diff --git a/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp b/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp
index 20c56a2..33de8a6 100644
--- a/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp
+++ b/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp
@@ -2,7 +2,7 @@
 
 using namespace NeuralNetwork::ConstructiveAlgorihtms;
 
-float CascadeCorrelation::trainOutputs(Cascade::Network &network, const std::vector<CascadeCorrelation::TrainingPattern> &patterns) {
+float CascadeCorrelation::trainOutputs(Cascade::Network &network, const std::vector <CascadeCorrelation::TrainingPattern> &patterns) {
 	std::size_t outputs = patterns[0].second.size();
 
 	FeedForward::Network p(network.getNeuronSize() - outputs - 1);
@@ -14,7 +14,7 @@ float CascadeCorrelation::trainOutputs(Cascade::Network &network, const std::vec
 		p[1][neuron + 1].setActivationFunction(network.getOutputNeurons()[neuron]->getActivationFunction());
 	}
 
-	std::vector<TrainingPattern> patternsForOutput;
+	std::vector <TrainingPattern> patternsForOutput;
 
 	for(auto &pattern:patterns) {
 		patternsForOutput.emplace_back(getInnerNeuronsOutput(network, pattern.first), pattern.second);
@@ -54,10 +54,10 @@ float CascadeCorrelation::trainOutputs(Cascade::Network &network, const std::vec
 }
 
 
-float CascadeCorrelation::trainOutputsRandom(std::size_t step, Cascade::Network &network, const std::vector<CascadeCorrelation::TrainingPattern> &patterns) {
+float CascadeCorrelation::trainOutputsRandom(std::size_t step, Cascade::Network &network, const std::vector <CascadeCorrelation::TrainingPattern> &patterns) {
 	std::size_t outputs = patterns[0].second.size();
 
-	std::vector<FeedForward::Network *> possibleOutputs;
+	std::vector < FeedForward::Network * > possibleOutputs;
 	{ // first networks is special
 		possibleOutputs.emplace_back(new FeedForward::Network(network.getNeuronSize() - outputs - 1));
 		FeedForward::Network &p = (*possibleOutputs.back());
@@ -92,7 +92,7 @@ float CascadeCorrelation::trainOutputsRandom(std::size_t step, Cascade::Network
 		}
 	}
 
-	std::vector<TrainingPattern> patternsForOutput;
+	std::vector <TrainingPattern> patternsForOutput;
 
 	for(auto &pattern:patterns) {
 		patternsForOutput.emplace_back(getInnerNeuronsOutput(network, pattern.first), pattern.second);
@@ -148,22 +148,22 @@ float CascadeCorrelation::trainOutputsRandom(std::size_t step, Cascade::Network
 	return bestScore;
 }
 
-std::pair<std::shared_ptr<NeuralNetwork::Neuron>, std::vector<float>> CascadeCorrelation::trainCandidates(Cascade::Network &network,
-																										  std::vector<std::shared_ptr<Neuron>> &candidates,
-																										  const std::vector<TrainingPattern> &patterns) {
+std::pair <std::shared_ptr<NeuralNetwork::Neuron>, std::vector<float>> CascadeCorrelation::trainCandidates(Cascade::Network &network,
+																										   std::vector <std::shared_ptr<Neuron>> &candidates,
+																										   const std::vector <TrainingPattern> &patterns) {
 	std::size_t outputs = patterns[0].second.size();
 
-	std::vector<TrainingPattern> patternsForOutput;
+	std::vector <TrainingPattern> patternsForOutput;
 
 	for(auto &pattern:patterns) {
 		patternsForOutput.emplace_back(getInnerNeuronsOutput(network, pattern.first), pattern.second);
 	}
 
-	std::vector<std::vector<float>> errors(patterns.size());
+	std::vector <std::vector<float>> errors(patterns.size());
 	std::vector<float> meanErrors(outputs);
 	float sumSquareError = 0;
 
-	std::vector<std::vector<float>> errorsReal(patterns.size());
+	std::vector <std::vector<float>> errorsReal(patterns.size());
 	for(std::size_t patternNumber = 0; patternNumber < patterns.size(); patternNumber++) {
 		auto &pattern = patterns[patternNumber];
 		errors[patternNumber].resize(network.outputs());
@@ -192,10 +192,10 @@ std::pair<std::shared_ptr<NeuralNetwork::Neuron>, std::vector<float>> CascadeCor
 		std::vector<float> lastCorrelations = {};
 		std::vector<float> slopes = {};
 		float sumVals = 0;
-		std::shared_ptr<Neuron> candidate = nullptr;
+		std::shared_ptr <Neuron> candidate = nullptr;
 	};
 
-	std::vector<CAND> candidatesRegister(candidates.size());
+	std::vector <CAND> candidatesRegister(candidates.size());
 
 	for(std::size_t i = 0; i < candidates.size(); i++) {
 		candidatesRegister[i].candidate = candidates[i];
@@ -208,7 +208,7 @@ std::pair<std::shared_ptr<NeuralNetwork::Neuron>, std::vector<float>> CascadeCor
 	std::size_t iterationsWithoutIprovement = 0;
 	float bestCorrelation = 0;
 	float lastCorrelation = 0;
-	std::shared_ptr<Neuron> bestCandidate = nullptr;
+	std::shared_ptr <Neuron> bestCandidate = nullptr;
 
 	std::vector<float> bestCorrelations(outputs);