From 0fe1e355a77c7ce06f0e54bd0211706663370012 Mon Sep 17 00:00:00 2001
From: hotsixman <hotsixman05@gmail.com>
Date: Fri, 24 Apr 2026 21:26:39 +0900
Subject: [PATCH] .

---
 .gemini/difficulty-factors.md |    48 +-
 predict-all.ts                |    68 +
 script/factorize.ts           |   119 +-
 script/predict.ts             |     4 +-
 script/train.py               |     8 +-
 spec.md                       |    25 +-
 test/factors.json             | 10264 --------------------------------
 test/model.pth                |   Bin 49814 -> 0 bytes
 test/scaler.json              |     1 -
 test/temp_factors.json        |     1 -
 test/validate_result_1.csv    |   201 -
 test/validate_result_2.csv    |   201 -
 test/validate_result_3.csv    |   201 -
 test/validate_result_4.csv    |   201 -
 test/validate_result_5.csv    |   201 -
 15 files changed, 204 insertions(+), 11339 deletions(-)
 create mode 100644 predict-all.ts
 delete mode 100644 test/factors.json
 delete mode 100644 test/model.pth
 delete mode 100644 test/scaler.json
 delete mode 100644 test/temp_factors.json
 delete mode 100644 test/validate_result_1.csv
 delete mode 100644 test/validate_result_2.csv
 delete mode 100644 test/validate_result_3.csv
 delete mode 100644 test/validate_result_4.csv
 delete mode 100644 test/validate_result_5.csv

diff --git a/.gemini/difficulty-factors.md b/.gemini/difficulty-factors.md
index 3af0dd7..7573a45 100644
--- a/.gemini/difficulty-factors.md
+++ b/.gemini/difficulty-factors.md
@@ -1,36 +1,28 @@
 # TJA 난이도 산정 핵심 요소 (Technical Factors)
 
-'상수' 산출을 위해 채보에서 추출해야 하는 5개 핵심 차원과 세부 지표입니다.
+학습 모델(DifficultyNet)에 입력되는 8가지 핵심 지표입니다. (spec.md 업데이트 반영)
 
-## 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.
+## 1. 물리적 밀도
+- **density_avg**: 평균 노트 밀도 (총 노트 수 / 곡 길이)
+- **density_peak**: 1초당 최대 밀도 (가장 촘촘한 구간의 노트 수)
 
-## 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.
+## 2. BPM 및 속도 변화
+- **bpm_avg**: 곡의 평균 BPM
+- **bpm_var**: BPM 변화율 (BPM 변속의 다양성)
 
-## 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.
+## 3. 구조적 특징
+- **note_count**: 총 노트 수
+- **long_note_duration**: 전체 대비 긴 노트(지속형) 점유율
 
-## 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.
+## 4. 패턴 및 연타
+- **complex_ratio**: 복합 패턴(큰 북/작은 북 혼합) 비율
+- **roll_count**: 연타(Roll) 노트의 총 개수
 
-## 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.
+## 계산 로직
+- **밀도 계열**: 노트 간 시간 간격(`delta`)의 역수(`1/delta`)로 순시 밀도를 계산하여 `avg`/`peak` 도출.
+- **BPM 계열**: `#BPM` 및 `#STOP` 명령을 기반으로 시간 축 전개 후 평균 및 표준편차(`var`) 계산.
+- **패턴 계열**: 슬라이딩 윈도우(4~8개 노트)에서 노트 타입 변화(`d↔k`) 횟수를 측정하여 비율 산정.
+- **연타 계열**: `#ROLL` 명령 및 입력 밀집 구간 카운트.
 
