diff --git a/EasyCL b/EasyCL
index b9023cdf..ef17a3e1 160000
--- a/EasyCL
+++ b/EasyCL
@@ -1 +1 @@
-Subproject commit b9023cdf25c10524ad06227275d3756bbe0c0ed2
+Subproject commit ef17a3e1b3d70b7ba89f94682500bc2d95ecb273
diff --git a/clMathLibraries/clBLAS b/clMathLibraries/clBLAS
index 34f83bf5..6df2f999 160000
--- a/clMathLibraries/clBLAS
+++ b/clMathLibraries/clBLAS
@@ -1 +1 @@
-Subproject commit 34f83bf5a4a71bbcb23a68f449bcde5bdc08baa7
+Subproject commit 6df2f999a80789efee3376e8fffeb26d891b464c
diff --git a/prototyping/clconvolve-fixedweights.cpp b/prototyping/clconvolve-fixedweights.cpp
index c989378f..433ddf64 100644
--- a/prototyping/clconvolve-fixedweights.cpp
+++ b/prototyping/clconvolve-fixedweights.cpp
@@ -57,11 +57,11 @@ class Config {
     // [[[cog
     // cog.outl('// generated using cog:')
     // for astring in strings:
-    //    cog.outl( 'string ' + astring + ';')
+    //    cog.outl('string ' + astring + ';')
     // for anint in ints:
-    //    cog.outl( 'int ' + anint + ';')
+    //    cog.outl('int ' + anint + ';')
     // for name in floats:
-    //    cog.outl( 'float ' + name + ';')
+    //    cog.outl('float ' + name + ';')
     // ]]]
     // generated using cog:
     string dataDir;
@@ -84,11 +84,11 @@ class Config {
         // [[[cog
         // cog.outl('// generated using cog:')
         // for astring in strings:
-        //    cog.outl( astring + ' = "";')
+        //    cog.outl(astring + ' = "";')
         // for anint in ints:
-        //    cog.outl( anint + ' = 0;')
+        //    cog.outl(anint + ' = 0;')
         // for name in floats:
-        //    cog.outl( name + ' = 0.0f;')
+        //    cog.outl(name + ' = 0.0f;')
         // ]]]
         // generated using cog:
         dataDir = "";
@@ -128,15 +128,15 @@ class Config {
     }
 };
 
-void sampleWeights( NeuralNet *net ) {
-    for( int layerId = 0; layerId < net->getNumLayers();  layerId++ ) {
+void sampleWeights(NeuralNet *net) {
+    for(int layerId = 0; layerId < net->getNumLayers();  layerId++) {
         Layer *layer = net->getLayer(layerId);
-        FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >( layer );
-        ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >( layer );
-        if( fc != 0 ) {
+        FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >(layer);
+        ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >(layer);
+        if(fc != 0) {
             conv = fc->convolutionalLayer;
         }
-        if( conv == 0 ) {
+        if(conv == 0) {
             continue;
         }
 
@@ -149,11 +149,11 @@ void sampleWeights( NeuralNet *net ) {
         int filterSize = dim.filterSize;
 
         initrand.seed(0);        
-        for( int i = 0; i < 10; i++ ) {
-            int thisrand = abs( (int)initrand() );
-            int seq = thisrand % ( numFilters * inputPlanes * filterSize * filterSize );
-            int planefilter = seq / ( filterSize * filterSize );
-            int rowcol = seq % ( filterSize * filterSize );
+        for(int i = 0; i < 10; i++) {
+            int thisrand = abs((int)initrand());
+            int seq = thisrand % (numFilters * inputPlanes * filterSize * filterSize);
+            int planefilter = seq / (filterSize * filterSize);
+            int rowcol = seq % (filterSize * filterSize);
             int filter = planefilter / inputPlanes;
             int inputPlane = planefilter % inputPlanes;
             int row = rowcol / filterSize;
@@ -180,24 +180,24 @@ void go(Config config) {
     int testAllocateN = 0;
 
 //    int totalLinearSize;
-    GenericLoader::getDimensions((config.dataDir + "/" + config.trainFile).c_str(), &Ntrain, &numPlanes, &imageSize );
+    GenericLoader::getDimensions((config.dataDir + "/" + config.trainFile).c_str(), &Ntrain, &numPlanes, &imageSize);
     Ntrain = config.numTrain == -1 ? Ntrain : config.numTrain;
 //    long allocateSize = (long)Ntrain * numPlanes * imageSize * imageSize;
     cout << "Ntrain " << Ntrain << " numPlanes " << numPlanes << " imageSize " << imageSize << endl;
     trainAllocateN = Ntrain;
     trainData = new float[ (long)trainAllocateN * numPlanes * imageSize * imageSize ];
     trainLabels = new int[trainAllocateN];
-    if( Ntrain > 0 ) {
-        GenericLoader::load((config.dataDir + "/" + config.trainFile).c_str(), trainData, trainLabels, 0, Ntrain );
+    if(Ntrain > 0) {
+        GenericLoader::load((config.dataDir + "/" + config.trainFile).c_str(), trainData, trainLabels, 0, Ntrain);
     }
 
