本文整理汇总了Python中hmm.HMM.learning方法的典型用法代码示例。如果您正苦于以下问题：Python HMM.learning方法的具体用法？Python HMM.learning怎么用？Python HMM.learning使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类hmm.HMM的用法示例。
在下文中一共展示了HMM.learning方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: clustering_by_chords
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import learning [as 别名]
def clustering_by_chords(musics, k, num_states=3):
N = len(musics)
ns = 2
nc = 12
n = num_states + num_states**2 + nc * num_states
feature_vectors = []
for idx, music in enumerate(musics):
suffix = []
chord = []
for dat in music.dat:
suffix.extend(dat[0])
chord.append(dat[1])
f = []
hmm_c = HMM(nc, num_states=num_states)
hmm_c.learning(chord)
Pic = hmm_c.Pi
a_sort = Pic.argsort()[-1::-1]
Pic = Pic[a_sort]
Ac = hmm_c.A[:, a_sort]
Bc = hmm_c.B[:, a_sort]
f.extend(list(Pic.flatten()))
f.extend(list(Ac.flatten()))
f.extend(list(Bc.flatten()))
f = np.array(f)
if any(np.isnan(f)):
print idx, music.title, music.keys[0]
feature_vectors.append(f)
feature_vectors = np.array(feature_vectors)
kmeans_model = KMeans(n_clusters=k, random_state=RS).fit(feature_vectors)
labels = kmeans_model.labels_
return labels, feature_vectors示例2: clustering_by_chords_and_lyrics
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import learning [as 别名]
def clustering_by_chords_and_lyrics(musics, k, num_states=3):
N = len(musics)
nc = 12
n = num_states + num_states**2 + nc * num_states
feature_vectors = []
for idx, music in enumerate(musics):
suffix = []
chord = []
for dat in music.dat:
suffix.append(dat[0])
chord.append(dat[1])
f = []
hmm_c = HMM(nc, num_states=num_states)
hmm_c.learning(chord)
Pic = hmm_c.Pi
a_sort = Pic.argsort()[-1::-1]
Pic = Pic[a_sort]
Ac = hmm_c.A[:, a_sort]
Bc = hmm_c.B[:, a_sort]
f.extend(list(Pic.flatten()))
f.extend(list(Ac.flatten()))
f.extend(list(Bc.flatten()))
f = np.array(f)
if any(np.isnan(f)):
print idx, music
feature_vectors.append(f)
Scores = []
docs = []
for music in musics:
a_words = parse_lyrics.get_affective_words(music.lyrics)
music.a_words = a_words
docs.append(a_words)
collection = nltk.TextCollection(docs)
terms = list(set(collection))
for idx, doc in enumerate(docs):
score = parse_lyrics.calc_score(collection, terms, doc)
Scores.append(score)
emo4 = parse_lyrics.calc_4emotions(score)
musics[idx].score = score
musics[idx].emo4 = emo4
Scores = np.array(Scores)
max_scores = Scores.max(axis=0)
min_scores = Scores.min(axis=0)
diff = max_scores - min_scores
Scores = (Scores - min_scores) / diff
for music in musics:
music.score = (music.score - min_scores) / diff
for idx, f in enumerate(feature_vectors):
np.append(f, Scores[idx])
feature_vectors = np.array(feature_vectors)
kmeans_model = KMeans(n_clusters=k, random_state=RS).fit(feature_vectors)
labels = kmeans_model.labels_
return labels, feature_vectors