public void Run()
{
var bestMethod = _model.Crossvalidate(5, true) as IMLRegression;
Console.WriteLine($"Training error: {_model.CalculateError(bestMethod, _model.TrainingDataset)}");
Console.WriteLine($"Validation error: {_model.CalculateError(bestMethod, _model.ValidationDataset)}");
var dataDivisions = new List <DataDivision> {
new DataDivision(0.5), new DataDivision(0.5)
};
_dataset.Divide(dataDivisions, true, new MersenneTwisterGenerateRandom());
foreach (var data in dataDivisions[0].Dataset)
{
var result = bestMethod.Compute(data.Input);
Console.WriteLine($"Result: {result}, Actual: {data.Ideal}");
}
EncogDirectoryPersistence.SaveObject(new FileInfo("SvmModel.eg"), bestMethod);
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
ErrorCalculation.Mode = ErrorCalculationMode.RMS;
// Download the data that we will attempt to model.
string filename = DownloadData(app.Args);
// Define the format of the data file.
// This area will change, depending on the columns and
// format of the file that you are trying to model.
var format = new CSVFormat('.', ' '); // decimal point and
// space separated
IVersatileDataSource source = new CSVDataSource(filename, true,
format);
var data = new VersatileMLDataSet(source);
data.NormHelper.Format = format;
ColumnDefinition columnSSN = data.DefineSourceColumn("SSN",
ColumnType.Continuous);
ColumnDefinition columnDEV = data.DefineSourceColumn("DEV",
ColumnType.Continuous);
// Analyze the data, determine the min/max/mean/sd of every column.
data.Analyze();
// Use SSN & DEV to predict SSN. For time-series it is okay to have
// SSN both as
// an input and an output.
data.DefineInput(columnSSN);
data.DefineInput(columnDEV);
data.DefineOutput(columnSSN);
// Create feedforward neural network as the model type.
// MLMethodFactory.TYPE_FEEDFORWARD.
// You could also other model types, such as:
// MLMethodFactory.SVM: Support Vector Machine (SVM)
// MLMethodFactory.TYPE_RBFNETWORK: RBF Neural Network
// MLMethodFactor.TYPE_NEAT: NEAT Neural Network
// MLMethodFactor.TYPE_PNN: Probabilistic Neural Network
var model = new EncogModel(data);
model.SelectMethod(data, MLMethodFactory.TypeFeedforward);
// Send any output to the console.
model.Report = new ConsoleStatusReportable();
// Now normalize the data. Encog will automatically determine the
// correct normalization
// type based on the model you chose in the last step.
data.Normalize();
// Set time series.
data.LeadWindowSize = 1;
data.LagWindowSize = WindowSize;
// Hold back some data for a final validation.
// Do not shuffle the data into a random ordering. (never shuffle
// time series)
// Use a seed of 1001 so that we always use the same holdback and
// will get more consistent results.
model.HoldBackValidation(0.3, false, 1001);
// Choose whatever is the default training type for this model.
model.SelectTrainingType(data);
// Use a 5-fold cross-validated train. Return the best method found.
// (never shuffle time series)
var bestMethod = (IMLRegression)model.Crossvalidate(5,
false);
// Display the training and validation errors.
Console.WriteLine(@"Training error: "
+ model.CalculateError(bestMethod,
model.TrainingDataset));
Console.WriteLine(@"Validation error: "
+ model.CalculateError(bestMethod,
model.ValidationDataset));
// Display our normalization parameters.
NormalizationHelper helper = data.NormHelper;
Console.WriteLine(helper.ToString());
// Display the final model.
Console.WriteLine(@"Final model: " + bestMethod);
// Loop over the entire, original, dataset and feed it through the
// model. This also shows how you would process new data, that was
// not part of your training set. You do not need to retrain, simply
// use the NormalizationHelper class. After you train, you can save
// the NormalizationHelper to later normalize and denormalize your
// data.
source.Close();
var csv = new ReadCSV(filename, true, format);
var line = new String[2];
// Create a vector to hold each time-slice, as we build them.
// These will be grouped together into windows.
var slice = new double[2];
var window = new VectorWindow(WindowSize + 1);
IMLData input = helper.AllocateInputVector(WindowSize + 1);
// Only display the first 100
int stopAfter = 100;
while (csv.Next() && stopAfter > 0)
{
var result = new StringBuilder();
line[0] = csv.Get(2); // ssn
line[1] = csv.Get(3); // dev
helper.NormalizeInputVector(line, slice, false);
// enough data to build a full window?
if (window.IsReady())
{
window.CopyWindow(((BasicMLData)input).Data, 0);
String correct = csv.Get(2); // trying to predict SSN.