-    GenericLoader::getDimensions((config.dataDir + "/" + config.validateFile).c_str(), &Ntest, &numPlanes, &imageSize );
+    GenericLoader::getDimensions((config.dataDir + "/" + config.validateFile).c_str(), &Ntest, &numPlanes, &imageSize);
     Ntest = config.numTest == -1 ? Ntest : config.numTest;
     testAllocateN = Ntest;
     testData = new float[ (long)testAllocateN * numPlanes * imageSize * imageSize ];
     testLabels = new int[testAllocateN]; 
-    if( Ntest > 0 ) {
-        GenericLoader::load((config.dataDir + "/" + config.validateFile).c_str(), testData, testLabels, 0, Ntest );
+    if(Ntest > 0) {
+        GenericLoader::load((config.dataDir + "/" + config.validateFile).c_str(), testData, testLabels, 0, Ntest);
     }
     
     timer.timeCheck("after load images");
@@ -206,15 +206,15 @@ void go(Config config) {
     float translate;
     float scale;
     int normalizationExamples = config.normalizationExamples > Ntrain ? Ntrain : config.normalizationExamples;
-    if( config.normalization == "stddev" ) {
+    if(config.normalization == "stddev") {
         float mean, stdDev;
-        NormalizationHelper::getMeanAndStdDev( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev );
+        NormalizationHelper::getMeanAndStdDev(trainData, normalizationExamples * inputCubeSize, &mean, &stdDev);
         cout << " image stats mean " << mean << " stdDev " << stdDev << endl;
         translate = - mean;
         scale = 1.0f / stdDev / config.normalizationNumStds;
-    } else if( config.normalization == "maxmin" ) {
+    } else if(config.normalization == "maxmin") {
         float mean, stdDev;
-        NormalizationHelper::getMinMax( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev );
+        NormalizationHelper::getMinMax(trainData, normalizationExamples * inputCubeSize, &mean, &stdDev);
         translate = - mean;
         scale = 1.0f / stdDev;
     } else {
@@ -228,48 +228,48 @@ void go(Config config) {
 //    const int numToTrain = Ntrain;
 //    const int batchSize = config.batchSize;
     EasyCL *cl = new EasyCL();
-    NeuralNet *net = new NeuralNet( cl );
+    NeuralNet *net = new NeuralNet(cl);
 //    net->inputMaker<unsigned char>()->numPlanes(numPlanes)->imageSize(imageSize)->insert();
-    net->addLayer( InputLayerMaker::instance()->numPlanes(numPlanes)->imageSize(imageSize) );
-    net->addLayer( NormalizationLayerMaker::instance()->translate(translate)->scale(scale) );
-    if( !NetdefToNet::createNetFromNetdef( net, config.netDef ) ) {
+    net->addLayer(InputLayerMaker::instance()->numPlanes(numPlanes)->imageSize(imageSize));
+    net->addLayer(NormalizationLayerMaker::instance()->translate(translate)->scale(scale));
+    if(!NetdefToNet::createNetFromNetdef(net, config.netDef)) {
         return;
     }
     net->print();
-    for( int i = 1; i < net->getNumLayers() - 1; i++ ) {
+    for(int i = 1; i < net->getNumLayers() - 1; i++) {
         Layer *layer = net->getLayer(i);
         FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >(layer);
         ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >(layer);
-        if( fc != 0 ) {
+        if(fc != 0) {
             conv = fc->convolutionalLayer;
         }
-        if( conv == 0 ) {
+        if(conv == 0) {
             continue;
         }
         initrand.seed(0);
         int weightsSize = conv->getWeightsSize();
     //int weightsSize = layer->getPersistSize();
-        if( weightsSize > 0 ) {
+        if(weightsSize > 0) {
             cout << "weightsSize " << weightsSize << endl;
             float *weights = new float[weightsSize];
-            for( int j = 0; j < weightsSize; j++ ) {
+            for(int j = 0; j < weightsSize; j++) {
                 int thisrand = (int)initrand();
-                float thisweight = ( thisrand % 100000 ) / 1000000.0f;
+                float thisweight = (thisrand % 100000) / 1000000.0f;
                 weights[j] = thisweight;
             }        
-            conv->initWeights( weights );
+            conv->initWeights(weights);
         }
-        if( conv->dim.biased ) {
+        if(conv->dim.biased) {
             initrand.seed(0);
             int biasedSize = conv->getBiasSize();
             float *biasWeights = new float[biasedSize];
-            for( int j = 0; j < biasedSize; j++ ) {
+            for(int j = 0; j < biasedSize; j++) {
                 int thisrand = (int)initrand();
-                float thisweight = ( thisrand % 100000 ) / 1000000.0f;
+                float thisweight = (thisrand % 100000) / 1000000.0f;
                 biasWeights[j] = thisweight;
                 //biasWeights[j] = 0;
             }        
-            conv->initBias( biasWeights );
+            conv->initBias(biasWeights);
         }
     }
 
