- numGibbs no longer part of EntityParameter

- refactored forward and reconstruct
- conditionally use optimizer for training

src/tests/test_xor.py

@@ -2,11 +2,13 @@ import os.path
 
 from rbm.layer import Layer
 from rbm.status import Status
-from rbm.train import train, TrainingParams
+from rbm.train import train, TrainingParams, Optimizer
 from rbm.matrix import Mat, np
 from rbm.entity import EntityParams
 
-work_dir = "../../results"
+WORK_DIR = "../../results"
+USE_OPTIMIZER = True
+
 def xor():
 	# Create params
 	entity_params = EntityParams()
@@ -21,16 +23,20 @@ def xor():
 	layer.init(0.01)
 
 	# Load weights (if exists)
-	layer.load(os.path.join(work_dir, "xor_layer0_state.npz"))
+	layer.load(os.path.join(WORK_DIR, "xor_layer0_state.npz"))
 
 	# Prepare training data
 	training_batch = Mat([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
 
 	# Train layer
-	train(layer.entity, training_batch, training_params, Status())
+	if USE_OPTIMIZER:
+		optim = Optimizer(layer.entity, training_params)
+		optim(training_batch, Status())
+	else:
+		train(layer.entity, training_batch, training_params, Status())
 
 	# Save weights
-	layer.save(os.path.join(work_dir, "xor_layer0_state.npz"))
+	layer.save(os.path.join(WORK_DIR, "xor_layer0_state.npz"))
 
 	# Test with test data
 	test_batch = Mat([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)