public void RegressionAdaBoostModel_Load()
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var reader = new StringReader(RegressionDecisionTreeModelString);
var sut = RegressionAdaBoostModel.Load(() => reader);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.22527472527472531, error, 0.0000001);
}
public void RegressionAdaBoostModel_Load()
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var reader = new StringReader(RegressionDecisionTreeModelString);
var sut = RegressionAdaBoostModel.Load(() => reader);
var predictions = sut.Predict(observations);
var evaluator = new MeanAbsolutErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.22527472527472531, error, 0.0000001);
}
/// <summary>
/// Predição de Floresta Aleatória e Rede Neural
/// </summary>
public void RegressionLearner_Learn_And_Predict()
{
#region Treinamento da Floresta Aleatória
var parser = new CsvParser(() => new StringReader(treinamento));
var targetName = "T";
var observations = parser.EnumerateRows(c => c != targetName)
.ToF64Matrix();
var targets = parser.EnumerateRows(targetName)
.ToF64Vector();
UltimaObservacao = new double[] { observations[observations.RowCount - 1, 0], observations[observations.RowCount - 1, 2], observations[observations.RowCount - 1, 3], observations[observations.RowCount - 1, 4], observations[observations.RowCount - 1, 5], targets[targets.Count() - 1] };
var learner = new RegressionRandomForestLearner(trees: 500);
model = learner.Learn(observations, targets);
#endregion
#region Teste da Floresta Aleatória
parser = new CsvParser(() => new StringReader(teste));
var observationsTeste = parser.EnumerateRows(c => c != targetName)
.ToF64Matrix();
var targetsTeste = parser.EnumerateRows(targetName)
.ToF64Vector();
// predict the training and test set.
var trainPredictions = model.Predict(observations);
var testPredictions = model.Predict(observationsTeste);
// create the metric
var metric = new MeanSquaredErrorRegressionMetric();
// measure the error on training and test set.
trainError = metric.Error(targets, trainPredictions);
testError = metric.Error(targetsTeste, testPredictions);
#endregion
#region Treinamento da Rede Neural
var net = new NeuralNet();
net.Add(new InputLayer(6));
net.Add(new DropoutLayer(0.2));
net.Add(new DenseLayer(800, Activation.Relu));
net.Add(new DropoutLayer(0.5));
net.Add(new DenseLayer(800, Activation.Relu));
net.Add(new DropoutLayer(0.5));
net.Add(new SquaredErrorRegressionLayer());
var learnernet = new RegressionNeuralNetLearner(net, iterations: 500, loss: new SquareLoss());
modelnet = learnernet.Learn(observations, targets);
#endregion
#region Teste da Rede Neural
trainPredictions = modelnet.Predict(observations);
testPredictions = modelnet.Predict(observationsTeste);
trainErrorNet = metric.Error(targets, trainPredictions);
testErrorNet = metric.Error(targetsTeste, testPredictions);
#endregion
#region Treinamento Ada
var learnerada = new RegressionAdaBoostLearner(maximumTreeDepth: 35, iterations: 2000, learningRate: 0.1);
modelada = learnerada.Learn(observations, targets);
#endregion
#region Teste Ada
trainPredictions = modelada.Predict(observations);
testPredictions = modelada.Predict(observationsTeste);
trainErrorAda = metric.Error(targets, trainPredictions);
testErrorAda = metric.Error(targetsTeste, testPredictions);
string stargets = "";
string strainPredictions = "";
string stargetsTeste = "";
string stestPredictions = "";
foreach (var i in targets)
{
stargets += i + ";";
}
foreach (var i in trainPredictions)
{
strainPredictions += i + ";";
}
foreach (var i in targetsTeste)
{
stargetsTeste += i + ";";
}
foreach (var i in testPredictions)
{
stestPredictions += i + ";";
}
#endregion
}
