public virtual void run()
{
Console.WriteLine("Creating training and test set from file...");
string trainingSetFileName = "data_sets/segment challenge.txt";
string testSetFileName = "data_sets/segment test.txt";
int inputsCount = 19;
int outputsCount = 7;
//Create training data set from file
DataSet trainingSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",");
Console.WriteLine("Training set size: " + trainingSet.Rows.Count);
trainingSet.shuffle();
trainingSet.shuffle();
//Normalizing training data set
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(trainingSet);
//Create test data set from file
DataSet testSet = DataSet.createFromFile(testSetFileName, inputsCount, outputsCount, ",");
Console.WriteLine("Test set size: " + testSet.Rows.Count);
Console.WriteLine("--------------------------------------------------");
testSet.shuffle();
testSet.shuffle();
//Normalizing training data set
normalizer.normalize(testSet);
Console.WriteLine("Creating neural network...");
//Create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 17, 10, outputsCount);
//attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.addListener(this);
learningRule.LearningRate = 0.01;
learningRule.MaxError = 0.001;
learningRule.MaxIterations = 12000;
Console.WriteLine("Training network...");
//train the network with training set
neuralNet.learn(trainingSet);
Console.WriteLine("Testing network...\n\n");
testNeuralNetwork(neuralNet, testSet);
Console.WriteLine("Done.");
Console.WriteLine("**************************************************");
// }
}
public virtual void normalizeBalancedTrainingSet()
{
//Normalizing balanced training set with MaxNormalizer
DataSet dataSet = DataSet.load(config.BalancedFileName);
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(dataSet);
Console.WriteLine("Saving normalized training data set to file... ");
dataSet.shuffle();
dataSet.shuffle();
dataSet.save(config.NormalizedBalancedFileName);
Console.WriteLine("Normalized training data set successfully saved!");
}
public virtual void run()
{
Console.WriteLine("Creating training and test set from file...");
string trainingSetFileName = "data_sets/diabetes.txt";
int inputsCount = 8;
int outputsCount = 2;
//Create data set from file
DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",");
dataSet.shuffle();
//Normalizing data set
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(dataSet);
//Creatinig training set (70%) and test set (30%)
DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
DataSet trainingSet = trainingAndTestSet[0];
DataSet testSet = trainingAndTestSet[1];
// for (int i = 0; i < 21; i++) {
Console.WriteLine("Creating neural network...");
//Create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 9, outputsCount);
// System.out.println("HIDDEN COUNT: " + i);
//attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.addListener(this);
learningRule.LearningRate = 0.05;
learningRule.MaxError = 0.01;
learningRule.MaxIterations = 10000;
Console.WriteLine("Training network...");
//train the network with training set
neuralNet.learn(trainingSet);
Console.WriteLine("Testing network...\n\n");
testNeuralNetwork(neuralNet, testSet);
Console.WriteLine("Done.");
Console.WriteLine("**************************************************");
// }
}
public virtual void run()
{
Console.WriteLine("Creating training set...");
string trainingSetFileName = "data_sets/cpu_data.txt";
int inputsCount = 7;
int outputsCount = 1;
// create training set from file
DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",", false);
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(dataSet);
Console.WriteLine("Creating neural network...");
// create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 16, outputsCount);
// attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.addListener(this);
// set learning rate and max error
learningRule.LearningRate = 0.2;
learningRule.MaxError = 0.01;
Console.WriteLine("Training network...");
// train the network with training set
neuralNet.learn(dataSet);
Console.WriteLine("Training completed.");
Console.WriteLine("Testing network...");
testNeuralNetwork(neuralNet, dataSet);
Console.WriteLine("Saving network");
// save neural network to file
neuralNet.save("MyNeuralNetCPU.nnet");
Console.WriteLine("Done.");
}
public virtual void createTrainingAndTestSet()
{
//Creating data set from file
DataSet dataSet = createDataSet();
dataSet.shuffle();
//Splitting main data set to training set (75%) and test set (25%)
DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(75, 25);
//Saving training set to file
DataSet trainingSet = trainingAndTestSet[0];
Console.WriteLine("Saving training set to file...");
trainingSet.save(config.TrainingFileName);
Console.WriteLine("Training set successfully saved!");
//Normalizing test set
DataSet testSet = trainingAndTestSet[1];
Console.WriteLine("Normalizing test set...");
Normalizer nor = new MaxNormalizer();
nor.normalize(testSet);
Console.WriteLine("Saving normalized test set to file...");
testSet.shuffle();
testSet.save(config.TestFileName);
Console.WriteLine("Normalized test set successfully saved!");
Console.WriteLine("Training set size: " + trainingSet.Rows.Count + " rows. ");
Console.WriteLine("Test set size: " + testSet.Rows.Count + " rows. ");
Console.WriteLine("-----------------------------------------------");
double percentTraining = (double)trainingSet.Rows.Count * 100.0 / (double)dataSet.Rows.Count;
double percentTest = (double)testSet.Rows.Count * 100.0 / (double)dataSet.Rows.Count;
Console.WriteLine("Training set takes " + formatDecimalNumber(percentTraining) + "% of main data set. ");
Console.WriteLine("Test set takes " + formatDecimalNumber(percentTest) + "% of main data set. ");
}