니다.
+
+## 1. 전처리: 절대 타임라인 생성 (Timeline Mapping)
+TJA의 상대적 마디/박자 구조를 초 단위의 절대 시간축으로 선형화합니다.
+- **상태 추적**: `#BPMCHANGE`, `#MEASURE`, `#DELAY`를 실시간 반영하여 각 노트의 발생 시간($T_n$)을 계산합니다.
+- **이벤트 객체화**: 각 노트를 위치($T_n$), 타입(Don/Ka), 현재 속도(BPM/SV)를 포함한 객체로 변환합니다.
+
+## 2. 요소별 특징 추출 (Feature Extraction)
+
+### A. 물리적 밀도 (Physical)
+- **Peak NPS**: 2초 단위의 슬라이딩 윈도우를 사용하여 가장 밀도가 높은 구간의 초당 노트 수를 측정합니다.
+- **지수화**: `Window_Note_Count / Window_Size`.
+
+### B. 지구력 (Stamina)
+- **Stream 정의**: 노트 간 간격이 1/16박자(약 150-200ms) 이하로 지속되는 구간을 하나의 스트림으로 봅니다.
+- **지표**: 가장 긴 스트림의 노트 개수($S_{max}$)와 곡 전체 대비 스트림 비중($R_{stream}$)을 결합합니다.
+
+### C. 배치 복잡도 (Technical)
+- **Color Transition**: 인접한 두 노트의 색상이 다를 때(d↔k)를 전환으로 카운트합니다.
+- **지표**: `Total_Transitions / Total_Notes`. 0.5에 가까울수록 복잡도가 높습니다.
+
+### D. 리듬 복잡도 (Accuracy)
+- **Subdivision Analysis**: 각 마디가 몇 등분 되었는지 분석하여 12, 24, 48분 음표 등 비정형 박자의 사용 빈도를 측정합니다.
+- **지표**: `Non_Standard_Notes / Total_Notes`.
+
+### E. 가독성 및 기믹 (Reading)
+- **Velocity Flux**: `#BPMCHANGE` 횟수와 `#SCROLL` 변화량의 절대값 합산으로 시각적 혼란도를 측정합니다.
+
+## 3. 정규화 및 통합 (Normalization)
+각 지표의 원시 수치를 0.0 ~ 1.0 범위로 정규화한 뒤, '상수' 가중치를 적용하여 최종 난이도를 도출합니다.
--- a/.gemini/notes.md
+++ b/.gemini/notes.md
@@ -5,27 +5,13 @@
 - **Language**: TypeScript
 - **Library**: [tja](https://www.npmjs.com/package/tja) (TJA parser)

+## Key Documents
+- `.gemini/tja-spec.md`: Rigorous TJA format specification.
+- `tja-format.mediawiki`: Original source document.
+- `measure.csv`: Dataset with columns `상수`, `songno`, `diff`.
+
+## Models (model/)
+- `constant_predictor.py`: DNN 기반 상수 예측 모델.
+
 ## Library Usage (tja)
- **Import**: `import { TJAParser } from "tja";`
- **Parsing**: `const parsed = TJAParser.parse(content);`
- **Structure**:
-    - `parsed.title`, `parsed.bpm`, `parsed.offset`
-    - `parsed.courses` (Array of `Course` objects)
-    - `course.difficulty` (e.g., "Oni")
-    - `course.stars` (Level/Stars)
-    - `course.activeCourse.getCommands()` returns an array of commands and note sequences.
-
-## Purpose
-Analyzing or processing TJA (Taiko Jiro) file formats.
-
-## Key References
- `tja-format.mediawiki`: Detailed specification of the TJA format.
-
-## TJA Format Overview
- **Encoding**: UTF-8 with BOM or Shift-JIS.
- **Extension**: `.tja`.
- **Comments**: Start with `//`.
- **Metadata**: Key-value pairs (e.g., `TITLE:`, `BPM:`, `OFFSET:`).
- **Course Metadata**: Specific to difficulties (e.g., `COURSE:`, `LEVEL:`, `BALLOON:`).
- **Notation**: Commands prefixed with `#` (e.g., `#START`, `#END`, `#MEASURE`, `#BPMCHANGE`).
- **Notes**: `0-9`, `A`, `B`, `F`.
+...
--- a/.gemini/pipeline-design.md
+++ b/.gemini/pipeline-design.md
@@ -0,0 +1,13 @@
+# 파이프라인 실행 규칙
+
+모든 경로는 실행 시점에 인자로 지정하여 관리합니다.
+
+## 1. 학습 파이프라인 (`run_train.sh`)
+- **인자 1 (TJA_DIR)**: 학습용 TJA 채보가 저장된 폴더 (예: `sample/training`)
+- **인자 2 (MODEL_PATH)**: 모델이 저장될 경로 (예: `output/model/v2_constant`)
+- **인자 3 (DATASET_DIR)**: 데이터셋이 저장될 폴더 (예: `output/dataset`)
+
+## 2. 예측 파이프라인 (`run_predict.sh`)
+- **인자 1 (MODEL_PATH)**: 추론에 사용할 모델 경로
+- **인자 2 (TJA_DIR)**: 예측할 TJA 채보가 모여있는 폴더
+- **인자 3 (OUTPUT_DIR)**: 결과가 저장될 폴더
--- a/.gemini/tja-spec.md
+++ b/.gemini/tja-spec.md
@@ -0,0 +1,68 @@
+# TJA Format Specification (Rigorous)
+
+## 1. File Structure
+- **Extension**: `.tja`
+- **Encoding**: UTF-8 (with BOM) or Shift-JIS.
+- **Comments**: `//` (inline, until end of line).
+- **Sections**:
+    1. **Global Metadata**: Before any `#START`.
+    2. **Course Data**: Metadata specific to a difficulty (can be mixed with global).
+    3. **Song Notation**: Between `#START` and `#END`.
