以下是几个引入AI功能的Python代码示例： ### 简单的线性回归模型（使用`scikit-learn`库） ```python from sklearn.linear_model import LinearRegression import numpy as np # 生成一些示例数据 X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) y = np.array([2, 4, 6, 8, 10]) # 创建线性回归模型 model = LinearRegression() # 训练模型 model.fit(X, y) # 预测新数据 new_X = np.array([6]).reshape(-1, 1) prediction = model.predict(new_X) print("预测值:", prediction) ``` ### 简单的图像识别（使用`TensorFlow`和`Keras`） ```python import tensorflow as tf from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Flatten, Dense # 加载MNIST数据集 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 数据预处理 x_train = x_train / 255.0 x_test = x_test / 255.0 # 创建模型 model = Sequential([ Flatten(input_shape=(28, 28)), Dense(128, activation='relu'), Dense(10, activation='softmax') ]) # 编译模型 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, epochs=5) # 评估模型 test_loss, test_acc = model.evaluate(x_test, y_test) print(f"测试准确率: {test_acc}") ``` ### 文本分类（使用`scikit-learn`） ```python from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline # 示例数据 documents = ["This is a positive review", "This is a negative review"] labels = [1, 0] # 创建管道 text_clf = Pipeline([ ('tfidf', TfidfVectorizer()), ('clf', MultinomialNB()) ]) # 训练模型 text_clf.fit(documents, labels) # 预测新文本 new_doc = ["This is a great product"] predicted = text_clf.predict(new_doc) print("预测结果:", predicted) ```