Warrior_EA/AI/Network.mqh

//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
#include <Arrays\ArrayDouble.mqh>
#include <Arrays\ArrayInt.mqh>
#include <Arrays\ArrayObj.mqh>
#include <OpenCL\OpenCL.mqh>
#include "..\System\AtomicFile.mqh"
//--- 3rd-tier CPU fallback (used when neither OpenCL nor DirectML/D3D12 GPU accel are available,
//--- e.g. a VM with no GPU passthrough, or this machine's OpenCL/DirectML init failed). Sizes
//--- WarriorCPU.dll's worker thread pool - no effect at all when a GPU tier (OpenCL or DirectML) is
//--- active, since neither one calls into WarriorCPU.dll.
//---
//--- A FIXED SMALL THREAD COUNT PER NETWORK, not a share of the machine. This replaced a TargetCPULoad
//--- input divided by the live chart count, which was wrong twice over:
//---  - The count is a SNAPSHOT taken when each net's pool is built, and charts are attached one at a
//---    time. Measured 2026-07-29 with five charts: they took 10/6/5/4/4% of the same budget, because
//---    the first chart only ever saw itself and the last saw all five. So the earliest chart got
//---    several times the threads of the latest - which silently skews any cross-topology comparison
//---    run on those charts, the exact thing the setting existed to make fair.
//---  - Nothing rebalances when a chart is added or removed, and rebalancing would mean tearing down a
//---    DLL context underneath a running trainer.
//--- Neither problem exists once the answer stops depending on how many charts are running.
//---
//--- Why 2 threads is not a compromise: since the topology became data-derived the widest dense layer
//--- is 64 units (ExpertSignalAIBase.mqh's ComputeFirstLayerWidth), so each ParallelFor has almost
//--- nothing to split and per-dispatch overhead dominates. The measurements agree - an MLP era cost
//--- ~66s at a wildly oversubscribed 12 threads and ~80s at 1 thread, a 20% spread across a 12x
//--- difference in thread count. Two per net also lands six concurrent charts exactly on a 12-core box.
//---
//--- Removing the input costs nothing on the product side: a Market build has no DLL tier at all (MQL5
//--- Market rule IV strips the #import blocks - see AI\NeuronDirectML.mqh), so it was already compiled
//--- out to a constant there and no buyer could ever reach it.
#define CPU_THREADS_PER_NETWORK 2
//+------------------------------------------------------------------+
//| The percentage CDirectMLMy needs in order to land on              |
//| CPU_THREADS_PER_NETWORK workers, given the detected core count.   |
//| (WarriorCPU.dll takes a percentage of cores, not a thread count.) |
//+------------------------------------------------------------------+
int EffectiveCpuLoadPercent()
  {
   int cores = (int)TerminalInfoInteger(TERMINAL_CPU_CORES);
   //--- Unknown core count: assume a small machine rather than a large one. Guessing high here would
   //--- reintroduce exactly the oversubscription this function exists to prevent.
   if(cores <= 0)
      cores = 4;
   //--- Fewer cores than we would ask for: take the machine as it is. 100 also means "all cores" to the
   //--- DLL, so this is the one value that needs no arithmetic.
   if(cores <= CPU_THREADS_PER_NETWORK)
      return 100;
   //--- Round UP: the DLL truncates when turning this back into a thread count, and landing one thread
   //--- short of the target is a worse error than landing one over.
   int pct = (int)MathCeil(100.0 * (double)CPU_THREADS_PER_NETWORK / (double)cores);
   return (int)MathMax(1, MathMin(100, pct));
  }
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
//--- Adam (Kingma & Ba, 2014) hyperparameters. Defaults are neuronetworksbook.pdf's own reference
//--- library defaults (defLearningRate/defBeta1/defBeta2 = 3.0e-4/0.9/0.999) - not the paper's
//--- abstract "0.001" mention, which the book's own worked examples don't actually use either.
//--- 3.0e-4 also happens to sit inside the noisy/non-stationary-trading-data range (0.0003-0.0005)
//--- this project had separately tuned lr to before this input existed, so no behavior conflict.
//--- Beta1 was previously hand-lowered to 0.8 as an experiment to fight a multi-era same-class-streak
//--- bug - that symptom's likely root cause (independent-sigmoid+BCE output gradient, since fixed to
//--- a joint softmax+CCE gradient in backProp()/backPropOCL()) is addressed elsewhere now, so this
//--- reverts to the literature/book default.
input double AdamLearningRate = 0.0003; // Adam learning rate
input double AdamBeta1         = 0.9;   // Adam beta1
input double AdamBeta2         = 0.999; // Adam beta2
//--- SGD+momentum hyperparameters. The book states no distinct default learning rate for this method
//--- (its own reference library reuses the same defLearningRate for every optimizer), so this reuses
//--- Adam's book-default rate as its starting point too. The book also states no numeric default for
//--- the momentum decay coefficient itself (just "in the range 0 to 1, exclusive") - 0.9 reuses
//--- Adam's beta1, the only concrete "momentum decay" value the book ever commits to a number for.
input double SgdLearningRate   = 0.0003; // SGD learning rate
input double SgdMomentum       = 0.9;    // SGD momentum
#define  lr AdamLearningRate
#define  b1 AdamBeta1
#define  b2 AdamBeta2
#define  momentum SgdMomentum
double eta = lr;
#define defConnect         0x7781
#define defArrayConnects   0x7782
#define defNeuronBase      0x7783
#define defNeuron          0x7784
#define defNeuronConv      0x7785
#define defNeuronPool     0x7786
#define defLayer           0x7787
#define defArrayLayer      0x7788
#define defNet             0x7789
#define defNeuronLSTM      0x7791
//--- Topology-descriptor form of the batch-normalization layer (CLayerDescription::type). Like
//--- defNeuronConv/defNeuronPool it has no scalar-CPU neuron class behind it - CNet's constructor maps
//--- it onto CNeuronBatchNormOCL, which is the only implementation. See AI\NeuronBatchNorm.mqh.
#define defNeuronBatchNorm 0x7792
//---
#define defBufferDouble    0x7882
#define defNeuronBaseOCL   0x7883
#define defNeuronLSTMOCL   0x7884
#define defNeuronConvOCL   0x7885
#define defNeuronPoolOCL   0x7886
#define defNeuronBatchNormOCL 0x7887
//---
#define def_k_FeedForward     0
#define def_k_ff_matrix_w     0
#define def_k_ff_matrix_i     1
#define def_k_ff_matrix_o     2
#define def_k_ff_inputs       3
#define def_k_ff_activation   4
//---
#define def_k_CaclOutputGradient 1
#define def_k_cog_matrix_t       0
#define def_k_cog_matrix_o       1
#define def_k_cog_matrix_ig      2
#define def_k_cog_activation     3
//---
#define def_k_CaclHiddenGradient 2
#define def_k_chg_matrix_w       0
#define def_k_chg_matrix_g       1
#define def_k_chg_matrix_o       2
#define def_k_chg_matrix_ig      3
#define def_k_chg_outputs        4
#define def_k_chg_activation     5
//---
#define def_k_UpdateWeightsMomentum      3
#define def_k_uwm_matrix_w        0
#define def_k_uwm_matrix_g        1
#define def_k_uwm_matrix_i        2
#define def_k_uwm_matrix_dw       3
#define def_k_uwm_inputs          4
#define def_k_uwm_learning_rates  5
#define def_k_uwm_momentum        6
#define def_k_uwm_optimizer       7
//---
#define def_k_UpdateWeightsAdam   4
#define def_k_uwa_matrix_w        0
#define def_k_uwa_matrix_g        1
#define def_k_uwa_matrix_i        2
#define def_k_uwa_matrix_m        3
#define def_k_uwa_matrix_v        4
#define def_k_uwa_inputs          5
#define def_k_uwa_l               6
#define def_k_uwa_b1              7
#define def_k_uwa_b2              8
//---
#define def_k_FeedForwardProof    15
#define def_k_ffp_matrix_i        0
#define def_k_ffp_matrix_o        1
#define def_k_ffp_inputs          2
#define def_k_ffp_window          3
#define def_k_ffp_step            4
//---
#define def_k_CalcInputGradientProof 16
#define def_k_cigp_matrix_i       0
#define def_k_cigp_matrix_g       1
#define def_k_cigp_matrix_o       2
#define def_k_cigp_matrix_ig      3
#define def_k_cigp_outputs        4
#define def_k_cigp_window         5
#define def_k_cigp_step           6
//---
#define def_k_FeedForwardConv     5
#define def_k_ffc_matrix_w        0
#define def_k_ffc_matrix_i        1
#define def_k_ffc_matrix_o        2
#define def_k_ffc_inputs          3
#define def_k_ffc_step            4
#define def_k_ffc_window_in       5
#define def_k_ffc_window_out      6
#define def_k_ffc_activation      7
//---
#define def_k_CalcHiddenGradientConv 6
#define def_k_chgc_matrix_w       0
#define def_k_chgc_matrix_g       1
#define def_k_chgc_matrix_o       2
#define def_k_chgc_matrix_ig      3
#define def_k_chgc_outputs        4
#define def_k_chgc_step           5
#define def_k_chgc_window_in      6
#define def_k_chgc_window_out     7
#define def_k_chgc_activation     8
//---
#define def_k_UpdateWeightsConvMomentum 7
#define def_k_uwcm_matrix_w       0
#define def_k_uwcm_matrix_g       1
#define def_k_uwcm_matrix_i       2
#define def_k_uwcm_matrix_dw      3
#define def_k_uwcm_inputs         4
#define def_k_uwcm_learning_rates 5
#define def_k_uwcm_momentum       6
#define def_k_uwcm_window_in      7
#define def_k_uwcm_window_out     8
#define def_k_uwcm_step           9
#define def_k_uwcm_optimizer      10
//---
#define def_k_UpdateWeightsConvAdam 8
#define def_k_uwca_matrix_w       0
#define def_k_uwca_matrix_g       1
#define def_k_uwca_matrix_i       2
#define def_k_uwca_matrix_m       3
#define def_k_uwca_matrix_v       4
#define def_k_uwca_inputs         5
#define def_k_uwca_l              6
#define def_k_uwca_b1             7
#define def_k_uwca_b2             8
#define def_k_uwca_window_in      9
#define def_k_uwca_window_out     10
#define def_k_uwca_step           11
//---
// LSTM (CNeuronLSTMOCL) - single-timestep-truncated BPTT (no gradient
// flows back into h_prev/c_prev from a prior step). Supports both Adam and
// SGD+momentum (LSTM_UpdateWeightsAdam/LSTM_UpdateWeightsMomentum below) -
// see CNeuronLSTMOCL::updateInputWeights for the optimizer dispatch.
