Learns Gaussian RBF function and centers from data.
Learns Gaussian RBF function and centers from data. The centers are chosen as the medoids of CLARANS. The standard deviation (i.e. width) of Gaussian radial basis function is estimated as the width of each cluster multiplied with a given scaling parameter r.
the training dataset.
an array to store centers on output. Its length is used as k of CLARANS.
the distance functor.
the scaling parameter.
Gaussian RBF functions with parameter learned from data.
Learns Gaussian RBF function and centers from data.
Learns Gaussian RBF function and centers from data. The centers are chosen as the medoids of CLARANS. The standard deviation (i.e. width) of Gaussian radial basis function is estimated by the p-nearest neighbors (among centers, not all samples) heuristic. A suggested value for p is 2.
the training dataset.
an array to store centers on output. Its length is used as k of CLARANS.
the distance functor.
the number of nearest neighbors of centers to estimate the width of Gaussian RBF functions.
Gaussian RBF functions with parameter learned from data.
Learns Gaussian RBF function and centers from data.
Learns Gaussian RBF function and centers from data. The centers are chosen as the medoids of CLARANS. Let d_{max} be the maximum distance between the chosen centers, the standard deviation (i.e. width) of Gaussian radial basis function is d_{max} / sqrt(2*k), where k is number of centers. In this way, the radial basis functions are not too peaked or too flat. This choice would be close to the optimal solution if the data were uniformly distributed in the input space, leading to a uniform distribution of medoids.
the training dataset.
an array to store centers on output. Its length is used as k of CLARANS.
the distance functor.
a Gaussian RBF function with parameter learned from data.
Learns Gaussian RBF function and centers from data.
Learns Gaussian RBF function and centers from data. The centers are chosen as the centroids of K-Means. The standard deviation (i.e. width) of Gaussian radial basis function is estimated as the width of each cluster multiplied with a given scaling parameter r.
the training dataset.
an array to store centers on output. Its length is used as k of k-means.
the scaling parameter.
Gaussian RBF functions with parameter learned from data.
Learns Gaussian RBF function and centers from data.
Learns Gaussian RBF function and centers from data. The centers are chosen as the centroids of K-Means. The standard deviation (i.e. width) of Gaussian radial basis function is estimated by the p-nearest neighbors (among centers, not all samples) heuristic. A suggested value for p is 2.
the training dataset.
an array to store centers on output. Its length is used as k of k-means.
the number of nearest neighbors of centers to estimate the width of Gaussian RBF functions.
Gaussian RBF functions with parameter learned from data.
Learns Gaussian RBF function and centers from data.
Learns Gaussian RBF function and centers from data. The centers are chosen as the centroids of K-Means. Let d_{max} be the maximum distance between the chosen centers, the standard deviation (i.e. width) of Gaussian radial basis function is d_{max} / sqrt(2*k), where k is number of centers. This choice would be close to the optimal solution if the data were uniformly distributed in the input space, leading to a uniform distribution of centroids.
the training dataset.
an array to store centers on output. Its length is used as k of k-means.
a Gaussian RBF function with parameter learned from data.
Returns the pairwise Euclidean distance matrix.
Returns the pairwise Euclidean distance matrix.
the data set.
if true, only the lower half of matrix is allocated to save space.
the lower half of proximity matrix.
Returns the proximity matrix of a dataset for given distance function.
Returns the proximity matrix of a dataset for given distance function.
the data set.
the distance function.
if true, only the lower half of matrix is allocated to save space.
the lower half of proximity matrix.
Measure running time of a function/block
Utility functions.