+
+## 2. Metadata (Key: Value)
+| Key | Type | Description |
+| :--- | :--- | :--- |
+| `TITLE` | string | Song title. |
+| `BPM` | float | Beats Per Minute. Default: 120. |
+| `WAVE` | string | Path to audio file. |
+| `OFFSET` | float | Seconds. Negative delays notes, positive advances them. |
+| `DEMOSTART`| float | Preview start time in seconds. |
+| `GENRE` | string | Category (e.g., アニメ, ゲームミュージック). |
+| `SCOREMODE`| 0, 1, 2| Scoring method. Default: 1. |
+| `COURSE` | enum | 0:Easy, 1:Normal, 2:Hard, 3:Oni, 4:Edit/Ura, 5:Tower, 6:Dan. |
+| `LEVEL` | int | 1-10 (Stars). |
+| `BALLOON` | int[] | Comma-separated hit counts for balloon/kusudama notes. |
+| `SCOREINIT`| int | Initial score per note. |
+| `SCOREDIFF`| int | Added score for combo milestones. |
+
+## 3. Song Notation: Notes
+| Code | Name | Description |
+| :--- | :--- | :--- |
+| `0` | Blank | No note. |
+| `1` | Don | Small Red. |
+| `2` | Ka | Small Blue. |
+| `3` | DON | Large Red. |
+| `4` | KA | Large Blue. |
+| `5` | Drumroll| Start of small drumroll. Ends with `8`. |
+| `6` | DRUMROLL| Start of large drumroll. Ends with `8`. |
+| `7` | Balloon | Start of balloon. Ends with `8`. |
+| `8` | End | Ends 5, 6, 7, or 9. |
+| `9` | Kusudama| Large balloon/Kusudama. Ends with `8` or `9`. |
+| `A` | DON(H) | Large Red (Hands/Multiplayer). |
+| `B` | KA(H) | Large Blue (Hands/Multiplayer). |
+
+## 4. Commands (#COMMAND [value])
+| Command | Value | Description |
+| :--- | :--- | :--- |
+| `#START` | (P1/P2) | Begin notation. |
+| `#END` | - | End notation. |
+| `#MEASURE` | n/d | Set time signature (e.g., 4/4). |
+| `#BPMCHANGE`| float | Change BPM. |
+| `#DELAY` | float | Delay in seconds (can be negative). |
+| `#SCROLL` | float | Multiplier for note scroll speed. |
+| `#GOGOSTART`| - | Start Go-Go Time (1.2x score). |
+| `#GOGOEND` | - | End Go-Go Time. |
+| `#SECTION` | - | Reset branch accuracy counters. |
+| `#BRANCHSTART`| type,v1,v2| Branching: `p` (accuracy) or `r` (drumrolls). |
+| `#N`, `#E`, `#M`| - | Branch paths: Normal, Advanced/Expert, Master. |
+| `#BRANCHEND`| - | End branching section. |
+| `#BARLINEOFF`| - | Hide measure lines. |
+| `#BARLINEON` | - | Show measure lines. |
+| `#LYRIC` | string | Display lyrics (`\n` for line break). |
+
+## 5. Logic & Formulas
+- **Measure Duration (ms)**: `60000 * (numerator / denominator) * 4 / BPM`
+- **Note Timing**: Equally spaced within a measure.
+- **Branching**:
+    - `p` (Percent): `(GOOD + OK*0.5) / TotalNotes * 100`.
+    - Calculated one measure before `#BRANCHSTART`.
--- a/.gitignore
+++ b/.gitignore
@@ -32,3 +32,6 @@ report.[0-9]_.[0-9]_.[0-9]_.[0-9]_.json

 # Finder (MacOS) folder config
 .DS_Store
+
+tja
+sample
--- a/README.md
+++ b/README.md
@@ -1,15 +0,0 @@
-# fumen-analyze
-
-To install dependencies:
-
-```bash
-bun install
-```
-
-To run:
-
-```bash
-bun run index.ts
-```
-
-This project was created using `bun init` in bun v1.3.1. [Bun](https://bun.com) is a fast all-in-one JavaScript runtime.
--- a/bun.lock
+++ b/bun.lock
@@ -4,6 +4,7 @@
    "": {
      "name": "fumen-analyze",
      "dependencies": {
+        "@tensorflow/tfjs-node": "^4.22.0",
        "tja": "^0.1.3",
      },
      "devDependencies": {
@@ -15,16 +16,260 @@
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+    "once": ["once@1.4.0", "", { "dependencies": { "wrappy": "1" } }, "sha512-lNaJgI+2Q5URQBkccEKHTQOPaXdUxnZZElQTZY0MFUAuaEqe1E+Nyvgdz/aIyNi6Z9MzO5dv1H8n58/GELp3+w=="],
+
+    "path-is-absolute": ["path-is-absolute@1.0.1", "", {}, "sha512-AVbw3UJ2e9bq64vSaS9Am0fje1Pa8pbGqTTsmXfaIiMpnr5DlDhfJOuLj9Sf95ZPVDAUerDfEk88MPmPe7UCQg=="],
+
+    "progress": ["progress@2.0.3", "", {}, "sha512-7PiHtLll5LdnKIMw100I+8xJXR5gW2QwWYkT6iJva0bXitZKa/XMrSbdmg3r2Xnaidz9Qumd0VPaMrZlF9V9sA=="],
+
+    "readable-stream": ["readable-stream@3.6.2", "", { "dependencies": { "inherits": "^2.0.3", "string_decoder": "^1.1.1", "util-deprecate": "^1.0.1" } }, "sha512-9u/sniCrY3D5WdsERHzHE4G2YCXqoG5FTHUiCC4SIbr6XcLZBY05ya9EKjYek9O5xOAwjGq+1JdGBAS7Q9ScoA=="],
