From d6ed27f5d2720e193b6258d24ac184d6557ac3ad Mon Sep 17 00:00:00 2001
From: Jens Ahrensfeld <jens@jayfield.org>
Date: Thu, 24 Oct 2019 18:51:45 +0000
Subject: [PATCH] - renamed Params member

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@570 b431acfa-c32f-4a4a-93f1-934dc6c82436
---
 source/Rbm.cpp | 26 +++++++++++++-------------
 source/Rbm.hpp | 36 ++++++++++++++++++------------------
 2 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/source/Rbm.cpp b/source/Rbm.cpp
index d91a3a5..9dbd12c 100644
--- a/source/Rbm.cpp
+++ b/source/Rbm.cpp
@@ -21,9 +21,9 @@ Rbm::Rbm(const Params& params, arma::mat &w, arma::mat &bv, arma::mat &bh)
 , m_bh(bh)
 {
 	Noise_Init(&m_noise, 0x32727155);
-	uniform(m_w, 0.0, m_params.m_weightInit);
-	uniform(m_bh, 0.0, m_params.m_weightInit);
-	uniform(m_bv, 0.0, m_params.m_weightInit);
+	uniform(m_w, 0.0, m_params.weightInit);
+	uniform(m_bh, 0.0, m_params.weightInit);
+	uniform(m_bv, 0.0, m_params.weightInit);
 }
 
 Rbm::Rbm(const Rbm& orig)
@@ -67,8 +67,8 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 		arma::mat miniBatch = batch.rows(batchRowIndex, batchRowIndex+miniBatchSizeActual-1);
 		trainingSizeRemain -= miniBatchSizeActual;
 		batchRowIndex += miniBatchSizeActual;
-		double learning_rate = m_params.m_learningRate/std::min(miniBatchSizeActual, trainingSize);
-		double weight_decay = m_params.m_weightDecay/std::min(miniBatchSizeActual, trainingSize);
+		double learning_rate = m_params.learningRate/std::min(miniBatchSizeActual, trainingSize);
+		double weight_decay = m_params.weightDecay/std::min(miniBatchSizeActual, trainingSize);
 
 		arma::mat vis_state(miniBatchSizeActual, m_w.n_rows);
 		arma::mat vis_probs(miniBatchSizeActual, m_w.n_rows);
@@ -79,7 +79,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 		{
 
 			// Create hidden layer base on training data
-			if (m_params.m_doSampleBatch)
+			if (m_params.doSampleBatch)
 			{
 				// When the hidden units are being driven by data, always use stochastic binary states
 				vis_state = sample(miniBatch);
@@ -93,7 +93,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 			hid_probs = probsLogistic(hid_state);
 
 			// Sample hidden
-			if (m_params.m_doRaoBlackwell)
+			if (m_params.doRaoBlackwell)
 			{
 				hid_state = hid_probs;
 			}
@@ -107,10 +107,10 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 			grad_bias_v = sum(vis_state, 0);
 			grad_bias_h = sum(hid_state, 0);
 
-			for (gibbs=0; gibbs < m_params.m_numGibbs; gibbs++)
+			for (gibbs=0; gibbs < m_params.numGibbs; gibbs++)
 			{
 				// Create visible reconstruction (a fantasy...) given hid
-				if (m_params.m_gibbsDoSampleHidden)
+				if (m_params.gibbsDoSampleHidden)
 				{
 					vis_probs = toVisibleProbs(sample(hid_probs));
 				}
@@ -120,7 +120,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 				}
 
 				// Create hidden representation given v
-				if (m_params.m_gibbsDoSampleVisible)
+				if (m_params.gibbsDoSampleVisible)
 				{
 					hid_state = toHiddenState(sample(vis_probs));
 				}
@@ -141,9 +141,9 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 			
 			status.L1 = accu(abs(m_w));
 			status.L2 = accu(m_w % m_w);
-			momentum_bias_v = m_params.m_momentum*momentum_bias_v + grad_bias_v;
-			momentum_bias_h = m_params.m_momentum*momentum_bias_h + grad_bias_h;
-			momentum_weights = m_params.m_momentum*momentum_weights + grad_weight - status.L2*penalty_weights;
+			momentum_bias_v = m_params.momentum*momentum_bias_v + grad_bias_v;
+			momentum_bias_h = m_params.momentum*momentum_bias_h + grad_bias_h;
+			momentum_weights = m_params.momentum*momentum_weights + grad_weight - status.L2*penalty_weights;
 			
 			m_bv += learning_rate*momentum_bias_v;
 			m_bh += learning_rate*momentum_bias_h;
diff --git a/source/Rbm.hpp b/source/Rbm.hpp
index 6bb61fb..4fabaac 100644
--- a/source/Rbm.hpp
+++ b/source/Rbm.hpp
@@ -24,27 +24,27 @@ public:
 	struct Params
 	{
 		Params()
-		: m_weightInit(0.01)
-		, m_weightDecay(0.001)
-		, m_learningRate(0.1)
-		, m_momentum(0.5)
-		, m_doRaoBlackwell(true)
-		, m_gibbsDoSampleVisible(false)
-		, m_gibbsDoSampleHidden(true)
-		, m_doSampleBatch(false)
-		, m_numGibbs(1)
+		: weightInit(0.01)
+		, weightDecay(0.001)
+		, learningRate(0.1)
+		, momentum(0.5)
+		, doRaoBlackwell(true)
+		, gibbsDoSampleVisible(false)
+		, gibbsDoSampleHidden(true)
+		, doSampleBatch(false)
+		, numGibbs(1)
 		{
 		}
 
-		double m_weightInit;
-		double m_weightDecay;
-		double m_learningRate;
-		double m_momentum;
-		bool m_doRaoBlackwell;
-		bool m_gibbsDoSampleVisible;
-		bool m_gibbsDoSampleHidden;
-		bool m_doSampleBatch;
-		size_t m_numGibbs;
+		double weightInit;
+		double weightDecay;
+		double learningRate;
+		double momentum;
+		bool doRaoBlackwell;
+		bool gibbsDoSampleVisible;
+		bool gibbsDoSampleHidden;
+		bool doSampleBatch;
+		size_t numGibbs;
 	};
 
 	struct Status