class FormWindow::Selection 是什么意思??

luhuijk 2011-03-30 04:34:47
class FormWindow::Selection
{
public:
Selection();
~Selection();

// Clear
void clear();

// Also clear out the pool. Call if reparenting of the main container occurs.
void clearSelectionPool();

void repaintSelection(QWidget *w);
void repaintSelection();

bool isWidgetSelected(QWidget *w) const;
QWidgetList selectedWidgets() const;

WidgetSelection *addWidget(FormWindow* fw, QWidget *w);
// remove widget, return new current widget or 0
QWidget* removeWidget(QWidget *w);

void raiseList(const QWidgetList& l);
void raiseWidget(QWidget *w);

void updateGeometry(QWidget *w);

void hide(QWidget *w);
void show(QWidget *w);

private:

typedef QList<WidgetSelection *> SelectionPool;
SelectionPool m_selectionPool;

typedef QHash<QWidget *, WidgetSelection *> SelectionHash;
SelectionHash m_usedSelections;
};

FormWindow::Selection::Selection()
{
}

class FormWindow::Selection{}这样是做什么?我们一般写一个类是
class A
{
public:
A();
~A();
};样啊!
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luhuijk 2011-03-30
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谢谢了,主要是在网上不知道输入什么关键字来查!
pengzhixi 2011-03-30
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额 Selection是一个嵌套类,嵌套在FormWindow里面
支持向量机源码,可在 www.csie.ntu.edu.tw/~cjlin/libsvm/ 下载到最新版本,该版本是 2013年4月更新的,3.17 版。压缩包里面有源代码和文档。以下摘自前述网站: Introduction LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM) Our goal is to help users from other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include Different SVM formulations Efficient multi-class classification Cross validation for model selection Probability estimates Various kernels (including precomputed kernel matrix) Weighted SVM for unbalanced data Both C++ and Java sources GUI demonstrating SVM classification and regression Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, OCaml, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available. It's also included in some data mining environments: RapidMiner, PCP, and LIONsolver. Automatic model selection which can generate contour of cross valiation accuracy.

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