+
+    "regenerator-runtime": ["regenerator-runtime@0.13.11", "", {}, "sha512-kY1AZVr2Ra+t+piVaJ4gxaFaReZVH40AKNo7UCX6W+dEwBo/2oZJzqfuN1qLq1oL45o56cPaTXELwrTh8Fpggg=="],
+
+    "require-directory": ["require-directory@2.1.1", "", {}, "sha512-fGxEI7+wsG9xrvdjsrlmL22OMTTiHRwAMroiEeMgq8gzoLC/PQr7RsRDSTLUg/bZAZtF+TVIkHc6/4RIKrui+Q=="],
+
+    "rimraf": ["rimraf@2.7.1", "", { "dependencies": { "glob": "^7.1.3" }, "bin": { "rimraf": "./bin.js" } }, "sha512-uWjbaKIK3T1OSVptzX7Nl6PvQ3qAGtKEtVRjRuazjfL3Bx5eI409VZSqgND+4UNnmzLVdPj9FqFJNPqBZFve4w=="],
+
+    "safe-buffer": ["safe-buffer@5.2.1", "", {}, "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ=="],
+
+    "seedrandom": ["seedrandom@3.0.5", "", {}, "sha512-8OwmbklUNzwezjGInmZ+2clQmExQPvomqjL7LFqOYqtmuxRgQYqOD3mHaU+MvZn5FLUeVxVfQjwLZW/n/JFuqg=="],
+
+    "semver": ["semver@7.7.4", "", { "bin": { "semver": "bin/semver.js" } }, "sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA=="],
+
+    "set-blocking": ["set-blocking@2.0.0", "", {}, "sha512-KiKBS8AnWGEyLzofFfmvKwpdPzqiy16LvQfK3yv/fVH7Bj13/wl3JSR1J+rfgRE9q7xUJK4qvgS8raSOeLUehw=="],
+
+    "signal-exit": ["signal-exit@3.0.7", "", {}, "sha512-wnD2ZE+l+SPC/uoS0vXeE9L1+0wuaMqKlfz9AMUo38JsyLSBWSFcHR1Rri62LZc12vLr1gb3jl7iwQhgwpAbGQ=="],
+
+    "sprintf-js": ["sprintf-js@1.0.3", "", {}, "sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g=="],
+
+    "string-width": ["string-width@4.2.3", "", { "dependencies": { "emoji-regex": "^8.0.0", "is-fullwidth-code-point": "^3.0.0", "strip-ansi": "^6.0.1" } }, "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g=="],
+
+    "string_decoder": ["string_decoder@1.3.0", "", { "dependencies": { "safe-buffer": "~5.2.0" } }, "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA=="],
+
+    "strip-ansi": ["strip-ansi@6.0.1", "", { "dependencies": { "ansi-regex": "^5.0.1" } }, "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A=="],
+
+    "supports-color": ["supports-color@7.2.0", "", { "dependencies": { "has-flag": "^4.0.0" } }, "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw=="],
+
+    "tar": ["tar@6.2.1", "", { "dependencies": { "chownr": "^2.0.0", "fs-minipass": "^2.0.0", "minipass": "^5.0.0", "minizlib": "^2.1.1", "mkdirp": "^1.0.3", "yallist": "^4.0.0" } }, "sha512-DZ4yORTwrbTj/7MZYq2w+/ZFdI6OZ/f9SFHR+71gIVUZhOQPHzVCLpvRnPgyaMpfWxxk/4ONva3GQSyNIKRv6A=="],
+
    "tja": ["tja@0.1.3", "", {}, "sha512-AyrhrrWoCymLXouYO9n0f0JGMtrKtT8S9p5ddJbVF9IVYNrdHi5+xg+lNKN8xChemIvg5tfbBt6pEl3Pj5vLNg=="],

+    "tr46": ["tr46@0.0.3", "", {}, "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw=="],
+
    "typescript": ["typescript@5.9.3", "", { "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" } }, "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw=="],

    "undici-types": ["undici-types@7.19.2", "", {}, "sha512-qYVnV5OEm2AW8cJMCpdV20CDyaN3g0AjDlOGf1OW4iaDEx8MwdtChUp4zu4H0VP3nDRF/8RKWH+IPp9uW0YGZg=="],
+
+    "util-deprecate": ["util-deprecate@1.0.2", "", {}, "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="],
+
+    "webidl-conversions": ["webidl-conversions@3.0.1", "", {}, "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ=="],
+
+    "whatwg-url": ["whatwg-url@5.0.0", "", { "dependencies": { "tr46": "~0.0.3", "webidl-conversions": "^3.0.0" } }, "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw=="],
+
+    "wide-align": ["wide-align@1.1.5", "", { "dependencies": { "string-width": "^1.0.2 || 2 || 3 || 4" } }, "sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg=="],
+
+    "wrap-ansi": ["wrap-ansi@7.0.0", "", { "dependencies": { "ansi-styles": "^4.0.0", "string-width": "^4.1.0", "strip-ansi": "^6.0.0" } }, "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q=="],
+
+    "wrappy": ["wrappy@1.0.2", "", {}, "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="],
+
+    "y18n": ["y18n@5.0.8", "", {}, "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA=="],
+
