private static void Main(string[] args)
{
Console.WriteLine("Укажите путь к файлу");
var path = Console.ReadLine();
Console.WriteLine("Укажите размерность прстранства");
var dimension =Convert.ToInt32(Console.ReadLine());
var ff = new FileFramework(@"E:\Users\Nikita\Documents\Visual Studio 2013\Projects\NSUBigData\LinearRegres\iris.csv", Convert.ToInt32(dimension));
Console.WriteLine("\nBegin Logistic Regression (binary) Classification demo");
Console.WriteLine("Goal is to demonstrate training using gradient descent");
var numFeatures = dimension-1;
var numRows = 100;
var seed = 1;
Console.WriteLine("\nGenerating " + numRows +
" artificial data items with " + numFeatures + " features");
var allData = ff.ParseFile();
Console.WriteLine("Creating train (80%) and test (20%) matrices");
double[][] trainData;
double[][] testData;
MakeTrainTest(allData, 0, out trainData, out testData);
Console.WriteLine("Done");
Console.WriteLine("\nTraining data: \n");
ShowData(trainData, 3, 2, true);
Console.WriteLine("\nTest data: \n");
ShowData(testData, 3, 2, true);
Console.WriteLine("Creating LR binary classifier");
var lc = new LogisticClassifier(numFeatures);
var maxEpochs = 1000;
Console.WriteLine("Setting maxEpochs = " + maxEpochs);
var alpha = 0.01;
Console.WriteLine("Setting learning rate = " + alpha.ToString("F2"));
Console.WriteLine("\nStarting training using (stochastic) gradient descent");
double[] weights = lc.Train(trainData, maxEpochs, alpha);
Console.WriteLine("Training complete");
Console.WriteLine("\nBest weights found:");
ShowVector(weights, 4, true);
double trainAcc = lc.Accuracy(trainData, weights);
Console.WriteLine("Prediction accuracy on training data = " +
trainAcc.ToString("F4"));
double testAcc = lc.Accuracy(testData, weights);
Console.WriteLine("Prediction accuracy on test data = " +
testAcc.ToString("F4"));
Console.WriteLine("\nEnd LR binary classification demo\n");
Console.ReadLine();
}
private static void Main(string[] args)
{
Console.WriteLine("Укажите путь к файлу");
var path = Console.ReadLine();
Console.WriteLine("Укажите размерность прстранства");
var dimension = Convert.ToInt32(Console.ReadLine());
var ff = new FileFramework(@"E:\Users\Nikita\Documents\Visual Studio 2013\Projects\NSUBigData\LinearRegres\iris.csv", Convert.ToInt32(dimension));
Console.WriteLine("\nBegin Logistic Regression (binary) Classification demo");
Console.WriteLine("Goal is to demonstrate training using gradient descent");
var numFeatures = dimension - 1;
var numRows = 100;
var seed = 1;
Console.WriteLine("\nGenerating " + numRows +
" artificial data items with " + numFeatures + " features");
var allData = ff.ParseFile();
Console.WriteLine("Creating train (80%) and test (20%) matrices");
double[][] trainData;
double[][] testData;
MakeTrainTest(allData, 0, out trainData, out testData);
Console.WriteLine("Done");
Console.WriteLine("\nTraining data: \n");
ShowData(trainData, 3, 2, true);
Console.WriteLine("\nTest data: \n");
ShowData(testData, 3, 2, true);
Console.WriteLine("Creating LR binary classifier");
var lc = new LogisticClassifier(numFeatures);
var maxEpochs = 1000;
Console.WriteLine("Setting maxEpochs = " + maxEpochs);
var alpha = 0.01;
Console.WriteLine("Setting learning rate = " + alpha.ToString("F2"));
Console.WriteLine("\nStarting training using (stochastic) gradient descent");
double[] weights = lc.Train(trainData, maxEpochs, alpha);
Console.WriteLine("Training complete");
Console.WriteLine("\nBest weights found:");
ShowVector(weights, 4, true);
double trainAcc = lc.Accuracy(trainData, weights);
Console.WriteLine("Prediction accuracy on training data = " +
trainAcc.ToString("F4"));
double testAcc = lc.Accuracy(testData, weights);
Console.WriteLine("Prediction accuracy on test data = " +
testAcc.ToString("F4"));
Console.WriteLine("\nEnd LR binary classification demo\n");
Console.ReadLine();
}
