Implement L1 regularization

Rbm::Params had no l1Lambda field at all -- L1 didn't exist anywhere in
the C++ codebase. Status::L1 was a dead field always -1 (never assigned
outside its constructor), and the GUI's "Lambda" control was explicitly
tooltipped "Unused" with an empty change-handler stub. Scaffolded, never
implemented.

Add Params::l1Lambda (default 0.0, inert unless set) with full toJson/
fromJson round-trip. Apply its subgradient (lambda*sign(W), matching
pyRBM's l1_lambda) directly to m_whv in Rbm::train, in the same place
and same decoupled-from-momentum manner as the weightDecay fix from the
previous commit -- folding it into inc_whv's momentum recursion would
cause the same momentum-amplification bug. Doesn't touch the biases,
matching pyRBM's state_adjust().

Also wire up Status::L1 to actually report something (the current L1
norm of the weights, sum(abs(W))) instead of permanently printing -1,
computed alongside status.err/err_total.

Inert by default (l1Lambda=0.0 in every existing .prj): confirmed via
unchanged poet.elf output and unchanged test-suite results (9 passed,
1 known issue, 2 failed).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016K8Gu7Qejd11JbdiHZqYAs

source/Rbm.hpp

@@ -28,6 +28,7 @@ public:
 		Params()
 		: learningRate(0.1)
 		, weightDecay(0.0)
+		, l1Lambda(0.0)
 		, momentum(0.9)
 		, doGaussianHidden(false)
 		, doGaussianVisible(false)
@@ -46,6 +47,7 @@ public:
 			std::cout << "Exporting Rbm::Params" << std::endl;
 			Json::Value params;
 			params["weightDecay"] = weightDecay;
+			params["l1Lambda"] = l1Lambda;
 			params["learningRate"] = learningRate;
 			params["momentum"] = momentum;
 			params["doGaussianVisible"] = (int)doGaussianVisible;
@@ -65,6 +67,7 @@ public:
 		{
 			std::cout << "Importing Rbm::Params" << std::endl;
 			weightDecay = params.get("weightDecay", weightDecay).asDouble();
+			l1Lambda = params.get("l1Lambda", l1Lambda).asDouble();
 			learningRate = params.get("learningRate", learningRate).asDouble();
 			momentum = params.get("momentum", momentum).asDouble();
 			doGaussianVisible = params.get("doGaussianVisible", doGaussianVisible) == 1;
@@ -79,6 +82,7 @@ public:
 		}
 
 		double weightDecay;
+		double l1Lambda;
 		double learningRate;
 		double momentum;
 		bool doGaussianVisible;