import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from tensorflow.keras.layers import Input, Dense, GRU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Transformer
from tensorflow.keras import backend as K

生成模拟数据

X = …

y = …

数据预处理

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

使用K均值聚类算法进行聚类

kmeans = KMeans(n_clusters=3)
clusters = kmeans.fit_predict(X_scaled)

使用PCA降维可视化聚类结果

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)

构建Transformer模型

inputs = Input(shape=(X.shape[1],))
transformer_output = Transformer(…)(inputs) # Transformer层
gru_output = GRU(…)(transformer_output) # GRU层
outputs = Dense(3, activation=‘softmax’)(gru_output) # 输出层

model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=Adam(), loss=‘categorical_crossentropy’, metrics=[‘accuracy’])

拆分数据集

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

训练模型

model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

预测

y_pred = model.predict(X_test)
y_pred_labels = np.argmax(y_pred, axis=1)

评估模型

accuracy = accuracy_score(np.argmax(y_test, axis=1), y_pred_labels)
conf_matrix = confusion_matrix(np.argmax(y_test, axis=1), y_pred_labels)

print(“模型准确率：”, accuracy)
print(“混淆矩阵：”, conf_matrix)