给我xgboost的matlab代码

XGBoost是一个用于梯度增强型决策树的优化库，它主要用于机器学习中的分类、回归、排序和预测建模问题。XGBoost的主要开发语言是C++，但也提供了Python、Java和R等语言的接口。目前还没有官方支持的Matlab接口，但是可以通过Matlab调用Python的方式来使用XGBoost。

下面是一个用Matlab调用Python的示例代码，以实现XGBoost对Iris数据集进行分类：

% Load the Iris dataset
load fisheriris
X = meas;
Y = species;

% Encode the categorical response variable as numeric
Y = grp2idx(Y);

% Split the dataset into training and testing sets
cv = cvpartition(Y,'Holdout',0.2);
Xtrain = X(cv.training,:);
Ytrain = Y(cv.training,:);
Xtest = X(cv.test,:);
Ytest = Y(cv.test,:);

% Convert the training and testing sets to Python arrays
py.Xtrain = py.numpy.array(Xtrain);
py.Ytrain = py.numpy.array(Ytrain);
py.Xtest = py.numpy.array(Xtest);

% Load the XGBoost Python module
py.xgboost = py.importlib.import_module('xgboost');

% Convert the training and testing sets to XGBoost DMatrix objects
py.dtrain = py.xgboost.DMatrix(py.Xtrain, label=py.Ytrain);
py.dtest = py.xgboost.DMatrix(py.Xtest);

% Set the XGBoost hyperparameters
params = py.dict(pyargs('objective','multi:softmax','num_class',3,'eval_metric','merror'));

% Train the XGBoost model
num_rounds = 10;
py.bst = py.xgboost.train(params, py.dtrain, num_rounds);

% Make predictions on the testing set
py.Ypred = py.bst.predict(py.dtest);
Ypred = double(py.array.array('d',py.numpy.nditer(py.Ypred)));

% Compute the classification accuracy
accuracy = sum(Ytest == Ypred) / numel(Ytest);
disp(['Classification accuracy: ' num2str(accuracy)]);

需要注意的是，上述代码需要先安装好Python和XGBoost库，并在Matlab中配置好Python环境。例如，可以通过以下代码设置Python环境：

% Set the Python executable path
pyExec = 'C:\Python39\python.exe';

% Set the Python library path
pyLib = 'C:\Python39\Lib';

% Setup the Python environment
pyversion(pyExec);
setenv('PATH', [pyLib ';' getenv('PATH')]);