private void RandomForest(List <Tuple <double[], double> > data)
{
Splitter splitter = new ShannonEntropySplitter();
RandomForestAlgorithm randomForestAlgorithm = new BoostingAlgorithmus();
foreach (var item in netMLObject.Options)
{
if (item == "shannonentropysplitter")
{
splitter = new ShannonEntropySplitter();
}
else if (item == "giniindexsplitter")
{
splitter = new GiniImpuritySplitter();
}
else if (item == "bagging")
{
randomForestAlgorithm = new BaggingAlgorithmus(10);
}
else if (item == "boosting")
{
randomForestAlgorithm = new BoostingAlgorithmus();
}
}
classification = new RandomForestClassifier(data, splitter, randomForestAlgorithm);
}
public void AnimalBoostingTrainAndClassify8020Test()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectTrainingAnimals(90);
for (double i = 0; i < 1; i = i + 1)
{
BoostingAlgorithmus boostingAlgorithmus =
new BoostingAlgorithmus(10);
boostingAlgorithmus.Train(animals, new ShannonEntropySplitter());
var animalsTest = dataSetLoader.SelectSelectingAnimals(10);
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = boostingAlgorithmus.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format(" i = {0} Data {1} - True {2} Verhältnis: {3}", i,
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
}