@@ -284,45 +284,45 @@ void go(Config config) {
 //    float restartLoss = 0;
 
     timer.timeCheck("before learning start");
-    if( config.dumpTimings ) {
-        StatefulTimer::dump( true );
+    if(config.dumpTimings) {
+        StatefulTimer::dump(true);
     }
     StatefulTimer::timeCheck("START");
 
-    SGD *sgd = SGD::instance( cl, config.learningRate, 0.0f );
+    SGD *sgd = SGD::instance(cl, config.learningRate, 0.0f);
     Trainable *trainable = net;
-    NetLearner netLearner( 
+    NetLearner netLearner(
         sgd, trainable,
         Ntrain, trainData, trainLabels,
         Ntest, testData, testLabels,
-        config.batchSize );
-    netLearner.setSchedule( config.numEpochs, afterRestart ? restartEpoch : 1 );
-//    netLearner.setBatchSize( config.batchSize );
-    netLearner.setDumpTimings( config.dumpTimings );  
-//    netLearner.learn( config.learningRate, 1.0f );
+        config.batchSize);
+    netLearner.setSchedule(config.numEpochs, afterRestart ? restartEpoch : 1);
+//    netLearner.setBatchSize(config.batchSize);
+    netLearner.setDumpTimings(config.dumpTimings);
+//    netLearner.learn(config.learningRate, 1.0f);
 
     cout << "forward output" << endl;
-    for( int layerId = 0; layerId < net->getNumLayers(); layerId++ ) {
+    for(int layerId = 0; layerId < net->getNumLayers(); layerId++) {
         Layer *layer = net->getLayer(layerId);
-        FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >( layer );
-        ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >( layer );
-        PoolingLayer *pool = dynamic_cast< PoolingLayer * >( layer );
-        SoftMaxLayer *softMax = dynamic_cast< SoftMaxLayer * >( layer );
-        if( fc != 0 ) {
+        FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >(layer);
+        ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >(layer);
+        PoolingLayer *pool = dynamic_cast< PoolingLayer * >(layer);
+        SoftMaxLayer *softMax = dynamic_cast< SoftMaxLayer * >(layer);
+        if(fc != 0) {
             conv = fc->convolutionalLayer;
         }
         int planes = 0;
         int imageSize = 0;
-        if( conv != 0 ) {
+        if(conv != 0) {
             cout << "convolutional (or conv based, ie fc)" << endl;
             planes = conv->dim.numFilters;
             imageSize = conv->dim.outputSize;
           //  continue;
-        } else if( pool != 0 ) {
+        } else if(pool != 0) {
             cout << "pooling" << endl;
             planes = pool->numPlanes;
             imageSize = pool->outputSize;
-        } else if( softMax != 0 ) {
+        } else if(softMax != 0) {
             cout << "softmax" << endl;
             planes = softMax->numPlanes;
             imageSize = softMax->imageSize;
@@ -332,16 +332,16 @@ void go(Config config) {
         cout << "layer " << layerId << endl;
 //        conv->getOutput();
         float const*output = layer->getOutput();
-//        for( int i = 0; i < 3; i++ ) {
+//        for(int i = 0; i < 3; i++) {
 //            cout << conv->getOutput()[i] << endl;
 //        }
         initrand.seed(0);
 //        LayerDimensions &dim = conv->dim;
-        for( int i = 0; i < 10; i++ ) {
-            int thisrand = abs( (int)initrand() );
-            int seq = thisrand % ( planes * imageSize * imageSize );
-            int outPlane = seq / ( imageSize * imageSize );
-            int rowcol = seq % ( imageSize * imageSize );
+        for(int i = 0; i < 10; i++) {
+            int thisrand = abs((int)initrand());
+            int seq = thisrand % (planes * imageSize * imageSize);
+            int outPlane = seq / (imageSize * imageSize);
+            int rowcol = seq % (imageSize * imageSize);
             int row = rowcol / imageSize;
             int col = rowcol % imageSize;
             cout << "out[" << outPlane << "," << row << "," << col << "]=" << output[ seq ] << endl;
@@ -352,14 +352,14 @@ void go(Config config) {
     sampleWeights(net);
 
     cout << "backprop output" << endl;
-    for( int layerId = net->getNumLayers() - 1; layerId >= 0; layerId-- ) {
+    for(int layerId = net->getNumLayers() - 1; layerId >= 0; layerId--) {
         Layer *layer = net->getLayer(layerId);
-        FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >( layer );
-        ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >( layer );
-        if( fc != 0 ) {
+        FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >(layer);
+        ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >(layer);
+        if(fc != 0) {
             conv = fc->convolutionalLayer;
         }
-        if( conv == 0 ) {
+        if(conv == 0) {
             continue;
         }
 
