[bugfix] - remove dead cd_jens; fix cd_gaussian_binary dbv; fix cd_binary_gaussian Gibbs sampling

- Remove cd_jens: dead code never called, contained multiple bugs.
- cd_gaussian_binary: dbv formula simplified — b_v terms cancelled between
  positive and negative phases, making the subtraction redundant.
- cd_binary_gaussian: Gibbs loop (CD-k > 1) now properly samples both visible
  (sample(prob(...))) and hidden (+ sample_gaussian) states when
  do_rao_blackwell=False. Previously used means throughout, giving biased
  gradient estimates for k > 1.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/rbm/train.py

@@ -2,46 +2,6 @@ from .matrix import sample, sample_gaussian, prob, rms_error_accu, Mat, np
 from .entity import Entity
 from .status import Status
 
-def cd_jens(entity: Entity, v_states: Mat):
-	params = entity.training_params
-	v_probs = prob(v_states)
-	h_states = entity.forward(v_states)
-	h_probs = h_states
-
-	if entity.params.do_gaussian_hidden:
-		h_states += sample_gaussian(h_states)
-	else:
-		h_probs = entity.forward(v_states)
-		if params.do_rao_blackwell:
-			h_states = h_probs
-		else:
-			h_states = sample(h_probs)
-
-	# Update weights (positive phase)
-	dw = np.dot(np.transpose(v_states), h_states)
-	dbv = np.sum(v_states, 0)
-	dbh = np.sum(h_states, 0)
-
-	# Gibbs	sampling with training params
-	for i in range(params.num_gibbs_samples):
-		if params.do_gibbs_sample_hidden:
-			v_probs = entity.reconstruct(sample(h_probs))
-		else:
-			v_probs = entity.reconstruct(h_probs)
-
-		# Create hidden representation given v
-		if params.do_gibbs_sample_visible:
-			h_probs = entity.forward(sample(v_probs))
-		else:
-			h_probs = entity.forward(v_probs)
-
-	# Update weights (negative phase)
-	dw -= np.dot(np.transpose(v_probs), h_probs)
-	dbv -= np.sum(v_probs, 0)
-	dbh -= np.sum(h_probs, 0)
-
-	return dw, dbv, dbh
-
 # %%%%%%%%% START POSITIVE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #   data = batchdata(:,:,batch);
 #   poshidprobs = 1./(1 + exp(-data*vishid - repmat(hidbiases,numcases,1)));
@@ -99,7 +59,7 @@ def cd_gaussian_binary(entity: Entity, data_pos: Mat):
 	# Update weights (positive phase)
 	dw = np.dot(np.transpose(data_pos), h_probs_pos)
 	dbh = np.sum(h_probs_pos, 0)
-	dbv = np.sum(data_pos-entity.state.b_v, 0)
+	dbv = np.sum(data_pos, 0)
 
 	# Negative phase — sample binary hidden states to get a sharper fantasy particle
 	data_neg = entity.v_given_h(sample(h_probs_pos))
@@ -108,7 +68,7 @@ def cd_gaussian_binary(entity: Entity, data_pos: Mat):
 	# Update weights (negative phase)
 	dw -= np.dot(np.transpose(data_neg), h_probs_neg)
 	dbh -= np.sum(h_probs_neg, 0)
-	dbv -= np.sum(data_neg-entity.state.b_v, 0)
+	dbv -= np.sum(data_neg, 0)
 
 	return dw, dbv, dbh
 
@@ -169,10 +129,13 @@ def cd_binary_gaussian(entity: Entity, data_pos: Mat):
 	data_neg = prob(entity.v_given_h(h_probs_pos))
 	h_probs_neg = entity.h_given_v(data_neg)
 
-	# Gibbs	sampling
+	# Gibbs sampling — sample both visible and hidden to continue the Markov chain
 	for _ in range(params.num_gibbs_samples-1):
-		data_neg = prob(entity.v_given_h(h_probs_neg))
+		v_probs     = prob(entity.v_given_h(h_probs_neg))
+		data_neg    = v_probs if params.do_rao_blackwell else sample(v_probs)
 		h_probs_neg = entity.h_given_v(data_neg)
+		if not params.do_rao_blackwell:
+			h_probs_neg = h_probs_neg + sample_gaussian(h_probs_neg)
 
 	# Update weights (negative phase)
 	dw -= np.dot(np.transpose(data_neg), h_probs_neg)