神经网络变得简单（第 71 部分）：目标条件预测编码（GCPC）·进阶篇
（2/3）·行为克隆只管模仿，轨迹怎么压缩才不丢信息？这篇拆开 GCPC 的预训练与政策学习双阶段
不少团队直接把离线轨迹喂给决策转换器，却从没追问过：那些次优轨迹里哪些片段真值得被编码。忽略轨迹表示学习目标与政策学习目标的差异，模型很容易在长周期任务里失焦。
◍ 编码器与解码器的层栈拼装
这段逻辑在 CNetDescription 的构建函数里，负责把编码器（encoder）和解码器（decoder）的每一层描述对象 CLayerDescription 逐个 new 出来并挂到容器上。任何一层 Add 失败就 delete 描述符并返回 false，只有全部通过才在末尾 return true。 编码器从第 11 层开始：第 11 层是 MLMHAttentionOCL 类型，节点数取上一层的两倍（prev_count * 2），滑动步长 step=4、输出窗口 window_out=16、子层 layers=4，优化器用 ADAM。第 12 层退回到 BaseOCL，count 直接等于 EmbeddingSize，激活函数为 None。第 13 层是 SoftMaxOCL，count 沿用 prev_count，step=1，同样无激活。 解码器先 Clear 再搭：输入层是 BaseOCL，count=EmbeddingSize；layer 1 把 (HistoryBars + PrecoderBars) * EmbeddingSize 个节点压成一层，激活用 LReLU。layer 2 又是 MLMHAttentionOCL，把节点数除回 EmbeddingSize，window=EmbeddingSize、step=4、window_out=16、layers=2。layer 3 换成 MultiModels，count=3、window=prev_wout、step=prev_count、无激活。 在 MT5 里跑这套网络描述时，若你改了 EmbeddingSize 或 HistoryBars，解码器 layer 1 的节点数会线性放大，可能明显拖慢 OpenCL 前向计算。建议先打印各层 descr.count 确认维度链没断。
} class=class="str">"cmt">//--- layer class="num">11 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronMLMHAttentionOCL; descr.count = prev_count * class="num">2; descr.window = prev_wout; descr.step = class="num">4; descr.window_out = class="num">16; descr.layers = class="num">4; descr.optimization = ADAM; if(!encoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">12 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; prev_count = descr.count = EmbeddingSize; descr.optimization = ADAM; descr.activation = None; if(!encoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">13 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronSoftMaxOCL; descr.count = prev_count; descr.step = class="num">1; descr.optimization = ADAM; descr.activation = None; if(!encoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- Decoder decoder.Clear(); class=class="str">"cmt">//--- Input layer if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; descr.count = EmbeddingSize; descr.activation = None; descr.optimization = ADAM; if(!decoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">1 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; prev_count = descr.count = (HistoryBars + PrecoderBars) * EmbeddingSize; descr.activation = LReLU; descr.optimization = ADAM; if(!decoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">2 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronMLMHAttentionOCL; prev_count = descr.count = prev_count / EmbeddingSize; prev_wout = descr.window = EmbeddingSize; descr.step = class="num">4; descr.window_out = class="num">16; descr.layers = class="num">2; descr.optimization = ADAM; if(!decoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">3 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronMultiModels; descr.count = class="num">3; descr.window = prev_wout; descr.step = prev_count; descr.activation = None; descr.optimization = ADAM; if(!decoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- class="kw">return true; }
「给强化学习网络搭描述层」
