public static Dictionary <int, double> getLabels(List <Instance> TrainList, List <Instance> TestList)
{
Dictionary <int, double> ret = new Dictionary <int, double>();
List <int> testLabels = new List <int>();
for (int i = 0; i < 10; i++)
{
testLabels.Add((i + 1) % 10);
}
List <Instance> instances = TrainList;// DataSet.readDataSet("examples/zoo.train");
MaxEnt me = new MaxEnt(instances);
me.Train();
for (int i = 0; i < testLabels.Count; i++)
{
TestList[TestList.Count - 1].Label = testLabels[i]; //最后一条记录的label更换为测试的label
List <Instance> trainInstances = TestList; // DataSet.readDataSet("examples/zoo.test");
int pass = 0;
foreach (Instance instance in trainInstances)
{
int predict = me.Classify(instance);
if (predict == instance.Label)
{
pass += 1;
}
}
ret.Add(testLabels[i], (double)1.0 * pass / trainInstances.Count);
}
return(ret);
//System.out.println("accuracy: " + 1.0 * pass / trainInstances.size());
}
public static double Run(List <Instance> TrainList, List <Instance> TestList)
{
List <Instance> instances = TrainList;// DataSet.readDataSet("examples/zoo.train");
MaxEnt me = new MaxEnt(instances);
me.Train();
List <Instance> trainInstances = TestList;// DataSet.readDataSet("examples/zoo.test");
int pass = 0;
foreach (Instance instance in trainInstances)
{
int predict = me.Classify(instance);
if (predict == instance.Label)
{
pass += 1;
}
}
return((double)1.0 * pass / trainInstances.Count);
//System.out.println("accuracy: " + 1.0 * pass / trainInstances.size());
}
