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);
}
