@ -1038,7 +1038,7 @@ double ERClassifierNM1::eval(const ERStat& stat)
@@ -1038,7 +1038,7 @@ double ERClassifierNM1::eval(const ERStat& stat)
( float ) ( 1 - stat . euler ) , //number of holes
stat . med_crossings ) ;
float votes = boost - > predict ( sample , noArray ( ) , DTrees : : PREDICT_SUM | StatModel : : RAW_OUTPUT ) ;
float votes = boost - > predict ( sample , noArray ( ) , ( int ) DTrees : : PREDICT_SUM | ( int ) StatModel : : RAW_OUTPUT ) ;
// Logistic Correction returns a probability value (in the range(0,1))
return ( double ) 1 - ( double ) 1 / ( 1 + exp ( - 2 * votes ) ) ;
@ -1070,7 +1070,7 @@ double ERClassifierNM2::eval(const ERStat& stat)
@@ -1070,7 +1070,7 @@ double ERClassifierNM2::eval(const ERStat& stat)
stat . med_crossings , stat . hole_area_ratio ,
stat . convex_hull_ratio , stat . num_inflexion_points ) ;
float votes = boost - > predict ( sample , noArray ( ) , DTrees : : PREDICT_SUM | StatModel : : RAW_OUTPUT ) ;
float votes = boost - > predict ( sample , noArray ( ) , ( int ) DTrees : : PREDICT_SUM | ( int ) StatModel : : RAW_OUTPUT ) ;
// Logistic Correction returns a probability value (in the range(0,1))
return ( double ) 1 - ( double ) 1 / ( 1 + exp ( - 2 * votes ) ) ;
@ -2152,6 +2152,11 @@ void MaxMeaningfulClustering::build_merge_info(double *Z, double *X, int N, int
@@ -2152,6 +2152,11 @@ void MaxMeaningfulClustering::build_merge_info(double *Z, double *X, int N, int
{
HCluster cluster ;
cluster . num_elem = ( int ) Z [ i + 3 ] ; //number of elements
cluster . nfa = 0 ;
cluster . dist_ext = 0.0f ;
cluster . max_meaningful = false ;
cluster . min_nfa_in_branch = 0 ;
cluster . probability = 0.0 ;
int node1 = ( int ) Z [ i ] ;
int node2 = ( int ) Z [ i + 1 ] ;
@ -2611,7 +2616,7 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
@@ -2611,7 +2616,7 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
sample . push_back ( ( float ) mean [ 0 ] ) ;
sample . push_back ( ( float ) std [ 0 ] ) ;
float votes_group = group_boost - > predict ( Mat ( sample ) , noArray ( ) , DTrees : : PREDICT_SUM | StatModel : : RAW_OUTPUT ) ;
float votes_group = group_boost - > predict ( Mat ( sample ) , noArray ( ) , ( int ) DTrees : : PREDICT_SUM | ( int ) StatModel : : RAW_OUTPUT ) ;
return ( double ) 1 - ( double ) 1 / ( 1 + exp ( - 2 * votes_group ) ) ;
}