The BrayCurtis Distance or Sorensen Distance is similar to the Manhattan Distance with normalization.
where \(\bf x\) and \(\bf x'\) are \(n\) dimensional feature vectors.
Imagine we have files with data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) as
features_a = RealFeatures(f_feats_a)
features_b = RealFeatures(f_feats_b)
features_a = RealFeatures(f_feats_a);
features_b = RealFeatures(f_feats_b);
RealFeatures features_a = new RealFeatures(f_feats_a);
RealFeatures features_b = new RealFeatures(f_feats_b);
features_a = Modshogun::RealFeatures.new f_feats_a
features_b = Modshogun::RealFeatures.new f_feats_b
features_a <- RealFeatures(f_feats_a)
features_b <- RealFeatures(f_feats_b)
features_a = modshogun.RealFeatures(f_feats_a)
features_b = modshogun.RealFeatures(f_feats_b)
RealFeatures features_a = new RealFeatures(f_feats_a);
RealFeatures features_b = new RealFeatures(f_feats_b);
auto features_a = some<CDenseFeatures<float64_t>>(f_feats_a);
auto features_b = some<CDenseFeatures<float64_t>>(f_feats_b);
We create an instance of CBrayCurtisDistance by passing it CDenseFeatures.
distance = BrayCurtisDistance(features_a, features_a)
distance = BrayCurtisDistance(features_a, features_a);
BrayCurtisDistance distance = new BrayCurtisDistance(features_a, features_a);
distance = Modshogun::BrayCurtisDistance.new features_a, features_a
distance <- BrayCurtisDistance(features_a, features_a)
distance = modshogun.BrayCurtisDistance(features_a, features_a)
BrayCurtisDistance distance = new BrayCurtisDistance(features_a, features_a);
auto distance = some<CBrayCurtisDistance>(features_a, features_a);
The distance matrix can be extracted as follows:
distance_matrix_aa = distance.get_distance_matrix()
distance_matrix_aa = distance.get_distance_matrix();
DoubleMatrix distance_matrix_aa = distance.get_distance_matrix();
distance_matrix_aa = distance.get_distance_matrix
distance_matrix_aa <- distance$get_distance_matrix()
distance_matrix_aa = distance:get_distance_matrix()
double[,] distance_matrix_aa = distance.get_distance_matrix();
auto distance_matrix_aa = distance->get_distance_matrix();
We can use the same instance with new CDenseFeatures to compute asymmetrical distance as follows:
distance.init(features_a, features_b)
distance_matrix_ab = distance.get_distance_matrix()
distance.init(features_a, features_b);
distance_matrix_ab = distance.get_distance_matrix();
distance.init(features_a, features_b);
DoubleMatrix distance_matrix_ab = distance.get_distance_matrix();
distance.init features_a, features_b
distance_matrix_ab = distance.get_distance_matrix
distance$init(features_a, features_b)
distance_matrix_ab <- distance$get_distance_matrix()
distance:init(features_a, features_b)
distance_matrix_ab = distance:get_distance_matrix()
distance.init(features_a, features_b);
double[,] distance_matrix_ab = distance.get_distance_matrix();
distance->init(features_a, features_b);
auto distance_matrix_ab = distance->get_distance_matrix();
‘BrayCurtis Distance <http://people.revoledu.com/kardi/tutorial/Similarity/BrayCurtisDistance.html>’