神经网络变得轻松（第二十三部分）：构建迁移学习工具·进阶篇
神经网络面板里的控件与标签生成
在 MT5 自建神经网络搭建面板时，先得把界面控件声明清楚。下面这段类成员定义了编辑框、下拉框、按钮和列表视图：m_edBatch 与 m_edProbability 负责批大小与概率参数输入，m_cbActivation 和 m_cbOptimization 切换激活函数与优化器，m_btAddLayer / m_btDeleteLayer 控制层数增减，m_lstNewModel 展示新建模型结构，m_btSave 存盘，m_arAddLayers 用 CArrayObj 托管动态层对象。 面板的 Create 与 OnEvent 被声明为虚函数，说明子类可重写交互逻辑；Destroy 直接转调 CAppDialog::Destroy，Run 与 ChartEvent 也只是薄封装，真正事件路由仍交给基类。 标签不是手填资源，而是靠 CreateLabel 批量造：传入 id、文本和坐标，函数内部 new 一个 CLabel，用 StringFormat("%s%d", LABEL_NAME, id) 自动拼出唯一控件名。任意一步 Create / Text / Add 失败就 delete 并返回 false，避免悬空指针。 在 MT5 里跑这套，你可以把 LABEL_NAME 前缀改掉，再数一下同面板最多能叠多少个 label——控件 ID 从 0 自增，超过 2^31 之前都不会撞名，但界面重绘开销会随标签数线性上升。
CEdit m_edBatch; CEdit m_edProbability; CComboBox m_cbActivation; CComboBox m_cbOptimization; CButton m_btAddLayer; CButton m_btDeleteLayer; class=class="str">"cmt">//--- new model CListView m_lstNewModel; CButton m_btSave; CArrayObj m_arAddLayers; class="kw">public: CNetCreatorPanel(); ~CNetCreatorPanel(); class=class="str">"cmt">//--- main application dialog creation and destroy class="kw">virtual class="type">bool Create(class="kw">const class="type">long chart, class="kw">const class="type">class="kw">string name, class="kw">const class="type">int subwin, class="kw">const class="type">int x1, class="kw">const class="type">int y1); class=class="str">"cmt">//--- chart event handler class="kw">virtual class="type">bool OnEvent(class="kw">const class="type">int id, class="kw">const class="type">long &lparam, class="kw">const class="type">class="kw">double &dparam, class="kw">const class="type">class="kw">string &sparam); class="kw">virtual class="type">void Destroy(class="kw">const class="type">int reason = REASON_PROGRAM) class="kw">override { CAppDialog::Destroy(reason); } class="type">bool Run(class="type">void) { class="kw">return CAppDialog::Run();} class="type">void ChartEvent(class="kw">const class="type">int id, class="kw">const class="type">long &lparam, class="kw">const class="type">class="kw">double &dparam, class="kw">const class="type">class="kw">string &sparam) { CAppDialog::ChartEvent(id, lparam, dparam, sparam); } }; class="type">bool CNetCreatorPanel::CreateLabel(class="kw">const class="type">int id, class="kw">const class="type">class="kw">string text, class="kw">const class="type">int x1, class="kw">const class="type">int y1, class="kw">const class="type">int x2, class="kw">const class="type">int y2) { CLabel *tmp_label = new CLabel(); if(!tmp_label) class="kw">return class="kw">false; if(!tmp_label.Create(m_chart_id, StringFormat("%s%d", LABEL_NAME, id), m_subwin, x1, y1, x2, y2)) { class="kw">delete tmp_label; class="kw">return class="kw">false; } if(!tmp_label.Text(text)) { class="kw">delete tmp_label; class="kw">return class="kw">false; } if(!Add(tmp_label)) { class="kw">delete tmp_label; class="kw">return class="kw">false; } class=class="str">"cmt">//--- class="kw">return true; } class="type">bool CNetCreatorPanel::CreateEdit(class="kw">const class="type">int id, CEdit& object,
