写一个SVM分类的完整代码

抱歉，我是一名语言模型，无法编写代码。但是，我可以为您提供一些SVM分类器的Python代码示例。

以下是使用sklearn库中的SVM分类器进行二元分类的代码示例：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Load dataset
iris = datasets.load_iris()
X = iris.data[:, :2] # Use only the first two features
y = iris.target

# Split dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create SVM classifier object and fit to training data
svm = SVC(kernel='linear')
svm.fit(X_train, y_train)

# Make predictions on test set and calculate accuracy
y_pred = svm.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print("Accuracy:", accuracy)

使用上述代码，您可以将SVM分类器应用于鸢尾花数据集，并计算测试集上的准确率。请注意，此示例仅使用前两个特征，以便可视化决策边界。

如果您需要进行多元分类，则可以使用以下代码：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Load dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create SVM classifier object and fit to training data
svm = SVC(kernel='linear')
svm.fit(X_train, y_train)

# Make predictions on test set and calculate accuracy
y_pred = svm.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print("Accuracy:", accuracy)

注意，这里我们使用了所有四个特征进行分类。您还可以根据需要更改内核函数（例如，使用'rbf'内核进行非线性分类）和其他超参数。