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; } }