public void AnimalDualPerceptronTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectAnimals();
for (double i = 0; i < 1; i = i + 1)
{
Kernel kernel = new LinearKernel();
DualPerceptronClassifier dualPerceptronClassifier = new DualPerceptronClassifier(animals, kernel);
dualPerceptronClassifier.Train();
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = dualPerceptronClassifier.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()));
}
}
public void AnimalBoostingTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectAnimals();
for (double i = 0; i < 1; i = i + 1)
{
BoostingAlgorithmus boostingAlgorithmus =
new BoostingAlgorithmus(10);
boostingAlgorithmus.Train(animals, new ShannonEntropySplitter());
var animalsTest = dataSetLoader.SelectAnimals();
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()));
}
}
public void AnimalDecisionTreeTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectAnimals();
DecisionTreeClassifier decisionTreeClassifier =
new DecisionTreeClassifier(animals, new ShannonEntropySplitter());
decisionTreeClassifier.Train();
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = decisionTreeClassifier.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 NaiveBayesAnimalTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectAnimals();
for (double i = 0; i < 1; i = i + 1)
{
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(animals);
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = naiveBayes.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()));
}
}
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()));
}
public void AnimalClassifyMethod()
{
DataSetLoader dataSetLoader = new DataSetLoader();
var animals = dataSetLoader.SelectAnimals();
var data = dataSetLoader.CalculatePercent(50, animals);
DecisionTreeClassifier decisionTreeClassifier =
new DecisionTreeClassifier(data.Item1, new ShannonEntropySplitter());
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(data.Item1);
var list = new List <NetML.Classification>();
Kernel kernel = new LinearKernel();
SVMClassifier animalSVMClassifier =
new SVMClassifier(animals, kernel, 0.001, 10.0);
var neuronalAnimals = dataSetLoader.SelectNeuronalNetworkAnimals();
NeuronalNetworkClassifier neuronalNetworkClassifier =
new NeuronalNetworkClassifier(neuronalAnimals, 16, 7, 16, 500, 0.1);
list.Add(decisionTreeClassifier);
list.Add(naiveBayes);
list.Add(animalSVMClassifier);
list.Add(neuronalNetworkClassifier);
Classifier classifier = new Classifier();
classifier.Classify(list, data.Item2);
}
public void AnimalRadialBasisFunctionNeuronalNetworkClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectNeuronalNetworkAnimals();
for (double i = 0; i < 1; i = i + 1)
{
OneHiddenLayerNeuronCounter oneHiddenLayerNeuronCounter = new OneHiddenLayerNeuronCounter(16, 8, 32);
RadialBasisFunctionNeuronalNetwork radialBasisFunctionNeuronalNetworkClassifier =
new RadialBasisFunctionNeuronalNetwork(oneHiddenLayerNeuronCounter, 10000, 0.5);
radialBasisFunctionNeuronalNetworkClassifier.Train(animals);
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = radialBasisFunctionNeuronalNetworkClassifier.Classify(item.Item1);
var resultString = String.Empty;
double maxValue = 0.0;
int innerCounter = 1;
int maxItem = 0;
foreach (var value in outputValue.OutputValues)
{
if (value > maxValue)
{
maxValue = value;
maxItem = innerCounter;
}
innerCounter++;
}
if (maxItem == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} {2} {3} {4} {5} {6} {7} = {8}",
item.Item2,
Convert.ToDecimal(outputValue.OutputValues[0]),
Convert.ToDecimal(outputValue.OutputValues[1]),
Convert.ToDecimal(outputValue.OutputValues[2]),
Convert.ToDecimal(outputValue.OutputValues[3]),
Convert.ToDecimal(outputValue.OutputValues[4]),
Convert.ToDecimal(outputValue.OutputValues[5]),
Convert.ToDecimal(outputValue.OutputValues[6]),
(maxItem == 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()));
}
}
public void AnimalNeuronalNetworkClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectNeuronalNetworkAnimals();
for (double i = 0; i < 1; i = i + 1)
{
NeuronalNetworkClassifier neuronalNetworkClassifier =
new NeuronalNetworkClassifier(animals, 16, 7, 16, 900, 0.1, NeuronalNetworkMode.Standard);
neuronalNetworkClassifier.Train();
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = neuronalNetworkClassifier.ClassifiyMultibleResultValue(item.Item1);
var resultString = String.Empty;
double maxValue = 0.0;
int innerCounter = 1;
int maxItem = 0;
foreach (var value in outputValue)
{
if (value > maxValue)
{
maxValue = value;
maxItem = innerCounter;
}
innerCounter++;
}
if (maxItem == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, maxItem, (maxItem == 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()));
}
}