写一下svm鸢尾花实验的代码

在Python中使用Scikit-learn库来做SVM鸢尾花分类的示例代码如下：

# 导入必要的库
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载鸢尾花数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 数据预处理：归一化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# 创建并训练SVM模型（默认的是RBF核）
clf = SVC(kernel='rbf', C=1, gamma='scale')  # 这里可以尝试不同的参数组合
clf.fit(X_train, y_train)

# 预测
y_pred = clf.predict(X_test)

# 评估性能
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

# 可视化决策边界（仅适用于二分类问题）
if len(np.unique(y)) == 2:
    from matplotlib.colors import ListedColormap
    X_combined = np.vstack((X_train, X_test))
    y_combined = np.hstack((y_train, y_test))

    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

    plt.figure(figsize=(10, 8))
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X_combined[y_combined == cl, 0], y=X_combined[y_combined == cl, 1],
                    alpha=0.8, c=cmap_bold(idx), edgecolor='black',
                    label=f'{cl}')
    plt.title("SVM Decision Boundaries")
    plt.xlabel('sepal length')
    plt.ylabel('sepal width')
    plt.legend(loc='upper left')
    plt.show()