「面板里编辑框与神经元下拉框的落地写法」
在 MT5 自定义面板里塞一个可编辑文本框，核心不是画出来，而是把坐标、只读属性和对象注册一次性跑通。下面这段把 x1/y1/x2/y2 作为控件矩形对角坐标传入，用 StringFormat 拼出唯一名避免和同图其他 EDIT 重名，ALIGN_RIGHT 让文字贴右，ReadOnly 决定用户能否改。 [CODE] const int x1, const int y1, const int x2, const int y2, bool read_only) { if(!object.Create(m_chart_id, StringFormat("%s%d", EDIT_NAME, id), m_subwin, x1, y1, x2, y2)) return false; if(!object.TextAlign(ALIGN_RIGHT)) return false; if(!object.ReadOnly(read_only)) return false; if(!Add(object)) return false; //--- return true; } [/CODE] 逐行拆解：前四行是函数入参，x1/y1 为左上角、x2/y2 为右下角像素坐标，read_only 控制交互权限。Create 调用在指定图表子窗口 m_subwin 建对象，名用 EDIT_NAME 加 id 防重。TextAlign(ALIGN_RIGHT) 设右对齐，ReadOnly 设只读态，Add 把对象挂进面板容器，任何一步失败直接返 false。 下拉框则要预灌神经元类型。CreateComboBoxType 里给 m_cbNewNeuronType 一次性 ItemAdd 了 11 种层类型：从 Base、Conv、Proof 到 LSTM、Attention、多头注意力、MLMH 注意力，再到 Dropout、BatchNorm、VAE。 [CODE] bool CNetCreatorPanel::CreateComboBoxType(const int x1, const int y1, const int x2, const int y2) { if(!m_cbNewNeuronType.Create(m_chart_id, "cbNewNeuronType", m_subwin, x1, y1, x2, y2)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronBaseOCL), defNeuronBaseOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronConvOCL), defNeuronConvOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronProofOCL), defNeuronProofOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronLSTMOCL), defNeuronLSTMOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronAttentionOCL), defNeuronAttentionOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronMHAttentionOCL), defNeuronMHAttentionOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronMLMHAttentionOCL), defNeuronMLMHAttentionOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronDropoutOCL), defNeuronDropoutOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronBatchNormOCL), defNeuronBatchNormOCL)) return false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronVAEOCL), defNeuronVAEOCL)) return false; if(!Add(m_cbNewNeuronType)) return false; //--- return true; } [/CODE] ItemAdd 第一个参数是显示字符串（由 LayerTypeToString 从枚举转），第二个是实际枚举值。11 次添加若任一项失败面板构建即中断，最后 Add 进容器才返 true。开 MT5 把这段塞进你的 CNetCreatorPanel 类，编译后拖面板出来就能看到下拉里齐了 11 种层——外汇与贵金属行情高波动，这类工具仅辅助结构搭建，实盘信号仍须自担风险。
class="kw">const class="type">int x1, class="kw">const class="type">int y1, class="kw">const class="type">int x2, class="kw">const class="type">int y2, class="type">bool read_only) { if(!object.Create(m_chart_id, StringFormat("%s%d", EDIT_NAME, id), m_subwin, x1, y1, x2, y2)) class="kw">return class="kw">false; if(!object.TextAlign(ALIGN_RIGHT)) class="kw">return class="kw">false; if(!object.ReadOnly(read_only)) class="kw">return class="kw">false; if(!Add(object)) class="kw">return class="kw">false; class=class="str">"cmt">//--- class="kw">return true; } class="type">bool CNetCreatorPanel::CreateComboBoxType(class="kw">const class="type">int x1, class="kw">const class="type">int y1, class="kw">const class="type">int