@@ -367,10 +367,10 @@ void go(Config config) {
         float const*weights = conv->getWeights();
         float const*biases = conv->getBias();
         int weightsSize = conv->getWeightsSize() / conv->dim.numFilters;
-        for( int i = 0; i < weightsSize; i++ ) {
+        for(int i = 0; i < weightsSize; i++) {
             cout << " weight " << i << " " << weights[i] << endl;
         }
-        for( int i = 0; i < 3; i++ ) {
+        for(int i = 0; i < 3; i++) {
             cout << " bias " << i << " " << biases[i] << endl;
         }
     }
@@ -380,32 +380,32 @@ void go(Config config) {
     delete net;
     delete cl;
 
-    if( trainData != 0 ) {
+    if(trainData != 0) {
         delete[] trainData;
     }
-    if( testData != 0 ) {
+    if(testData != 0) {
         delete[] testData;
     }
-    if( testLabels != 0 ) {
+    if(testLabels != 0) {
         delete[] testLabels;
     }
-    if( trainLabels != 0 ) {
+    if(trainLabels != 0) {
         delete[] trainLabels;
     }
 }
 
-void printUsage( char *argv[], Config config ) {
+void printUsage(char *argv[], Config config) {
     cout << "Usage: " << argv[0] << " [key]=[value] [[key]=[value]] ..." << endl;
     cout << endl;
     cout << "Possible key=value pairs:" << endl;
     // [[[cog
     // cog.outl('// generated using cog:')
     // for name in strings:
-    //    cog.outl( 'cout << "    ' + name.lower() + '=[' + descriptions[name.lower()] + '] (" << config.' + name + ' << ")" << endl;')
+    //    cog.outl('cout << "    ' + name.lower() + '=[' + descriptions[name.lower()] + '] (" << config.' + name + ' << ")" << endl;')
     // for name in ints:
-    //    cog.outl( 'cout << "    ' + name.lower() + '=[' + descriptions[name.lower()] + '] (" << config.' + name + ' << ")" << endl;')
+    //    cog.outl('cout << "    ' + name.lower() + '=[' + descriptions[name.lower()] + '] (" << config.' + name + ' << ")" << endl;')
     // for name in floats:
-    //    cog.outl( 'cout << "    ' + name.lower() + '=[' + descriptions[name.lower()] + '] (" << config.' + name + ' << ")" << endl;')
+    //    cog.outl('cout << "    ' + name.lower() + '=[' + descriptions[name.lower()] + '] (" << config.' + name + ' << ")" << endl;')
     // ]]]
     // generated using cog:
     cout << "    datadir=[directory to search for train and validate files] (" << config.dataDir << ")" << endl;
@@ -425,14 +425,14 @@ void printUsage( char *argv[], Config config ) {
     // [[[end]]]
 }
 
