LogisticRegression意图识别模型demo

# 导入所需的库
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression

# 定义训练数据
sentences = [
    "我想听音乐",
    "打开冰箱",
    "今天天气怎么样",
    "给我打电话",
    "我想打开电视"
]
intents = [
    "play_music",
    "open_fridge",
    "check_weather",
    "call_me",
    "turn_on_tv"
]

# 使用 CountVectorizer 来提取语句中的特征
vectorizer = CountVectorizer()
vectors = vectorizer.fit_transform(sentences)

# 使用逻辑回归模型进行训练
classifier = LogisticRegression()
classifier.fit(vectors, intents)


# 定义一个函数，用来预测一个语句的意图
def predict_intent(sentence):
    vector = vectorizer.transform([sentence])
    intent = classifier.predict(vector)[0]
    return intent


# 测试一下函数
sentence = "有点无聊，想刷视频"
predicted_intent = predict_intent(sentence)
print(predicted_intent)  # 输出：turn_on_tv