public void NaivebayersLanguageTest()
{
var netMLString = "create classification naivebayers linearbayeskernel";
NetMLCreator netMLCreator = new NetMLCreator(netMLString);
DataSetLoader dataSetLoader = new DataSetLoader();
var data = dataSetLoader.SelectAnimals();
netMLCreator.Create(data);
netMLCreator.Train();
var testData = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in testData)
{
var outputValue = netMLCreator.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("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
public void BackprobpagationLanguageTest()
{
var netMLString =
"create classification backpropagation inputneurons = 16 outputneurons = 1 firsthiddenlayerneurons = 16 evolutions = 100 learningrate = 0.1 ";
NetMLParser netMLParser = new NetMLParser();
var result = netMLParser.Parse(netMLString);
NetMLCreator netMLCreator = new NetMLCreator(result);
DataSetLoader dataSetLoader = new DataSetLoader();
var data = dataSetLoader.SelectAnimals();
netMLCreator.Create(data);
netMLCreator.Train();
var testData = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in testData)
{
var outputValue = netMLCreator.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("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