// Derived from scratch from the standard LSTM equations - NOT ported from
// the NeuroNet_DNG reference, whose LSTM_HiddenGradient kernel overwrites
// the live weights buffer instead of writing to weights_gradient.
#define def_k_LSTM_Gates          9
#define def_k_lstmg_matrix_w      0
#define def_k_lstmg_hidden_prev   1
#define def_k_lstmg_inputs        2
#define def_k_lstmg_concatenated  3
#define def_k_lstmg_hidden_size   4
#define def_k_lstmg_input_size    5
//---
#define def_k_LSTM_State          10
#define def_k_lstms_concatenated  0
#define def_k_lstms_memory        1
#define def_k_lstms_hidden_prev   2
#define def_k_lstms_hidden_cache  3
#define def_k_lstms_output        4
#define def_k_lstms_hidden_size   5
//---
#define def_k_LSTM_GateGradient   11
#define def_k_lstmgg_gradient     0
#define def_k_lstmgg_memory       1
#define def_k_lstmgg_concatenated 2
#define def_k_lstmgg_concatenated_gradient 3
#define def_k_lstmgg_hidden_size  4
//---
#define def_k_LSTM_WeightsGradient 12
#define def_k_lstmwg_concatenated_gradient 0
#define def_k_lstmwg_hidden_cache 1
#define def_k_lstmwg_inputs       2
#define def_k_lstmwg_weights_gradient 3
#define def_k_lstmwg_hidden_size  4
#define def_k_lstmwg_input_size   5
//---
#define def_k_LSTM_InputsGradient 13
#define def_k_lstmig_concatenated_gradient 0
#define def_k_lstmig_matrix_w     1
#define def_k_lstmig_inputs_gradient 2
#define def_k_lstmig_hidden_size  3
#define def_k_lstmig_input_size   4
//---
#define def_k_LSTM_UpdateWeightsAdam 14
#define def_k_lstmuwa_matrix_w    0
#define def_k_lstmuwa_weights_gradient 1
#define def_k_lstmuwa_matrix_m    2
#define def_k_lstmuwa_matrix_v    3
#define def_k_lstmuwa_l           4
#define def_k_lstmuwa_b1          5
#define def_k_lstmuwa_b2          6
//---
// SGD+momentum counterpart to LSTM_UpdateWeightsAdam above - see
// AI\Network.cl's LSTM_UpdateWeightsMomentum for the kernel body.
#define def_k_LSTM_UpdateWeightsMomentum 17
#define def_k_lstmuwm_matrix_w    0
#define def_k_lstmuwm_weights_gradient 1
#define def_k_lstmuwm_matrix_dw   2
#define def_k_lstmuwm_learning_rates 3
#define def_k_lstmuwm_momentum    4
#define def_k_lstmuwm_optimizer   5
//---
// Sequence LSTM. One launch PER TIMESTEP - the recurrence is sequential and OpenCL
// barriers only span a work-group, so the host loop is what provides the global
// ordering. See the block comment above LSTM_SeqStepForward in AI\Network.cl.
#define def_k_LSTM_SeqStepForward 18
#define def_k_lsf_matrix_w        0
#define def_k_lsf_inputs          1
#define def_k_lsf_cache_gates     2
#define def_k_lsf_cache_cell      3
#define def_k_lsf_cache_hidden    4
#define def_k_lsf_output          5
#define def_k_lsf_hidden_size     6
#define def_k_lsf_step_inputs     7
#define def_k_lsf_steps           8
#define def_k_lsf_t               9
//---
#define def_k_LSTM_SeqStepGateGrad 19
#define def_k_lsgg_out_gradient   0
#define def_k_lsgg_dh_buf         1
#define def_k_lsgg_dc_buf         2
#define def_k_lsgg_cache_gates    3
#define def_k_lsgg_cache_cell     4
#define def_k_lsgg_gate_grad      5
#define def_k_lsgg_hidden_size    6
#define def_k_lsgg_steps          7
#define def_k_lsgg_t              8
//---
#define def_k_LSTM_SeqStepWeightGrad 20
#define def_k_lswg_gate_grad      0
#define def_k_lswg_cache_hidden   1
#define def_k_lswg_inputs         2
#define def_k_lswg_weights_gradient 3
#define def_k_lswg_hidden_size    4
#define def_k_lswg_step_inputs    5
#define def_k_lswg_t              6
//---
#define def_k_LSTM_SeqStepInputGrad 21
#define def_k_lsig_gate_grad      0
#define def_k_lsig_matrix_w       1
#define def_k_lsig_inputs_gradient 2
#define def_k_lsig_dh_buf         3
#define def_k_lsig_hidden_size    4
#define def_k_lsig_step_inputs    5
#define def_k_lsig_t              6
//---
// Mini-batch gradient accumulation - see the block comment above AccumulateWeightGrad in
// AI\Network.cl. The APPLY is a kernel too on OpenCL since 2026-08-09 (def_k_ApplyAccumAdam below);
// the host-side MQL5 step in NeuronOCLBase.mqh remains the implementation for the DLL and
// pure-MQL5 tiers, and the reference the kernels must stay line-for-line identical to.
#define def_k_AccumulateWeightGrad 22
#define def_k_awg_matrix_acc      0
#define def_k_awg_matrix_g        1
#define def_k_awg_matrix_i        2
#define def_k_awg_inputs          3
//---
#define def_k_AccumulateWeightGradConv 23
#define def_k_awgc_matrix_acc     0
#define def_k_awgc_matrix_g       1
#define def_k_awgc_matrix_i       2
#define def_k_awgc_inputs         3
#define def_k_awgc_window_in      4
#define def_k_awgc_window_out     5
#define def_k_awgc_step           6
//---
#define def_k_AccumulateBufferInto 24
#define def_k_abi_dst             0
#define def_k_abi_src             1
//---
// Mini-batch APPLY. Replaces eight full-weight-matrix transfers per batch per block with one
// dispatch - see the block comment above ApplyAccumAdam in AI\Network.cl for why that dominated
// OpenCL training time, and why it matters more than it looks (Market builds forbid DLL imports,
// so OpenCL is the tier paying clients run).
#define def_k_ApplyAccumAdam      25
#define def_k_aaa_matrix_w        0
#define def_k_aaa_matrix_acc      1
#define def_k_aaa_matrix_m        2
#define def_k_aaa_matrix_v        3
#define def_k_aaa_scale           4
#define def_k_aaa_l               5
#define def_k_aaa_b1              6
#define def_k_aaa_b2              7
//---
#define def_k_ApplyAccumMomentum  26
#define def_k_aam_matrix_w        0
#define def_k_aam_matrix_acc      1
#define def_k_aam_matrix_dw       2
#define def_k_aam_scale           3
#define def_k_aam_lr              4
#define def_k_aam_momentum        5
//--- One latch for the whole process, not per net: the apply kernels either built on this machine's
//--- OpenCL device or they did not, and that answer cannot change mid-run. Set false by the first
//--- dispatch failure so the fallback costs one warning instead of a failed Execute every batch.
//--- Deliberately NOT reset between nets - four charts share the device.
bool g_applyAccumKernelUsable = true;
//---
// Batch-norm kernels (2026-08-09) - see the BATCH NORM block in AI\Network.cl. Same speed-only
// degradation contract as the apply kernels: without them the layer computes host-side exactly as
// before. g_bnKernelUsable is the same style of process-wide latch, cleared at init when the kernels
// fail to build, or at runtime by a failed dispatch OR a failed SELF-CHECK - each kernel is compared
// against its host twin on first use (NeuronBatchNorm.mqh), because this layer's persisted running
// statistics turn a wrong kernel into a permanently poisoned .nnw, and a check that runs once per
// process is cheap insurance against exactly that.
#define def_k_BatchNormForward    27
#define def_k_bnf_matrix_i        0
#define def_k_bnf_matrix_o        1
#define def_k_bnf_options         2
#define def_k_bnf_w               3
#define def_k_bnf_frozen          4
//---
#define def_k_BatchNormHiddenGrad 28
#define def_k_bnh_matrix_g        0
#define def_k_bnh_prev_o          1
#define def_k_bnh_prev_g          2
#define def_k_bnh_options         3
#define def_k_bnh_activation      4
//---
#define def_k_BatchNormAccumGammaBeta 29
#define def_k_bna_matrix_g        0
#define def_k_bna_options         1
#define def_k_bna_acc             2
//---
#define def_k_BatchNormApplyGammaBeta 30
#define def_k_bnp_options         0
#define def_k_bnp_acc             1
#define def_k_bnp_scale           2
#define def_k_bnp_lt              3
#define def_k_bnp_b1              4
#define def_k_bnp_b2              5
#define def_k_bnp_lr              6
#define def_k_bnp_momentum        7
#define def_k_bnp_optimizer       8
bool g_bnKernelUsable = true;
//---
// b1/b2 are now the AdamBeta1/AdamBeta2 inputs declared above (book defaults 0.9/0.999) - see
// AdamLearningRate's declaration comment for why the earlier 0.8 experiment (fighting a multi-era
// same-class-streak bug via a shorter momentum window) was reverted: that symptom's likely root
// cause was the independent-sigmoid+BCE output gradient, since replaced with a joint softmax+CCE
// gradient in backProp()/backPropOCL(), which addresses it more directly than shortening b1 ever
// could. b1/b2 are passed as runtime parameters into every backend (not baked into compiled
// kernels - see DirectML\WarriorCPU.cpp/WarriorDML.cpp/AI\Network.cl's UpdateWeightsAdam
// signatures), so they're safe to expose as ordinary inputs.
