public void GenericXmlDataContractSerializer_Deserialize_Dont_PreserveObjectReferences()
{
var sut = new GenericXmlDataContractSerializer(preserveObjectReferences: false);
var reader = new StringReader(m_serializationString_DontPreserveObjectReferences);
var actual = sut.Deserialize <Dictionary <string, int> >(() => reader);
CollectionAssert.AreEqual(m_serializationData, actual);
}
public void GenericXmlDataContractSerializer_Deserialize()
{
var sut = new GenericXmlDataContractSerializer();
var reader = new StringReader(m_serializationString);
var actual = sut.Deserialize <Dictionary <string, int> >(() => reader);
CollectionAssert.AreEqual(m_serializationData, actual);
}
public void GenericXmlDataContractSerializer_Serialize_Dont_PreserveObjectReferences()
{
var writer = new StringWriter();
var sut = new GenericXmlDataContractSerializer(preserveObjectReferences: false);
sut.Serialize(m_serializationData, () => writer);
Assert.AreEqual(m_serializationString_DontPreserveObjectReferences, writer.ToString());
}
public void GenericXmlDataContractSerializer_Serialize()
{
var writer = new StringWriter();
var sut = new GenericXmlDataContractSerializer();
sut.Serialize(m_serializationData, () => writer);
Assert.AreEqual(m_serializationString, writer.ToString());
}
public void FeatureNormalization_Normalize()
{
// Use StreamReader(filepath) when running from filesystem
var parser = new CsvParser(() => new StringReader(Resources.winequality_white));
var targetName = "quality";
// read feature matrix (all columns different from the targetName)
var observations = parser.EnumerateRows(c => c != targetName)
.ToF64Matrix();
// create minmax normalizer (normalizes each feature from 0.0 to 1.0)
var minMaxTransformer = new MinMaxTransformer(0.0, 1.0);
// transforms features using the feature normalization transform
minMaxTransformer.Transform(observations, observations);
// read targets
var targets = parser.EnumerateRows(targetName)
.ToF64Vector();
// Create neural net.
var net = new NeuralNet();
net.Add(new InputLayer(observations.ColumnCount));
net.Add(new SquaredErrorRegressionLayer());
// Create regression learner.
var learner = new RegressionNeuralNetLearner(net, new SquareLoss());
// learns a neural net regression model.
var model = learner.Learn(observations, targets);
// serializer for saving the MinMaxTransformer
var serializer = new GenericXmlDataContractSerializer();
// Serialize transform for use with the model.
// Replace this with StreamWriter for use with file system.
var data = new StringBuilder();
var writer = new StringWriter(data);
serializer.Serialize(minMaxTransformer, () => writer);
// Deserialize transform for use with the model.
// Replace this with StreamReader for use with file system.
var reader = new StringReader(data.ToString());
var deserializedMinMaxTransform = serializer.Deserialize <MinMaxTransformer>(() => reader);
// Normalize observation and predict using the model.
var normalizedObservation = deserializedMinMaxTransform.Transform(observations.Row(0));
var prediction = model.Predict(normalizedObservation);
Trace.WriteLine($"Prediction: {prediction}");
}
public void RandomForest_Default_Parameters_Save_Load_Model_Using_Serializer()
{
#region read and split data
// Use StreamReader(filepath) when running from filesystem
var parser = new CsvParser(() => new StringReader(Resources.winequality_white));
var targetName = "quality";
// read feature matrix
var observations = parser.EnumerateRows(c => c != targetName)
.ToF64Matrix();
// read regression targets
var targets = parser.EnumerateRows(targetName)
.ToF64Vector();
// creates training test splitter,
// Since this is a regression problem, we use the random training/test set splitter.
// 30 % of the data is used for the test set.
var splitter = new RandomTrainingTestIndexSplitter <double>(trainingPercentage: 0.7, seed: 24);
var trainingTestSplit = splitter.SplitSet(observations, targets);
var trainSet = trainingTestSplit.TrainingSet;
var testSet = trainingTestSplit.TestSet;
#endregion
// create learner with default parameters
var learner = new RegressionRandomForestLearner(trees: 100);
// learn model with found parameters
var model = learner.Learn(trainSet.Observations, trainSet.Targets);
// predict the training and test set.
var trainPredictions = model.Predict(trainSet.Observations);
var testPredictions = model.Predict(testSet.Observations);
// since this is a regression problem we are using square error as metric
// for evaluating how well the model performs.
var metric = new MeanSquaredErrorRegressionMetric();
// measure the error on training and test set.
var trainError = metric.Error(trainSet.Targets, trainPredictions);
var testError = metric.Error(testSet.Targets, testPredictions);
TraceTrainingAndTestError(trainError, testError);
//Save/load model as xml, in the file system use new StreamWriter(filePath);
var xmlSerializer = new GenericXmlDataContractSerializer();
var savedModelXml = new StringWriter();
xmlSerializer
.Serialize <IPredictorModel <double> >(model, () => savedModelXml);
var loadedModelXml = xmlSerializer
.Deserialize <IPredictorModel <double> >(() => new StringReader(savedModelXml.ToString()));
//Save/load model as binary, in the file system use new StreamWriter(filePath);
var binarySerializer = new GenericBinarySerializer();
var savedModelBinary = new StringWriter();
binarySerializer
.Serialize <IPredictorModel <double> >(model, () => savedModelBinary);
var loadedModelBinary = binarySerializer
.Deserialize <IPredictorModel <double> >(() => new StringReader(savedModelBinary.ToString()));
}
