smile.util

Class SmileUtils

java.lang.Object

smile.util.SmileUtils

public class SmileUtils
extends Object

Some useful functions.

Author:

Haifeng Li

Constructor Summary

Constructors

Constructor and Description
SmileUtils()

Method Summary

Methods

Modifier and Type	Method and Description
static GaussianRadialBasis	learnGaussianRadialBasis(double[][] x, double[][] centers) Learns Gaussian RBF function and centers from data.
static GaussianRadialBasis[]	learnGaussianRadialBasis(double[][] x, double[][] centers, double r) Learns Gaussian RBF function and centers from data.
static GaussianRadialBasis[]	learnGaussianRadialBasis(double[][] x, double[][] centers, int p) Learns Gaussian RBF function and centers from data.
static <T> GaussianRadialBasis	learnGaussianRadialBasis(T[] x, T[] centers, Metric<T> distance) Learns Gaussian RBF function and centers from data.
static <T> GaussianRadialBasis[]	learnGaussianRadialBasis(T[] x, T[] centers, Metric<T> distance, double r) Learns Gaussian RBF function and centers from data.
static <T> GaussianRadialBasis[]	learnGaussianRadialBasis(T[] x, T[] centers, Metric<T> distance, int p) Learns Gaussian RBF function and centers from data.
static int[][]	sort(Attribute[] attributes, double[][] x) Sorts each variable and returns the index of values in ascending order.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SmileUtils

public SmileUtils()

Method Detail

sort

public static int[][] sort(Attribute[] attributes,
           double[][] x)

Sorts each variable and returns the index of values in ascending order. Only numeric attributes will be sorted. Note that the order of original array is NOT altered.

Parameters:

x - a set of variables to be sorted. Each row is an instance. Each column is a variable.

Returns:

the index of values in ascending order

learnGaussianRadialBasis

public static GaussianRadialBasis learnGaussianRadialBasis(double[][] x,
                                           double[][] centers)

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.

Parameters:

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.

learnGaussianRadialBasis

public static GaussianRadialBasis[] learnGaussianRadialBasis(double[][] x,
                                             double[][] centers,
                                             int p)

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.

Parameters:

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.

learnGaussianRadialBasis

public static GaussianRadialBasis[] learnGaussianRadialBasis(double[][] x,
                                             double[][] centers,
                                             double r)

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.

Parameters:

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.

learnGaussianRadialBasis

public static <T> GaussianRadialBasis learnGaussianRadialBasis(T[] x,
                                               T[] centers,
                                               Metric<T> distance)

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.

Parameters:

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.

learnGaussianRadialBasis

public static <T> GaussianRadialBasis[] learnGaussianRadialBasis(T[] x,
                                                 T[] centers,
                                                 Metric<T> distance,
                                                 int p)

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.

Parameters:

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.

learnGaussianRadialBasis

public static <T> GaussianRadialBasis[] learnGaussianRadialBasis(T[] x,
                                                 T[] centers,
                                                 Metric<T> distance,
                                                 double r)

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.

Parameters:

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.