// Tightened from 1.0e6 - that ceiling was so loose it never actually engaged before training had
// already gone unstable (real collapses were happening at weight magnitudes several orders of
// magnitude below it). 100.0 matches the equivalent clamp in Dmitriy Gizlyk's reference NeuroNet.mqh
// engine (references\MQL5\Experts\NeuroNet_DNG\NeuroNet.mqh) and gives a hard ceiling that's actually
// reachable-and-meaningful given MAX_WEIGHT_DELTA=0.1 per step below.
#define MAX_WEIGHT                100.0
// Decoupled (AdamW-style) weight decay applied inside every Adam weight update below and in
// DirectML\WarriorCPU.cpp/WarriorDML.cpp/AI\Network.cl (all four backends kept in sync) - see
// WarriorCPU.cpp's WEIGHT_DECAY comment for the full rationale: MAX_WEIGHT only stops outright
// +-Infinity blowups, it does nothing to stop weights slowly, unboundedly growing over hundreds of
// training eras on a fixed, heavily class-balance-oversampled dataset, which was producing multi-
// hour climb-to-90%+-then-collapse-to-single-digits OOS accuracy cycles.
// 0.001, NOT the 0.01 Loshchilov & Hutter default: decay here is applied per SAMPLE (online updates,
// ~20k+ steps per era), and AdamW's data term is invariant to gradient scale, so a weight's
// sustainable magnitude is roughly (its gradient stream's signal-to-noise ratio)/WEIGHT_DECAY. For a
// weak-signal domain like this one, 0.01 was observed (2026-07-19, SP500 H4) to grind the
// discriminative weights down until the per-bar logit spread (avg 0.19 at era 1) fell BELOW the
// calibration-capped class-prior offsets (~0.008): recall stayed healthy for ~30 eras while the
// spread decayed monotonically, then argmax degenerated to constant-Neutral once the evidence tilt
// dropped under the prior tilt. The prior offsets are capped by calibration regardless of decay
// strength; the evidence tilts scale with 1/WEIGHT_DECAY - so decay strength decides which one wins
// argmax. 0.001 lifts the evidence ceiling 10x while still bounding long-run weight growth.
#define WEIGHT_DECAY              0.001
// Per-step update clip - see WarriorCPU.cpp's matching MAX_WEIGHT_DELTA comment for the full
// rationale: weight decay alone didn't stop the collapse cycles, since they turned out to be sudden
// Adam overshoot events (OOS accuracy falling below the 3-class random-guess floor within ~20 eras),
// most likely from 5x back-to-back oversampling replay building artificially correlated momentum.
// Applied to the raw delta BEFORE it's added to the weight, unlike MAX_WEIGHT which only clamps the
// post-update weight value and is far too loose (1e6) to prevent this.
#define MAX_WEIGHT_DELTA          0.1
// Floor on |activationFunctionDerivative()| for saturated tanh/sigmoid units (see
// SigmoidFunctionDerivative/TanhFunctionDerivative below) - without this, a neuron pinned near its
// activation extremes (output near -1/0/1) produces a near-zero derivative, which zeroes that
// neuron's entire backprop gradient contribution regardless of how wrong its output is. A saturated
// unit can then never receive a corrective signal to unstick it. 1e-4 matches the equivalent floor in
// Dmitriy Gizlyk's reference NeuroNet.mqh/NeuroNet.cl engine.
// 2026-07-27: Increased from 1.0e-4 to 1.0e-3 — must stay in sync with AI\Network.cl's matching
// constant. See that file's comment for the full rationale (fp32 OpenCL saturation floor fix).
#define MIN_ACTIVATION_DERIVATIVE 1.0e-3
// Logit temperature for the 3-class softmax head (training gradient in backProp/backPropOCL AND
// read-time ApplyClassificationSoftmax - the two MUST stay in sync or the model is scored against a
// different distribution than it was trained on). The classification outputs are SIGMOID-bounded to
// [0,1], so the raw logit spread can never exceed 1 and the softmax winner caps at e/(e+2)=0.576 -
// the one-hot 1.0 target is unreachable, per-sample gradients never decay below ~0.42, and training
// can only orbit, never converge (observed as IS error frozen at sqrt(1/3)=0.58 with all three
// outputs saturated at 0). Scaling the logits by 6 stretches the spread to [0,6], raising the
// ceiling to e^6/(e^6+2)=0.995: targets effectively reachable, gradients can vanish, and the focal
// modulation's pt finally spans (0,1) instead of (0.21,0.58). The gradient deliberately stays
// (target - softmax) WITHOUT the extra 6x chain-rule factor - the scale is defined as part of the
// loss, keeping gradient magnitudes (and thus eta tuning) unchanged.
#define CLASS_LOGIT_SCALE         6.0

//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
#resource "Network.cl" as string cl_program
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
enum ENUM_ACTIVATION
  {
   NONE,
   TANH,
   SIGMOID,
   PRELU     // fixed param=0.01, matches CNeuronConv's CPU activationFunction
  };
//+------------------------------------------------------------------+
//| Translates ENUM_ACTIVATION to the "activation" int code every     |
//| native compute backend actually understands (Network.cl's         |
//| kernels, and the mirrored Activation()/inline switches in         |
//| WarriorCPU.cpp / WarriorDML.cpp): 0=TANH, 1=SIGMOID, 2=PRELU, and |
//| deliberately anything else (incl. NONE) falls through every one   |
//| of those switches unmatched, which is exactly linear passthrough -|
//| there's no case 3 anywhere on the native side, so PRELU relies on |
//| the FeedForwardConv-family kernels specifically, and NONE never   |
//| needs a case at all. This is NOT the same numbering as            |
//| ENUM_ACTIVATION itself (NONE=0, TANH=1, SIGMOID=2, PRELU=3) - a   |
//| raw (int)activation cast at a kernel call site silently sends the |
//| WRONG activation to the GPU/DLL tier (e.g. MQL5 TANH -> native    |
//| SIGMOID). Only use this at actual kernel-dispatch call sites -    |
//| CNeuronBase::Save()/CNeuronBaseOCL::Save() persist the raw        |
//| ENUM_ACTIVATION value instead, and must keep using (int)activation|
//| directly so saved topology files round-trip through Load() as-is. |
//+------------------------------------------------------------------+
int NativeActivationCode(ENUM_ACTIVATION value)
  {
   switch(value)
     {
      case TANH:    return 0;
      case SIGMOID: return 1;
      case PRELU:   return 2;
      default:      return -1; // NONE (and anything unrecognized) - no kernel/DLL case matches
     }
  }
//+------------------------------------------------------------------+
//| Human-readable ENUM_ACTIVATION, for diagnostics only. Used by the |
//| load-time architecture repair (CNet::EnforceOutputActivation) so  |
//| the log names the stale value it found instead of printing a bare |
//| integer nobody can decode months later.                           |
//+------------------------------------------------------------------+
string ActivationName(ENUM_ACTIVATION value)
  {
   switch(value)
     {
      case NONE:    return "NONE";
      case TANH:    return "TANH";
      case SIGMOID: return "SIGMOID";
      case PRELU:   return "PRELU";
     }
   return "UNKNOWN(" + IntegerToString((int)value) + ")";
  }
//---
//--- Guarded so an identical copy can live in Enumerations\InputEnums.mqh too: that lets Variables\
//--- Inputs.mqh (which needs this type for the TrainingOptimizer input) be included FIRST - ahead of
//--- this AI header - without a duplicate-definition error. Whichever file is parsed first defines it;
//--- the other's copy is skipped. Keep the two definitions in sync.
