我们要将数据显示到QTreeView中,按照Model/View框架介绍,需要定义2个类TreeModel和TreeItem,TreeModel继承于QAbstractItemModel,用于向View...已经满足了TreeModel获取任意数据的要求。 下一步,我们来定义TreeModel类。...(3)定义TreeModel类 我们需要继承自QAbstractItemModel,让我们来看看它有哪些接口。...QVariant TreeModel::data(const QModelIndex &index, int role) const { if (!...QModelIndex TreeModel::parent(const QModelIndex &index) const { if (!
:337) Item.refreshChildren (treeModel.js:381) (anonymous) (treeModel.js:245) (anonymous) (treeModel.js...:65) Promise_ctor (winjs.base.js:1656) Lock.run (treeModel.js:60) Item.expand (treeModel.js:240) TreeModel.expand...(treeModel.js:783) TreeModel.expandAll (treeModel.js:796) Tree.expandAll (treeImpl.js:130) (anonymous...:332) Item.refreshChildren (treeModel.js:381) (anonymous) (treeModel.js:245) (anonymous) (treeModel.js...(treeModel.js:783) TreeModel.expandAll (treeModel.js:796) Tree.expandAll (treeImpl.js:130) (anonymous
我们写一个自定义的TreeModel,继承自该类,实现里面的一些重载函数: #include "TreeItem.h" #include #include #include #include "define.h" class TreeModel : public QAbstractItemModel { Q_OBJECT...public: explicit TreeModel(QStringList headers,QObject *parent = 0); ~TreeModel(); //以下为自定义...另外介绍几个核心函数实现: TreeModel::data():视图获取数据时调用的函数,里面通过具体的TreeItem::data()获取最终数据 QVariant TreeModel::data(
此时应该使用 TreeModel 或者 GraphLinksModel。 TreeModel TreeModel 适用于比较简单的场景。...TreeModel 只用 nodeDataArray 一个数组就可以,不需要用一个额外的数组定义连线,只需在定义节点的时候指定父节点即可。...语法: const myTreeModel = new go.TreeModel(nodeDataArray); 演示代码: // 创建画布 this.diagram = $(go.Diagram...parent: "Beta" }, { key: "D", parent: "Beta" }, { key: "E", parent: "Beta" }, ]; // 创建 TreeModel...parent: "Beta" }, { key: "D", parent: "Beta" }, { key: "E", parent: "Beta" }, ]; // 创建 TreeModel
pygtk/2.0/demos/tree_store.pyc /usr/lib64/pygtk/2.0/demos/tree_store.pyo /usr/lib64/pygtk/2.0/demos/treemodel.py.../usr/lib64/pygtk/2.0/demos/treemodel.pyc /usr/lib64/pygtk/2.0/demos/treemodel.pyo /usr/lib64/pygtk/2.0
JMeterUtils.applyHiDPIOnFonts(); PluginManager.install(this, true); JMeterTreeModel treeModel...splash.setProgress(60); treeLis.setActionHandler(instance); GuiPackage.initInstance(treeLis, treeModel...); splash.setProgress(80); MainFrame main = new MainFrame(treeModel, treeLis);...Create non-GUI version to avoid headless problems JMeterTreeNode root = (JMeterTreeNode) treeModel.getRoot...(); treeModel.addSubTree(tree, root); // Hack to resolve ModuleControllers in
double"> </TreeModel
contentPane; private JButton btnSelectFolder; private JTree tree; private DefaultTreeModel treeModel...DefaultMutableTreeNode root = new DefaultMutableTreeNode(folder.getName()); treeModel...= new DefaultTreeModel(root); tree.setModel(treeModel); calculateFolderSize
accuracy") accuracy = evaluator.evaluate(predictions) print("Test Error = %g " % (1.0 - accuracy)) treeModel...= model.stages[2] # summary only print(treeModel) 2)回归算法概述 回归也是一种重要的机器学习和数据挖掘技术。...= evaluator.evaluate(predictions) print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) treeModel...= model.stages[1] # summary only print(treeModel) spark.stop() 3)无监督学习概述 利用无标签的数据学习数据的分布或数据与数据之间的关系被称为无监督学习
JTree需要TreeModel来展示数据。 避免方法:创建并设置DefaultTreeModel。 3. JTextArea JTextArea用于多行文本输入和显示。
accuracy") accuracy = evaluator.evaluate(dfpredictions) print("Test Error = %g " % (1.0 - accuracy)) treeModel...= model.stages[2] print(treeModel) +----------+------------+--------------------+ |prediction|indexedLabel...evaluator.evaluate(dfpredictions) print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) treeModel...= model.stages[1] print(treeModel) +----------+-----+--------------------+ |prediction|label|
"continuous" dataType="double"> </TreeModel
TreeModel <- rpart(Creditability ~ ., data = credit[i_calibration1, ]) library(rpart.plot) prp(TreeModel
#train tree of appropriate depth and accumulate #out of sample (oos) errors treeModel...= DecisionTreeRegressor(max_depth=iDepth) treeModel.fit(xTrain, yTrain) treePrediction...= treeModel.predict(xTest) error = [yTest[r] - treePrediction[r] \ for r in range
TreeModel <- rpart(Creditability ~ ., data = credit\[i_calibration1, \]) library(rpart.plot) prp(TreeModel
// Fetch best model val bestModel = cvModel.bestModel val treeModel = bestModel.asInstanceOf[org.apache.spark.ml.PipelineModel...stages(3).asInstanceOf[DecisionTreeClassificationModel] println("Learned classification tree model:\n"+ treeModel.toDebugString
"}, {key: "c31", text: '服务重启', parent: "c3"}, ]; diagram.model = new go.TreeModel
TreeModel <- rpart(Creditability ~ ., data = credit[i_calibration1, ])library(rpart.plot)prp(TreeModel
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