public void testDataSetPopulation()
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
INumerizer numerizer = new IrisDataSetNumerizer();
NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet();
innds.CreateExamplesFromDataSet(irisDataSet, numerizer);
NeuralNetworkConfig config = new NeuralNetworkConfig();
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6);
config.SetConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.SetConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.SetTrainingScheme(new BackPropagationLearning(0.1, 0.9));
ffnn.TrainOn(innds, 10);
innds.RefreshDataset();
ffnn.TestOnDataSet(innds);
}
internal static void backPropogationDemo()
{
try
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
INumerizer numerizer = new IrisDataSetNumerizer();
NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet();
innds.CreateExamplesFromDataSet(irisDataSet, numerizer);
NeuralNetworkConfig config = new NeuralNetworkConfig();
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS,
6);
config.SetConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.SetConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.SetTrainingScheme(new BackPropagationLearning(0.1, 0.9));
ffnn.TrainOn(innds, 1000);
innds.RefreshDataset();
int[] result = ffnn.TestOnDataSet(innds);
System.Console.WriteLine(result[0] + " right, " + result[1] + " wrong");
}
catch (Exception e)
{
throw e;
}
}
public void testPerceptron()
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
INumerizer numerizer = new IrisDataSetNumerizer();
NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet();
innds.CreateExamplesFromDataSet(irisDataSet, numerizer);
Perceptron perc = new Perceptron(3, 4);
perc.TrainOn(innds, 10);
innds.RefreshDataset();
perc.TestOnDataSet(innds);
}
public void testPerceptron()
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
Numerizer numerizer = new IrisDataSetNumerizer();
NNDataSet innds = new IrisNNDataSet();
innds.createExamplesFromDataSet(irisDataSet, numerizer);
Perceptron perc = new Perceptron(3, 4);
perc.trainOn(innds, 10);
innds.refreshDataset();
perc.testOnDataSet(innds);
}
public void testNumerizesAndDeNumerizesIrisDataSetExample3()
{
DataSet ds = DataSetFactory.getIrisDataSet();
Example first = ds.getExample(100);
INumerizer n = new IrisDataSetNumerizer();
Pair <ICollection <double>, ICollection <double> > io = n.Numerize(first);
Assert.AreEqual(CollectionFactory.CreateQueue <double>(new[] { 6.3, 3.3, 6.0, 2.5 }), io.GetFirst());
Assert.AreEqual(CollectionFactory.CreateQueue <double>(new[] { 1.0, 0.0, 0.0 }), io.getSecond());
string plant_category = n.Denumerize(CollectionFactory.CreateQueue <double>(new[] { 1.0, 0.0, 0.0 }));
Assert.AreEqual("virginica", plant_category);
}
public void testNumerizesAndDeNumerizesIrisDataSetExample2()
{
DataSet ds = DataSetFactory.getIrisDataSet();
Example first = ds.getExample(51);
INumerizer n = new IrisDataSetNumerizer();
Pair <ICollection <double>, ICollection <double> > io = n.Numerize(first);
Assert.AreEqual(CollectionFactory.CreateQueue <double>(new[] { 6.4, 3.2, 4.5, 1.5 }), io.GetFirst());
Assert.AreEqual(CollectionFactory.CreateQueue <double>(new[] { 0.0, 1.0, 0.0 }), io.getSecond());
string plant_category = n.Denumerize(CollectionFactory.CreateQueue <double>(new[] { 0.0, 1.0, 0.0 }));
Assert.AreEqual("versicolor", plant_category);
}
public void testNumerizesAndDeNumerizesIrisDataSetExample3()
{
DataSet ds = DataSetFactory.getIrisDataSet();
Example first = ds.getExample(100);
Numerizer n = new IrisDataSetNumerizer();
Pair <List <Double>, List <Double> > io = n.numerize(first);
AssertListsEqual <double>(new List <double>()
{
6.3, 3.3, 6.0, 2.5
}, io.getFirst());
AssertListsEqual <double>(new List <double>()
{
1.0, 0.0, 0.0
}, io.getSecond());
String plant_category = n.denumerize(new List <double>()
{
1.0, 0.0, 0.0
});
Assert.AreEqual("virginica", plant_category);
}
public void testNumerizesAndDeNumerizesIrisDataSetExample2()
{
DataSet ds = DataSetFactory.getIrisDataSet();
Example first = ds.getExample(51);
Numerizer n = new IrisDataSetNumerizer();
Pair <List <Double>, List <Double> > io = n.numerize(first);
AssertListsEqual <double>(new List <double>()
{
6.4, 3.2, 4.5, 1.5
}, io.getFirst());
AssertListsEqual <double>(new List <double>()
{
0.0, 1.0, 0.0
}, io.getSecond());
String plant_category = n.denumerize(new List <double>()
{
0.0, 1.0, 0.0
});
Assert.AreEqual("versicolor", plant_category);
}
static void perceptronDemo()
{
try
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
INumerizer numerizer = new IrisDataSetNumerizer();
NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet();
innds.CreateExamplesFromDataSet(irisDataSet, numerizer);
Perceptron perc = new Perceptron(3, 4);
perc.TrainOn(innds, 10);
innds.RefreshDataset();
int[] result = perc.TestOnDataSet(innds);
System.Console.WriteLine(result[0] + " right, " + result[1] + " wrong");
}
catch (Exception e)
{
throw e;
}
}