-## 상수 추정 가중치 (Proposed)
-`Constant ∝ (Peak_NPS * 0.4) + (Complexity * 0.3) + (Stamina * 0.2) + (Reading * 0.1)`
+## 중요 주의사항
+- 모든 Factor는 모델 학습 전 `StandardScaler` 또는 `RobustScaler`를 통해 반드시 이상치를 처리해야 합니다.
diff --git a/predict-all.ts b/predict-all.ts
new file mode 100644
index 0000000..9d1c1ba
--- /dev/null
+++ b/predict-all.ts
@@ -0,0 +1,68 @@
+import { readdirSync, writeFileSync, existsSync } from 'fs';
+import { join, extname, dirname } from 'path';
+import { execSync } from 'child_process';
+
+async function predictAll() {
+    const args = process.argv.slice(2);
+    if (args.length < 3) {
+        console.log("Usage: bun script/predict-all.ts <workingDir> <tjaDir> <outputPath>");
+        process.exit(1);
+    }
+
+    const workingDir = args[0];
+    const tjaDir = args[1];
+    const outputPath = args[2];
+    const targetDifficulties = ['oni', 'edit'];
+
+    if (!existsSync(tjaDir)) {
+        console.error(`❌ Directory not found: ${tjaDir}`);
+        process.exit(1);
+    }
+
+    const files = readdirSync(tjaDir).filter(file => extname(file).toLowerCase() === '.tja');
+    console.log(`📂 Found ${files.length} TJA files. Processing Oni & Edit...`);
+
+    const finalResults: any[] = [];
+
+    for (const file of files) {
+        const fullPath = join(tjaDir, file);
+        
+        for (const diff of targetDifficulties) {
+            try {
+                // 개별 예측 실행
+                const cmd = `bun script/predict.ts "${workingDir}" "${fullPath}" "${diff}"`;
+                const output = execSync(cmd, { stdio: ['pipe', 'pipe', 'ignore'] }).toString();
+                
+                const match = output.match(/(?:Prediction|Predicted Difficulty):\s*(\d+\.\d+)/);
+                
+                if (match) {
+                    const value = parseFloat(match[1]);
+                    console.log(`✅ [${file}] ${diff.toUpperCase()}: ${value}`);
+                    finalResults.push({
+                        filename: file,
+                        difficulty_type: diff,
+                        predicted_measure: value
+                    });
+                }
+            } catch (e) {
+                // 해당 난이도가 없는 경우 조용히 넘어감
+            }
+        }
+    }
+
+    // 결과 저장 (확장자에 따라 분기)
+    if (outputPath.endsWith('.json')) {
+        writeFileSync(outputPath, JSON.stringify(finalResults, null, 2));
+    } else {
+        // CSV 저장 (기본값)
+        const csvContent = [
+            "filename,difficulty,predicted_measure",
+            ...finalResults.map(r => `"${r.filename}",${r.difficulty_type},${r.predicted_measure}`)
+        ].join('\n');
+        writeFileSync(outputPath, csvContent);
+    }
+
+    console.log(`\n✨ Prediction complete! Results saved to: ${outputPath}`);
+}
+
+predictAll();
\ No newline at end of file
diff --git a/script/factorize.ts b/script/factorize.ts
index dafbf7a..b3b83ab 100644
--- a/script/factorize.ts
+++ b/script/factorize.ts
@@ -1,55 +1,114 @@
-import { Course, Bar } from 'tja-parser';
+import { Course, Bar, Note, HitNote } from 'tja-parser';
 
 export namespace Factor {
-    // 노트 추출 및 정렬 (시간순)
+    export function filterHitNotes(course: Course) {
+        const notes: HitNote[] = [];
+        course.noteGroups.forEach((g) => {
+            if (g instanceof Bar) {
+                for (const note of g.getNotes()) {
+                    if (note instanceof HitNote) {
+                        notes.push(note);
+                    }
+                }
+            }
+        });
+        return notes;
+    }
+
     export function getAllNotes(course: Course) {
         const notes: { type: number, time: number }[] = [];
         for (const group of course.noteGroups) {
             if (group instanceof Bar) {
-                // 파서 구조에 따라 노트 추출 방식 조정 필요
-                notes.push(...group.getNotes().map((note) => {
-                    return {
-                        //@ts-expect-error
-                        type: note.type,
-                        time: note.getTimingMS()
-                    }
-                }));
+                notes.push(...group.getNotes().map((note) => ({
+                    //@ts-expect-error
+                    type: note.type,
+                    time: note.getTimingMS()
+                })));
             }
         }
         return notes.sort((a, b) => a.time - b.time).filter(n => [1, 2, 3, 4].includes(n.type));
     }
 
