hpo - 副本.PY

Signed-off-by: autotest <32b7dbc8@leinao.ai>

hpo - 副本.PY

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+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+import argparse
+
+# 加载数据集
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+# 划分数据集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 参数解析
+parser = argparse.ArgumentParser(description='SVM Model for Iris dataset')
+parser.add_argument('--C', type=float, default=1.0, help='Regularization parameter')
+parser.add_argument('--kernel', type=str, default='rbf', help='Kernel type')
+parser.add_argument('--gamma', type=float, default='scale', help='Kernel coefficient')
+
+args = parser.parse_args()
+
+# 定义支持向量机模型并设置参数
+svm = SVC(C=args.C, kernel=args.kernel, gamma=args.gamma)
+
+# 在训练集上拟合模型
+svm.fit(X_train, y_train)
+
+# 输出模型在测试集上的准确率
+test_accuracy = svm.score(X_test, y_test)
+print(f"Accuracy={test_accuracy}")
+