-int main( int argc, char *argv[] ) {
+int main(int argc, char *argv[]) {
     Config config;
-    if( argc == 2 && ( string(argv[1]) == "--help" || string(argv[1]) == "--?" || string(argv[1]) == "-?" || string(argv[1]) == "-h" ) ) {
-        printUsage( argv, config );
+    if(argc == 2 && (string(argv[1]) == "--help" || string(argv[1]) == "--?" || string(argv[1]) == "-?" || string(argv[1]) == "-h")) {
+        printUsage(argv, config);
     } 
-    for( int i = 1; i < argc; i++ ) {
-        vector<string> splitkeyval = split( argv[i], "=" );
-        if( splitkeyval.size() != 2 ) {
+    for(int i = 1; i < argc; i++) {
+        vector<string> splitkeyval = split(argv[i], "=");
+        if(splitkeyval.size() != 2) {
           cout << "Usage: " << argv[0] << " [key]=[value] [[key]=[value]] ..." << endl;
           exit(1);
         } else {
@@ -441,69 +441,69 @@ int main( int argc, char *argv[] ) {
 //            cout << "key [" << key << "]" << endl;
             // [[[cog
             // cog.outl('// generated using cog:')
-            // cog.outl('if( false ) {')
+            // cog.outl('if(false) {')
             // for name in strings:
-            //    cog.outl( '} else if( key == "' + name.lower() + '" ) {')
-            //    cog.outl( '    config.' + name + ' = value;')
+            //    cog.outl('} else if(key == "' + name.lower() + '") {')
+            //    cog.outl('    config.' + name + ' = value;')
             // for name in ints:
-            //    cog.outl( '} else if( key == "' + name.lower() + '" ) {')
-            //    cog.outl( '    config.' + name + ' = atoi( value );')
+            //    cog.outl('} else if(key == "' + name.lower() + '") {')
+            //    cog.outl('    config.' + name + ' = atoi(value);')
             // for name in floats:
-            //    cog.outl( '} else if( key == "' + name.lower() + '" ) {')
-            //    cog.outl( '    config.' + name + ' = atof( value );')
+            //    cog.outl('} else if(key == "' + name.lower() + '") {')
+            //    cog.outl('    config.' + name + ' = atof(value);')
             // ]]]
             // generated using cog:
-            if( false ) {
-            } else if( key == "datadir" ) {
+            if(false) {
+            } else if(key == "datadir") {
                 config.dataDir = value;
-            } else if( key == "trainfile" ) {
+            } else if(key == "trainfile") {
                 config.trainFile = value;
-            } else if( key == "validatefile" ) {
+            } else if(key == "validatefile") {
                 config.validateFile = value;
-            } else if( key == "netdef" ) {
+            } else if(key == "netdef") {
                 config.netDef = value;
-            } else if( key == "normalization" ) {
+            } else if(key == "normalization") {
                 config.normalization = value;
-            } else if( key == "dataset" ) {
+            } else if(key == "dataset") {
                 config.dataset = value;
-            } else if( key == "numtrain" ) {
-                config.numTrain = atoi( value );
-            } else if( key == "numtest" ) {
-                config.numTest = atoi( value );
-            } else if( key == "batchsize" ) {
-                config.batchSize = atoi( value );
-            } else if( key == "numepochs" ) {
-                config.numEpochs = atoi( value );
-            } else if( key == "dumptimings" ) {
-                config.dumpTimings = atoi( value );
-            } else if( key == "normalizationexamples" ) {
-                config.normalizationExamples = atoi( value );
-            } else if( key == "learningrate" ) {
-                config.learningRate = atof( value );
-            } else if( key == "normalizationnumstds" ) {
-                config.normalizationNumStds = atof( value );
+            } else if(key == "numtrain") {
+                config.numTrain = atoi(value);
+            } else if(key == "numtest") {
+                config.numTest = atoi(value);
+            } else if(key == "batchsize") {
+                config.batchSize = atoi(value);
+            } else if(key == "numepochs") {
+                config.numEpochs = atoi(value);
+            } else if(key == "dumptimings") {
+                config.dumpTimings = atoi(value);
+            } else if(key == "normalizationexamples") {
+                config.normalizationExamples = atoi(value);
+            } else if(key == "learningrate") {
+                config.learningRate = atof(value);
+            } else if(key == "normalizationnumstds") {
+                config.normalizationNumStds = atof(value);
             // [[[end]]]
             } else {
                 cout << endl;
                 cout << "Error: key '" << key << "' not recognised" << endl;
                 cout << endl;
-                printUsage( argv, config );
+                printUsage(argv, config);
                 cout << endl;
                 return -1;
             }
         }
     }
-    string dataset = toLower( config.dataset );
-    if( dataset != "" ) {
-        if( dataset == "mnist" ) {
+    string dataset = toLower(config.dataset);
+    if(dataset != "") {
+        if(dataset == "mnist") {
             config.dataDir = "../data/mnist";
             config.trainFile = "train-dat.mat";
             config.validateFile = "t10k-dat.mat";
-        } else if( dataset == "norb" ) {
+        } else if(dataset == "norb") {
             config.dataDir = "../data/norb";
             config.trainFile = "training-shuffled-dat.mat";
             config.validateFile = "testing-sampled-dat.mat";
-        } else if( dataset == "cifar10" ) {
+        } else if(dataset == "cifar10") {
             config.dataDir = "../data/cifar10";
             config.trainFile = "train-dat.mat";
             config.validateFile = "test-dat.mat";
@@ -517,8 +517,8 @@ int main( int argc, char *argv[] ) {
         cout << "   validatefile: " << config.validateFile << ":" << endl;
     }
     try {
-        go( config );
-    } catch( runtime_error e ) {
+        go(config);
+    } catch(runtime_error e) {
         cout << "Something went wrong: " << e.what() << endl;
         return -1;
     }
diff --git a/prototyping/qlearning/ScenarioImage.cpp b/prototyping/qlearning/ScenarioImage.cpp
index ba31291d..a30f9839 100644
--- a/prototyping/qlearning/ScenarioImage.cpp
+++ b/prototyping/qlearning/ScenarioImage.cpp
@@ -15,7 +15,7 @@ using namespace std;
 #undef VIRTUAL
 #define VIRTUAL
 
