public void RandomForest_all_training_samples()
{
initData_Jason_Bagging();
BuildRandomForest build =
new BuildRandomForest();
ModelRandomForest model =
(ModelRandomForest)build.BuildModel(
_trainingData,
_attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < _trainingData[0].Length; row++)
{
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
if (value ==
_trainingData[_indexTargetAttribute][row])
{
count++;
}
}
Assert.AreEqual(9, count);
}
public void RandomForest_single_training_sample_value_0()
{
initData_Jason_Bagging();
BuildRandomForest build =
new BuildRandomForest();
ModelRandomForest model =
(ModelRandomForest)build.BuildModel(
_trainingData,
_attributeHeaders,
_indexTargetAttribute);
int row = 0;
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
Assert.AreEqual(value, _trainingData[_indexTargetAttribute][row]);
}
public void RandomForest_single_training_sample_3_features()
{
initData_Jason_3_features();
BuildRandomForest build =
new BuildRandomForest();
ModelRandomForest model =
(ModelRandomForest)build.BuildModel(
_trainingData,
_attributeHeaders,
_indexTargetAttribute);
int row = 4;//5.38 2.1 0
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
//Can be both 1 or 0
//Assert.IsTrue(value==0 | value==1);
Assert.AreEqual(value,
_trainingData[_indexTargetAttribute][row]);
}