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script/train.ts

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+import { parseTja } from './parse';
+import { factorize, Factors } from './factorize';
+import { join } from 'path';
+import { readFileSync, writeFileSync, readdirSync } from 'fs';
+import { parse } from 'csv-parse/sync';
+
+async function train() {
+    const args = process.argv.slice(2);
+    const workingDir = args[0];
+    const scriptDir = args[1];
+    const dataDir = args[2];
+    const trainCount = parseInt(args[3]) || 100;
+    const validateCount = parseInt(args[4]) || 20;
+    const margin = parseFloat(args[5]) || 0.1;
+
+    const factorJsonPath = join(scriptDir, 'factor.json');
+ // 항상 script/ 위치 참조
+    let weights: Factors = JSON.parse(readFileSync(factorJsonPath, 'utf-8'));
+    
+    // 결과물(로그 등)을 저장할 경로 (필요 시 활용)
+    const logPath = join(workingDir, 'training_log.txt'); 
+
+    const measureCsv = readFileSync(join(dataDir, 'measure.csv'), 'utf-8');
+    const records = parse(measureCsv, { columns: true, skip_empty_lines: true });
+
+    const tjaFiles = readdirSync(join(dataDir, 'tja')).filter(f => f.endsWith('.tja'));
+    const shuffled = tjaFiles.sort(() => 0.5 - Math.random());
+    const trainFiles = shuffled.slice(0, trainCount);
+    const validateFiles = shuffled.slice(trainCount, trainCount + validateCount);
+
+    console.log(`Training with ${trainFiles.length} files...`);
+
+    // 학습 로직 (단순화된 경사 하강법 또는 반복 최적화)
+    let error = Infinity;
+    let iterations = 0;
+    while (error > margin && iterations < 100) {
+        let totalError = 0;
+        let count = 0;
+
+        for (const file of trainFiles) {
+            const songno = file.replace(/\D/g, '');
+            const tjaContent = readFileSync(join(dataDir, 'tja', file), 'utf-8');
+            const parsed = parseTja(tjaContent);
+            if (!parsed) continue;
+
+            for (const diff of ['oni', 'edit'] as const) {
+                const course = parsed[diff];
+                if (!course) continue;
+
+                const target = records.find((r: any) => r.songno === songno && (r.diff === (diff === 'oni' ? 'oni' : 'ura')));
+                if (!target) {
+                    // console.log(`[!] No target for ${songno} diff=${diff}`);
+                    continue;
+                }
+
+                const factors = factorize(course);
+                const prediction = Object.keys(factors).reduce((sum, key) => 
+                    sum + (factors[key as keyof Factors] * weights[key as keyof Factors]), 0);
+                
+                const targetValue = parseFloat(target.상수);
+                const diff_val = targetValue - prediction;
+                totalError += Math.abs(diff_val);
+                count++;
+
+                // 가중치 업데이트
+                for (const key in weights) {
+                    const k = key as keyof Factors;
+                    weights[k] += diff_val * factors[k] * 0.05; // 학습률 조정
+                }
+            }
+        }
+        error = totalError / (count || 1);
+        console.log(`Iteration ${iterations}: Mean Error = ${error.toFixed(4)}`);
+        iterations++;
+    }
+
+    writeFileSync(factorJsonPath, JSON.stringify(weights, null, 2));
+    writeFileSync(join(workingDir, 'training_result.json'), JSON.stringify({ finalError: error, weights }, null, 2));
+    console.log(`Training complete. Weights saved to ${factorJsonPath}, result saved to ${workingDir}`);
+
+    // 검증 로직
+    console.log('\nValidation Results:');
+    for (const file of validateFiles) {
+        const songno = file.replace(/\D/g, '');
+        const tjaContent = readFileSync(join(dataDir, 'tja', file), 'utf-8');
+        const parsed = parseTja(tjaContent);
+        if (!parsed) continue;
+
+        for (const diff of ['oni', 'edit'] as const) {
+            const course = parsed[diff];
+            if (!course) continue;
+
+            const target = records.find((r: any) => r.songno === songno && (r.diff === (diff === 'oni' ? 'oni' : 'ura')));
+            if (!target) {
+                console.log(`[!] No match for ${songno} diff=${diff === 'oni' ? 'oni' : 'ura'}`);
+                continue;
+            }
+
+            const factors = factorize(course);
+            const prediction = Object.keys(factors).reduce((sum, key) => 
+                sum + (factors[key as keyof Factors] * weights[key as keyof Factors]), 0);
+            
+            console.log(`[${songno}] Target: ${target.상수}, Predicted: ${prediction.toFixed(2)}, Diff: ${Math.abs(parseFloat(target.상수) - prediction).toFixed(2)}`);
+        }
+    }
+}
+
+train();