#ifndef WARRIOR_ENUM_OPTIMIZATION_DEFINED
#define WARRIOR_ENUM_OPTIMIZATION_DEFINED
//--- 2026-07-28: a third DFA entry was removed. It was never Direct Feedback Alignment: its feedback
//--- signal multiplied dL/dw by a DETERMINISTIC sign pattern (connectionIndex % 2), which makes half of
//--- every weight tensor perform gradient ASCENT permanently - it diverges by construction, with no
//--- hyperparameter able to rescue it. Real DFA (Nokland 2016) works because a FIXED RANDOM matrix gives
//--- a consistent feedback direction the forward weights can align to; an index-parity sign flip has no
//--- such alignment property. Its backward pass was also structurally incompatible with the OpenCL/
//--- DirectML neuron model this project actually runs on (one CNeuronBaseOCL object holds a whole layer
//--- in a device buffer, so the per-neuron host-scalar loops it used saw layer.Total()==1 and updated
//--- nothing). SGD/ADAM keep their ordinal values 0/1 - m_optimizationAlgo feeds the weights-filename
//--- fingerprint, so these must never be renumbered.
enum ENUM_OPTIMIZATION
  {
   SGD,  // SGD + Momentum (heavy-ball, simpler, needs more eras)
   ADAM  // Adam (adaptive step, faster convergence, can overfit)
  };
#endif
//---
enum ENUM_BUFFERS
  {
   WEIGHTS,
   DELTA_WEIGHTS,
   OUTPUT,
   GRADIENT,
   FIRST_MOMENTUM,
   SECOND_MOMENTUM
  };
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
#include "NeuronPrimitives.mqh"
class CLayer;
//---
class CNeuronBase    :  public CObject
  {
protected:
   double            outputVal;
   double            prevVal;
   uint              m_myIndex;
   double            gradient;
   CArrayCon         *Connections;
   ENUM_ACTIVATION   activation;
   ENUM_OPTIMIZATION optimization;
   int               t;
   //---
   virtual bool      feedForward(CLayer *prevLayer)               {  return false;     }
   virtual bool      calcHiddenGradients(CLayer *&nextLayer)     {  return false;     }
   virtual double    activationFunction(double x);
   virtual double    SigmoidFunction(double x)                    {  return MathPow(1 + exp(-x), -1); }
   virtual double    TanhFunction(double x)                       {  return tanh(x);   }
   virtual CLayer    *getOutputLayer(void)                        {  return NULL;      }
public:
                     CNeuronBase(void);
                    ~CNeuronBase(void);
   virtual bool      Init(uint numOutputs, uint myIndex, ENUM_OPTIMIZATION optimization_type, double weighScale = -1.0);
   virtual void      SetActivationFunction(ENUM_ACTIVATION value) {  activation = value; }
   //--- Mirrors CNeuronBaseOCL::Activation(). Needed so CNet::EnforceOutputActivation() can read back
   //--- what a Load() restored from disk without caring which neuron model the net was built from.
   virtual ENUM_ACTIVATION Activation(void)                       {  return activation;  }
   //---
   static double            alpha;
   //---
   virtual void      setOutputVal(double val)                     {  prevVal = outputVal;   outputVal = val;    }
   virtual double    getOutputVal()                               {  return outputVal; }
   virtual double    getPrevVal()                                 {  return prevVal;   }
   virtual void      setGradient(double val)                      {  gradient = val;     }
   virtual double    getGradient()                                {  return gradient;  }
   virtual CArrayCon *getConnections()                            {  return Connections;}
   virtual double    activationFunctionDerivative(double x);
   virtual double    SigmoidFunctionDerivative(double x)          {  return MathMax(MIN_ACTIVATION_DERIVATIVE, x * (1 - x));   }
   virtual double    TanhFunctionDerivative(double x)             {  return MathMax(MIN_ACTIVATION_DERIVATIVE, (1 + x) * (1 - x));  }
   //---
   virtual bool      feedForward(CObject *&SourceObject);
   virtual bool      calcHiddenGradients(CObject *&TargetObject);
   virtual bool      updateInputWeights(CLayer *prevLayer)       {  return false;     }
   virtual bool      updateInputWeights(CObject *SourceObject);
   //---
   virtual bool      Save(int const file_handle);
   virtual bool      Load(int const file_handle)
     {
      activation = (ENUM_ACTIVATION)FileReadInteger(file_handle, INT_VALUE);
      optimization = (ENUM_OPTIMIZATION)FileReadInteger(file_handle, INT_VALUE);
      t = (ENUM_OPTIMIZATION)FileReadInteger(file_handle, INT_VALUE);
      return(Connections.Load(file_handle));
     }
   //--- Forget the optimizer's trajectory memory (Adam m/v, SGD momentum, step counter) while leaving
   //--- the weights untouched - see CNet::ResetOptimizerState for when and why. Scope matches
   //--- CNet::CaptureWeights on this legacy hierarchy: the neuron's OWN connections (gate/inner-layer
   //--- weights of the scalar pool/LSTM subclasses are outside the checkpoint too, so resetting more
   //--- than the checkpoint restores would desynchronize the pair).
   virtual bool      ResetOptimizerState(void)
     {
      t = 1;
      if(CheckPointer(Connections) == POINTER_INVALID)
         return true;
      for(int i = 0; i < Connections.Total(); i++)
        {
         CConnection *con = Connections.At(i);
         if(CheckPointer(con) == POINTER_INVALID)
            continue;
         con.deltaWeight = 0.0;
         con.mt = 0.0;
         con.vt = 0.0;
        }
      return true;
     }
   //---
   virtual int       Type(void)        const                      {  return defNeuronBase;                  }
  };
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
#include "NeuronCPU.mqh"
class COpenCLMy   :  public COpenCL
  {
public:
                     COpenCLMy(void)   {};
                    ~COpenCLMy(void)   {};
   template<typename T>
   int               AddBufferFromArray(T &data[], const uint data_array_offset, const uint data_array_count, const uint flags);
  };
#include "NeuronDirectML.mqh"
class CLayer: public CArrayObj
  {
private:
   uint              iOutputs;
   int               iFileHandle;
   COpenCLMy         *OpenCL;
   CDirectMLMy       *DirectML;

public:
                     CLayer(uint outputs = 0, int handle = INVALID_HANDLE, COpenCLMy *OpenCL = NULL, CDirectMLMy *DirectML = NULL);
                    ~CLayer(void) {};
   //--- Fan-in-scaled element factory. Deliberately NOT named CreateElement: see the override below.
   bool              CreateElementScaled(int const index, double weighScale);
   //--- CRITICAL: this MUST keep CArrayObj::CreateElement's EXACT signature - `virtual bool
   //--- CreateElement(const int index)` - because it is the real virtual override that CArrayObj::Load()
   //--- dispatches through, and CArrayObj::Load() is how EVERY saved layer is read back (CLayer::Load ->
   //--- CNet::Load). In MQL5 an override must match the base parameter list
   //--- exactly; adding even a DEFAULTED parameter makes it a separate method that merely hides the base
   //--- one, silently leaving the base's `return(false)` stub in the vtable slot. That is exactly what a
   //--- `double weighScale = -1.0` parameter added here did: from then on every single model load failed
   //--- at the first layer ("REJECTED: only loaded 0 of N layers (failed at layer 0)") no matter how
   //--- healthy the .nnw was, so every restart retrained from era 0 and every best-checkpoint restore
   //--- silently kept the current weights. Never add parameters to this method - extend
   //--- CreateElementScaled() and call it explicitly instead.
   virtual bool      CreateElement(const int index) { return CreateElementScaled(index, -1.0); }
   virtual void      IncreaseTotal()   {  m_data_total++;   }
   virtual int       Type(void)  const { return defLayer; }
   virtual bool      Load(const int file_handle);
  };
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
#include "ArrayLayer.mqh"
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
class CNeuronPool : public CNeuronBase
  {
protected:
   CLayer            *OutputLayer;
   int               iWindow;
   int               iStep;

   virtual bool      feedForward(CLayer *prevLayer);
   virtual bool      calcHiddenGradients(CLayer *&nextLayer);

public:
                     CNeuronPool(void) {};
                    ~CNeuronPool(void);
   virtual bool      Init(uint numOutputs, uint myIndex, int window, int step, int units_count, ENUM_OPTIMIZATION optimization_type);
   //---
   virtual CLayer    *getOutputLayer(void)  { return OutputLayer;  }
   virtual bool      calcInputGradients(CLayer *prevLayer) ;
   virtual bool      calcInputGradients(CNeuronBase *prevNeuron, uint index) ;
   //--- methods for working with files
   virtual bool      Save(int const file_handle);
   virtual bool      Load(int const file_handle);
   virtual int       Type(void)   const   {  return defNeuronPool;   }
  };
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
class CNeuronConv  :  public CNeuronPool
  {
protected:
   double            param;   //PReLU param
   virtual bool      feedForward(CLayer *prevLayer);
   virtual bool      calcHiddenGradients(CLayer *&nextLayer);
   virtual double    activationFunction(double x);
   virtual bool      updateInputWeights(CLayer *prevLayer);
public:
                     CNeuronConv() :   param(0.01) { };
                    ~CNeuronConv(void)             { };
   //---
   virtual bool      calcInputGradients(CLayer *prevLayer) ;
   virtual bool      calcInputGradients(CNeuronBase *prevNeuron, uint index) ;
   virtual double    activationFunctionDerivative(double x);
   virtual int       Type(void)   const   {  return defNeuronConv;   }
   //--- methods for working with files
   virtual bool      Save(int const file_handle);
   virtual bool      Load(int const file_handle);
  };
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
#include "LayerDescription.mqh"
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
class CNet
  {
protected:
   double            dLogitAdjust[3];
   bool              bLogitAdjust;
   void              backPropOCL(CArrayDouble *targetVals, double sampleWeight = 1.0);
   bool              InitOpenCL(void);
   bool              InitDirectML(void);
   //--- Pure-MQL5 forward pass over OCL-format layers loaded host-only (no backend) - see SetCpuInference.
   bool              feedForwardCPU(CArrayDouble *inputVals);
public:
                     CNet(CArrayObj *Description);
                    ~CNet(void);
   bool              feedForward(CArrayDouble *inputVals);
   //--- sampleWeight scales this example's output-layer gradient before it propagates back through the
   //--- hidden layers - see the matching declaration comment on ExpertSignalAIBase.mqh's oversampling
   //--- replacement for why (inverse-class-frequency loss weighting instead of replaying the same
   //--- example multiple times).
   void              backProp(CArrayDouble *targetVals, double sampleWeight = 1.0);
   //--- LOGIT ADJUSTMENT (Menon et al. 2021, "Long-tail learning via logit adjustment"). Per-class
   //--- additive offsets tau*log(prior_c) folded into the 3-class softmax during the BACKWARD pass
   //--- only. Minimizing softmax CE on adjusted logits is consistent for BALANCED error - which is
   //--- exactly the metric checkpoint selection already ranks on (macro-recall), so this is the first
   //--- time the loss and the selection criterion optimize the same thing.
