python 实现 无监督转有监督学 KMeans to SVM

import numpy as np
from sklearn.cluster import KMeans,DBSCAN
import pandas as pd
from sklearn import datasets
from sklearn.preprocessing import MaxAbsScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载鸢尾花数据集
iris = pd.read_csv("../data/iris.csv",sep=",",header=None,index_col=None)
#
# scaler = MaxAbsScaler()
# X:np.ndarray = scaler.fit_transform(iris.iloc[:,:4])

X:np.ndarray = iris.iloc[:,:3]

# y = iris.iloc[:,4]  # 标签

print(X)



kmeans_model = KMeans(n_clusters=3,max_iter=300)
# kmeans_model = DBSCAN(min_samples=3)
kmeans_model.fit(X)
y_km_train =  kmeans_model.predict(X)
print(y_km_train)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y_km_train, test_size=0.3, random_state=42)
# print(X_train)
print(y_train)

# 创建SVM分类器（使用线性核）
svm_model = SVC(kernel='rbf',max_iter=200,gamma=1, C=1.0)

# 训练模型
svm_model.fit(X_train, y_train)

# 预测测试集
y_pred = svm_model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"模型准确率: {accuracy:.2f}")