在 MT5 里用面向对象方式堆策略网络时，先要把 actor、goal、encoder 三组层描述容器准备好。函数入口处若传入空指针就当场 new 一个 CArrayObj，任一分配失败直接 return false，避免后面挂空引用把 EA 拖崩。 状态编码器（encoder）第一层用 defNeuronBaseOCL 类型，节点数等于 EmbeddingSize，优化器选 ADAM、激活函数 None；第二层切到 defNeuronConcatenate，count 设 LatentCount，window 接上一层节点数、step 取 AccountDescr，激活走 SIGMOID。这两层构成了把账户状态压成隐变量的前端。 actor 侧稍重一些：输入层同样是 defNeuronBaseOCL + LatentCount，随后 concatenate 层 step 换成 NRewards、激活改 LReLU；再叠两层 base 层，倒数第二层 SIGMOID，末层节点数直接拉到 2 * NActions 且不带激活——输出成对参数供后续采样动作。外汇与贵金属波动剧烈，这类网络只是概率建模，实盘前务必在策略测试器跑多周期回测。 逐行看这段构建代码，能发现所有 Add 失败分支都先 delete descr 再返 false，防止描述对象泄漏。复制进你的 .mqh 头文件，把 EmbeddingSize / LatentCount / NActions 换成自己的宏，就能在终端里编译验证结构是否撑得住。
class="type">bool CreateDescriptions(CArrayObj *actor, CArrayObj *goal, CArrayObj *encoder) { class=class="str">"cmt">//--- CLayerDescription *descr; class=class="str">"cmt">//--- if(!actor) { actor = new CArrayObj(); if(!actor) class="kw">return false; } if(!goal) { goal = new CArrayObj(); if(!goal) class="kw">return false; } if(!encoder) { encoder = new CArrayObj(); if(!encoder) class="kw">return false; } class=class="str">"cmt">//--- State Encoder encoder.Clear(); class=class="str">"cmt">//--- Input layer if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; class="type">int prev_count = descr.count = EmbeddingSize; descr.activation = None; descr.optimization = ADAM; if(!encoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">1 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronConcatenate; descr.count = LatentCount; descr.window = prev_count; descr.step = AccountDescr; descr.optimization = ADAM; descr.activation = SIGMOID; if(!encoder.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- Actor actor.Clear(); class=class="str">"cmt">//--- Input layer if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; prev_count = descr.count = LatentCount; descr.activation = None; descr.optimization = ADAM; if(!actor.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">1 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronConcatenate; descr.count = LatentCount; descr.window = prev_count; descr.step = NRewards; descr.optimization = ADAM; descr.activation = LReLU; if(!actor.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">2 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; descr.count = LatentCount; descr.activation = SIGMOID; descr.optimization = ADAM; if(!actor.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">3 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; descr.count = class="num">2 * NActions; descr.activation = None; descr.optimization = ADAM; if(!actor.Add(descr)) { class="kw">delete descr;
Actor 与 Goal 网络的层定义和初始化加载
这段逻辑在构建强化学习智能体的两个子网络：Actor 负责输出动作，Goal 网络评估状态价值与目标偏差。第四层 Actor 用了 defNeuronVAEOCL 类型，节点数绑定 NActions，优化器统一走 ADAM；若 Add 失败立即 delete 描述符并返回 false，避免内存泄漏。 Goal 网络则从输入层开始：首层 defNeuronBaseOCL，节点数等于 EmbeddingSize，激活函数为 None；随后两层隐层均为 LatentCount 个节点、LReLU 激活。最后输出层用 defNeuronFQF 类型，节点数 NRewards，window_out 显式设为 32，这是分位数回归输出窗口，影响奖励分布的分位粒度。 网络对象在类域声明为 Encoder、StateEncoder、Actor、Goal 四个 CNet 实例。OnInit 里先尝试从 FileName+"Enc.nnw" 加载已训练模型，三个 temp 浮点作占位参数；若加载失败则现场 new 两个 CArrayObj 描述符容器，调用 CreateTrajNetDescriptions 重建结构，任一环节失败返回 INIT_FAILED，EA 不会启动。 外汇与贵金属市场高杠杆、高波动，此类模型在实盘仅具概率优势，请先在 MT5 策略测试器用历史数据验证层参数与加载逻辑。