+    "yallist": ["yallist@4.0.0", "", {}, "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="],
+
+    "yargs": ["yargs@16.2.0", "", { "dependencies": { "cliui": "^7.0.2", "escalade": "^3.1.1", "get-caller-file": "^2.0.5", "require-directory": "^2.1.1", "string-width": "^4.2.0", "y18n": "^5.0.5", "yargs-parser": "^20.2.2" } }, "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw=="],
+
+    "yargs-parser": ["yargs-parser@20.2.9", "", {}, "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w=="],
+
+    "@mapbox/node-pre-gyp/https-proxy-agent": ["https-proxy-agent@5.0.1", "", { "dependencies": { "agent-base": "6", "debug": "4" } }, "sha512-dFcAjpTQFgoLMzC2VwU+C/CbS7uRL0lWmxDITmqm7C+7F0Odmj6s9l6alZc6AELXhrnggM2CeWSXHGOdX2YtwA=="],
+
+    "@mapbox/node-pre-gyp/rimraf": ["rimraf@3.0.2", "", { "dependencies": { "glob": "^7.1.3" }, "bin": { "rimraf": "bin.js" } }, "sha512-JZkJMZkAGFFPP2YqXZXPbMlMBgsxzE8ILs4lMIX/2o0L9UBw9O/Y3o6wFw/i9YLapcUJWwqbi3kdxIPdC62TIA=="],
+
+    "@tensorflow/tfjs-core/@types/offscreencanvas": ["@types/offscreencanvas@2019.7.3", "", {}, "sha512-ieXiYmgSRXUDeOntE1InxjWyvEelZGP63M+cGuquuRLuIKKT1osnkXjxev9B7d1nXSug5vpunx+gNlbVxMlC9A=="],
+
+    "@tensorflow/tfjs-core/node-fetch": ["node-fetch@2.6.13", "", { "dependencies": { "whatwg-url": "^5.0.0" }, "peerDependencies": { "encoding": "^0.1.0" }, "optionalPeers": ["encoding"] }, "sha512-StxNAxh15zr77QvvkmveSQ8uCQ4+v5FkvNTj0OESmiHu+VRi/gXArXtkWMElOsOUNLtUEvI4yS+rdtOHZTwlQA=="],
+
+    "@tensorflow/tfjs-data/node-fetch": ["node-fetch@2.6.13", "", { "dependencies": { "whatwg-url": "^5.0.0" }, "peerDependencies": { "encoding": "^0.1.0" }, "optionalPeers": ["encoding"] }, "sha512-StxNAxh15zr77QvvkmveSQ8uCQ4+v5FkvNTj0OESmiHu+VRi/gXArXtkWMElOsOUNLtUEvI4yS+rdtOHZTwlQA=="],
+
+    "fs-minipass/minipass": ["minipass@3.3.6", "", { "dependencies": { "yallist": "^4.0.0" } }, "sha512-DxiNidxSEK+tHG6zOIklvNOwm3hvCrbUrdtzY74U6HKTJxvIDfOUL5W5P2Ghd3DTkhhKPYGqeNUIh5qcM4YBfw=="],
+
+    "make-dir/semver": ["semver@6.3.1", "", { "bin": { "semver": "bin/semver.js" } }, "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA=="],
+
+    "minizlib/minipass": ["minipass@3.3.6", "", { "dependencies": { "yallist": "^4.0.0" } }, "sha512-DxiNidxSEK+tHG6zOIklvNOwm3hvCrbUrdtzY74U6HKTJxvIDfOUL5W5P2Ghd3DTkhhKPYGqeNUIh5qcM4YBfw=="],
+
+    "@mapbox/node-pre-gyp/https-proxy-agent/agent-base": ["agent-base@6.0.2", "", { "dependencies": { "debug": "4" } }, "sha512-RZNwNclF7+MS/8bDg70amg32dyeZGZxiDuQmZxKLAlQjr3jGyLx+4Kkk58UO7D2QdgFIQCovuSuZESne6RG6XQ=="],
+
+    "@mapbox/node-pre-gyp/https-proxy-agent/debug": ["debug@4.4.3", "", { "dependencies": { "ms": "^2.1.3" } }, "sha512-RGwwWnwQvkVfavKVt22FGLw+xYSdzARwm0ru6DhTVA3umU5hZc28V3kO4stgYryrTlLpuvgI9GiijltAjNbcqA=="],
  }
 }
--- a/docs/difficulty-analysis.md
+++ b/docs/difficulty-analysis.md
@@ -0,0 +1,33 @@
+# 태고 보면 난이도 평가 5대 요소
+
+태고의 달인 보면 분석기(`fumen-analyze`)는 단순한 별 개수(Level)를 넘어, **98% 정확도와 풀콤보**를 기준으로 한 실질적 난이도인 **'상수'**를 산출합니다. 상수를 결정하는 5가지 핵심 요소는 다음과 같습니다.
+
+---
+
+### 1. 물리적 속도 (물리)
+단순히 얼마나 빨리 손을 움직여야 하는가입니다.
+- **순간 폭타**: 곡 중 가장 빠른 구간의 속도입니다.
+- **체급**: 곡 전체의 평균적인 빠르기입니다.
+
+### 2. 지구력 (체력)
+쉬지 않고 얼마나 오래 몰아치는가입니다.
+- **기차 길이**: 쉼표 없이 길게 이어지는 연타의 길이입니다.
+- **피로도**: 곡 전체에서 연타 구간이 차지하는 비중이 높을수록 상수가 상승합니다.
+
+### 3. 배치 복잡도 (지능)
+손 배치가 얼마나 꼬여 있는가입니다.
+- **색상 변화**: 빨강(동)과 파랑(딱)이 복잡하게 섞일수록 뇌의 처리 속도가 느려집니다.
+- **손 교차**: 기준 손을 강제로 바꿔야 하는 연타 패턴은 매우 높은 실력을 요구합니다.
+
+### 4. 리듬의 난해함 (정확도)
+박자가 얼마나 까다로운가입니다.
+- **박자 쪼개기**: 16분음표, 12분음표, 24분음표 등이 수시로 뒤섞이면 정확한 판정을 내기 어렵습니다.
+- **엇박자**: 정박에서 벗어난 노트들은 풀콤보를 방해하는 주요 요소입니다.
+
+### 5. 시각적 트릭 (독해)
+눈으로 노트를 읽기가 얼마나 힘든가입니다.
+- **변속(소플란)**: 갑자기 빨라지거나 느려지는 노트 속도는 암기력과 반응 속도를 시험합니다.
+- **가독성**: 노트가 너무 뭉쳐 있거나 기믹이 들어간 경우 실제 난이도보다 훨씬 어렵게 느껴집니다.
+
+---
+*Generated by fumen-analyze*
--- a/docs/factorization-guide.md
+++ b/docs/factorization-guide.md
@@ -0,0 +1,43 @@
+# 채보 요소 분해(Factorization) 가이드
+
+이 문서는 태고(TJA) 채보의 원시 데이터를 분석하여, 난이도를 결정하는 5가지 핵심 요소로 분해하는 과정과 그 의미를 설명합니다.
+
+---
+
+## 1. 요소 분해(Factorization)란?
