用Python写出完成如下功能的代码：构建LDA分类模型；优化超参数；预测；测试模型的性能；

以下是使用Python实现LDA分类模型的代码：

# 导入必要的库
from sklearn.datasets import load_iris
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# 构建LDA分类模型
lda = LinearDiscriminantAnalysis()

# 设置超参数搜索范围
params = {'solver': ['svd', 'lsqr', 'eigen'], 'shrinkage': [None, 'auto', 0.1, 0.5, 0.9]}

# 优化超参数
grid_search = GridSearchCV(lda, param_grid=params, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)
best_lda = grid_search.best_estimator_

# 预测
y_pred = best_lda.predict(X_test)

# 测试模型的性能
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

运行上述代码后，将输出LDA分类模型的准确率。如果需要更好的性能，可以进一步调整超参数搜索范围。