IMLData output = bestMethod.Compute(input);
String predicted = helper
.DenormalizeOutputVectorToString(output)[0];
result.Append(line);
result.Append(" -> predicted: ");
result.Append(predicted);
result.Append("(correct: ");
result.Append(correct);
result.Append(")");
Console.WriteLine(result.ToString());
}
// Add the normalized slice to the window. We do this just after
// the after checking to see if the window is ready so that the
// window is always one behind the current row. This is because
// we are trying to predict next row.
window.Add(slice);
stopAfter--;
}
csv.Close();
// Delete data file and shut down.
File.Delete(filename);
EncogFramework.Instance.Shutdown();
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
// Download the data that we will attempt to model.
string irisFile = DownloadData(app.Args);
// Define the format of the data file.
// This area will change, depending on the columns and
// format of the file that you are trying to model.
IVersatileDataSource source = new CSVDataSource(irisFile, false,
CSVFormat.DecimalPoint);
var data = new VersatileMLDataSet(source);
data.DefineSourceColumn("sepal-length", 0, ColumnType.Continuous);
data.DefineSourceColumn("sepal-width", 1, ColumnType.Continuous);
data.DefineSourceColumn("petal-length", 2, ColumnType.Continuous);
data.DefineSourceColumn("petal-width", 3, ColumnType.Continuous);
// Define the column that we are trying to predict.
ColumnDefinition outputColumn = data.DefineSourceColumn("species", 4,
ColumnType.Nominal);
// Analyze the data, determine the min/max/mean/sd of every column.
data.Analyze();
// Map the prediction column to the output of the model, and all
// other columns to the input.
data.DefineSingleOutputOthersInput(outputColumn);
// Create feedforward neural network as the model type. MLMethodFactory.TYPE_FEEDFORWARD.
// You could also other model types, such as:
// MLMethodFactory.SVM: Support Vector Machine (SVM)
// MLMethodFactory.TYPE_RBFNETWORK: RBF Neural Network
// MLMethodFactor.TYPE_NEAT: NEAT Neural Network
// MLMethodFactor.TYPE_PNN: Probabilistic Neural Network
var model = new EncogModel(data);
model.SelectMethod(data, MLMethodFactory.TypeFeedforward);
// Send any output to the console.
model.Report = new ConsoleStatusReportable();
// Now normalize the data. Encog will automatically determine the correct normalization
// type based on the model you chose in the last step.
data.Normalize();
// Hold back some data for a final validation.
// Shuffle the data into a random ordering.
// Use a seed of 1001 so that we always use the same holdback and will get more consistent results.
model.HoldBackValidation(0.3, true, 1001);
// Choose whatever is the default training type for this model.
model.SelectTrainingType(data);
// Use a 5-fold cross-validated train. Return the best method found.
var bestMethod = (IMLRegression)model.Crossvalidate(5, true);
// Display the training and validation errors.
Console.WriteLine(@"Training error: " + model.CalculateError(bestMethod, model.TrainingDataset));
Console.WriteLine(@"Validation error: " + model.CalculateError(bestMethod, model.ValidationDataset));
// Display our normalization parameters.
NormalizationHelper helper = data.NormHelper;
Console.WriteLine(helper.ToString());
// Display the final model.
Console.WriteLine(@"Final model: " + bestMethod);
// Loop over the entire, original, dataset and feed it through the model.
// This also shows how you would process new data, that was not part of your
// training set. You do not need to retrain, simply use the NormalizationHelper
// class. After you train, you can save the NormalizationHelper to later
// normalize and denormalize your data.
source.Close();
var csv = new ReadCSV(irisFile, false, CSVFormat.DecimalPoint);
var line = new String[4];
IMLData input = helper.AllocateInputVector();
while (csv.Next())
{
var result = new StringBuilder();
line[0] = csv.Get(0);
line[1] = csv.Get(1);
line[2] = csv.Get(2);
line[3] = csv.Get(3);
String correct = csv.Get(4);
helper.NormalizeInputVector(line, ((BasicMLData)input).Data, false);
IMLData output = bestMethod.Compute(input);
String irisChosen = helper.DenormalizeOutputVectorToString(output)[0];
result.Append(line);
result.Append(" -> predicted: ");
result.Append(irisChosen);
result.Append("(correct: ");
result.Append(correct);
result.Append(")");
Console.WriteLine(result.ToString());
}
csv.Close();
// Delete data file ande shut down.
File.Delete(irisFile);
EncogFramework.Instance.Shutdown();
}