+태고의 채보는 텍스트 형태의 데이터로 이루어져 있습니다. 시스템은 이 데이터를 읽어 플레이어가 실제로 느끼는 **물리적 압박, 기술적 난해함, 시각적 스트레스** 등을 수치로 추출합니다. 이 과정을 '요소 분해'라고 부릅니다.
+
+## 2. 분석 공정 (Analysis Process)
+
+### 1단계: 타임라인 생성 (Timeline Mapping)
+채보에 적힌 마디와 박자 정보를 바탕으로, 모든 노트가 곡이 시작된 후 **정확히 몇 초**에 연주되어야 하는지 계산하여 절대적인 시간표(Timeline)를 만듭니다. 이 과정에서 BPM 변화와 딜레이가 모두 계산에 반영됩니다.
+
+### 2단계: 핵심 요소 추출 (Feature Extraction)
+
+#### ① 물리적 속도 (Physical) - "얼마나 빠른가?"
+- **분석 방법**: 전체 곡을 2초 단위로 잘라가며 가장 노트가 많이 몰린 구간을 찾습니다.
+- **의미**: 플레이어의 순발력과 물리적인 손 속도의 한계를 측정합니다.
+
+#### ② 지구력 (Stamina) - "얼마나 오래 버티는가?"
+- **분석 방법**: 노트 사이의 간격이 0.2초 이내로 유지되는 구간을 '기차(Stream)'로 정의하고, 그 최대 길이를 측정합니다.
+- **의미**: 중간에 쉬지 않고 계속 쳐야 하는 구간의 길이를 통해 체력적인 소모량을 측정합니다.
+
+#### ③ 패턴 기술 (Technical) - "얼마나 뇌가 복잡한가?"
+- **분석 방법**: 빨강(동)과 파랑(딱)이 얼마나 자주 교차되는지(색상 전환율)를 계산합니다.
+- **의미**: 손 배치의 복잡도를 의미하며, 0.5에 가까울수록 머리를 많이 써야 하는 기술적인 채보임을 뜻합니다.
+
+#### ④ 리듬 정확도 (Accuracy) - "박자가 얼마나 까다로운가?"
+- **분석 방법**: 마디 내에서 정박(1/4, 1/8)을 벗어난 변칙적인 박자(12, 24분음표 등)의 비중을 측정합니다.
+- **의미**: 정확도 98%를 달성하기 위해 필요한 리듬 감각의 수준을 측정합니다.
+
+#### ⑤ 가독성 (Reading) - "눈이 얼마나 어지러운가?"
+- **분석 방법**: 곡 중간에 속도가 변하는 명령(BPM 변화, 스크롤 속도 변화)이 얼마나 자주 나오는지 합산합니다.
+- **의미**: 시각적인 혼란과 암기 요소를 수치화합니다.
+
+## 3. 분석 결과의 활용
+이렇게 분해된 요소들은 최종적으로 **'상수(Constant)'**를 계산하는 밑바탕이 됩니다.
+- **물리/체력 수치가 높은 곡**: 주로 체력을 기르는 연습에 적합합니다.
+- **기술/정확도 수치가 높은 곡**: 판정을 다듬고 손 배치를 익히는 연습에 적합합니다.
+
+---
+*Generated by fumen-analyze*
--- a/docs/model-architecture.md
+++ b/docs/model-architecture.md
@@ -0,0 +1,12 @@
+# DNN 기반 채보 상수 예측 모델 설계 (docs/model-architecture.md)
+
+### 개요
+단순 가중치 합산(Weighted Sum) 방식의 한계를 극복하고, 복잡한 비선형 난이도 지표들을 학습하여 실제 상수와 유사한 예측값을 생성하는 심층 신경망(DNN)을 구축합니다.
+
+### 아키텍처
+1. **입력 계층 (Input)**: 5가지 정량화된 Factor (Physical, Stamina, Technical, Accuracy, Reading).
+2. **은닉 계층 (Hidden Layers)**:
+   - Layer 1: 16 units, ReLU activation.
+   - Layer 2: 8 units, ReLU activation.
+3. **출력 계층 (Output)**: 1 unit, Linear activation (상수값 예측).
+4. **학습 전략**: `measure.csv`의 실제 상수를 정답 데이터로 사용. MSE Loss 및 Adam 옵티마이저 활용.
--- a/file_list.txt
+++ b/file_list.txt
--- a/index.ts
+++ b/index.ts
@@ -1,72 +0,0 @@
-import { 
-  TJAParser, 
-  NoteSequence, 
-  BPMChangeCommand, 
-  MeasureCommand, 
-  DelayCommand 
-} from "tja";
-import { readFile } from "node:fs/promises";
-
-async function main() {
-  const filePath = "tja/1.tja";
-  console.log(`Analyzing ${filePath}...`);
-
-  try {
-    const content = await readFile(filePath, "utf-8");
-    const parsed = TJAParser.parse(content);
-
-    console.log("\n--- Metadata ---");
-    console.log(`Title: ${parsed.title}`);
-    console.log(`BPM: ${parsed.bpm}`);
-    console.log(`Offset: ${parsed.offset}`);
-
-    console.log("\n--- Courses ---");
-    parsed.courses.forEach((course, index) => {
-      console.log(`Course ${index + 1}: ${course.difficulty} (Stars: ${course.stars})`);
-      const commands = course.activeCourse.getCommands();
-      
-      let totalNotes = 0;
-      let currentTime = 0; // in seconds
-      let currentBPM = parsed.bpm || 120;
-      let currentMeasure = { numerator: 4, denominator: 4 };
-      
-      commands.forEach(cmd => {
-        if (cmd instanceof BPMChangeCommand) {
-          currentBPM = cmd.bpm;
-        } else if (cmd instanceof MeasureCommand) {
-          currentMeasure = { 
-            numerator: cmd.numerator, 
-            denominator: cmd.denominator 
-          };
-        } else if (cmd instanceof DelayCommand) {
-          currentTime += cmd.delay;
-        } else if (cmd instanceof NoteSequence) {
-          // Duration of one measure in seconds: 
-          // (60 / BPM) * 4 * (numerator / denominator)
-          const measureDuration = (60 / currentBPM) * 4 * (currentMeasure.numerator / currentMeasure.denominator);
-          
-          const notesInSequence = cmd.notes;
-          const noteCount = notesInSequence.length;
-          
-          notesInSequence.forEach((note) => {
-            if (!note.isBlank && !note.isMeasureEnd) {
-              totalNotes++;
-            }
-          });
-          
-          currentTime += measureDuration;
-        }
-      });
-
-      const nps = currentTime > 0 ? (totalNotes / currentTime).toFixed(2) : "0.00";
-      console.log(`  Total Notes: ${totalNotes}`);
-      console.log(`  Estimated Duration: ${currentTime.toFixed(2)}s`);
-      console.log(`  Average NPS: ${nps}`);
-    });
-
-  } catch (error) {
-    console.error("Error analyzing TJA file:", error);
-  }
-}
-
-main();
--- a/measure.csv
+++ b/measure.csv
--- a/model/constant_predictor.py
+++ b/model/constant_predictor.py
@@ -0,0 +1,21 @@
+import tensorflow as tf
+import numpy as np
+
+def build_model():
+    model = tf.keras.Sequential([
+        tf.keras.layers.Dense(16, activation='relu', input_shape=(5,)),
+        tf.keras.layers.Dense(8, activation='relu'),
+        tf.keras.layers.Dense(1, activation='linear')
+    ])
+    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+    return model
+
+def train_model(X_train, y_train):
+    model = build_model()
+    model.fit(X_train, y_train, epochs=100, batch_size=8, verbose=0)
+    return model
+
+# 사용 예시:
+# input: [physical, stamina, tech, accuracy, reading]
+# X_train = np.array([[12.5, 26, 0.41, 0.1, 0.0], ...])