-ScenarioImage::ScenarioImage( int size, bool appleMoves ) :
+ScenarioImage::ScenarioImage(int size, bool appleMoves) :
         net(0), // this is simply used by the showQRepresentation method
         size(size),
         appleMoves(appleMoves) {
@@ -31,7 +31,7 @@ ScenarioImage::ScenarioImage( int size, bool appleMoves ) :
     reset();
     print();
 }
-void ScenarioImage::setNet( NeuralNet *net ) {
+void ScenarioImage::setNet(NeuralNet *net) {
     this->net = net;
 }
 void ScenarioImage::printQRepresentation() {
@@ -39,29 +39,29 @@ void ScenarioImage::printQRepresentation() {
     cout << "q directions:" << endl;
     int size = scenario->getPerceptionSize();
     float *input = new float[ size * size * 2 ];
-    arrayZero( input, size * size * 2 );
+    arrayZero(input, size * size * 2);
     input[ scenario->appleY * size + scenario->appleX ] = 1;
-    for( int y = 0; y < size; y++ ) {
+    for(int y = 0; y < size; y++) {
         string thisLine = "";
-        for( int x = 0; x < size; x++ ) {
+        for(int x = 0; x < size; x++) {
             float highestQ = 0;
             int bestAction = 0;
             input[ size * size + y * size + x ] = 1;
-            net->forward( input );
+            net->forward(input);
             input[ size * size + y * size + x ] = 0;
             float const*output = net->getOutput();
-            for( int action = 0; action < 4; action++ ) {
+            for(int action = 0; action < 4; action++) {
                 float thisQ = output[action];
-                if( action == 0 || thisQ > highestQ ) {
+                if(action == 0 || thisQ > highestQ) {
                     highestQ = thisQ;
                     bestAction = action;
                 }
             }
-            if( bestAction == 0 ) {
+            if(bestAction == 0) {
                 thisLine += ">";
-            } else if( bestAction == 1 ) {  
+            } else if(bestAction == 1) {
                 thisLine += "<";
-            } else if( bestAction == 2 ) {
+            } else if(bestAction == 2) {
                 thisLine += "V";
             } else {
                 thisLine += "^";
@@ -73,12 +73,12 @@ void ScenarioImage::printQRepresentation() {
 }
 
 VIRTUAL void ScenarioImage::print() {
-    for( int y = 0; y < size; y++ ) {
+    for(int y = 0; y < size; y++) {
         string line = "";
-        for( int x = 0; x < size; x++ ) {
-            if( x == posX && y == posY ) {
+        for(int x = 0; x < size; x++) {
+            if(x == posX && y == posY) {
                 line += "X";
-            } else if( x == appleX && y == appleY ) {
+            } else if(x == appleX && y == appleY) {
                 line += "O";
             } else {
                 line += ".";
@@ -92,11 +92,11 @@ VIRTUAL ScenarioImage::~ScenarioImage() {
 VIRTUAL int ScenarioImage::getNumActions() {
     return 4;
 }
-VIRTUAL float ScenarioImage::act( int index ) { // returns reward
+VIRTUAL float ScenarioImage::act(int index) { // returns reward
     numMoves++;
     int dx = 0;
     int dy = 0;
-    switch( index ) {
+    switch(index) {
         case 0:
             dx = 1;
             break;
@@ -112,10 +112,10 @@ VIRTUAL float ScenarioImage::act( int index ) { // returns reward
     }
     int newX = posX + dx;
     int newY = posY + dy;
-    if( newX < 0 || newX >= size || newY < 0 || newY >= size ) {
+    if(newX < 0 || newX >= size || newY < 0 || newY >= size) {
         return -0.5f;
     }
-    if( newX == appleX && newY == appleY ) {
+    if(newX == appleX && newY == appleY) {
         finished = true;
         posX = newX;
         posY = newY;
@@ -135,8 +135,8 @@ VIRTUAL int ScenarioImage::getPerceptionSize() {
 VIRTUAL int ScenarioImage::getPerceptionPlanes() {
     return 2;
 }
-VIRTUAL void ScenarioImage::getPerception( float *perception ) {
-    for( int i = 0; i < size * size * 2; i++ ) {
+VIRTUAL void ScenarioImage::getPerception(float *perception) {
+    for(int i = 0; i < size * size * 2; i++) {
         perception[i] = 0;
     }
     perception[appleY * size + appleX] = 1;
@@ -146,12 +146,12 @@ VIRTUAL int ScenarioImage::getWorldSize() {
     return size;
 }
 VIRTUAL void ScenarioImage::reset() {
-    if( net != 0 ) {
+    if(net != 0) {
         this->print();
         this->printQRepresentation();
         cout << "game: " << game << " moves: " << numMoves << endl;
     }
-    if( appleMoves ) {
+    if(appleMoves) {
         appleX = myrand() % size;
         appleY = myrand() % size;
     } else {
@@ -159,7 +159,7 @@ VIRTUAL void ScenarioImage::reset() {
     }
     finished = false;
     bool sampledOnce = false;
-    while( !sampledOnce || ( posX == appleX && posY == appleY ) ) {
+    while(!sampledOnce || (posX == appleX && posY == appleY)) {
         posX = myrand() % size;
         posY = myrand() % size;
         sampledOnce = true;
diff --git a/test/testGenericLoader.cpp b/test/testGenericLoader.cpp
index d6f0e28d..204aeda5 100644
--- a/test/testGenericLoader.cpp
+++ b/test/testGenericLoader.cpp
@@ -14,42 +14,42 @@
 
 using namespace std;
 