x2, class="kw">const class="type">int y2) { if(!m_cbNewNeuronType.Create(m_chart_id, "cbNewNeuronType", m_subwin, x1, y1, x2, y2)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronBaseOCL), defNeuronBaseOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronConvOCL), defNeuronConvOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronProofOCL), defNeuronProofOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronLSTMOCL), defNeuronLSTMOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronAttentionOCL), defNeuronAttentionOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronMHAttentionOCL), defNeuronMHAttentionOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronMLMHAttentionOCL), defNeuronMLMHAttentionOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronDropoutOCL), defNeuronDropoutOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronBatchNormOCL), defNeuronBatchNormOCL)) class="kw">return class="kw">false; if(!m_cbNewNeuronType.ItemAdd(LayerTypeToString(defNeuronVAEOCL), defNeuronVAEOCL)) class="kw">return class="kw">false; if(!Add(m_cbNewNeuronType)) class="kw">return class="kw">false; class=class="str">"cmt">//--- class="kw">return true; }
◍ 神经网络面板初始化时的控件布局逻辑
在 MT5 自定义对话框里搭一个神经网络配置面板，核心就是按坐标把标签、编辑框、下拉框和列表依次堆上去。下面这段 Create 方法先调父类 CAppDialog::Create 定出整体尺寸，再用 INDENT_LEFT、INDENT_TOP 等常量算每个控件的左上角和右下角。 PANEL_WIDTH 和 PANEL_HEIGHT 决定面板总大小，LIST_WIDTH、EDIT_WIDTH、ADDS_WIDTH 以及 CONTROLS_GAP_X/Y 控制横向分区与间距。比如预训练模型名编辑框默认文本是 "Select file"，Layers Total 编辑框初值写死 "0"，说明建面板时还没载入任何模型结构。 自旋钮 m_spPTModelLayers 的 MinValue、MaxValue、Value 全设 0，是因为此时不知道模型有几层可迁移，得等文件读入后再动态改上限。列表 m_lstPTModel 占左侧剩余高度（ClientAreaHeight() - INDENT_BOTTOM），并开了垂直滚动，模型层多了不会撑爆界面。 右侧区域从 lx2 + CONTROLS_GAP_X 起，放「Add layer」标签、类型下拉框和神经元数编辑框（默认常量 DEFAULT_NEURONS）。这种左模型预览、右手动加层的排布，你在自己写 EA 面板时可以直接抄坐标算法。
class="type">bool CNetCreatorPanel::Create(class="kw">const class="type">long chart, class="kw">const class="type">class="kw">string name, class="kw">const class="type">int subwin, class="kw">const class="type">int x1, class="kw">const class="type">int y1) { if(!CAppDialog::Create(chart, name, subwin, x1, y1, x1 + PANEL_WIDTH, y1 + PANEL_HEIGHT)) class="kw">return class="kw">false; class="type">int lx1 = INDENT_LEFT; class="type">int ly1 = INDENT_TOP; class="type">int lx2 = lx1 + LIST_WIDTH; class="type">int ly2 = ly1 + EDIT_HEIGHT; if(!CreateLabel(class="num">0, "PreTrained model", lx1, ly1, lx2, ly2)) class="kw">return class="kw">false; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ly1 + EDIT_HEIGHT; if(!CreateEdit(class="num">0, m_edPTModel, lx1, ly1, lx2, ly2, true)) class="kw">return class="kw">false; if(!m_edPTModel.Text("Select file")) class="kw">return class="kw">false; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ly1 + EDIT_HEIGHT; if(!CreateLabel(class="num">1, "Layers Total", lx1, ly1, lx1 + EDIT_WIDTH, ly2)) class="kw">return class="kw">false; class=class="str">"cmt">//--- if(!CreateEdit(class="num">1, m_edPTModelLayers, lx2 - EDIT_WIDTH, ly1, lx2, ly2, true)) class="kw">return class="kw">false; if(!m_edPTModelLayers.Text("class="num">0")) class="kw">return class="kw">false; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ly1 + EDIT_HEIGHT; if(!CreateLabel(class="num">2, "Transfer Layers", lx1, ly1, lx1 + EDIT_WIDTH, ly2)) class="kw">return class="kw">false; class=class="str">"cmt">//--- if(!m_spPTModelLayers.Create(m_chart_id, "spPTMCopyLayers", m_subwin, lx2 - class="num">100, ly1, lx2, ly2)) class="kw">return class="kw">false; m_spPTModelLayers.MinValue(class="num">0); m_spPTModelLayers.MaxValue(class="num">0); m_spPTModelLayers.Value(class="num">0); if(!Add(m_spPTModelLayers)) class="kw">return class="kw">false; lx1 = INDENT_LEFT; lx2 = lx1 + LIST_WIDTH; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ClientAreaHeight() - INDENT_BOTTOM; if(!m_lstPTModel.Create(m_chart_id, "lstPTModel", m_subwin, lx1, ly1, lx2, ly2)) class="kw">return class="kw">false; if(!m_lstPTModel.VScrolled(true)) class="kw">return class="kw">false; if(!Add(m_lstPTModel)) class="kw">return class="kw">false; lx1 = lx2 + CONTROLS_GAP_X; lx2 = lx1 + ADDS_WIDTH; ly1 = INDENT_TOP; ly2 = ly1 + EDIT_HEIGHT; if(!CreateLabel(class="num">3, "Add layer", lx1, ly1, lx2, ly2)) class="kw">return class="kw">false; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ly1 + EDIT_HEIGHT; if(!CreateComboBoxType(lx1, ly1, lx2, ly2)) class="kw">return class="kw">false; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ly1 + EDIT_HEIGHT; if(!CreateLabel(class="num">4, "Neurons", lx1, ly1, lx1 + EDIT_WIDTH, ly2)) class="kw">return class="kw">false; class=class="str">"cmt">//--- if(!CreateEdit(class="num">2, m_edCount, lx2 - EDIT_WIDTH, ly1, lx2, ly2, class="kw">false)) class="kw">return class="kw">false; if(!m_edCount.Text((class="type">class="kw">string)DEFAULT_NEURONS))
神经网络面板里的图层控件与描述提取
在 MT5 自建神经网络配置面板时，图层的新增与删除靠两个按钮对象完成。代码里 m_btAddLayer 和 m_btDeleteLayer 分别用 Create 在子窗口坐标 (lx1, ly1) 到 (lx1+ADDS_WIDTH/2, ly2) 与对称右半区生成，文本写死为 "ADD LAYER" 与 "DELETE"，且都设了 Locking(false) 允许重复点击。
描述类继承上，CNetModify 公开派生自 CNet，只补了两个接口：LayersTotal() 返回图层数、GetLayersDiscriptions() 回传 CArrayObj* 指针。后者遍历 LayersTotal() 次，每次取 layers.At(i) 的首神经元调 GetLayerInfo() 塞进数组，任一步空指针就 break 提前退出。
GetLayerInfo 的基类版把 type、count（=Output.Total()）、optimization、activation 以及 batch（LS 优化时取 iBatch，否则恒为 1）写进新 CLayerDescription；CNeuronProofOCL 覆写时再补 window=iWindow、step=iStep。这意味着你改了窗口或步长参数，描述对象里会立刻反映，不用动面板其它逻辑。
想验证这套结构，直接把 CNetModify::GetLayersDiscriptions 抄进自己的 EA，断点看返回的 CLayerDescription 数量是否等于 LayersTotal()；外汇与贵金属模型训练波动大、过拟合风险高，数值仅供本地回测参照。