-    export function getAverageDensity(notes: { time: number }[]): number {
-        if (notes.length < 2) return 0;
-        const duration = (notes[notes.length - 1].time - notes[0].time) / 1000;
-        return duration > 0 ? notes.length / duration : 0;
+    // 밀도 관련
+    export function getAverageDensity(notes: HitNote[]) {
+        return notes.length / (notes[notes.length - 1].getTimingMS() - notes[0].getTimingMS()) * 1000
     }
 
-    export function getPeakDensity(notes: { time: number }[]): number {
-        if (notes.length === 0) return 0;
-        let maxPeak = 0;
-        const windowSize = 1000;
+    export function getPeakDensity(notes: HitNote[]) {
+        let peakDensity = 0;
         for (let i = 0; i < notes.length; i++) {
-            let count = 0;
-            const startTime = notes[i].time;
-            for (let j = i; j < notes.length && notes[j].time < startTime + windowSize; j++) {
-                count++;
+            let noteCount = 1;
+            for (let j = i + 1; j < notes.length && (notes[j].getTimingMS() - notes[i].getTimingMS() <= 1000); j++) {
+                noteCount++;
+            }
+            if (peakDensity < noteCount) {
+                peakDensity = noteCount;
             }
-            maxPeak = Math.max(maxPeak, count);
         }
-        return maxPeak;
+        return peakDensity;
     }
 
-    export function getMaxCombo(notes: any[]): number {
-        return notes.length;
+    // BPM 관련
+    export function getAverageBPM(notes: HitNote[]) {
+
+        const averageBPM = notes.reduce((p, note) => p + note.getBPM().valueOf(), 0) / notes.length;
+        return averageBPM;
+    }
+
+    export function getBpmChange(notes: HitNote[]) {
+        let bpmChange = 0;
+        for (let i = 1; i < notes.length; i++) {
+            if (Math.abs(notes[i].getBPM() - notes[i - 1].getBPM()) > 1.5) {
+                bpmChange++;
+            }
+        };
+        return bpmChange;
+    }
+
+    // 복잡성
+    export function getComplexity(notes: HitNote[]) {
+        let complexity = 0;
+        for (let i = 2; i < notes.length; i++) {
+            let localComplexity = 0;
+
+            // ddk 또는 dkk류면 1, 아니면 0.5
+            if (
+                (notes[i].type % 2 === notes[i - 1].type % 2 && notes[i - 1].type % 2 !== notes[i - 2].type % 2) ||
+                (notes[i].type % 2 !== notes[i - 1].type % 2 && notes[i - 1].type % 2 === notes[i - 2].type % 2)
+            ) {
+                localComplexity = 1;
+            } else {
+                localComplexity = 0.5
+            }
+
+            // 시간 차가 짧을 수록 complexity 증가
+            localComplexity *= (1 / (notes[i].getTimingMS() - notes[i - 1].getTimingMS()) + 1 / (notes[i - 1].getTimingMS() - notes[i - 2].getTimingMS()))
+            complexity += localComplexity;
+        }
+        return complexity / notes.length;
+    }
+
+    // 스크롤 변화
+    export function getScrollChange(notes: HitNote[]) {
+        let bpmChange = 0;
+        for (let i = 1; i < notes.length; i++) {
+            if (Math.abs(notes[i].getBPM() * notes[i].getScroll() - notes[i - 1].getBPM() * notes[i - 1].getScroll()) > 1.5) {
+                bpmChange++;
+            }
+        };
+        return bpmChange;
     }
 }
 
 export function factorize(course: Course) {
-    const notes = Factor.getAllNotes(course);
+    const notes = Factor.filterHitNotes(course)
     return {
-        average_density: Factor.getAverageDensity(notes),
-        peak_density: Factor.getPeakDensity(notes),
-        max_combo: Factor.getMaxCombo(notes)
+        note_count: notes.length,
+        density_avg: Factor.getAverageDensity(notes),
+        density_peak: Factor.getPeakDensity(notes),
+        bpm_avg: Factor.getAverageBPM(notes),
+        bpm_change: Factor.getBpmChange(notes),
+        complexity: Factor.getComplexity(notes),
+        scroll_change: Factor.getScrollChange(notes)
     };
 }
\ No newline at end of file
diff --git a/script/predict.ts b/script/predict.ts
index 0fa53b0..363a698 100644
--- a/script/predict.ts
+++ b/script/predict.ts
@@ -52,7 +52,7 @@ def predict():
 
