util

Value Members

1. def gaussrbf[T <: AnyRef](x: Array[T], centers: Array[T], distance: Metric[T], r: Double): Array[GaussianRadialBasis]

   

   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.

   x

   the training dataset.

   centers

   an array to store centers on output. Its length is used as k of CLARANS.

   distance

   the distance functor.

   r

   the scaling parameter.

   returns

   Gaussian RBF functions with parameter learned from data.

2. def gaussrbf[T <: AnyRef](x: Array[T], centers: Array[T], distance: Metric[T], p: Int): Array[GaussianRadialBasis]

   

   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.

   x

   the training dataset.

   centers

   an array to store centers on output. Its length is used as k of CLARANS.

   distance

   the distance functor.

   p

   the number of nearest neighbors of centers to estimate the width of Gaussian RBF functions.

   returns

   Gaussian RBF functions with parameter learned from data.

3. def gaussrbf[T <: AnyRef](x: Array[T], centers: Array[T], distance: Metric[T]): GaussianRadialBasis

   

   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.

   x

   the training dataset.

   centers

   an array to store centers on output. Its length is used as k of CLARANS.

   distance

   the distance functor.

   returns

   a Gaussian RBF function with parameter learned from data.

4. def gaussrbf(x: Array[Array[Double]], centers: Array[Array[Double]], r: Double): Array[GaussianRadialBasis]

   

   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.

   x

   the training dataset.

   centers

   an array to store centers on output. Its length is used as k of k-means.

   r

   the scaling parameter.

   returns

   Gaussian RBF functions with parameter learned from data.

5. def gaussrbf(x: Array[Array[Double]], centers: Array[Array[Double]], p: Int): Array[GaussianRadialBasis]

   

   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.

   x

   the training dataset.

   centers

   an array to store centers on output. Its length is used as k of k-means.

   p

   the number of nearest neighbors of centers to estimate the width of Gaussian RBF functions.

   returns

   Gaussian RBF functions with parameter learned from data.

6. def gaussrbf(x: Array[Array[Double]], centers: Array[Array[Double]]): GaussianRadialBasis

   

   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.

   x

   the training dataset.

   centers

   an array to store centers on output. Its length is used as k of k-means.

   returns

   a Gaussian RBF function with parameter learned from data.

7. lazy val logger: Logger

   

   Definition Classes

   Logging

8. def pdist(data: Array[Array[Double]], half: Boolean = true): Array[Array[Double]]

   

   Returns the pairwise Euclidean distance matrix.

   Returns the pairwise Euclidean distance matrix.

   data

   the data set.

   half

   if true, only the lower half of matrix is allocated to save space.

   returns

   the lower half of proximity matrix.

9. def proximity[T](data: Array[T], dist: Distance[T], half: Boolean = true): Array[Array[Double]]

   

   Returns the proximity matrix of a dataset for given distance function.

   Returns the proximity matrix of a dataset for given distance function.

   data

   the data set.

   dist

   the distance function.

   half

   if true, only the lower half of matrix is allocated to save space.

   returns

   the lower half of proximity matrix.

10. object time

    

    Measure running time of a function/block