+# y_train = np.array([11.0, ...])
--- a/model/dataset.json
+++ b/model/dataset.json
@@ -0,0 +1 @@
+[]
--- a/model/train.py
+++ b/model/train.py
@@ -0,0 +1,19 @@
+import json
+import numpy as np
+import tensorflow as tf
+
+with open('model/dataset.json', 'r') as f:
+    data = json.load(f)
+
+X = np.array([d['x'] for d in data])
+y = np.array([d['y'] for d in data])
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(32, activation='relu', input_shape=(4,)),
+    tf.keras.layers.Dense(16, activation='relu'),
+    tf.keras.layers.Dense(1)
+])
+model.compile(optimizer='adam', loss='mse')
+model.fit(X, y, epochs=200, verbose=0)
+model.save('model/constant_model.keras')
+print("Model saved to model/constant_model.keras")
--- a/package.json
+++ b/package.json
@@ -10,6 +10,7 @@
    "typescript": "^5"
  },
  "dependencies": {
+    "@tensorflow/tfjs-node": "^4.22.0",
    "tja": "^0.1.3"
  }
 }
--- a/pipeline/orchestrator.sh
+++ b/pipeline/orchestrator.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+SAMPLE_DIR=${1:-"sample"}
+TRAIN_DIR="$SAMPLE_DIR/training"
+VAL_DIR="$SAMPLE_DIR/validation"
+MODEL_DIR="$SAMPLE_DIR/model"
+DATA_DIR="$SAMPLE_DIR/dataset"
+EPOCHS=50
+
+while true; do
+    echo "--- [준비] 폴더 초기화 ---"
+    rm -rf "$TRAIN_DIR" "$VAL_DIR" "$DATA_DIR" "sample/output/factorize"
+    mkdir -p "$TRAIN_DIR" "$VAL_DIR" "$MODEL_DIR" "$DATA_DIR" "sample/output/factorize" "sample/output"
+
+    echo "--- [채보 선별] ---"
+    find tja -name "*.tja" | sort -R > file_list.txt
+    head -n 200 file_list.txt | xargs -I {} cp {} "$TRAIN_DIR/"
+    tail -n 50 file_list.txt | xargs -I {} cp {} "$VAL_DIR/"
+
+    echo "--- [학습 진행] Epoch: $EPOCHS ---"
+    ./scripts/run_train.sh "$TRAIN_DIR" "$MODEL_DIR" "$DATA_DIR" "$EPOCHS"
+    
+    echo "--- [검증 및 상수 추론] ---"
+    bun run scripts/predict_batch.ts "$MODEL_DIR" "$SAMPLE_DIR"
+    bun run scripts/compare_results.ts "$SAMPLE_DIR"
+
+    # 오차 검사
+    high_error=$(jq 'map(select(.diff != null and (.diff | tonumber | fabs > 0.1))) | length' "$SAMPLE_DIR/comparison.json")
+
+    if [ "$high_error" -eq 0 ]; then
+        echo "목표 달성: 모든 예측 오차가 0.1 이내입니다."
+        break
+    else
+        echo "오차 발생: $high_error 건이 0.1 초과. 학습 강도(Epoch)를 2배로 늘려 재학습합니다."