         with torch.no_grad():
             input_tensor = torch.from_numpy(X_scaled).float()
-            pred = model(input_tensor) * 11 + 1
+            pred = model(input_tensor) * 12 + 0.5
         print(float(pred.item()))
     except Exception as e:
         print(f"ERROR: {e}", file=sys.stderr)
@@ -89,7 +89,7 @@ function main() {
         console.error(`Error: Difficulty '${difficulty}' not found.`);
         process.exit(1);
     }
-
+    
     const score = runInference(workingDir, factorize(chart));
     console.log(`\n🎯 Predicted Difficulty: ${score.toFixed(2)}`);
 }
diff --git a/script/train.py b/script/train.py
index a3da3fa..c91b148 100644
--- a/script/train.py
+++ b/script/train.py
@@ -6,7 +6,7 @@ import torch.nn as nn
 import torch.optim as optim
 from sklearn.preprocessing import StandardScaler
 
-MAX_MARGIN_LIMIT = 3
+MAX_MARGIN_LIMIT = 10
 
 class DifficultyNet(nn.Module):
     def __init__(self, input_dim):
@@ -83,7 +83,7 @@ def train(script_dir, working_dir, data_dir, train_count, val_count, margin, val
             
             # 현재 학습 데이터에 대한 오차 분석
             with torch.no_grad():
-                diff = torch.abs((preds_train * 11 + 1) - (y_train * 11 + 1))
+                diff = torch.abs((preds_train * 12 + 0.5) - (y_train * 12 + 0.5))
                 train_mae = torch.mean(diff).item()
                 train_max = torch.max(diff).item()
             
@@ -114,7 +114,7 @@ def train(script_dir, working_dir, data_dir, train_count, val_count, margin, val
                 val_df = df.sample(n=min(int(val_count), len(df)))
                 X_val = torch.FloatTensor(val_df[X_cols].values).to(device)
                 y_val_raw = val_df['상수'].values
-                preds = model(X_val) * 11 + 1
+                preds = model(X_val) * 12 + 0.5
                 y_val_tensor = torch.FloatTensor(y_val_raw).unsqueeze(1).to(device)
                 diff_tensor = torch.abs(preds - y_val_tensor)
                 mae = torch.mean(diff_tensor).item()
@@ -135,7 +135,7 @@ def train(script_dir, working_dir, data_dir, train_count, val_count, margin, val
                 if mae <= margin and max_error <= (margin * MAX_MARGIN_LIMIT):
                     print(f"   [Iter {i}] ✅ PASS (MAE: {mae:.4f}, MAX: {max_error:.4f})")
                 else:
-                    print(f"   [Iter {i}] ❌ FAIL (MAE: {mae:.4f} > {margin}, MAX: {max_error:.4f} > {margin * MAX_MARGIN_LIMIT})")
+                    print(f"   [Iter {i}] ❌ FAIL (MAE: {mae:.4f} > {margin} || MAX: {max_error:.4f} > {margin * MAX_MARGIN_LIMIT})")
                     all_passed = False
                     break 
 
diff --git a/spec.md b/spec.md
index 313101c..cbf2734 100644
--- a/spec.md
+++ b/spec.md
@@ -43,10 +43,27 @@
 - 로드된 모델과 학습 시 사용했던 스케일링 파라미터(Mean, Std)를 동일하게 적용하여 예측을 진행한다.
 
