/// <summary>
/// Trains each model to recognize each of the output labels.
/// </summary>
/// <returns>The sum log-likelihood for all models after training.</returns>
///
protected double Run<T>(T[] inputs, int[] outputs)
{
int classes = classifier.Classes;
double[] logLikelihood = new double[classes];
// For each model,
#if !DEBUG
GABIZ.Base.Parallel.For(0, classes, i =>
#else
for (int i = 0; i < classes; i++)
#endif
{
// Select the input/output set corresponding
// to the model's specialization class
int[] inx = outputs.Find(y => y == i);
T[] observations = inputs.Submatrix(inx);
if (observations.Length > 0)
{
// Create and configure the learning algorithm
IUnsupervisedLearning teacher = algorithm(i);
// Train the current model in the input/output subset
logLikelihood[i] = teacher.Run(observations as Array[]);
}
}
public double Run(int[][] observations_db, int[] class_labels)
{
ValidationHelper.ValidateObservationDb(observations_db, 0, mClassifier.SymbolCount);
int class_count = mClassifier.ClassCount;
double[] logLikelihood = new double[class_count];
int K = class_labels.Length;
DiagnosticsHelper.Assert(observations_db.Length == K);
int[] class_label_counts = new int[class_count];
Parallel.For(0, class_count, i =>
{
IUnsupervisedLearning teacher = mAlgorithmEntity(i);
List <int> match_record_index_set = new List <int>();
for (int k = 0; k < K; ++k)
{
if (class_labels[k] == i)
{
match_record_index_set.Add(k);
}
}
int K2 = match_record_index_set.Count;
class_label_counts[i] = K2;
if (K2 != 0)
{
int[][] observations_subdb = new int[K2][];
for (int k = 0; k < K2; ++k)
{
int record_index = match_record_index_set[k];
observations_subdb[k] = observations_db[record_index];
}
logLikelihood[i] = teacher.Run(observations_subdb);
}
});
if (mEmpirical)
{
for (int i = 0; i < class_count; i++)
{
mClassifier.Priors[i] = (double)class_label_counts[i] / K;
}
}
//if (mRejection)
//{
// mClassifier.Threshold = Threshold();
//}
return(logLikelihood.Sum());
}
/// <summary>
/// Trains each model to recognize each of the output labels.
/// </summary>
/// <returns>The sum log-likelihood for all models after training.</returns>
///
protected double Run<T>(T[] inputs, int[] outputs)
{
if (inputs == null) throw new ArgumentNullException("inputs");
if (outputs == null) throw new ArgumentNullException("outputs");
if (inputs.Length != outputs.Length)
throw new DimensionMismatchException("outputs",
"The number of inputs and outputs does not match.");
for (int i = 0; i < outputs.Length; i++)
if (outputs[i] < 0 || outputs[i] >= Classifier.Classes)
throw new ArgumentOutOfRangeException("outputs");
int classes = Classifier.Classes;
double[] logLikelihood = new double[classes];
int[] classCounts = new int[classes];
// For each model,
#if !DEBUG
Parallel.For(0, classes, i =>
#else
for (int i = 0; i < classes; i++)
#endif
{
// We will start the class model learning problem
var args = new GenerativeLearningEventArgs(i, classes);
OnGenerativeClassModelLearningStarted(args);
// Select the input/output set corresponding
// to the model's specialization class
int[] inx = outputs.Find(y => y == i);
T[] observations = inputs.Submatrix(inx);
classCounts[i] = observations.Length;
if (observations.Length > 0)
{
// Create and configure the learning algorithm
IUnsupervisedLearning teacher = Algorithm(i);
// Train the current model in the input/output subset
logLikelihood[i] = teacher.Run(observations as Array[]);
}
// Update and report progress
OnGenerativeClassModelLearningFinished(args);
}
/// <summary>
/// Constructs a new <see cref="BagOfAudioWords"/>.
/// </summary>
///
/// <param name="extractor">The feature extractor to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfAudioWords(IAudioFeatureExtractor <TFeature> extractor,
IUnsupervisedLearning <IClassifier <double[], int>, double[], int> algorithm)
{
base.Init(extractor, algorithm);
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/>.
