The Euclidean distance for real valued features is the square root of the sum of squared disparity between the corresponding feature dimensions of two data points.
where \(\bf x\) and \(\bf x'\) are \(d\) 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)
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 CEuclideanDistance by passing it CDenseFeatures.
distance = EuclideanDistance(features_a, features_a)
distance = EuclideanDistance(features_a, features_a);
EuclideanDistance distance = new EuclideanDistance(features_a, features_a);
distance = Modshogun::EuclideanDistance.new features_a, features_a
distance <- EuclideanDistance(features_a, features_a)
EuclideanDistance distance = new EuclideanDistance(features_a, features_a);
auto distance = some<CEuclideanDistance>(features_a, features_a);
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$get_distance_matrix()
double[,] distance_matrix_aa = distance.get_distance_matrix();
auto distance_matrix_aa = distance->get_distance_matrix();
We can use the same instance with new CDenseFeatures to compute distance.
distance.init(features_a, features_b)
distance.init(features_a, features_b);
distance.init(features_a, features_b);
distance.init features_a, features_b
distance$init(features_a, features_b)
distance.init(features_a, features_b);
distance->init(features_a, features_b);
If desired, squared distance can be extracted like:
distance.set_disable_sqrt(True)
distance_matrix_ab = distance.get_distance_matrix()
distance.set_disable_sqrt(true);
distance_matrix_ab = distance.get_distance_matrix();
distance.set_disable_sqrt(true);
DoubleMatrix distance_matrix_ab = distance.get_distance_matrix();
distance.set_disable_sqrt 1
distance_matrix_ab = distance.get_distance_matrix
distance$set_disable_sqrt(TRUE)
distance_matrix_ab <- distance$get_distance_matrix()
distance.set_disable_sqrt(true);
double[,] distance_matrix_ab = distance.get_distance_matrix();
distance->set_disable_sqrt(true);
auto distance_matrix_ab = distance->get_distance_matrix();