class="kw">return class="kw">false; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ly1 + EDIT_HEIGHT; if(!CreateLabel(class="num">5, "Activation", lx1, ly1, lx1 + EDIT_WIDTH, ly2)) class="kw">return class="kw">false; class=class="str">"cmt">//--- if(!CreateComboBoxActivation(lx2 - EDIT_WIDTH, ly1, lx2, ly2)) class="kw">return class="kw">false; ly1 = ly2 + CONTROLS_GAP_Y; ly2 = ly1 + BUTTON_HEIGHT; if(!m_btAddLayer.Create(m_chart_id, "btAddLayer", m_subwin, lx1, ly1, lx1 + ADDS_WIDTH / class="num">2, ly2)) class="kw">return class="kw">false; if(!m_btAddLayer.Text("ADD LAYER")) class="kw">return class="kw">false; m_btAddLayer.Locking(class="kw">false); if(!Add(m_btAddLayer)) class="kw">return class="kw">false; class=class="str">"cmt">//--- if(!m_btDeleteLayer.Create(m_chart_id, "btDeleteLayer", m_subwin, lx2 - ADDS_WIDTH / class="num">2, ly1, lx2, ly2)) class="kw">return class="kw">false; if(!m_btDeleteLayer.Text("DELETE")) class="kw">return class="kw">false; m_btDeleteLayer.Locking(class="kw">false); if(!Add(m_btDeleteLayer)) class="kw">return class="kw">false; class CNetModify : class="kw">public CNet { class="kw">public: CNetModify(class="type">void) {}; ~CNetModify(class="type">void) {}; class=class="str">"cmt">//--- class="type">uint LayersTotal(class="type">void); CArrayObj* GetLayersDiscriptions(class="type">void); }; CLayerDescription* CNeuronBaseOCL::GetLayerInfo(class="type">void) { CLayerDescription* result = new CLayerDescription(); if(!result) class="kw">return result; class=class="str">"cmt">//--- result.type = Type(); result.count = Output.Total(); result.optimization = optimization; result.activation = activation; result.batch = (class="type">int)(optimization == LS ? iBatch : class="num">1); result.layers = class="num">1; class=class="str">"cmt">//--- class="kw">return result; } CLayerDescription* CNeuronProofOCL::GetLayerInfo(class="type">void) { CLayerDescription *result = CNeuronBaseOCL::GetLayerInfo(); if(!result) class="kw">return result; result.window = (class="type">int)iWindow; result.step = (class="type">int)iStep; class=class="str">"cmt">//--- class="kw">return result; } CArrayObj* CNetModify::GetLayersDiscriptions(class="type">void) { CArrayObj* result = new CArrayObj(); for(class="type">uint i = class="num">0; i < LayersTotal(); i++) { CLayer* layer = layers.At(i); if(!layer) class="kw">break; CNeuronBaseOCL* neuron = layer.At(class="num">0); if(!neuron) class="kw">break; if(!result.Add(neuron.GetLayerInfo())) class="kw">break; } class=class="str">"cmt">//--- class="kw">return result; } class="type">bool CNetCreatorPanel::LoadModel(class="type">class="kw">string file_name) { class="type">class="kw">float error, undefine, forecast; class="type">class="kw">datetime time; ResetLastError();
「预训练模型载入与图层管理的代码落点」
在 MT5 里做神经网络面板时，载入 .nnw 预训练模型最怕遇到文件损坏却无任何提示。下面这段逻辑会在 Load 失败时先清空列表、写入错误文案，再用 GetLastError 区分「文件损坏（err==0）」和「其他错误码」两种情形，交易者调试自己的模型文件时可直接照抄这个分支结构。 [CODE] if(!m_Model.Load(file_name, error, undefine, forecast, time, false)) { m_lstPTModel.ItemsClear(); m_lstPTModel.ItemAdd("Error of load model", 0); m_lstPTModel.ItemAdd(file_name, 1); int err = GetLastError(); if(err == 0) m_lstPTModel.ItemAdd("The file is damaged"); else m_lstPTModel.ItemAdd(StringFormat("error id: %d", GetLastError()), 2); m_edPTModel.Text("Select file"); return false; } [/CODE] 载入成功后才去读层数：m_Model.LayersTotal() 返回整数，写入编辑框；随后用 GetLayersDiscriptions 拿描述数组，循环拼出「类型 (units N)」的条目塞进列表。外汇与贵金属行情的高波动特性，可能让训练好的模型在样本外失效，因此图层结构可视化是排查泛化问题的第一步。 OpenPreTrainedModel 用 FileSelectDialog 限定后缀 *.nnw，只取 filenames[0] 交给 LoadModel；事件映射里把 m_edPTModel 的点击绑到这个函数，点编辑框就弹文件框。CNetModify 的 GetLayer / AddLayer 则负责运行时按索引取层或追加新层，索引越界直接返 NULL，避免空指针崩面板。