class="kw">return false; } class=class="str">"cmt">//--- layer class="num">4 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronVAEOCL; descr.count = NActions; descr.optimization = ADAM; if(!actor.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- Goal goal.Clear(); class=class="str">"cmt">//--- Input layer if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; prev_count = descr.count = EmbeddingSize; descr.activation = None; descr.optimization = ADAM; if(!goal.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">1 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; descr.count = LatentCount; descr.activation = LReLU; descr.optimization = ADAM; if(!goal.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">3 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronBaseOCL; descr.count = LatentCount; descr.activation = LReLU; descr.optimization = ADAM; if(!goal.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- layer class="num">4 if(!(descr = new CLayerDescription())) class="kw">return false; descr.type = defNeuronFQF; descr.count = NRewards; descr.window_out = class="num">32; descr.optimization = ADAM; descr.activation = None; if(!goal.Add(descr)) { class="kw">delete descr; class="kw">return false; } class=class="str">"cmt">//--- class="kw">return true; } CNet Encoder; CNet StateEncoder; CNet Actor; CNet Goal; class=class="str">"cmt">//+------------------------------------------------------------------+ class=class="str">"cmt">//| Expert initialization function | class=class="str">"cmt">//+------------------------------------------------------------------+ class="type">int OnInit() { class=class="str">"cmt">//--- ........ ........ class=class="str">"cmt">//--- load models class="type">float temp; if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true)) { CArrayObj *encoder = new CArrayObj(); CArrayObj *decoder = new CArrayObj(); if(!CreateTrajNetDescriptions(encoder, decoder)) { class="kw">delete encoder; class="kw">delete decoder; class="kw">return INIT_FAILED; }
◍ EA初始化时神经网络模型的加载与维度校验
在 MT5 智能交易系统的 OnInit 阶段，若本地已存在训练好的网络文件（后缀 StEnc.nnw、Goal.nnw、Act.nnw），会直接 Load 载入；任一文件载入失败则进入重建分支，用 CreateDescriptions 生成 actor / goal / encoder 的三套网络描述对象并 Create 构建，任何一步返回 false 都 delete 掉相关指针并以 INIT_FAILED 退出。 载入或构建完成后，代码会把 Actor 的 OpenCL 上下文同步给 StateEncoder、Encoder、Goal，并关闭 Encoder 的训练模式（TrainMode(false)）。随后立刻做维度对账：Actor 输出节点数必须等于 NActions，Encoder 输出必须等于 EmbeddingSize，二者不符就 PrintFormat 报错并 INIT_FAILED。 Encoder 第 0 层输出维度还要等于 HistoryBars * BarDescr，例如 HistoryBars=30、BarDescr=5 时期望 150 维，错位会触发 'Input size of Encoder doesn't match state description' 并终止初始化。最后把账户余额与净值读入 PrevBalance / PrevEquity 作为回合基线，返回 INIT_SUCCEEDED 才算过闸。 外汇与贵金属市场杠杆高、滑点随机，这类依赖神经网络的 EA 在模型维度校验未过闸时绝不会下单，但过闸后实盘仍可能因分布偏移而表现漂移，需先在策略测试器用历史数据验证网络文件与参数匹配。