 # Factor
-## 종류
-- average_density: 평균 밀도
-- peak_density: 최고 초당 밀도
-- max_combo: 노트의 수
+## 추천 Factor 세트 (난이도 결정 핵심 지표)
+1.  **density_avg**: 평균 노트 밀도 (총 노트 수 / 곡 길이)
+2.  **density_peak**: 1초당 최대 밀도 (가장 촘촘한 구간의 노트 수)
+3.  **bpm_avg**: 곡의 평균 BPM
+4.  **bpm_change**: BPM 변화율 (BPM 변속의 다양성)
+5.  **note_count**: 총 노트 수
+6.  **complexity**: 동캇 꼬임
+
+## Factor 계산 로직
+- **밀도 계열**: 
+    - 노트 간 시간 간격을 `delta`라고 할 때, `1 / delta`로 순시 밀도를 구한 뒤 최대값(`peak`)과 평균값(`avg`)을 도출합니다.
+- **패턴 계열**:
+    - 노트 타입을 배열로 매핑(예: 0=none, 1=don, 2=ka, 3=big_don, 4=big_ka)합니다.
+    - 슬라이딩 윈도우(예: 4~8개 노트)를 사용하여 인접한 노트의 타입 변화가 잦은 구간을 복합 패턴으로 판정(`complex_ratio`)합니다.
+- **연타 계열**:
+    - `#ROLL` 명령 혹은 특정 시간 내에 입력이 몰려있는 노트를 연타로 간주하여 카운트합니다.
+
+## 구현 가이드
+- `factorize.ts`에서 각 곡의 `Course` 데이터를 순회하며 위 지표들을 계산합니다.
+- 계산된 값은 `Record<string, number>` 형태로 반환하여 학습기에 입력합니다.
+- 모든 Factor는 이상치(Outlier) 처리를 위해 학습 단계에서 `StandardScaler` 또는 `RobustScaler`를 반드시 거쳐야 합니다.
 
 # Scripts
 - `parse.ts`: TJA 문자열을 `Course` 객체로 변환.
diff --git a/test/factors.json b/test/factors.json
deleted file mode 100644
index 3f1cc90..0000000
--- a/test/factors.json
+++ /dev/null
@@ -1,10264 +0,0 @@
-[
-  {
-    "songno": "449",
-    "diff": "oni",
-    "average_density": 5.587527839643653,
-    "peak_density": 12,
-    "max_combo": 784
-  },
-  {
-    "songno": "307",
-    "diff": "oni",
-    "average_density": 4.531287297527706,
-    "peak_density": 9,
-    "max_combo": 604
-  },
-  {
-    "songno": "461",
-    "diff": "oni",
-    "average_density": 5.787089080466525,
-    "peak_density": 13,
-    "max_combo": 657
-  },
-  {
-    "songno": "313",
-    "diff": "oni",
-    "average_density": 3.6810344827586206,
-    "peak_density": 8,
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diff --git a/test/scaler.json b/test/scaler.json
deleted file mode 100644
index 26d3f9e..0000000
--- a/test/scaler.json
+++ /dev/null
@@ -1 +0,0 @@
-{"mean": [5.390975807838123, 11.135695764909247, 630.3327571305099], "std": [1.4301422132137462, 3.0709973724562003, 211.64659003608378], "cols": ["average_density", "peak_density", "max_combo"]}
\ No newline at end of file
diff --git a/test/temp_factors.json b/test/temp_factors.json
deleted file mode 100644
index d535832..0000000
--- a/test/temp_factors.json
+++ /dev/null
@@ -1 +0,0 @@
-{"average_density":6.492187500000001,"peak_density":14,"max_combo":831}
\ No newline at end of file
diff --git a/test/validate_result_1.csv b/test/validate_result_1.csv
deleted file mode 100644
index a5edb1a..0000000
--- a/test/validate_result_1.csv
+++ /dev/null
@@ -1,201 +0,0 @@
-songno,diff,상수,predicted_measure,error
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diff --git a/test/validate_result_2.csv b/test/validate_result_2.csv
deleted file mode 100644
index 209f32e..0000000
--- a/test/validate_result_2.csv
+++ /dev/null
@@ -1,201 +0,0 @@
-songno,diff,상수,predicted_measure,error
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diff --git a/test/validate_result_3.csv b/test/validate_result_3.csv
deleted file mode 100644
index f20f7de..0000000
--- a/test/validate_result_3.csv
+++ /dev/null
@@ -1,201 +0,0 @@
-songno,diff,상수,predicted_measure,error
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diff --git a/test/validate_result_4.csv b/test/validate_result_4.csv
deleted file mode 100644
index 4eec548..0000000
--- a/test/validate_result_4.csv
+++ /dev/null
@@ -1,201 +0,0 @@
-songno,diff,상수,predicted_measure,error
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diff --git a/test/validate_result_5.csv b/test/validate_result_5.csv
deleted file mode 100644
index 4e06aa0..0000000
--- a/test/validate_result_5.csv
+++ /dev/null
@@ -1,201 +0,0 @@
-songno,diff,상수,predicted_measure,error
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