docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거

Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
2026-07-05 00:33:48 +09:00
parent 1cfd3bbb56
commit 9148c358d0
6455 changed files with 1 additions and 86875 deletions
@@ -0,0 +1,187 @@
+---
+id: wiki-2026-0508-hmm
+title: Hidden Markov Model (HMM)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [HMM, hidden markov model, Viterbi, forward-backward, Baum-Welch]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.95
+verification_status: applied
+tags: [machine-learning, hmm, sequence, viterbi, baum-welch, speech]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: hmmlearn / pomegranate
+---
+
+# Hidden Markov Model (HMM)
+
+## 매 한 줄
+> **"매 hidden state + observable emission 의 의 sequence model"**. 매 transition + emission probability. 매 Viterbi (MAP), forward-backward (filter), Baum-Welch (EM training). 매 modern: 매 LSTM/Transformer 의 의 의 displace, 매 still relevant in 매 bioinformatics, speech.
+
+## 매 핵심
+
+### 매 component
+- **States**: hidden.
+- **Observations**: emitted from state.
+- **Transition matrix** A: state → state.
+- **Emission matrix** B: state → obs.
+- **Initial distribution** π.
+
+### 매 task
+- **Evaluation**: P(O|λ) — forward.
+- **Decoding**: best state sequence — Viterbi.
+- **Learning**: λ from O — Baum-Welch (EM).
+
+### 매 응용
+1. **Speech recognition** (legacy, pre-DL).
+2. **POS tagging**.
+3. **Bioinformatics** (gene, protein domains).
+4. **Finance** (regime detection).
+5. **Activity recognition**.
+
+## 💻 패턴
+
+### hmmlearn
+```python
+from hmmlearn import hmm
+import numpy as np
+
+# 매 Gaussian emissions
+model = hmm.GaussianHMM(n_components=3, covariance_type='full', n_iter=100)
+model.fit(X_observations)
+
+states = model.predict(X_test)  # 매 Viterbi
+log_prob = model.score(X_test)  # 매 forward
+```
+
+### Viterbi (manual)
+```python
+def viterbi(obs, A, B, pi):
+    """매 most likely state sequence."""
+    n_states = len(pi)
+    T = len(obs)
+    delta = np.zeros((T, n_states))
+    psi = np.zeros((T, n_states), dtype=int)
+    
+    delta[0] = pi * B[:, obs[0]]
+    for t in range(1, T):
+        for j in range(n_states):
+            trans = delta[t-1] * A[:, j]
+            psi[t, j] = trans.argmax()
+            delta[t, j] = trans.max() * B[j, obs[t]]
+    
+    states = [delta[-1].argmax()]
+    for t in range(T-1, 0, -1):
+        states.insert(0, psi[t, states[0]])
+    return states
+```
+
+### Forward (P(O|λ))
+```python
+def forward(obs, A, B, pi):
+    n_states = len(pi)
+    T = len(obs)
+    alpha = np.zeros((T, n_states))
+    alpha[0] = pi * B[:, obs[0]]
+    for t in range(1, T):
+        for j in range(n_states):
+            alpha[t, j] = sum(alpha[t-1, i] * A[i, j] for i in range(n_states)) * B[j, obs[t]]
+    return alpha[-1].sum()
+```
+
+### Backward
+```python
+def backward(obs, A, B):
+    n_states = A.shape[0]
+    T = len(obs)
+    beta = np.ones((T, n_states))
+    for t in range(T-2, -1, -1):
+        for i in range(n_states):
+            beta[t, i] = sum(A[i, j] * B[j, obs[t+1]] * beta[t+1, j] for j in range(n_states))
+    return beta
+```
+
+### Baum-Welch (EM)
+```python
+def baum_welch(obs, n_states, max_iter=100):
+    n_obs = len(obs)
+    pi = np.random.dirichlet(np.ones(n_states))
+    A = np.random.dirichlet(np.ones(n_states), size=n_states)
+    n_symbols = max(obs) + 1
+    B = np.random.dirichlet(np.ones(n_symbols), size=n_states)
+    
+    for _ in range(max_iter):
+        alpha = compute_alpha(obs, A, B, pi)
+        beta = compute_beta(obs, A, B)
+        gamma = (alpha * beta) / (alpha * beta).sum(axis=1, keepdims=True)
+        xi = compute_xi(obs, A, B, alpha, beta)
+        
+        # 매 M-step
+        pi = gamma[0]
+        A = xi.sum(axis=0) / gamma[:-1].sum(axis=0, keepdims=True).T
+        for k in range(n_symbols):
+            B[:, k] = gamma[obs == k].sum(axis=0) / gamma.sum(axis=0)
+    
+    return pi, A, B
+```
+
+### POS tagging (toy)
+```python
+states = ['noun', 'verb', 'adj']
+words = ['cat', 'eats', 'red', 'apple']
+# 매 P(state | word) via HMM
+```
+
+### Gaussian Mixture HMM (continuous)
+```python
+model = hmm.GMMHMM(n_components=4, n_mix=3, covariance_type='full')
+model.fit(X)
+```
+
+### Regime detection (finance)
+```python
+returns = stock.pct_change().dropna()
+model = hmm.GaussianHMM(n_components=2).fit(returns.values.reshape(-1, 1))
+regimes = model.predict(returns.values.reshape(-1, 1))
+# 매 0 = bull, 1 = bear (or vice versa — interpret)
+```
+
+## 매 결정 기준
+| 상황 | Use |
+|---|---|
+| Sequence + small data | HMM |
+| Speech (modern) | DL |
+| Bioinformatics | Profile HMM |
+| Regime detection | Gaussian HMM |
+| Long sequence | RNN / Transformer |
+
+**기본값**: 매 small data sequence = HMM. 매 large data = DL. 매 bioinformatics 의 still HMM 의 standard.
+
+## 🔗 Graph
+- 부모: [[Probabilistic-Graphical-Models]]
+- 응용: [[Bioinformatics]]
+- Adjacent: [[Markov-Chain]] · [[Kalman-Filter-and-State-Tracking|Kalman-Filter]]
+
+## 🤖 LLM 활용
+**언제**: 매 small-data sequence. 매 explainable.
+**언제 X**: 매 modern ML 매 DL win.
+
+## ❌ 안티패턴
+- **HMM for image**: 매 wrong domain.
+- **No prior**: 매 EM stuck.
+- **Too many states**: 매 overfit.
+
+## 🧪 검증 / 중복
+- Verified (Rabiner 1989, hmmlearn docs).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Viterbi / forward-backward / Baum-Welch / hmmlearn code |