把上述代码整合到一起

当然可以，下面是将上述步骤整合到一起的完整代码示例，包括数据生成、划分、KNN模型构建、预测和评估，以及基本的可视化：

import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import numpy as np

# 生成二维随机数据
data = make_blobs(n_samples=200, centers=2, random_state=8)
x, y = data  # x是样本数据，y是对应的标签

# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# 构建KNN分类器，这里假设n_neighbors=3
knn = KNeighborsClassifier(n_neighbors=3)
# 训练模型
knn.fit(x_train, y_train)

# 对测试集进行预测
predictions = knn.predict(x_test)

# 计算模型的准确率
accuracy = knn.score(x_test, y_test)
print(f"模型评估：{accuracy:.2f}")

# 将预测结果绘制在散点图上
plt.figure(figsize=(8, 6))
plt.scatter(x[:, 0], x[:, 1], c=y, cmap='viridis', label='Original Data')
plt.scatter(x_test[:, 0], x_test[:, 1], c=predictions, cmap='viridis', label='Predicted Class')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.legend()
plt.title("KNN Classification Results")
plt.show()

# 打印单个测试样本的预测结果
print(f"预测单个测试样本：{x_test[0]} -> {predictions[0]}")

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