/// </summary>
///
/// <param name="detector">The feature detector to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IFeatureDetector <TPoint, TFeature> detector,
//IClusteringAlgorithm<TFeature>
IUnsupervisedLearning <IClassifier <TFeature, int>, TFeature, int> algorithm)
{
Init(detector, algorithm);
}
protected double Run <T>(T[] inputs, int[] outputs)
{
if (inputs == null)
{
throw new ArgumentNullException("inputs");
}
if (outputs == null)
{
throw new ArgumentNullException("outputs");
}
if (inputs.Length != outputs.Length)
{
throw new DimensionMismatchException("outputs",
"The number of inputs and outputs does not match.");
}
for (int i = 0; i < outputs.Length; i++)
{
if (outputs[i] < 0 || outputs[i] >= Classifier.Classes)
{
throw new ArgumentOutOfRangeException("outputs");
}
}
int classes = Classifier.Classes;
double[] logLikelihood = new double[classes];
int[] classCounts = new int[classes];
// For each model,
Parallel.For(0, classes, i =>
{
// We will start the class model learning problem
var args = new GenerativeLearningEventArgs(i, classes);
OnGenerativeClassModelLearningStarted(args);
// Select the input/output set corresponding
// to the model's specialization class
int[] inx = outputs.Find(y => y == i);
T[] observations = inputs.Get(inx);
classCounts[i] = observations.Length;
if (observations.Length > 0)
{
// Create and configure the learning algorithm
IUnsupervisedLearning teacher = Algorithm(i);
// Train the current model in the input/output subset
logLikelihood[i] = teacher.Run(observations as Array[]);
}
// Update and report progress
OnGenerativeClassModelLearningFinished(args);
});
if (Empirical)
{
for (int i = 0; i < classes; i++)
{
Classifier.Priors[i] = (double)classCounts[i] / inputs.Length;
}
}
if (Rejection)
{
Classifier.Threshold = Threshold();
}
// Returns the sum log-likelihood for all models.
return(logLikelihood.Sum());
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/> using a
/// <see cref="SpeededUpRobustFeaturesDetector">surf</see>
/// feature detector to identify features.
/// </summary>
///
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IUnsupervisedLearning <IClassifier <double[], int>, double[], int> algorithm)
{
base.Init(new SpeededUpRobustFeaturesDetector(), algorithm);
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/>.
/// </summary>
///
/// <param name="detector">The feature detector to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IFeatureDetector <TPoint> detector, //IClusteringAlgorithm<double[]>
IUnsupervisedLearning <IClassifier <double[], int>, double[], int> algorithm)
{
base.Init(detector, algorithm);
}
/// <summary>
/// Constructs a new <see cref="BagOfAudioWords"/>.
/// </summary>
///
/// <param name="extractor">The feature extractor to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfAudioWords(IAudioFeatureExtractor <TFeature> extractor,
IUnsupervisedLearning <IClassifier <TPoint, int>, TPoint, int> algorithm)
{
Init(extractor, algorithm);
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/>.
/// </summary>
///
/// <param name="extractor">The feature extractor to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IImageFeatureExtractor <TPoint> extractor, //IClusteringAlgorithm<double[]>
IUnsupervisedLearning <IClassifier <double[], int>, double[], int> algorithm)
{
base.Init(extractor, algorithm);
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/>.
/// </summary>
///
/// <param name="extractor">The feature extractor to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IImageFeatureExtractor <TPoint> extractor,
IUnsupervisedLearning <IClassifier <TFeature, int>, TFeature, int> algorithm)
{
Init(extractor, algorithm);
}
/// <summary>
/// Constructs a new <see cref="BagOfAudioWords"/> using a
/// <see cref="MelFrequencyCepstrumCoefficient">MFCC</see>
/// feature detector to identify features.
/// </summary>
///
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfAudioWords(
IUnsupervisedLearning <IClassifier <double[], int>, double[], int> algorithm)
{
base.Init(new MelFrequencyCepstrumCoefficient(), algorithm);
}