From 6ed30e56c416ae42d9068cc853ecf0c8cf9aecf1 Mon Sep 17 00:00:00 2001
From: Michael Mandl <mandlm@molez.net>
Date: Thu, 22 Oct 2015 22:09:35 +0200
Subject: [PATCH] Finished the max-value net (2/3/1 neurons) with 10k learning
 iterations. No good.

---
 Layer.cpp | 12 ++++++------
 Net.cpp   |  5 ++---
 Neuro.cpp | 31 +++++++++++++++++++------------
 3 files changed, 27 insertions(+), 21 deletions(-)

diff --git a/Layer.cpp b/Layer.cpp
index 76d524a..ea15997 100644
--- a/Layer.cpp
+++ b/Layer.cpp
@@ -54,16 +54,16 @@ void Layer::updateInputWeights(Layer & prevLayer)
 {
 	static const double trainingRate = 0.2;
 
-	for (size_t currentLayerIndex = 0; currentLayerIndex < sizeWithoutBiasNeuron(); ++currentLayerIndex)
+	for (size_t targetLayerIndex = 0; targetLayerIndex < sizeWithoutBiasNeuron(); ++targetLayerIndex)
 	{
-		Neuron &targetNeuron = at(currentLayerIndex);
+		const Neuron &targetNeuron = at(targetLayerIndex);
 
-		for (size_t prevLayerIndex = 0; prevLayerIndex < prevLayer.size(); ++prevLayerIndex)
+		for (size_t sourceLayerIndex = 0; sourceLayerIndex < prevLayer.size(); ++sourceLayerIndex)
 		{
-			Neuron &sourceNeuron = prevLayer.at(prevLayerIndex);
+			Neuron &sourceNeuron = prevLayer.at(sourceLayerIndex);
 		
-			sourceNeuron.setOutputWeight(currentLayerIndex, 
-				sourceNeuron.getOutputWeight(currentLayerIndex) + 
+			sourceNeuron.setOutputWeight(targetLayerIndex, 
+				sourceNeuron.getOutputWeight(targetLayerIndex) + 
 				sourceNeuron.getOutputValue() * targetNeuron.getGradient() * trainingRate);
 		}
 	}
diff --git a/Net.cpp b/Net.cpp
index 4e6d68c..50ea0cc 100644
--- a/Net.cpp
+++ b/Net.cpp
@@ -73,11 +73,10 @@ void Net::backProp(const std::vector<double> &targetValues)
 
 	for (unsigned int i = 0; i < numResultValues; ++i)
 	{
-		double delta = resultValues[i] - targetValues[i];
-		rmsError += delta * delta;
+		rmsError += std::pow(resultValues[i] - targetValues[i], 2);
 	}
 
-	rmsError = sqrt(rmsError / numResultValues);
+	rmsError = std::sqrt(rmsError / numResultValues);
 
 	// calculate output neuron gradients 
 	for (unsigned int i = 0; i < numResultValues; ++i)
diff --git a/Neuro.cpp b/Neuro.cpp
index 7f57d99..85ee5c2 100644
--- a/Neuro.cpp
+++ b/Neuro.cpp
@@ -12,25 +12,32 @@ int main()
 
 		Net myNet({ 2, 3, 1 });
 
-		std::vector<double> inputValues =
+		size_t numIterations = 10000;
+		for (size_t iteration = 0; iteration < numIterations; ++iteration)
 		{
-			0.1,
-			0.7,
-		};
+			std::vector<double> inputValues =
+			{
+				std::rand() / (double)RAND_MAX,
+				std::rand() / (double)RAND_MAX
+			};
 
-		std::vector<double> targetValues = { 0.7 };
+			std::vector<double> targetValues =
+			{
+				*std::max_element(inputValues.begin(), inputValues.end())
+			};
 
-		myNet.feedForward(inputValues);
+			myNet.feedForward(inputValues);
 
-		std::vector<double> outputValues = myNet.getOutput();
+			std::vector<double> outputValues = myNet.getOutput();
 
-		double error = outputValues[0] - targetValues[0];
+			double error = outputValues[0] - targetValues[0];
 
-		std::cout << "Error: ";
-		std::cout << std::abs(error);
-		std::cout << std::endl;
+			std::cout << "Error: ";
+			std::cout << std::abs(error);
+			std::cout << std::endl;
 
-		myNet.backProp(targetValues);
+			myNet.backProp(targetValues);
+		}
 	}
 	catch (std::exception &ex)
 	{