   //--- It replaces minority REPLAY (which duplicated rare bars up to 28x and made Buy and Sell
   //--- compete for the same replicated capacity - the measured failure was each model taking one
   //--- direction to ~50% recall and abandoning the other, with the direction chosen arbitrarily) and
   //--- the post-hoc inference prior, which becomes double-counting once the offsets are trained in.
   //--- Offsets are NEGATIVE (log of a probability), so a rare class gets its logit pushed DOWN during
   //--- training, forcing the weights to produce a larger raw logit to compensate. At inference the
   //--- offsets are absent, so that surplus becomes the calibrated boost the rare class needs.
   void              SetLogitAdjustment(const double &offsets[]);
   void              ClearLogitAdjustment(void)   {  bLogitAdjust = false;  }
   void              getResults(CArrayDouble *&resultVals) ;
   double            getRecentAverageError()  { return recentAverageError; }
   //--- indicatorParams: flattened AutoTuneIndicators "winning" AD indicator param values (see
   //--- CExpertSignalAIBase::FlattenIndicatorParams/UnflattenIndicatorParams); pass an empty array
   //--- when there is nothing to persist/restore.
   bool              Save(string file_name, double error, double undefine, double forecast, datetime time, bool common, long era, bool trainingComplete, const double &indicatorParams[]);
   //--- `quiet` suppresses the on-reject diagnostic Prints for callers that EXPECT a miss and handle it
   //--- gracefully (the EMA shadow-net bootstrap on a CPU-DLL box, which can't hold a 2nd full net - see
   //--- EnsureShadowNet). The main-model load leaves it false so a real failure is still loud.
   bool              Load(string file_name, double &error, double &undefine, double &forecast, datetime &time, bool common, long &era, bool &trainingComplete, double &indicatorParams[], bool quiet=false);
   //--- In-MEMORY weight checkpoint (host-only, zero extra device tensors). CaptureWeights() snapshots
   //--- every neuron's weights (base/conv/LSTM) into host arrays; RestoreWeights() writes them back IN
   //--- PLACE via setWeights - reusing the existing neuron objects and their already-allocated device
   //--- buffers, exactly like BlendWeightsFrom(). This REPLACED an earlier file-based checkpoint pair
   //--- (since removed - it had no callers left) for the mid-run stability restore, because a file path RE-CREATES every neuron on
   //--- load (fresh CLayer + Init), and the multithreaded CPU-DLL backend (CDirectMLMy/WarriorCPU.dll)
   //--- cannot allocate a second full set of neuron tensors while the live set still exists - so the
   //--- file restore failed ("read 0 layers"), the model could never roll back a regressed era, and it
   //--- drifted into a Neutral collapse. In-place weight copy uses only getWeights/setWeights, which the
   //--- per-era shadow blend already exercises successfully on that backend. Snapshots WEIGHTS only (not
   //--- Adam moments); the regression handler decays eta on restore and per-step deltas are clipped, so
   //--- stale moments can't overshoot. In-memory => valid only within a single Train() run (same as the
   //--- ephemeral _ckpt.tmp was), which is exactly its scope.
   //--- Per-layer weight-norm change since the previous call - the direct test for "is this stage
   //--- receiving gradient at all". See the definition for why a loss curve cannot answer that.
   string            LayerLearningReport(void);
   bool              CaptureWeights(void);
   bool              RestoreWeights(void);
   //--- MINI-BATCH CONTROL (2026-08-09 audit, F4).
   //--- SetBatchSize() is how training asks for accumulation; 1 restores the exact per-sample path the
   //--- engine used before. The REQUEST is not always granted - see BatchSize(), which refuses when the
   //--- backend cannot accumulate. FlushBatch() applies whatever a partial batch has accumulated and is
   //--- what every save/checkpoint/scoring boundary must call so no caller ever observes weights with
   //--- unapplied gradients behind them.
   void              SetBatchSize(int size)  {  m_batchSizeRequested = (size > 1 ? size : 1);  }
   int               BatchSize(void);
   bool              FlushBatch(void);
   int               PendingBatchSamples(void) const  {  return m_batchCount;  }
   //--- Zero every neuron's optimizer state - Adam first/second moments, SGD momentum deltas, the
   //--- per-neuron bias-correction step counters, and batch-norm's gamma/beta moment slots - while
   //--- leaving weights, activations and batch-norm running STATISTICS untouched (those belong to the
   //--- model, not the optimizer). 2026-08-09 audit, F3: RestoreWeights() rolls the WEIGHTS back to the
   //--- best checkpoint but the moments still encode the rejected trajectory, so the first updates
   //--- after a restore push straight back toward the state that was just rolled back - observed as the
   //--- restore -> regress-again -> restore oscillation. Called after every mid-run checkpoint restore
   //--- and at every plateau warm restart (a restart is a new schedule; stale momentum is not part of
   //--- it). Resetting t restarts Adam's bias-correction warm-up, whose larger early effective rate is
   //--- bounded by MAX_WEIGHT_DELTA like every other step.
   bool              ResetOptimizerState(void);
   //--- EMA shadow-weight deployment: blends this net's weights a small step (tau) toward another
   //--- net's weights, layer by layer, neuron by neuron - this.weight = (1-tau)*this.weight +
   //--- tau*live.weight. Intended usage: `this` is a persistent "shadow" net that live trading/OOS
   //--- checkpointing reads from, and `live` is the net Train()'s era loop actually backprops
   //--- against. A single bad era's raw weights (e.g. an Adam overshoot) can only ever nudge the
   //--- shadow by `tau`, so the deployed model can no longer whipsaw between 90%+ and single-digit
   //--- OOS accuracy the way a directly-deployed live net can - the shadow is a running average over
   //--- many eras, not a snapshot of whichever one happened to look best (or worst) in isolation.
   //--- Requires `this` and `live` to share identical topology (same layer/neuron/window counts) -
   //--- true whenever the shadow was cloned from live via Save()/Load() and never independently
   //--- rebuilt. Silently skips (rather than fails) any layer/neuron pair that doesn't line up, so a
   //--- topology mismatch degrades to a partial blend instead of corrupting unrelated layers.
   bool              BlendWeightsFrom(CNet &live, double tau);
   //--- Cold-start fix: overwrites just the bias term (not the per-input weights, which stay randomly
   //--- initialized and carry the real learning signal) of each output neuron's incoming weight block,
   //--- on the layer immediately before the output layer - see ExpertSignalAIBase.mqh's call site
   //--- (AdvanceLabelCachePrebuild()) for why: a freshly-initialized network's argmax is close to
   //--- uniform noise across classes, so on a heavily imbalanced label distribution it fires far more
   //--- non-majority classes than the true base rate warrants until backProp corrects it over many
   //--- steps. biasValues.Size() must equal the output layer's neuron count. Only supports the
   //--- OpenCL/DirectML batched neuron model (CNeuronBaseOCL) this project actually runs on - returns
   //--- false (no-op) rather than corrupt anything if that assumption doesn't hold.
   bool              SeedOutputLayerBias(const double &biasValues[]);
   //--- Pure-MQL5 (no OpenCL/DirectML/DLL) inference mode. Set BEFORE Load() in an inference-only
   //--- backtest: it makes InitOpenCL()/InitDirectML() no-op (both backends stay NULL), so the OCL
   //--- neurons load their weights host-side only and feedForward() runs the double-precision MQL5
   //--- path (feedForwardCPU) reading those same host buffers. Training/optimization never set this
   //--- (they always want a backend), so their behaviour is unchanged. See ExpertSignalAIBase.mqh's
   //--- inference-only wiring and the deploy-time validation that gates it.
   void              SetCpuInference(bool v)       {  m_cpuInference = v;   }
   bool              CpuInference(void)      const {  return m_cpuInference; }
   //--- Re-assert the output layer's activation after a Load(), and report what it used to be.
   //--- WHY THIS EXISTS: a .nnw persists the ARCHITECTURE, not just the weights. CNeuronBase::Save/
   //--- CNeuronBaseOCL::Save write (int)activation per neuron and the matching Load() reads it straight
   //--- back into the live object, so the activation chosen in BuildFreshTopology() only ever applies to
   //--- a genuinely NEW topology. Every reload restores whatever is on disk and the next Save() writes it
   //--- back out - a wrong value can never heal on its own, while the source file reads as though it were
   //--- already fixed. That is exactly how models kept training with an unbounded NONE classification head
   //--- for a full day after BuildFreshTopology() had been reverted to SIGMOID (2026-07-29): confirmed by
   //--- parsing the binaries, `layer N: BaseOCL act=NONE out=3`, while a freshly reset model of the same
   //--- config read act=SIGMOID. Symptom was negative "OOS raw out" values (impossible under sigmoid)
   //--- escalating to a 4.1e13 logit spread with all three classes numerically identical.
   //--- Only the output layer is repaired here: it is always a plain dense layer whose activation is a
   //--- single unambiguous expression in BuildFreshTopology(). Hidden layers are deliberately left alone -
   //--- they legitimately differ per stage (PRELU dense, PRELU conv, NONE pool, TANH LSTM), so blanket
   //--- re-assertion there would corrupt exactly the topologies it was meant to protect.
   //--- Returns true when a repair was actually made, and reports the stale value through `previous`.
   bool              EnforceOutputActivation(ENUM_ACTIVATION intended, ENUM_ACTIVATION &previous);
   //--- Receptive field of the first conv layer as LOADED, for the stale-architecture check in
   //--- CExpertSignalAIBase::EnforceTopologyContract. 0 when the net has no conv layer.
   uint              FirstConvWindow(void);
   //--- Freeze/unfreeze every batch-normalization layer's running statistics (AI\NeuronBatchNorm.mqh).
