Fix Bernoulli-only sample() applied to Gaussian visible/hidden units

Matutils::sample() always binary-thresholds (src > uniform(src)), but it
was the only sampler in the codebase and was called unconditionally on
h_probs/v_probs/miniBatch in several CD Gibbs-loop branches regardless
of doGaussianVisible/doGaussianHidden. Binary-thresholding a Gaussian
unit's continuous activation is meaningless -- it would corrupt any
Gaussian-visible/hidden RBM (image-domain experiments via the GUI or
TEST target); doesn't affect poet's plain BB-RBM path since both flags
are false there.

Add sample_gaussian() (mean + N(0,1) noise) alongside the existing
Bernoulli sample() in matutils.hpp, plus Rbm::sampleVisible/sampleHidden
helpers that dispatch to the right one per the RBM's configured type.
Replace every visible/hidden Gibbs-step sample() call in cd_jens (the
active path) and cd_hinton (compiled but currently unused, behind
USE_CD_HINTON) with the appropriate dispatch helper, and fix the same
issue in Rbm::train's doSampleBatch path.

Behavior is unchanged for any RBM with doGaussianVisible/doGaussianHidden
both false (confirmed: poet.elf f output identical before/after).

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

source/Rbm.cpp

@@ -88,6 +88,16 @@ arma::mat Rbm::toVisibleProbs(const arma::mat& hidden) const
 	return prob(state);
 }
 
+arma::mat Rbm::sampleVisible(const arma::mat& visible) const
+{
+	return m_params.doGaussianVisible ? sample_gaussian(visible) : sample(visible);
+}
+
+arma::mat Rbm::sampleHidden(const arma::mat& hidden) const
+{
+	return m_params.doGaussianHidden ? sample_gaussian(hidden) : sample(hidden);
+}
+
 void Rbm::weightsAssign(const arma::mat& w, const arma::mat& bh, const arma::mat& bv)
 {
 	m_whv.submat(0, 0, w.n_rows - 1, w.n_cols - 1) = w;
@@ -210,7 +220,7 @@ void Rbm::cd_hinton(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma
 	}
 	else
 	{
-		posprods = v_data.t() * sample(poshidprobs);
+		posprods = v_data.t() * sampleHidden(poshidprobs);
 	}
 
 	arma::mat negprods;
@@ -218,11 +228,11 @@ void Rbm::cd_hinton(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma
 	arma::mat negvisact;
 	for (int i=0; i < m_params.numGibbs; i++)
 	{
-		// Start negative phase	
+		// Start negative phase
 		arma::mat negdata;
 		if (m_params.gibbsDoSampleHidden)
 		{
-			negdata = toVisibleProbs(sample(poshidstates));
+			negdata = toVisibleProbs(sampleHidden(poshidstates));
 		}
 		else
 		{
@@ -231,11 +241,11 @@ void Rbm::cd_hinton(arma::mat const &v_data, arma::mat &dw, arma::mat &dbh, arma
 		arma::mat neghidprobs;
 		if (m_params.gibbsDoSampleVisible)
 		{
-			neghidprobs = toHiddenProbs(sample(negdata)); 
+			neghidprobs = toHiddenProbs(sampleVisible(negdata));
 		}
 		else
 		{
-			neghidprobs = toHiddenProbs(negdata); 
+			neghidprobs = toHiddenProbs(negdata);
 		}
 		negprods = negdata.t() * neghidprobs; 
 		neghidact = arma::sum(neghidprobs);
@@ -280,7 +290,7 @@ void Rbm::cd_jens(arma::mat const &v_states, arma::mat &dw, arma::mat &dbh, arma
 		// Create visible reconstruction (a fantasy...) given hid
 		if (m_params.gibbsDoSampleHidden)
 		{
-			v_probs = toVisibleProbs(sample(h_probs));
+			v_probs = toVisibleProbs(sampleHidden(h_probs));
 		}
 		else
 		{
@@ -290,7 +300,7 @@ void Rbm::cd_jens(arma::mat const &v_states, arma::mat &dw, arma::mat &dbh, arma
 		// Create hidden representation given v
 		if (m_params.gibbsDoSampleVisible)
 		{
-			h_probs = toHiddenProbs(sample(v_probs));
+			h_probs = toHiddenProbs(sampleVisible(v_probs));
 		}
 		else
 		{
@@ -342,8 +352,8 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
 			// Create hidden layer base on training data
 			if (m_params.doSampleBatch)
 			{
-				// When the hidden units are being driven by data, always use stochastic binary states
-				v_states = sample(miniBatch);
+				// Sample the visible batch (respects doGaussianVisible)
+				v_states = sampleVisible(miniBatch);
 			}
 
 			// Contrastive divergence learning: calculate gradients

source/Rbm.hpp

@@ -164,7 +164,14 @@ private:
 	void cd(arma::mat const &v_states, arma::mat &dwhv, arma::mat &dbhv, arma::mat &dbv);
 	arma::mat v_to_h(const arma::mat &visible) const;
 	arma::mat h_to_v(const arma::mat &hidden) const;
-	
+
+	// Sample a visible/hidden unit's state from its probability/mean matrix,
+	// dispatching to the Bernoulli or Gaussian sampler per doGaussianVisible/
+	// doGaussianHidden -- CD's Gibbs steps must not binary-threshold a
+	// Gaussian unit's continuous activation.
+	arma::mat sampleVisible(const arma::mat &visible) const;
+	arma::mat sampleHidden(const arma::mat &hidden) const;
+
 };
 
 #endif /* RBM_HPP */

source/matutils.hpp

@@ -31,7 +31,15 @@ namespace Matutils
 		arma::umat res = (src > uniform(src));
 		return arma::conv_to<arma::mat>::from(res);
 	}
-	
+
+	// src holds a Gaussian unit's mean; returns a stochastic sample around it.
+	// Binary-thresholding a continuous Gaussian activation (via sample() above)
+	// is meaningless, so Gaussian-visible/hidden units need this instead.
+	inline arma::mat sample_gaussian(const arma::mat &src)
+	{
+		return src + arma::randn(arma::size(src));
+	}
+
 	inline arma::mat prob(const arma::mat &src)
 	{
 		return 1 / (1 + (arma::exp(-src)));