-void go( string trainFilepath, int startN, int numExamples ) {
+void go(string trainFilepath, int startN, int numExamples) {
     int N;
     int numPlanes;
     int imageSize;
 //    int totalSize;
-    GenericLoader::getDimensions(trainFilepath.c_str(), &N, &numPlanes, &imageSize );
+    GenericLoader::getDimensions(trainFilepath.c_str(), &N, &numPlanes, &imageSize);
     cout << "N " << N << " numplanes " << numPlanes << " imageSize " << imageSize << endl;
     float *images = new float[ numExamples * numPlanes * imageSize * imageSize ];
     int *labels = new int[ numExamples ];
-    GenericLoader::load(trainFilepath.c_str(), images, labels, startN, numExamples );
+    GenericLoader::load(trainFilepath.c_str(), images, labels, startN, numExamples);
 //    float *images = new float[ N * numPlanes * imageSize * imageSize ];
-//    for( int i = 0; i < N * numPlanes * imageSize * imageSize; i++ ) {
+//    for(int i = 0; i < N * numPlanes * imageSize * imageSize; i++) {
 //        images[i] = imagesUchar[i];
 //    }
     float thismin;
     float thismax;
-    NormalizationHelper::getMinMax( images, numExamples * numPlanes * imageSize * imageSize, &thismin, &thismax );
+    NormalizationHelper::getMinMax(images, numExamples * numPlanes * imageSize * imageSize, &thismin, &thismax);
     cout << "min: " << thismin << " max: " << thismax << endl;
-    ImagePng::writeImagesToPng( "testGenericLoader.png", images, numExamples * numPlanes, imageSize );
-    for( int i = 0; i < numExamples; i++ ) {
+    ImagePng::writeImagesToPng("testGenericLoader.png", images, numExamples * numPlanes, imageSize);
+    for(int i = 0; i < numExamples; i++) {
         cout << "labels[" << i << "]=" << labels[i] << endl;
     }
 //    float *translated = new float[N * numPlanes * imageSize * imageSize];
-//    Translator::translate( n, numPlanes, imageSize, translateRows, translateCols, images, translated );
-//    ImagePng::writeImagesToPng( "testTranslator-2.png", translated + n * numPlanes * imageSize * imageSize, numPlanes, imageSize );
+//    Translator::translate(n, numPlanes, imageSize, translateRows, translateCols, images, translated);
+//    ImagePng::writeImagesToPng("testTranslator-2.png", translated + n * numPlanes * imageSize * imageSize, numPlanes, imageSize);
 }
 
-int main( int argc, char *argv[] ) {
-    if( argc != 4 ) {
+int main(int argc, char *argv[]) {
+    if(argc != 4) {
         cout << "Usage: [trainfilepath] [startn] [numexamples]" << endl;
         return -1;
     }
-    string trainFilepath = string( argv[1] );
-    int startN = atoi( argv[2] );
-    int numExamples = atoi( argv[3] );
-    go( trainFilepath, startN, numExamples );
+    string trainFilepath = string(argv[1]);
+    int startN = atoi(argv[2]);
+    int numExamples = atoi(argv[3]);
+    go(trainFilepath, startN, numExamples);
     return 0;
 }
 
diff --git a/test/testPatchExtractor.cpp b/test/testPatchExtractor.cpp
index 75307ebb..e75a8c07 100644
--- a/test/testPatchExtractor.cpp
+++ b/test/testPatchExtractor.cpp
@@ -7,35 +7,35 @@
 
 using namespace std;
 
-void go( string dataDir, string setName, int n, int patchSize, int patchRow, int patchCol ) {
+void go(string dataDir, string setName, int n, int patchSize, int patchRow, int patchCol) {
     int N;
     int numPlanes;
     int imageSize;
-    unsigned char *imagesUchar = NorbLoader::loadImages( dataDir + "/" + setName + "-dat.mat", &N, &numPlanes, &imageSize, n + 1 );
+    unsigned char *imagesUchar = NorbLoader::loadImages(dataDir + "/" + setName + "-dat.mat", &N, &numPlanes, &imageSize, n + 1);
     cout << "n " << n << " N " << N << endl;
     N = n + 1;
     float *images = new float[ N * numPlanes * imageSize * imageSize ];
-    for( int i = 0; i < N * numPlanes * imageSize * imageSize; i++ ) {
+    for(int i = 0; i < N * numPlanes * imageSize * imageSize; i++) {
         images[i] = imagesUchar[i];
     }
-    ImagePng::writeImagesToPng( "testPatchExtractor-1.png", images + n * numPlanes * imageSize * imageSize, numPlanes, imageSize );
+    ImagePng::writeImagesToPng("testPatchExtractor-1.png", images + n * numPlanes * imageSize * imageSize, numPlanes, imageSize);
     float *patches = new float[N * numPlanes * patchSize * patchSize];
-    PatchExtractor::extractPatch( n, numPlanes, imageSize, patchSize, patchRow, patchCol, images, patches );
-    ImagePng::writeImagesToPng( "testPatchExtractor-2.png", patches + n * numPlanes * patchSize * patchSize, numPlanes, patchSize );
+    PatchExtractor::extractPatch(n, numPlanes, imageSize, patchSize, patchRow, patchCol, images, patches);
+    ImagePng::writeImagesToPng("testPatchExtractor-2.png", patches + n * numPlanes * patchSize * patchSize, numPlanes, patchSize);
 }
 
