neuralnetworkdoc.cpp

来自「使用神经网络开发包实现图形化的神经网络模拟」· C++ 代码 · 共 99 行

// NeuralNetworkDoc.cpp : implementation of the CNeuralNetworkDoc class
//

#include "stdafx.h"
#include "NeuralNetwork.h"

#include "NeuralNetworkDoc.h"

#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
static char THIS_FILE[] = __FILE__;
#endif

/////////////////////////////////////////////////////////////////////////////
// CNeuralNetworkDoc

IMPLEMENT_DYNCREATE(CNeuralNetworkDoc, CDocument)

BEGIN_MESSAGE_MAP(CNeuralNetworkDoc, CDocument)
	//{{AFX_MSG_MAP(CNeuralNetworkDoc)
		// NOTE - the ClassWizard will add and remove mapping macros here.
		//    DO NOT EDIT what you see in these blocks of generated code!
	//}}AFX_MSG_MAP
END_MESSAGE_MAP()

/////////////////////////////////////////////////////////////////////////////
// CNeuralNetworkDoc construction/destruction

CNeuralNetworkDoc::CNeuralNetworkDoc()
{
	// TODO: add one-time construction code here

}

CNeuralNetworkDoc::~CNeuralNetworkDoc()
{
}

BOOL CNeuralNetworkDoc::OnNewDocument()
{
	if (!CDocument::OnNewDocument())
		return FALSE;
	// TODO: add reinitialization code here
	// (SDI documents will reuse this document)

	return TRUE;
}



/////////////////////////////////////////////////////////////////////////////
// CNeuralNetworkDoc serialization

void CNeuralNetworkDoc::Serialize(CArchive& ar)
{
	//SetModifiedFlag(true);
	if (ar.IsStoring())
	{
		// TODO: add storing code here
	}
	else
	{
		// TODO: add loading code here
	}
	neuralNetwork.Serialize(ar);
}

/////////////////////////////////////////////////////////////////////////////
// CNeuralNetworkDoc diagnostics

#ifdef _DEBUG
void CNeuralNetworkDoc::AssertValid() const
{
	CDocument::AssertValid();
}

void CNeuralNetworkDoc::Dump(CDumpContext& dc) const
{
	CDocument::Dump(dc);
}
#endif //_DEBUG

/////////////////////////////////////////////////////////////////////////////
// CNeuralNetworkDoc commands

void CNeuralNetworkDoc::DeleteContents()
{
	while(!neuralNetwork.getLayerNeurons().empty())
	{
		neuralNetwork.getLayerNeurons().pop_back();
	}
	while(!neuralNetwork.getNeuralContainer().empty())
	{
		neuralNetwork.getNeuralContainer().pop_back();
	}
	
}