写一段python代码，包含很多复杂的算法

以下是一段包含多种复杂算法的 Python 代码示例：

import numpy as np
from sklearn import svm, metrics
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import fetch_openml

# 载入 MNIST 数据集
mnist = fetch_openml('mnist_784')
X, y = mnist["data"], mnist["target"]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

# 调用 SVM 算法，使用网格搜索调参
param_grid = {'C': [0.1, 1, 10, 100], 'kernel': ['linear', 'rbf']}
svm_clf = svm.SVC()
grid_search = GridSearchCV(svm_clf, param_grid, cv=3, n_jobs=-1)
grid_search.fit(X_train, y_train)

# 输出最优参数
print("SVM best parameters: ", grid_search.best_params_)

# 计算 SVM 在测试集上的准确率
svm_predictions = grid_search.predict(X_test)
svm_accuracy = metrics.accuracy_score(y_test, svm_predictions)
print("SVM accuracy: ", svm_accuracy)

# 使用随机森林算法
rf_clf = RandomForestClassifier(n_estimators=100)
rf_clf.fit(X_train, y_train)

# 计算随机森林在测试集上的准确率
rf_predictions = rf_clf.predict(X_test)
rf_accuracy = metrics.accuracy_score(y_test, rf_predictions)
print("Random Forest accuracy: ", rf_accuracy)

这段代码中包含了 SVM (支持向量机) 算法和随机森林算法，它们都是常用的机器学习算法。其中SVM算法使用了网格搜索来调参，能够找到最优的超参数组合。最后，