   //--- Frozen, a forward pass is a pure function of its input; unfrozen (the default) it also advances
   //--- the statistics. Anything that COMPARES two forward passes must freeze first or it measures its
   //--- own side effect. No-op on a net with no normalization layers.
   void              SetBatchNormFrozen(bool frozen);
   //---
   static double     recentAverageSmoothingFactor;

private:
   CArrayLayer       *layers;
   COpenCLMy         *opencl;
   CDirectMLMy       *directml;
   double            recentAverageError;
   bool              m_cpuInference;
   //--- In-memory best-weights checkpoint (see CaptureWeights/RestoreWeights). One CArrayDouble per
   //--- neuron in layer-major order; host-only, no device tensors. NULL/false until the first capture.
   CArrayObj        *m_weightSnapshot;
   bool              m_haveWeightSnapshot;
   //--- Previous call's per-layer weight L2 norms, for LayerLearningReport(). Sized lazily to the layer
   //--- count; -1 marks "no baseline yet" so the first report says (init) instead of a bogus 0% change.
   double            m_prevLayerNorm[];
   //--- Previous call's per-layer weight VECTORS, for the |dW| term of LayerLearningReport(). One
   //--- CArrayDouble per layer index (empty for layers that own no weights), host-only.
   //--- WHY BOTH: |d|W||/|W| - the change in NORM - cannot distinguish "this layer only shrank under
   //--- weight decay" from "this layer moved somewhere useful". Pure decay and a genuine rotation of a
   //--- constant-norm weight vector can print the same number, and on 2026-07-31 the LSTM layers printed
   //--- a suspiciously constant ~1.05%/era while a sibling conv oscillated - a difference the norm-change
   //--- statistic could only hint at. |dW|/|W| - the norm of the CHANGE - separates them outright: under
   //--- decay alone it equals the decay rate exactly, while any gradient component adds in quadrature
   //--- (sqrt(decay^2 + (g/|W|)^2)). Reading them side by side is the whole diagnostic: |dW| >> |d|W||
   //--- means the layer is rotating (learning); |dW| ~= |d|W|| with the norm falling means it is only
   //--- being decayed away. See [[feedback_verify_in_situ_not_offline]] - this is the in-situ check.
   CArrayObj        *m_prevLayerWeights;
   //--- One-shot latch for BlendWeightsFrom's skip warning - it runs every era, and the condition it
   //--- reports is permanent, so the second print would only be noise.
   bool              m_blendSkipLogged;
   //--- PROCESS-WIDE compute-probe latches (shared by every CNet in this terminal process).
   //--- A run legitimately builds SEVERAL CNet objects - the main Net, the EMA shadow net, the OOS-sim
   //--- clone, the deploy-time MQL5-inference self-check clone - and each one probed the backends
   //--- independently. On a host without OpenCL that reprinted the same 3-line banner per net
   //--- ("OpenCL not found, error code=5100" comes from the STDLIB COpenCL::Initialize, so it can't be
   //--- silenced at our call site), which read in the log like the EA was initializing twice.
   //--- s_openclUnavailable: latched only on FAILURE, and only ever skips a probe that is already known
   //--- to fail - OpenCL availability cannot change inside a process. A host that HAS OpenCL never
   //--- latches, so every CNet still gets its own COpenCLMy. Skipping also keeps GetLastError() free of
   //--- the harmless 5100 for later callers.
   //--- s_computeTierLogged: suppresses only the repeat of the informational "tier active" line (the
   //--- tier is a property of the HOST, identical for every net). Failure messages stay loud every time.
   static bool       s_openclUnavailable;
   static bool       s_computeTierLogged;
   //--- Mini-batch state. m_batchSizeRequested is what training asked for; m_batchCount is how many
   //--- samples have been accumulated into the current batch. m_batchKernelsOk records whether this
   //--- net's OpenCL device actually built the accumulation kernels - false forces per-sample updates
   //--- rather than failing, so an old device trains exactly as it did before (see InitOpenCL).
   //--- m_batchBegun guards BeginBatch so the first sample of a batch zeroes the accumulators exactly
   //--- once. m_batchWarned latches the one-time "cannot batch on this tier" notice.
   int               m_batchSizeRequested;
   int               m_batchCount;
   bool              m_batchKernelsOk;
   //--- Whether the DEVICE-SIDE apply kernels built (def_k_ApplyAccumAdam). Separate from
   //--- m_batchKernelsOk because they degrade differently: without the accumulation kernels the net
   //--- cannot batch at all and must fall back to per-sample updates, whereas without these it batches
   //--- normally and merely pays a host round-trip per batch. Conflating them would turn a missing
   //--- optimisation into a change of optimizer.
   bool              m_applyKernelsOk;
   bool              m_batchBegun;
   bool              m_batchWarned;
   //--- Zero every accumulator, starting a fresh batch.
   bool              BeginBatch(void);
  };
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
class CNeuronLSTM    :  public CNeuronPool
  {
protected:
   CLayer            *ForgetGate;
   CLayer            *InputGate;
   CLayer            *OutputGate;
   CLayer            *NewContent;
   CArrayDouble      *Memory;
   CArrayDouble      *PrevMemory;
   CArrayDouble      *Input;
   CArrayDouble      *InputGradient;
   //---
   virtual bool      feedForward(CLayer *prevLayer);
   virtual bool      calcHiddenGradients(CLayer *&nextLayer);
   virtual bool      updateInputWeights(CLayer *prevLayer);
   virtual bool      updateInputWeights(CLayer *gate, CArrayDouble *input_data);
   virtual bool      InitLayer(CLayer *layer, int numOutputs, int numUnits, ENUM_OPTIMIZATION optimization_type);
   virtual CArrayDouble *CalculateGate(CLayer *gate, CArrayDouble *sequence);

public:
                     CNeuronLSTM(void);
                    ~CNeuronLSTM(void);
   virtual bool      Init(uint numOutputs, uint myIndex, int window, int step, int units_count, ENUM_OPTIMIZATION optimization_type);
   //---
   virtual CLayer    *getOutputLayer(void)  { return OutputLayer;  }
   virtual bool      calcInputGradients(CLayer *prevLayer) ;
   virtual bool      calcInputGradients(CNeuronBase *prevNeuron, uint index) ;
   //--- methods for working with files
   virtual bool      Save(int const file_handle);
   virtual bool      Load(int const file_handle);
   virtual int       Type(void)   const   {  return defNeuronLSTM;   }
  };
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
template<typename T>
int COpenCLMy::AddBufferFromArray(T &data[], const uint data_array_offset, const uint data_array_count, const uint flags)
  {
   int result = -1;
   for(int i = 0; i < m_buffers_total; i++)
     {
      if(m_buffers[i] != INVALID_HANDLE)
         continue;
      result = i;
      break;
     }
//---
   if(result < 0)
     {
      if(ArrayResize(m_buffers, m_buffers_total + 1) > 0)
        {
         m_buffers_total = ArraySize(m_buffers);
         result = m_buffers_total - 1;
         m_buffers[result] = INVALID_HANDLE;
        }
      else
         return result;
     }
//---
   if(!BufferFromArray(result, data, data_array_offset, data_array_count, flags))
      return -1;
//---
   return result;
  }
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
#include "BufferDouble.mqh"
//+------------------------------------------------------------------+
//|                                                                   |
//+------------------------------------------------------------------+
class CNeuronBaseOCL    :  public CObject
  {
protected:
   COpenCLMy         *OpenCL;
   CDirectMLMy       *DirectML;
   CBufferDouble     *Output;
   CBufferDouble     *PrevOutput;
   CBufferDouble     *Weights;
   CBufferDouble     *DeltaWeights;
   CBufferDouble     *Gradient;
   CBufferDouble     *FirstMomentum;
   CBufferDouble     *SecondMomentum;
   //--- MINI-BATCH ACCUMULATOR (2026-08-09 audit, F4). Same shape as Weights; holds the SUM of this
   //--- weight block's per-sample gradients over the current batch. Allocated lazily by
   //--- EnsureGradAccum() the first time a batched update runs, so a batch size of 1 (or a net that
   //--- only ever runs inference) pays nothing. Deliberately NOT persisted: it is transient within a
   //--- batch, and every save point flushes the batch first (see CNet::FlushBatch).
   CBufferDouble     *GradAccum;
   //---
   const double      alpha;
   int               t;
   //---
   ENUM_ACTIVATION   activation;
   ENUM_OPTIMIZATION optimization;
   //---
   virtual bool      feedForward(CNeuronBaseOCL *NeuronOCL);
   virtual bool      calcHiddenGradients(CNeuronBaseOCL *NeuronOCL);
   //--- Create a buffer's device-side storage on whichever backend is active, or leave it host-only
   //--- (its CArrayDouble m_data already holds the values just Load()ed) when neither backend exists -
   //--- the pure-MQL5 inference path (see CNet::SetCpuInference / feedForwardCPU).
   bool              BackendBufferCreate(CBufferDouble *buf)
     {
      if(CheckPointer(buf) == POINTER_INVALID)
         return false;
      if(CheckPointer(OpenCL) != POINTER_INVALID)
         return buf.BufferCreate(OpenCL);
      if(CheckPointer(DirectML) != POINTER_INVALID)
         return buf.BufferCreate(DirectML);
      return true; // no backend: keep host m_data, skip device allocation
     }
   //--- THE optimizer step, and the only copy of it in the batched path: one weight block, one Adam or
   //--- SGD+momentum update on `acc * scale`, then the accumulator is zeroed. Dense, conv and LSTM all
   //--- route their blocks through this, so the four compute tiers and the three layer types cannot
   //--- drift apart the way four separate kernel copies have in this engine before. Does NOT advance
   //--- t - the caller does that once per batch, after all of its blocks are stepped.
