/// <summary>
/// Creates a new (labeled) training set from a file.
/// </summary>
/// <param name="trainingSetFileName">The name of the file containing the training set.</param>
public TrainingSet(string trainingSetFileName)
{
TextReader textReader = new StreamReader(trainingSetFileName);
const char separator = ' ';
//
// 1. Read the input vector length and the input vector length.
//
string line = textReader.ReadLine();
string[] words = line.Split(separator);
// Validate the input vector length.
int inputVectorLength = Int32.Parse(words[0]);
Utilities.NumberPositive(inputVectorLength, "inputVectorLength");
this.inputVectorLength = inputVectorLength;
// Validate the output vector length.
int outputVectorLength = Int32.Parse(words[1]);
Utilities.NumberPositive(outputVectorLength, "outputVectorLength");
this.outputVectorLength = outputVectorLength;
// Skip the blank line.
textReader.ReadLine();
//
// 2. Create the training patterns.
//
trainingPatterns = new List <TrainingPattern>();
while ((line = textReader.ReadLine()) != null)
{
words = line.Split(separator);
// 2.1. Create the input vector.
double[] inputVector = new double[inputVectorLength];
for (int i = 0; i < inputVectorLength; i++)
{
inputVector[i] = Double.Parse(words[i]);
}
// 2.2. Create the output vector length.
double[] outputVector = new double[outputVectorLength];
for (int i = 0; i < outputVectorLength; i++)
{
outputVector[i] = Double.Parse(words[inputVectorLength + i]);
}
// 2.3. Add the training pattern into the training set.
TrainingPattern trainingPattern = new TrainingPattern(inputVector, outputVector);
Add(trainingPattern);
}
}
/// <summary>
/// <para>
/// Adds a (supervised) training pattern to the training set.
/// </para>
/// <para>
/// Note that if the training pattern already exists in the set, it will be replaced.
/// </para>
/// </summary>
/// <param name="trainingPattern">The training pattern to add.</param>
/// <exception cref="ArgumentNullException">
/// Condition: <c>trainingPattern</c> is <c>null</c>.
/// </exception>
/// <exception cref="ArgumentException">
/// Condition: the lengths of input vector or output vector (from the training pattern) differ from their expected lengths (from the training set).
/// </exception>
public void Add(TrainingPattern trainingPattern)
{
// Validatate the arguments.
Utilities.ObjectNotNull(trainingPattern, "trainingPattern");
// Validate the input vector length.
if (trainingPattern.InputVector.Length != inputVectorLength)
{
throw new ArgumentException("The input vector must be of size " + inputVectorLength, "trainingPattern");
}
// Validate the output vector length.
if (trainingPattern.OutputVector.Length != outputVectorLength)
{
throw new ArgumentException("The output vector must be of size " + outputVectorLength, "trainingPattern");
}
// Add the training pattern to the training set.
trainingPatterns.Add(trainingPattern);
}
/// <summary>
/// Removes a training pattern from the training set.
/// </summary>
/// <param name="trainingPattern">The training sample to remove.</param>
/// <returns>
/// <c>True</c> if successful, <c>false</c> otherwise.
/// </returns>
public bool Remove(TrainingPattern trainingPattern)
{
return(trainingPatterns.Remove(trainingPattern));
}
/// <summary>
/// Determines whether the training set contains a training pattern.
/// </summary>
/// <param name="trainingPattern">The training sample to check for containment.</param>
/// <returns>
/// <c>True</c> if contains, <c>false</c> otherwise.
/// </returns>
public bool Contains(TrainingPattern trainingPattern)
{
return(trainingPatterns.Contains(trainingPattern));
}