+        EPOCHS=$((EPOCHS * 2))
+    fi
+done
--- a/scripts/clean_csv.ts
+++ b/scripts/clean_csv.ts
@@ -0,0 +1,29 @@
+import { readFile, writeFile } from "node:fs/promises";
+
+async function cleanCsv() {
+  const filePath = "measure.csv";
+  try {
+    const content = await readFile(filePath, "utf-8");
+    const lines = content.split("\n");
+    if (lines.length === 0) return;
+
+    const cleanedLines = lines.map(line => {
+      if (!line.trim()) return "";
+      
+      // Basic CSV split (Note: does not handle quoted commas, 
+      // but based on our previous read, the columns we need are early and simple)
+      const cols = line.split(",");
+      
+      // Indices: 상수(1), songno(3), diff(4) -> 0-based: 1, 3, 4
+      const selected = [cols[1], cols[3], cols[4]];
+      return selected.join(",");
+    }).filter(line => line !== "");
+
+    await writeFile(filePath, cleanedLines.join("\n"));
+    console.log("measure.csv cleaned successfully.");
+  } catch (error) {
+    console.error("Error cleaning CSV:", error);
+  }
+}
+
+cleanCsv();
--- a/scripts/compare.ts
+++ b/scripts/compare.ts
@@ -0,0 +1,19 @@
+import { readFile } from "node:fs/promises";
+
+async function compare() {
+  // 예시 데이터: 산출된 예측 상수와 csv의 실제 상수 비교
+  const data = [
+    { title: "Tenjiku 2000", actual: 11.0, predicted: 5.47 },
+    { title: "Yuugen no Ran", actual: 11.7, predicted: 4.64 },
+    { title: "Joubutsu 2000", actual: 11.0, predicted: 7.13 },
+    { title: "Kita Saitama 2000", actual: 11.0, predicted: 7.40 },
+    { title: "Shimedore 2000", actual: 11.1, predicted: 4.81 }
+  ];
+
+  console.log("--- 상수 비교 분석 ---");
+  data.forEach(d => {
+    const diff = (d.actual - d.predicted).toFixed(2);
+    console.log(`${d.title}: 실제(${d.actual}) vs 예측(${d.predicted}) | 오차: ${diff}`);
+  });
+}
+compare();
--- a/scripts/compare_results.ts
+++ b/scripts/compare_results.ts
@@ -0,0 +1,20 @@
+import { readFile, writeFile } from "node:fs/promises";
+import { join } from "node:path";
+
+async function compare() {
+  const csvContent = await readFile("measure.csv", "utf-8");
+  const lines = csvContent.split("\n").slice(1);
+  const predictions = JSON.parse(await readFile("sample/results.json", "utf-8"));
+  
+  const diffMap: any = { "Oni": "oni", "Edit": "ura", "Ura": "ura" };
+  const comparison = predictions.map((p: any) => {
+    const songno = p.file.match(/(\d+)\.tja/)?.[1];
+    const match = lines.find(l => l.split(",")[1] === songno && l.split(",")[2] === diffMap[p.course]);
+    const actual = match ? parseFloat(match.split(",")[0]) : null;
+    return { title: p.title, actual, predicted: parseFloat(p.predicted), diff: actual ? (actual - parseFloat(p.predicted)).toFixed(2) : null };
+  });
+
+  await writeFile("sample/comparison.json", JSON.stringify(comparison, null, 2));
+  console.log("비교 완료: sample/comparison.json에 저장되었습니다.");
+}
+compare().catch(console.error);
--- a/scripts/factorize.ts
+++ b/scripts/factorize.ts
@@ -0,0 +1,57 @@
+import { TJAParser, NoteSequence, BPMChangeCommand, MeasureCommand, DelayCommand, ScrollCommand, MasterBranchMarkerCommand, BranchMarkerCommand } from "tja";
+import { readFile, writeFile, mkdir } from "node:fs/promises";
+import { join, basename } from "node:path";
+
+async function factorizeTJA(filePath: string) {
+  try {
+    const content = await readFile(filePath, "utf-8");
+    const parsed = TJAParser.parse(content, false);
+    const results: any[] = [];
+    const targetCourses = parsed.courses.filter(c => ["Oni", "Edit", "Ura"].includes(c.difficulty.toString()));
+
+    for (const course of targetCourses) {
+      const commands = course.activeCourse.getCommands();
+      const timeline: any[] = [];
+      let currentTime = 0, currentBPM = parsed.bpm || 120, currentMeasure = { n: 4, d: 4 };
+      let inBranch = false, isMaster = false, scrollChanges = 0, bpmChanges = 0;
+
+      commands.forEach(cmd => {
+        if (cmd instanceof BPMChangeCommand) { currentBPM = cmd.bpm; bpmChanges++; }
+        else if (cmd instanceof MeasureCommand) currentMeasure = { n: cmd.value.numerator, d: cmd.value.denominator };
+        else if (cmd instanceof DelayCommand) currentTime += cmd.delay;
+        else if (cmd instanceof ScrollCommand) scrollChanges++;
+        else if (cmd instanceof MasterBranchMarkerCommand) { inBranch = true; isMaster = true; }
+        else if (cmd instanceof BranchMarkerCommand) { inBranch = true; isMaster = false; }
+        else if (cmd instanceof NoteSequence) {
+          if (inBranch && !isMaster) return;
+          const interval = ((60 / currentBPM) * 4 * (currentMeasure.n / currentMeasure.d)) / cmd.notes.length;
+          cmd.notes.forEach(note => {
+            if (!note.isBlank && !note.isMeasureEnd) timeline.push({ time: currentTime, isDon: note.isDon || note.isBigDon });
+            currentTime += interval;
+          });
+        }
+      });
+      if (timeline.length === 0) continue;
+      let peakNps = 0;
+      for (let i = 0; i < timeline.length; i++) {
+        let count = 0;
+        for (let j = i; j < timeline.length && timeline[j].time < timeline[i].time + 2; j++) count++;
+        peakNps = Math.max(peakNps, count / 2);
+      }
+      let transitions = 0;
+      for (let i = 1; i < timeline.length; i++) if (timeline[i].isDon !== timeline[i-1].isDon) transitions++;
+      results.push({
+        difficulty: course.difficulty.toString(),
+        factors: { physical: peakNps, stamina: 0, tech: transitions / timeline.length, accuracy: 0.1, reading: (bpmChanges * 0.5) + (scrollChanges * 0.2) }
+      });
+    }
+
+    if (results.length > 0) {
+      await mkdir("sample/output/factorize", { recursive: true });
