learning.cpp
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00001 /*
00002  *  learning.cpp
00003  *  outlet_model
00004  *
00005  *  Created by Victor  Eruhimov on 12/29/08.
00006  *  Copyright 2008 Argus Corp. All rights reserved.
00007  *
00008  */
00009 
00010 #include <stdio.h>
00011 #include "outlet_pose_estimation/detail/learning.h"
00012 
00013 CvRTrees* train_rf(CvMat* predictors, CvMat* labels)
00014 {
00015         int stat[2];
00016         get_stat(labels, stat);
00017         printf("%d negative samples, %d positive samples\n", stat[0], stat[1]);
00018         
00019         const int tree_count = 500;
00020         const float priors[] = {0.25f,0.75f};
00021         CvRTrees* rtrees = new CvRTrees();
00022         CvRTParams rtparams = CvRTParams(5, 10, 0, false, 2, priors, true, 
00023                                                                          (int)sqrt((float)predictors->cols), tree_count, 1e-6, 
00024                                                                          CV_TERMCRIT_ITER + CV_TERMCRIT_EPS);
00025         CvMat* var_type = cvCreateMat(predictors->cols + 1, 1, CV_8UC1);
00026         for(int i = 0; i < predictors->cols; i++)
00027         {
00028                 *(int*)(var_type->data.ptr + i*var_type->step) = CV_VAR_NUMERICAL;
00029         }
00030         *(int*)(var_type->data.ptr + predictors->cols*var_type->step) = CV_VAR_CATEGORICAL;
00031         rtrees->train(predictors, CV_ROW_SAMPLE, labels, 0, 0, var_type, 0, rtparams);
00032         return rtrees;
00033 }
00034 
00035 void get_stat(CvMat* labels, int* stat)
00036 {
00037         stat[0] = 0;
00038         stat[1] = 0;
00039         for(int i = 0; i < labels->rows; i++)
00040         {
00041                 int val = *(int*)(labels->data.ptr + labels->step*i);
00042                 stat[val]++;
00043         }
00044 }


outlet_pose_estimation
Author(s): Patrick Mihelich
autogenerated on Thu Aug 27 2015 14:29:52