static void Regression(int classKey) { List<DataItem> training = LoadData(classKey, "training", 50); LogisticReg regression = new LogisticReg(50, classKey, 1); regression.Training(training); List<DataItem> crsvalid = LoadData(0, "crsvalid", 50); double precision, recall; regression.Testing(training, out precision, out recall); regression.Testing(crsvalid, out precision, out recall); Console.WriteLine("class: {0}, precision: {1}, recall: {2}", classKey, precision, recall); }
static void Regression() { string[] classLabels = Directory.GetFiles(@"C:\Users\chentao\Desktop\Workspace1\Github\DocModel\DocumentModel\bin\Debug\training_data"); for (int i = 0; i < classLabels.Length; i++) { int classKey = int.Parse( classLabels[i].Substring(classLabels[i].LastIndexOf('_') + 1) ); List<DataItem> training = LoadData(classKey, "training", 50); LogisticReg regression = new LogisticReg(50, classKey, 1); regression.Training(training); List<DataItem> crsvalid = LoadData(0, "crsvalid", 50); double precision, recall; regression.Testing(training, out precision, out recall); regression.Testing(crsvalid, out precision, out recall); Console.WriteLine("class: {0}, precision: {1}, recall: {2}", classKey, precision, recall); } }
static void Test() { LogisticReg regression = new LogisticReg(2, 1, 1); List<DataItem> items = new List<DataItem>(); StreamReader reader = new StreamReader(new FileStream("ex2data2.txt", FileMode.Open)); string line; while ((line = reader.ReadLine()) != null) { string[] ss = line.Split(','); DataItem item = new DataItem(); item.AddItem(0, double.Parse(ss[0])); item.AddItem(1, double.Parse(ss[1])); item.AddClassLabel(int.Parse(ss[2])); items.Add(item); } double[] theta = { 0, 0 }; double r = regression.CostFunc(items, theta); }