if(!Encoder.Create(encoder)) { class="kw">delete encoder; class="kw">delete decoder; class="kw">return INIT_FAILED; } class="kw">delete encoder; class="kw">delete decoder; class=class="str">"cmt">//--- } if(!StateEncoder.Load(FileName + "StEnc.nnw", temp, temp, temp, dtStudied, true) || !Goal.Load(FileName + "Goal.nnw", temp, temp, temp, dtStudied, true) || !Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true)) { CArrayObj *actor = new CArrayObj(); CArrayObj *goal = new CArrayObj(); CArrayObj *encoder = new CArrayObj(); if(!CreateDescriptions(actor, goal, encoder)) { class="kw">delete actor; class="kw">delete goal; class="kw">delete encoder; class="kw">return INIT_FAILED; } if(!Actor.Create(actor) || !StateEncoder.Create(encoder) || !Goal.Create(goal)) { class="kw">delete actor; class="kw">delete goal; class="kw">delete encoder; class="kw">return INIT_FAILED; } class="kw">delete actor; class="kw">delete goal; class="kw">delete encoder; class=class="str">"cmt">//--- } StateEncoder.SetOpenCL(Actor.GetOpenCL()); Encoder.SetOpenCL(Actor.GetOpenCL()); Goal.SetOpenCL(Actor.GetOpenCL()); Encoder.TrainMode(false); Actor.getResults(Result); if(Result.Total() != NActions) { PrintFormat("The scope of the actor does not match the actions count(%d <> %d)", NActions, Result.Total()); class="kw">return INIT_FAILED; } Encoder.getResults(Result); if(Result.Total() != EmbeddingSize) { PrintFormat("The scope of the Encoder does not match the embedding size(%d <> %d)", EmbeddingSize, Result.Total()); class="kw">return INIT_FAILED; } class=class="str">"cmt">//--- Encoder.GetLayerOutput(class="num">0, Result); if(Result.Total() != (HistoryBars * BarDescr)) { PrintFormat("Input size of Encoder doesn&class="macro">#x27;t match state description(%d <> %d)", Result.Total(), (HistoryBars * BarDescr)); class="kw">return INIT_FAILED; } class=class="str">"cmt">//--- PrevBalance = AccountInfoDouble(ACCOUNT_BALANCE); PrevEquity = AccountInfoDouble(ACCOUNT_EQUITY); class=class="str">"cmt">//--- class="kw">return(INIT_SUCCEEDED); }
「编码器前向传播与模型懒加载」
这段逻辑把轨迹自编码器的推理链路直接铺在 EA 初始化与逐帧计算里。先看前向部分：先把缓冲状态 bState 喂给 Encoder 做一层前向（feedForward 第三个参数 false 表示不训练），再把 Encoder 输出转交 StateEncoder 得到账户状态向量 bAccount，随后 Goal 网络以 NULL 为输出缓冲只算表征，最后 Actor 基于 StateEncoder 出动作。任意一步返回 false 就直接 return，避免脏状态往下传。 初始化函数 OnInit 里先 ResetLastError，再 LoadTotalBase 装载学习数据；若失败会打印错误码并回 INIT_FAILED。模型文件默认从 FileName+"Enc.nnw" 与 "Dec.nnw" 读取，读取时传入 dtStudied 与 true 标记。 若本地没有现成神经网络权重，代码会走新建分支：建两个 CArrayObj 描述编码器与解码器结构，调 CreateTrajNetDescriptions 填充层定义，再分别 Encoder.Create / Decoder.Create。注意 Iterations 输入参数写死 1e4（即 10000 次），这是训练或回放轨迹的生成上限，开 MT5 改这个值能直接拉长轨迹缓冲。 OpenCL 上下文在模型创建或加载后由 Encoder.GetOpenCL() 统一取出，后续若接小布盯盘的 GPU 批处理脚本，可复用同一设备句柄省去重复初始化。外汇与贵金属行情下用这类自编码推理，信号延迟和过拟合风险都偏高，参数务必在模拟盘先验证。
class=class="str">"cmt">//--- if(!Encoder.feedForward((CBufferFloat*)GetPointer(bState), class="num">1, false, (CNet*)GetPointer(Encoder)) || !StateEncoder.feedForward((CNet *)GetPointer(Encoder), -class="num">1, (CBufferFloat *)GetPointer(bAccount)) || !Goal.feedForward((CNet *)GetPointer(Encoder), -class="num">1, (CBufferFloat *)NULL) || !Actor.feedForward((CNet *)GetPointer(StateEncoder), -class="num">1, (CNet *)GetPointer(Goal))) class="kw">return; class=class="str">"cmt">//+------------------------------------------------------------------+ class=class="str">"cmt">//| Input parameters | class=class="str">"cmt">//+------------------------------------------------------------------+ class="kw">input class="type">int Iterations = class="num">1e4; class=class="str">"cmt">//+------------------------------------------------------------------+ class=class="str">"cmt">//| | class=class="str">"cmt">//+------------------------------------------------------------------+ STrajectory Buffer[]; CNet Encoder; CNet Decoder; CBufferFloat LastEncoder; class=class="str">"cmt">//+------------------------------------------------------------------+ class=class="str">"cmt">//| Expert initialization function | class=class="str">"cmt">//+------------------------------------------------------------------+ class="type">int OnInit() { class=class="str">"cmt">//--- ResetLastError(); if(!LoadTotalBase()) { PrintFormat("Error of load study data: %d", GetLastError()); class="kw">return INIT_FAILED; } class=class="str">"cmt">//--- load models class="type">float temp; if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) || !Decoder.Load(FileName + "Dec.nnw", temp, temp, temp, dtStudied, true)) { Print("Init new models"); CArrayObj *encoder = new CArrayObj(); CArrayObj *decoder = new CArrayObj(); if(!CreateTrajNetDescriptions(encoder, decoder)) { class="kw">delete encoder; class="kw">delete decoder; class="kw">return INIT_FAILED; } if(!Encoder.Create(encoder) || !Decoder.Create(decoder)) { class="kw">delete encoder; class="kw">delete decoder; class="kw">return INIT_FAILED; } class="kw">delete encoder; class="kw">delete decoder; class=class="str">"cmt">//--- } OpenCL = Encoder.GetOpenCL();
初始化校验与训练循环的真实落点
在 MT5 里跑自编码器类策略，初始化阶段最容易栽在维度不匹配上。下面这段校验逻辑强制要求 Encoder 输出维度等于 EmbeddingSize，且第一层输出必须等于 HistoryBars * BarDescr；Decoder 输入也必须等于 EmbeddingSize，否则直接 INIT_FAILED。 [CODE] Decoder.SetOpenCL(OpenCL); Encoder.getResults(Result); if(Result.Total() != EmbeddingSize) { PrintFormat("The scope of the Encoder does not match the embedding size count (%d <> %d)", EmbeddingSize, Result.Total()); return INIT_FAILED; } //--- Encoder.GetLayerOutput(0, Result); if(Result.Total() != (HistoryBars * BarDescr)) { PrintFormat("Input size of Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr)); return INIT_FAILED; } //--- Decoder.GetLayerOutput(0, Result); if(Result.Total() != EmbeddingSize) { PrintFormat("Input size of Decoder doesn't match Encoder output (%d <> %d)", Result.Total(), EmbeddingSize); return INIT_FAILED; }
| if(!LastEncoder.BufferInit(EmbeddingSize,0) |
|---|
| !Gradient.BufferInit(EmbeddingSize,0) |
| !LastEncoder.BufferCreate(OpenCL) |
!Gradient.BufferCreate(OpenCL)) { PrintFormat("Error of create buffers: %d", GetLastError()); return INIT_FAILED; } if(!EventChartCustom(ChartID(), 1, 0, 0, "Init")) { PrintFormat("Error of create study event: %d", GetLastError()); return INIT_FAILED; } //--- return(INIT_SUCCEEDED); } //+------------------------------------------------------------------+
| // | Train function |
|---|
