public void DecisionTreeLanguageTest()
{
var netMLString = "create classification decisiontree";
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()));
}
public void RadialbasisfunctionLanguageTest()
{
var netMLString =
"create classification radialbasisfunction inputneurons = 5 outputneurons = 15 firsthiddenlayerneurons = 2 evolutions = 100 learningrate = 0.1 ";
NetMLParser netMLParser = new NetMLParser();
var result = netMLParser.Parse(netMLString);
}
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()));
}
public void ClusteringLanguageTest()
{
var netMLString =
"create clustering kmetroids euclidmetric ";
NetMLParser netMLParser = new NetMLParser();
var result = netMLParser.Parse(netMLString);
NetMLCreator netMLCreator = new NetMLCreator(result);
DataSetLoader dataSetLoader = new DataSetLoader();
var irises = dataSetLoader.SelectClusteringIrises();
netMLCreator.Create(irises);
var clusters = netMLCreator.Cluster(3);
var clusterCounter = 0;
Dictionary <int, int> clusterDictonary = new Dictionary <int, int>();
foreach (var cluster in clusters)
{
Debug.WriteLine(string.Format("Cluster {0} - Count {1}", clusterCounter, cluster.Count));
clusterDictonary.Add(clusterCounter, 0);
clusterCounter++;
}
var irisesTest = dataSetLoader.SelectClusteringIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = netMLCreator.CalculateClusterAffinity(item);
Debug.WriteLine(string.Format("Value {0} - Predicted {1}", item, outputValue));
clusterDictonary[outputValue]++;
counter++;
trueCounter++;
}
clusterCounter = 0;
foreach (var cluster in clusters)
{
var calculatedCluster = clusterDictonary[clusterCounter];
Debug.WriteLine(string.Format("Cluster {0} - Original Count {1} - Calculated Count {2}",
clusterCounter, cluster.Count, calculatedCluster));
clusterCounter++;
}
}