if(!m_Model.Load(file_name, error, undefine, forecast, time, class="kw">false)) { m_lstPTModel.ItemsClear(); m_lstPTModel.ItemAdd("Error of load model", class="num">0); m_lstPTModel.ItemAdd(file_name, class="num">1); class="type">int err = GetLastError(); if(err == class="num">0) m_lstPTModel.ItemAdd("The file is damaged"); else m_lstPTModel.ItemAdd(StringFormat("error id: %d", GetLastError()), class="num">2); m_edPTModel.Text("Select file"); class="kw">return class="kw">false; } m_edPTModel.Text(file_name); m_edPTModelLayers.Text((class="type">class="kw">string)m_Model.LayersTotal()); if(!!m_arPTModelDescription) class="kw">delete m_arPTModelDescription; m_arPTModelDescription = m_Model.GetLayersDiscriptions(); m_lstPTModel.ItemsClear(); class="type">int total = m_arPTModelDescription.Total(); for(class="type">int i = class="num">0; i < total; i++) { CLayerDescription* temp = m_arPTModelDescription.At(i); if(!temp) class="kw">return class="kw">false; class=class="str">"cmt">//--- class="type">class="kw">string item = StringFormat("%s(units %d)", LayerTypeToString(temp.type), temp.count); if(!m_lstPTModel.AddItem(item, i)) class="kw">return class="kw">false; } m_spPTModelLayers.MaxValue(total); m_spPTModelLayers.Value(total); class=class="str">"cmt">//--- class="kw">return true; } class="type">bool CNetCreatorPanel::OpenPreTrainedModel(class="type">void) { class="type">class="kw">string filenames[]; if(FileSelectDialog("Select a file to load data", NULL, "Neuron Net(*.nnw)|*.nnw|All files(*.*)|*.*", FSD_FILE_MUST_EXIST, filenames, NULL) > class="num">0) { if(!LoadModel(filenames[class="num">0])) class="kw">return class="kw">false; } else m_edPTModel.Text("Files not selected"); class=class="str">"cmt">//--- class="kw">return true; } EVENT_MAP_BEGIN(CNetCreatorPanel) ON_EVENT(ON_CLICK, m_edPTModel, OpenPreTrainedModel) ON_EVENT(ON_CLICK, m_btAddLayer, OnClickAddButton) ON_EVENT(ON_CLICK, m_btDeleteLayer, OnClickDeleteButton) ON_EVENT(ON_CLICK, m_btSave, OnClickSaveButton) ON_EVENT(ON_CHANGE, m_spPTModelLayers, ChangeNumberOfLayers) ON_EVENT(ON_CHANGE, m_lstPTModel, OnChangeListPTModel) EVENT_MAP_END(CAppDialog) CLayer* CNetModify::GetLayer(class="type">uint layer) { if(!layers || LayersTotal() <= layer) class="kw">return NULL; class=class="str">"cmt">//--- class="kw">return layers.At(layer); } class="type">bool CNetModify::AddLayer(CLayer *new_layer) { if(!new_layer) class="kw">return class="kw">false; if(!layers) { layers = new CArrayLayer(); if(!layers) class="kw">return class="kw">false; } class=class="str">"cmt">//--- class="kw">return layers.Add(new_layer); }