EmguCV/Emgu.CV.ML/Boost.cs

using System;
using System.Collections.Generic;
using System.Text;
using Emgu.CV;
using Emgu.CV.Structure;
using Emgu.CV.ML.Structure;
using System.Runtime.InteropServices;

namespace Emgu.CV.ML
{
   /// <summary>
   /// Boost Tree 
   /// </summary>
   public class Boost : Emgu.CV.ML.StatModel
   {
      /// <summary>
      /// Create a default Boost classifier
      /// </summary>
      public Boost()
      {
         _ptr = MlInvoke.CvBoostCreate();
      }

      /// <summary>
      /// Train the boost tree using the specific traning data
      /// </summary>
      /// <param name="trainData">The training data. A 32-bit floating-point, single-channel matrix, one vector per row</param>
      /// <param name="tflag">data layout type</param>
      /// <param name="responses">A floating-point matrix of the corresponding output vectors, one vector per row. </param>
      /// <param name="varIdx">Can be null if not needed. When specified, identifies variables (features) of interest. It is a Matrix&gt;int&lt; of nx1</param>
      /// <param name="sampleIdx">Can be null if not needed. When specified, identifies samples of interest. It is a Matrix&gt;int&lt; of nx1</param>
      /// <param name="varType">The types of input variables</param>
      /// <param name="missingMask">Can be null if not needed. When specified, it is an 8-bit matrix of the same size as <paramref name="trainData"/>, is used to mark the missed values (non-zero elements of the mask)</param>
      /// <param name="param">The parameters for training the boost tree</param>
      /// <param name="update">specifies whether the classifier needs to be updated (i.e. the new weak tree classifiers added to the existing ensemble), or the classifier needs to be rebuilt from scratch</param>
      /// <returns></returns>
      public bool Train(
         Matrix<float> trainData,
         MlEnum.DATA_LAYOUT_TYPE tflag,
         Matrix<float> responses,
         Matrix<int> varIdx,
         Matrix<int> sampleIdx,
         Matrix<int> varType,
         Matrix<int> missingMask,
         MCvBoostParams param, 
         bool update)
      {
         return MlInvoke.CvBoostTrain(
            _ptr,
            trainData.Ptr,
            tflag,
            responses.Ptr,
            varIdx == null ? IntPtr.Zero : varIdx.Ptr,
            sampleIdx == null ? IntPtr.Zero : sampleIdx.Ptr,
            varType == null ? IntPtr.Zero : varType.Ptr,
            missingMask == null ? IntPtr.Zero : missingMask.Ptr,
            param,
            update);
      }


      /// <summary>
      /// The method takes the feature vector and the optional missing measurement mask on input, traverses the random tree and returns the cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates)
      /// </summary>
      /// <param name="sample">The sample to be predicted</param>
      /// <param name="missingDataMask">Can be null if not needed. When specified, it is an 8-bit matrix of the same size as <paramref name="trainData"/>, is used to mark the missed values (non-zero elements of the mask)</param>
      /// <param name="weakResponses">Can be null if not needed. a floating-point vector, of responses from each individual weak classifier. The number of elements in the vector must be equal to the slice length.</param>
      /// <param name="slice">The continuous subset of the sequence of weak classifiers to be used for prediction</param>
      /// <param name="rawMode">Normally set to false that implies a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..num_of_categoriesi-1 ranges. (as the decision tree uses such normalized representation internally). It is useful for faster prediction with tree ensembles. For ordered input variables the flag is not used. </param>    
      /// <returns>The cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates)</returns>
      public float Predict(
         Matrix<float> sample,
         Matrix<int> missingDataMask, 
         Matrix<float> weakResponses,
         MCvSlice slice,
         bool rawMode
         )
      {
         return MlInvoke.CvBoostPredict(
            _ptr, 
            sample.Ptr, 
            missingDataMask == null ? IntPtr.Zero : missingDataMask.Ptr,
            weakResponses == null? IntPtr.Zero : weakResponses.Ptr,
            slice,
            rawMode);
      }

      /// <summary>
      /// Release the Boost classifier and all memory associate with it
      /// </summary>
      protected override void DisposeObject()
      {
         MlInvoke.CvBoostRelease(_ptr);
      }
   }
}