//+------------------------------------------------------------------+ void Train(void) { //--- vector<float> probability = GetProbTrajectories(Buffer, 0.9); vector<float> result, target; matrix<float> targets; STD = vector<float>::Zeros((HistoryBars + PrecoderBars) * 3); int std_count = 0; uint ticks = GetTickCount(); for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++) { int tr = SampleTrajectory(probability); int batch = GPTBars + 50; int state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3 - PrecoderBars - batch)); if(state <= 0) { iter--; continue; } Encoder.Clear(); Decoder.Clear(); LastEncoder.BufferInit(EmbeddingSize,0); [/CODE] 代码逐行拆解：Decoder.SetOpenCL 把计算丢给 OpenCL 设备；Encoder.getResults 取回结构输出，随后三个 if 分别卡死嵌入维数、状态描述长度、解码器输入维数。LastEncoder 与 Gradient 的 BufferInit/BufferCreate 失败会打印 GetLastError 并退出。EventChartCustom 发自定义事件 "Init" 通知图表端。 Train 函数里 probability 用 0.9 温度采样轨迹；batch 固定为 GPTBars+50；state 用 MathRand 平方归一化做偏置抽样，若算出小于等于 0 就重试本次迭代。外汇与贵金属行情高波动，这类模型维度错配会直接让 EA 加载失败，开 MT5 把 EmbeddingSize 和 HistoryBars*BarDescr 打印出来对一遍再编译。
Decoder.SetOpenCL(OpenCL); Encoder.getResults(Result); if(Result.Total() != EmbeddingSize) { PrintFormat("The scope of the Encoder does not match the embedding size count(%d <> %d)", EmbeddingSize, Result.Total()); class="kw">return INIT_FAILED; } class=class="str">"cmt">//--- Encoder.GetLayerOutput(class="num">0, Result); if(Result.Total() != (HistoryBars * BarDescr)) { PrintFormat("Input size of Encoder doesn&class="macro">#x27;t match state description(%d <> %d)", Result.Total(), (HistoryBars * BarDescr)); class="kw">return INIT_FAILED; } class=class="str">"cmt">//--- Decoder.GetLayerOutput(class="num">0, Result); if(Result.Total() != EmbeddingSize) { PrintFormat("Input size of Decoder doesn&class="macro">#x27;t match Encoder output(%d <> %d)", Result.Total(), EmbeddingSize); class="kw">return INIT_FAILED; } if(!LastEncoder.BufferInit(EmbeddingSize,class="num">0) || !Gradient.BufferInit(EmbeddingSize,class="num">0) || !LastEncoder.BufferCreate(OpenCL) || !Gradient.BufferCreate(OpenCL)) { PrintFormat("Error of create buffers: %d", GetLastError()); class="kw">return INIT_FAILED; } if(!EventChartCustom(ChartID(), class="num">1, class="num">0, class="num">0, "Init")) { PrintFormat("Error of create study event: %d", GetLastError()); class="kw">return INIT_FAILED; } class=class="str">"cmt">//--- class="kw">return(INIT_SUCCEEDED); } class=class="str">"cmt">//+------------------------------------------------------------------+ class=class="str">"cmt">//| Train function | class=class="str">"cmt">//+------------------------------------------------------------------+ class="type">void Train(class="type">void) { class=class="str">"cmt">//--- vector<class="type">float> probability = GetProbTrajectories(Buffer, class="num">0.9); vector<class="type">float> result, target; matrix<class="type">float> targets; STD = vector<class="type">float>::Zeros((HistoryBars + PrecoderBars) * class="num">3); class="type">int std_count = class="num">0; class="type">uint ticks = GetTickCount(); for(class="type">int iter = class="num">0; (iter < Iterations && !IsStopped()); iter ++) { class="type">int tr = SampleTrajectory(probability); class="type">int batch = GPTBars + class="num">50; class="type">int state = (class="type">int)((MathRand() * MathRand() / MathPow(class="num">32767, class="num">2)) * (Buffer[tr].Total - class="num">3 - PrecoderBars - batch)); if(state <= class="num">0) { iter--; class="kw">continue; } Encoder.Clear(); Decoder.Clear(); LastEncoder.BufferInit(EmbeddingSize,class="num">0);