import $ivy.`com.github.haifengl::smile-scala:3.1.0`
import $ivy.`org.slf4j:slf4j-simple:2.0.13`  

import scala.language.postfixOps
import smile._
import smile.math._
import smile.math.distance._
import smile.math.kernel._
import smile.math.matrix._
import smile.math.matrix.Matrix._
import smile.math.rbf._
import smile.stat.distribution._
import smile.data._
import smile.data.formula._
import smile.data.measure._
import smile.data.`type`._
import smile.clustering._

import java.awt.Color.{BLACK, BLUE, CYAN, DARK_GRAY, GRAY, GREEN, LIGHT_GRAY, MAGENTA, ORANGE, PINK, RED, WHITE, YELLOW}
import smile.plot.swing.Palette.{DARK_RED, VIOLET_RED, DARK_GREEN, LIGHT_GREEN, PASTEL_GREEN, FOREST_GREEN, GRASS_GREEN, NAVY_BLUE, SLATE_BLUE, ROYAL_BLUE, CADET_BLUE, MIDNIGHT_BLUE, SKY_BLUE, STEEL_BLUE, DARK_BLUE, DARK_MAGENTA, DARK_CYAN, PURPLE, LIGHT_PURPLE, DARK_PURPLE, GOLD, BROWN, SALMON, TURQUOISE, BURGUNDY, PLUM}
import smile.plot.swing._
import smile.plot.show
import smile.plot.Render._

val x = read.csv("../data/clustering/gaussian/six.txt", header = false, delimiter = ' ').toArray
val clusters = hclust(x, "complete")
show(dendrogram(clusters))

val clusters = kmeans(x, 6, runs = 20)
show(plot(x, clusters.y, '.'))

val elongate = read.csv("../data/clustering/gaussian/elongate.txt", header = false, delimiter = '\t').toArray
val clusters = kmeans(elongate, 2, runs = 20)
show(plot(elongate, clusters.y, '.'))

val clusters = xmeans(x, 50)
show(plot(x, clusters.y, '.'))

val clusters = gmeans(x, 50)
show(plot(x, clusters.y, '.'))

val clusters = dac(x, 12, 0.9)
show(plot(x, clusters.y, '.'))

val data = read.libsvm("../data/libsvm/news20.dat")
val sparse = (0 until data.size).map(i => data(i).x).toArray
val clusters = sib(sparse, 20, 100, 8)

val clusters = clarans(x, new EuclideanDistance(), 6, 10, 20)
show(plot(x, clusters.y, '.'))

val t48 = read.csv("../data/clustering/chameleon/t4.8k.txt", header = false, delimiter = ' ').toArray
val clusters = dbscan(t48, 20, 10)
show(plot(t48, clusters.y, '.'))

val clusters = denclue(x, 1.0, 50)
show(plot(x, clusters.y, '.'))

val sincos = read.csv("../data/clustering/nonconvex/sincos.txt", header = false, delimiter = '\t').toArray
val clusters = specc(sincos, 2, 0.2)
show(plot(sincos, clusters.y, 'o'))

val clusters = mec(x, 20, 2.0)
show(plot(x, clusters.y, '.'))