   //--- OpenCL fast path for the above: one dispatch, nothing crosses the bus. Returns false without
   //--- reporting when the kernels or buffers are not there, so the caller falls back to the host step.
   bool              ApplyAccumOnDevice(CBufferDouble *w, CBufferDouble *acc, CBufferDouble *m,
                                        CBufferDouble *v, CBufferDouble *dw, double scale, int total);
   bool              ApplyAccumToBlock(CBufferDouble *w, CBufferDouble *acc, CBufferDouble *m,
                                       CBufferDouble *v, CBufferDouble *dw, double scale);
   //--- Lazily allocate the mini-batch accumulator to match `src` (Weights / WeightsConv / WeightsLSTM,
   //--- whichever block the caller accumulates into). Returns false when there is nothing to shadow,
   //--- which is the normal case for an output-layer neuron that owns no weight block at all.
   //--- General form - `target` is taken by reference-to-pointer so a subclass can allocate its OWN
   //--- accumulator member (conv's kernel block, for one) through the same code path.
   bool              EnsureGradAccumFor(CBufferDouble *&target, CBufferDouble *src)
     {
      if(CheckPointer(src) == POINTER_INVALID || src.Total() <= 0)
         return false;
      if(CheckPointer(target) != POINTER_INVALID && target.Total() == src.Total())
         return true;
      if(CheckPointer(target) != POINTER_INVALID)
         delete target;
      target = new CBufferDouble();
      if(CheckPointer(target) == POINTER_INVALID)
         return false;
      if(!target.BufferInit(src.Total(), 0.0))
         return false;
      return BackendBufferCreate(target);
     }
   bool              EnsureGradAccum(CBufferDouble *src)  {  return EnsureGradAccumFor(GradAccum, src);  }

public:
                     CNeuronBaseOCL(void);
                    ~CNeuronBaseOCL(void);
   virtual bool      Init(uint numOutputs, uint myIndex, COpenCLMy *open_cl, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
   virtual bool      Init(uint numOutputs, uint myIndex, CDirectMLMy *direct_ml, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
   virtual void      SetActivationFunction(ENUM_ACTIVATION value) {  activation = value; }
   //---
   virtual int       getOutputIndex(void)          {  return Output.GetIndex();        }
   virtual int       getPrevOutIndex(void)         {  return PrevOutput.GetIndex();    }
   virtual int       getGradientIndex(void)        {  return Gradient.GetIndex();      }
   virtual int       getWeightsIndex(void)         {  return Weights.GetIndex();       }
   virtual int       getDeltaWeightsIndex(void)    {  return DeltaWeights.GetIndex();  }
   virtual int       getFirstMomentumIndex(void)   {  return FirstMomentum.GetIndex(); }
   virtual int       getSecondMomentumIndex(void)  {  return SecondMomentum.GetIndex();}
   //---
   virtual int       getOutputVal(double &values[])   {  return Output.GetData(values);      }
   virtual int       getOutputVal(CArrayDouble *values)   {  return Output.GetData(values);  }
   virtual int       getPrevVal(double &values[])     {  return PrevOutput.GetData(values);  }
   virtual int       getGradient(double &values[])    {  return Gradient.GetData(values);    }
   //--- pushes locally-modified gradient values back to this buffer's GPU/CPU-DLL-side copy - used by
   //--- CNet::backPropOCL() to apply per-sample loss weighting after the native CalcOutputGradient call
   //--- (which only computes the raw, unweighted delta) and before the backward pass reads this same
   //--- buffer to propagate into the hidden layers.
   virtual bool      setGradient(const double &values[])
     {
      int count = ArraySize(values);
      for(int i = 0; i < count; i++)
         if(!Gradient.Update(i, values[i]))
            return false;
      return Gradient.BufferWrite();
     }
   // Guarded: output-layer neurons (numOutputs==0) have Weights deleted in Init() but not re-created,
   // so BlendWeightsFrom() walking every neuron would otherwise dereference a dead pointer here.
   virtual int       getWeights(double &values[])     {  return (CheckPointer(Weights) == POINTER_INVALID ? 0 : Weights.GetData(values));     }
   // Paired with getWeights() above for CNet::BlendWeightsFrom()'s EMA shadow-weight deployment
   // (see that method's declaration comment) - writes a full replacement weight array back to this
   // buffer's device-side (DLL/OpenCL/DirectML) storage. Unlike Weights.Update(i,...) (per-element,
   // used by the Adam kernels' own writes), this replaces the whole buffer in one bulk assignment
   // then pushes it to the device - the shape callers use when blending two already-read-out arrays.
   virtual bool      setWeights(double &values[])
     {
      if(CheckPointer(Weights) == POINTER_INVALID)
         return false;
      if(!Weights.AssignArray(values))
         return false;
      return Weights.BufferWrite();
     }
   //--- Forget the optimizer's trajectory memory: Adam first/second moments, SGD's previous-delta
   //--- buffer, and the bias-correction step counter, weights untouched. Subclasses that own extra
   //--- weight blocks (conv/LSTM/batch-norm) extend this with their own moment storage. See
   //--- CNet::ResetOptimizerState for the restore/warm-restart rationale.
   virtual bool      ResetOptimizerState(void)
     {
      t = 1;
      bool ok = ZeroOptimizerBuffer(FirstMomentum);
      ok = ZeroOptimizerBuffer(SecondMomentum) && ok;
      ok = ZeroOptimizerBuffer(DeltaWeights) && ok;
      return ok;
     }
   virtual int       Neurons(void)                    {  return Output.Total();              }
   virtual ENUM_ACTIVATION Activation(void)           {  return activation;                  }
   virtual int       getConnections(void)             {  return (CheckPointer(Weights) != POINTER_INVALID && CheckPointer(Gradient) != POINTER_INVALID && Gradient.Total() > 0 ? Weights.Total() / Gradient.Total() : 0);   }
   //--- Host-side (no device round-trip) buffer element access for the pure-MQL5 inference path. Safe to
   //--- call after Load() with no backend: the CArrayDouble m_data holds the values, unlike getWeights()/
   //--- getOutputVal() which route through BufferRead() (a device read that fails without a backend).
   double            OutputHost(int i)                {  return (CheckPointer(Output) != POINTER_INVALID && i >= 0 && i < Output.Total()) ? Output.At(i) : 0.0;   }
   double            WeightHost(int i)                {  return (CheckPointer(Weights) != POINTER_INVALID && i >= 0 && i < Weights.Total()) ? Weights.At(i) : 0.0;  }
   int               WeightsCount(void)               {  return (CheckPointer(Weights) != POINTER_INVALID) ? Weights.Total() : 0;   }
   //--- Pure-MQL5, double-precision forward pass mirroring Network.cl's FeedForward kernel, reading only
   //--- host buffers. Used exclusively when no compute backend exists (CNet::SetCpuInference). Dense
   //--- (fully-connected) here; conv/pool/LSTM subclasses override with their own kernel math.
   virtual bool      feedForwardCPU(CNeuronBaseOCL *NeuronOCL);
   //--- Host-side input write for the CPU inference path's layer 0, and host-side output read for
   //--- getResults() - both bypass the device buffer that the CPU path deliberately never allocates.
   bool              SetInputsCPU(CArrayDouble *inputVals);
   int               GetOutputsCPU(CArrayDouble *values);
   //---
   virtual bool      feedForward(CObject *SourceObject);
   virtual bool      calcHiddenGradients(CObject *TargetObject);
   virtual bool      calcOutputGradients(CArrayDouble *Target);
   virtual bool      updateInputWeights(CNeuronBaseOCL *NeuronOCL);
   virtual bool      updateInputWeights(CObject *SourceObject);
   //--- MINI-BATCH PAIR (2026-08-09 audit, F4). accumulateInputWeightGrads() adds THIS sample's
   //--- per-weight gradient into GradAccum without touching the weights; ApplyAccumulatedGradients()
   //--- then takes ONE optimizer step on the batch mean and clears the accumulator. Together they are
   //--- the batched equivalent of updateInputWeights(), and at batch size 1 they must reproduce it
   //--- exactly - the accumulation kernels are copied from the Adam update kernels for that reason.
   //--- The dispatch pair mirrors updateInputWeights: the accumulate half is native (it is the O(n^2)
   //--- outer product); the apply half is a kernel on OpenCL and host-side MQL5 everywhere else. It was
   //--- host-side on ALL tiers until 2026-08-09, so that the four backends shared one optimizer
   //--- implementation - a good instinct that turned out to cost eight full weight-matrix transfers per
   //--- batch per block, which made the GPU tier several times slower than a CPU thread pool. Since
   //--- Market builds forbid DLL imports, that tier is the one clients run. The host copy remains the
   //--- reference; ApplyAccumOnDevice is transcribed from it and the two must be edited together.
   virtual bool      accumulateInputWeightGrads(CNeuronBaseOCL *NeuronOCL);
   virtual bool      accumulateInputWeightGrads(CObject *SourceObject);
   virtual bool      ApplyAccumulatedGradients(double scale);
   //--- Zero the accumulator at the start of a batch. Separate from ApplyAccumulatedGradients so a
   //--- discarded partial batch (a stopped run) can be cleared without taking a step from it.
   virtual bool      BeginGradAccum(void);
   //---
   virtual bool      Save(int const file_handle);
   virtual bool      Load(int const file_handle);
   //---
   virtual int       Type(void)        const                      {  return defNeuronBaseOCL;                  }
  };
//+------------------------------------------------------------------+
#include "NeuronOCLConvPool.mqh"
#include "NeuronBatchNorm.mqh"
//+------------------------------------------------------------------+
//--- Marks a .nnw LSTM record as the SEQUENCE format. Chosen so it cannot collide with any value the
//--- pre-sequence format could have written in that slot (an input width, i.e. -1 or a positive count).
#define LSTM_SEQ_SAVE_TAG (-424242)
//--- Initial bias of the FORGET gate (gate 0). Every other weight starts near zero; this one must not.