-int main( int argc, char *argv[] ) {
-    if( argc != 7 ) {
+int main(int argc, char *argv[]) {
+    if(argc != 7) {
         cout << "Usage: [datadir] [setname] [n] [patchsize] [patchrow] [patchcol]" << endl;
         return -1;
     }
-    string dataDir = string( argv[1] );
-    string setName = string( argv[2] );
-    int n = atoi( argv[3] );
-    int patchSize = atoi( argv[4] );
-    int patchRow = atoi( argv[5] );
-    int patchCol = atoi( argv[6] );
-    go( dataDir, setName, n, patchSize, patchRow, patchCol );
+    string dataDir = string(argv[1]);
+    string setName = string(argv[2]);
+    int n = atoi(argv[3]);
+    int patchSize = atoi(argv[4]);
+    int patchRow = atoi(argv[5]);
+    int patchCol = atoi(argv[6]);
+    go(dataDir, setName, n, patchSize, patchRow, patchCol);
     return 0;
 }
 
diff --git a/test/testTranslator.cpp b/test/testTranslator.cpp
index 15cd48b9..909cbaf2 100644
--- a/test/testTranslator.cpp
+++ b/test/testTranslator.cpp
@@ -7,38 +7,38 @@
 
 using namespace std;
 
-void go( string dataDir, string setName, int n, int translateRows, int translateCols ) {
+void go(string dataDir, string setName, int n, int translateRows, int translateCols) {
     int N;
     int numPlanes;
     int imageSize;
     string filePath = dataDir + "/" + setName + "-dat.mat";
-    NorbLoader::getDimensions( filePath, &N, &numPlanes, &imageSize );
+    NorbLoader::getDimensions(filePath, &N, &numPlanes, &imageSize);
     N = n + 1;
     unsigned char *imagesUchar = new unsigned char[ N * numPlanes * imageSize * imageSize ];
     int *labels = new int[ N ];
-    NorbLoader::load( filePath, imagesUchar, labels, 0, N );
+    NorbLoader::load(filePath, imagesUchar, labels, 0, N);
     cout << "n " << n << " N " << N << endl;
     float *images = new float[ N * numPlanes * imageSize * imageSize ];
-    for( int i = 0; i < N * numPlanes * imageSize * imageSize; i++ ) {
+    for(int i = 0; i < N * numPlanes * imageSize * imageSize; i++) {
         images[i] = imagesUchar[i];
     }
-    ImagePng::writeImagesToPng( "testTranslator-1.png", images + n * numPlanes * imageSize * imageSize, numPlanes, imageSize );
+    ImagePng::writeImagesToPng("testTranslator-1.png", images + n * numPlanes * imageSize * imageSize, numPlanes, imageSize);
     float *translated = new float[N * numPlanes * imageSize * imageSize];
-    Translator::translate( n, numPlanes, imageSize, translateRows, translateCols, images, translated );
-    ImagePng::writeImagesToPng( "testTranslator-2.png", translated + n * numPlanes * imageSize * imageSize, numPlanes, imageSize );
+    Translator::translate(n, numPlanes, imageSize, translateRows, translateCols, images, translated);
+    ImagePng::writeImagesToPng("testTranslator-2.png", translated + n * numPlanes * imageSize * imageSize, numPlanes, imageSize);
 }
 
-int main( int argc, char *argv[] ) {
-    if( argc != 6 ) {
+int main(int argc, char *argv[]) {
+    if(argc != 6) {
         cout << "Usage: [datadir] [setname] [n] [translaterows] [translatecols]" << endl;
         return -1;
     }
-    string dataDir = string( argv[1] );
-    string setName = string( argv[2] );
-    int n = atoi( argv[3] );
-    int translateRows = atoi( argv[4] );
-    int translateCols = atoi( argv[5] );
-    go( dataDir, setName, n, translateRows, translateCols );
+    string dataDir = string(argv[1]);
+    string setName = string(argv[2]);
+    int n = atoi(argv[3]);
+    int translateRows = atoi(argv[4]);
+    int translateCols = atoi(argv[5]);
+    go(dataDir, setName, n, translateRows, translateCols);
     return 0;
 }