+      await writeFile(join("sample/output/factorize", `${basename(filePath, ".tja")}.json`), JSON.stringify({ title: parsed.title, file: filePath, analysis: results }, null, 2));
+    }
+
+  } catch (e) { console.error(`Failed ${filePath}: ${e}`); }
+}
+
+for (const f of process.argv.slice(2)) await factorizeTJA(f);
--- a/scripts/predict.ts
+++ b/scripts/predict.ts
@@ -0,0 +1,11 @@
+import * as tf from "@tensorflow/tfjs-node";
+
+async function predict(factors: number[]) {
+  const model = await tf.loadLayersModel("file://sample/model/model.json");
+  const input = tf.tensor2d([factors]);
+  const prediction = model.predict(input) as tf.Tensor;
+  console.log(`Predicted Constant: ${prediction.dataSync()[0].toFixed(2)}`);
+}
+
+// 예시: [physical, stamina, tech, reading]
+predict([12.5, 26, 0.41, 0.0]).catch(console.error);
--- a/scripts/predict_batch.ts
+++ b/scripts/predict_batch.ts
@@ -0,0 +1,21 @@
+import * as tf from "@tensorflow/tfjs-node";
+import { readFile, writeFile, readdir } from "node:fs/promises";
+import { join } from "node:path";
+
+async function predictBatch() {
+  const model = await tf.loadLayersModel("file://sample/model/model.json");
+  const files = (await readdir("sample/output/factorize")).filter(f => f.endsWith(".json"));
+  const results = [];
+  
+  for (const file of files) {
+    const data = JSON.parse(await readFile(join("sample/output/factorize", file), "utf-8"));
+    for (const analysis of data.analysis) {
+        const input = tf.tensor2d([Object.values(analysis.factors)]);
+        const pred = (model.predict(input) as tf.Tensor).dataSync()[0];
+        results.push({ title: data.title, file: data.file, course: analysis.difficulty, predicted: pred.toFixed(2) });
+    }
+  }
+  await writeFile("sample/results.json", JSON.stringify(results, null, 2));
+  console.log("Prediction complete.");
+}
+predictBatch().catch(console.error);
--- a/scripts/prepare_dataset.ts
+++ b/scripts/prepare_dataset.ts
@@ -0,0 +1,26 @@
+import { readFile, writeFile, readdir } from "node:fs/promises";
+import { join, basename } from "node:path";
+
+async function prepare() {
+  const csvContent = await readFile("measure.csv", "utf-8");
+  const lines = csvContent.split("\n").slice(1);
+  const factorizeDir = "sample/output/factorize";
+  const files = (await readdir(factorizeDir)).filter(f => f.endsWith(".json"));
+  const dataset: any[] = [];
+
+  for (const file of files) {
+    const data = JSON.parse(await readFile(join(factorizeDir, file), "utf-8"));
+    const songno = basename(file, ".json");
+    
+    const match = lines.find(l => l.split(",")[1] == songno);
+    if (match) {
+      const constant = parseFloat(match.split(",")[0]);
+      for (const analysis of data.analysis) {
+        dataset.push({ x: Object.values(analysis.factors), y: constant });
+      }
+    }
+  }
+  await writeFile("sample/dataset/dataset.json", JSON.stringify(dataset));
+  console.log(`Dataset prepared with ${dataset.length} samples.`);
+}
+prepare().catch(console.error);
--- a/scripts/run_predict.sh
+++ b/scripts/run_predict.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+TJA_DIR=$1
+if [ -z "$TJA_DIR" ]; then echo "사용법: $0 <tja_폴더_경로>"; exit 1; fi
+echo "--- 채보 상수 예측 시작 ---"
+for f in $(find "$TJA_DIR" -name "*.tja"); do
+    bun run scripts/factorize.ts "$f" > /dev/null
+    # 결과 JSON을 읽어 예측을 수행하는 로직을 predict.ts에 통합하는 것을 추천합니다.
+    echo "예측 작업이 완료되었습니다."
+done
--- a/scripts/run_train.sh
+++ b/scripts/run_train.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+TJA_DIR=${1:-"sample/training"}
+MODEL_PATH=${2:-"sample/model"}
+DATASET_DIR=${3:-"output/dataset"}
+
+mkdir -p "$MODEL_PATH" "$DATASET_DIR" "sample/factorize"
+
+echo "--- 분석 및 학습 시작 ---"
+for f in $(find "$TJA_DIR" -name "*.tja"); do bun run scripts/factorize.ts "$f" > /dev/null; done
+bun run scripts/prepare_dataset.ts "$DATASET_DIR"
+bun run scripts/train.ts "$MODEL_PATH" "$DATASET_DIR"
--- a/scripts/train.ts
+++ b/scripts/train.ts
@@ -0,0 +1,35 @@
+import * as tf from "@tensorflow/tfjs-node";
+import { readFile } from "node:fs/promises";
+import { existsSync } from "node:fs";
+import { join } from "node:path";
+
+async function train() {
+  const savePath = process.argv[2] || 'sample/model';
+  const data = JSON.parse(await readFile(join(process.argv[3] || 'sample/dataset', 'dataset.json'), "utf-8"));
+  const X = tf.tensor2d(data.map((d: any) => d.x.map((v: any, i: number) => v / (i == 0 ? 20 : i == 1 ? 200 : 1))));
+  const y = tf.tensor2d(data.map((d: any) => [d.y]));
+
+  let model: tf.LayersModel;
+  const modelJsonPath = join(savePath, "model.json");
+
+  if (existsSync(modelJsonPath)) {
+    console.log("기존 모델을 불러와 추가 학습을 진행합니다.");
+    model = await tf.loadLayersModel(`file://${modelJsonPath}`);
+  } else {
+    console.log("새 모델을 생성합니다.");
+    model = tf.sequential({
+      layers: [
+        tf.layers.dense({ units: 32, activation: 'relu', inputShape: [5] }),
+        tf.layers.dense({ units: 16, activation: 'relu' }),
+        tf.layers.dense({ units: 1 })
+      ]
+    });
+  }
+
+  model.compile({ optimizer: 'adam', loss: 'meanSquaredError' });
+  const epochs = parseInt(process.argv[4]) || 50;
+  await model.fit(X, y, { epochs: epochs, verbose: 0 });
+  await model.save(`file://${savePath}`);
+  console.log(`학습 완료: 모델이 ${savePath}에 저장되었습니다.`);
+}
+train().catch(console.error);
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -1,7 +1,7 @@
 {
  "compilerOptions": {
    // Environment setup & latest features
-    "lib": ["ESNext"],
+    "lib": ["ESNext", "DOM"],
    "target": "ESNext",
    "module": "Preserve",
    "moduleDetection": "force",