//--- See the long rationale and the measured numbers at its use in CNeuronLSTMOCL::SetInputs - in
//--- short, a zero bias means sigmoid(0)=0.5, which halves the cell state every bar and leaves a
//--- 20-bar window with the memory and the gradient reach of a single bar.
#define LSTM_FORGET_BIAS_INIT (1.0)
//+------------------------------------------------------------------+
//| GPU-accelerated LSTM layer (OpenCL + DirectML). Derived from      |
//| scratch from the standard LSTM equations - NOT ported from the    |
//| NeuroNet_DNG reference (see the note above the LSTM kernels in    |
//| Network.cl for why). Single-timestep-truncated BPTT: gradient     |
//| does not flow back into h_prev/c_prev from an earlier step.       |
//| Adam-only - Init fails for any other optimization type.           |
//+------------------------------------------------------------------+
class CNeuronLSTMOCL : public CNeuronBaseOCL
  {
protected:
   int               m_iInputs;
   //--- Mini-batch running total of WeightsGradient across the samples of one batch - see the note on
   //--- accumulateInputWeightGrads below for why WeightsGradient itself cannot serve as it.
   CBufferDouble     *GradAccumLSTM;
   //--- Sequence shape. m_iStepInputs is the width of ONE timestep (the per-bar feature count reaching
   //--- this layer), set from the layer descriptor by SetStepWidth() before the first feedForward;
   //--- m_iSteps is then m_iInputs / m_iStepInputs. When m_iStepInputs <= 0 the layer falls back to the
   //--- LEGACY single-timestep behaviour (whole input as one step), which is what every .nnw written
   //--- before the sequence rewrite contains.
   int               m_iStepInputs;
   int               m_iSteps;
   //--- Per-timestep caches, required by backpropagation-through-time: the backward pass needs each
   //--- step's gate activations, cell state and hidden state, which the single-buffer Concatenated/
   //--- Memory/HiddenCache trio below cannot hold because every step overwrites the last.
   CBufferDouble     *CacheGates;    // T * 4H, gate order [f,i,o,g]
   CBufferDouble     *CacheCell;     // T * H,  c_t
   CBufferDouble     *CacheHidden;   // T * H,  h_t
   CBufferDouble     *WeightsLSTM;
   CBufferDouble     *FirstMomentumLSTM;
   CBufferDouble     *SecondMomentumLSTM;
   CBufferDouble     *DeltaWeightsLSTM;
   CBufferDouble     *WeightsGradient;
   CBufferDouble     *Concatenated;
   CBufferDouble     *ConcatenatedGradient;
   CBufferDouble     *Memory;
   CBufferDouble     *HiddenCache;
   //---
   virtual bool      feedForward(CNeuronBaseOCL *NeuronOCL);
   virtual bool      feedForwardCPU(CNeuronBaseOCL *NeuronOCL); // pure-MQL5 mirror of LSTM_Gates + LSTM_State
   virtual bool      updateInputWeights(CNeuronBaseOCL *NeuronOCL);
   virtual bool      SetInputs(int count);
   bool              AllocateSequenceCaches(void);

public:
                     CNeuronLSTMOCL(void) : m_iInputs(-1), m_iStepInputs(-1), m_iSteps(-1)
     {
      GradAccumLSTM = NULL;
      CacheGates = NULL;
      CacheCell = NULL;
      CacheHidden = NULL;
      WeightsLSTM = NULL;
      FirstMomentumLSTM = NULL;
      SecondMomentumLSTM = NULL;
      DeltaWeightsLSTM = NULL;
      WeightsGradient = NULL;
      Concatenated = NULL;
      ConcatenatedGradient = NULL;
      Memory = NULL;
      HiddenCache = NULL;
     }
                    ~CNeuronLSTMOCL(void);
   virtual bool      Init(uint numOutputs, uint myIndex, COpenCLMy *open_cl, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
   virtual bool      Init(uint numOutputs, uint myIndex, CDirectMLMy *direct_ml, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
   //--- Per-timestep input width, from CLayerDescription::window (see AddLstmStage). Must be called
   //--- between Init() and the first feedForward; <= 0 keeps the legacy single-timestep behaviour.
   //--- Not persisted from here - Save/Load carry it, so a loaded model does not depend on call order.
   void              SetStepWidth(int stepInputs) {  m_iStepInputs = (stepInputs > 0 ? stepInputs : -1);  }
   bool              IsSequenceMode(void) const {  return (m_iStepInputs > 0 && m_iSteps > 1);  }
   virtual bool      calcInputGradients(CNeuronBaseOCL *NeuronOCL);
   virtual bool      Save(int const file_handle);
   virtual bool      Load(int const file_handle);
   virtual int       Type(void)   const   {  return defNeuronLSTMOCL;  }
   // See CNeuronBaseOCL::getWeights/setWeights - same pair, targeting WeightsLSTM instead of the
   // base class's Weights, for CNet::BlendWeightsFrom()'s EMA shadow-weight deployment.
   virtual int       getWeightsLSTM(double &values[])     {  return (CheckPointer(WeightsLSTM) == POINTER_INVALID ? 0 : WeightsLSTM.GetData(values));  }
   //--- Build this layer's weight block to match `src`, for a net that was cloned from one whose LSTM
   //--- had not yet run a forward pass. Save() writes m_iInputs = -1 and omits EVERY LSTM buffer in that
   //--- state, so the clone comes back with WeightsLSTM == NULL - and if that clone is an EMA shadow,
   //--- which only ever receives BlendWeightsFrom and never runs forward itself, nothing would ever
   //--- allocate it. m_iStepInputs must be copied FIRST: SetInputs reads it to decide whether the block
   //--- is 4H(H + stepInputs + 1) (sequence) or 4H(H + inputs + 1) (single timestep).
   virtual bool      AdoptShapeFrom(CNeuronLSTMOCL &src)
     {
      if(src.m_iInputs <= 0 || Neurons() != src.Neurons())
         return false;
      m_iStepInputs = src.m_iStepInputs;
      return SetInputs(src.m_iInputs);
     }
   virtual bool      setWeightsLSTM(double &values[])
     {
      if(CheckPointer(WeightsLSTM) == POINTER_INVALID)
         return false;
      if(!WeightsLSTM.AssignArray(values))
         return false;
      return WeightsLSTM.BufferWrite();
     }
   //--- The LSTM's gate-weight block keeps its own moment/momentum buffers beside the base class's -
   //--- see CNet::ResetOptimizerState.
   virtual bool      ResetOptimizerState(void)
     {
      bool ok = CNeuronBaseOCL::ResetOptimizerState();
      ok = ZeroOptimizerBuffer(FirstMomentumLSTM) && ok;
      ok = ZeroOptimizerBuffer(SecondMomentumLSTM) && ok;
      ok = ZeroOptimizerBuffer(DeltaWeightsLSTM) && ok;
      return ok;
     }
   //--- MINI-BATCH. This layer needs no outer-product accumulation kernel of its own: by the time the
   //--- update pass reaches it, WeightsGradient already holds THIS sample's complete dW, summed over
   //--- the T timesteps of BPTT by calcInputGradients. Batching it is therefore just a running total.
   //--- It cannot be done by leaving WeightsGradient un-zeroed between samples, which was the obvious
   //--- approach and is wrong: CPU_LSTMSeqBackward/DML_LSTMSeqBackward memset that buffer at the top of
   //--- every call, so the DLL would wipe the batch on each sample no matter what the host did. Hence a
   //--- separate accumulator plus a generic elementwise add (AccumulateBufferInto), which also avoids
   //--- changing the signature of an already-deployed export.
   virtual bool      accumulateInputWeightGrads(CNeuronBaseOCL *NeuronOCL);
   virtual bool      BeginGradAccum(void)
     {
      bool ok = CNeuronBaseOCL::BeginGradAccum();
      if(CheckPointer(GradAccumLSTM) != POINTER_INVALID && GradAccumLSTM.Total() > 0)
         ok = ZeroOptimizerBuffer(GradAccumLSTM) && ok;
      return ok;
     }
   virtual bool      ApplyAccumulatedGradients(double scale)
     {
      bool ok = ApplyAccumToBlock(Weights, GradAccum, FirstMomentum, SecondMomentum, DeltaWeights, scale);
      ok = ApplyAccumToBlock(WeightsLSTM, GradAccumLSTM, FirstMomentumLSTM, SecondMomentumLSTM,
                             DeltaWeightsLSTM, scale) && ok;
      if(optimization == ADAM)
         t++;
      return ok;
     }
  };
//+------------------------------------------------------------------+
//+------------------------------------------------------------------+
//| Implementation bodies.                                            |
//|                                                                   |
//| Everything above this line is DECLARATIONS - the nine classes     |
//| plus the include chain that orders them (each nested include      |
//| sits exactly where its base class becomes visible, so the order   |
//| here is a dependency graph, not a preference).                    |
//|                                                                   |
//| Everything below is method BODIES, grouped by the class they      |
//| belong to. They were interleaved with the declarations in one     |
//| 6266-line file; splitting them out is behaviour-neutral by        |
//| construction, since a body cannot run during compilation and      |
//| every declaration it could need is already visible above.         |
//+------------------------------------------------------------------+
#include "Impl\NeuronBase.mqh"
#include "Impl\NeuronConvPool.mqh"
#include "Impl\NeuronLSTM.mqh"
#include "Impl\Layer.mqh"
#include "Impl\NetBuild.mqh"
#include "Impl\NetForward.mqh"
#include "Impl\NetPersistence.mqh"
#include "Impl\NetWeights.mqh"
#include "Impl\NeuronOCLBase.mqh"
#include "Impl\NeuronOCLLSTM.mqh"
//+------------------------------------------------------------------+