//::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
/** @author  Michael Cotterell, John Miller
 *  @version 2.0
 *  @date    Mon Apr 22 14:53:24 EDT 2019
 *  @see     LICENSE (MIT style license file).
 *
 *  @note    Model: K-Means ++ Clustering (smart initialization of centroids)
 */

package scalation
package modeling
package clustering

import scalation.mathstat._
import scalation.random.{Discrete, Randi, RandomSeeds}

//:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
/** The `KMeansClustererPP` class cluster several vectors/points using
 *  the Hartigan-Wong algorithm.
 *  @param x      the vectors/points to be clustered stored as rows of a matrix
 *  @param k      the number of clusters to make
 *  @param flags  the flags used to adjust the algorithm
 */
class KMeansClustererPP (x: MatrixD, k: Int, flags: Array [Boolean] = Array (false, false))
      extends KMeansClustererHW (x, k, flags):

    private   val debug    = debugf ("KMeansClustererPP", false)         // debug function
    private   val NSTREAMS = RandomSeeds.seeds.length                    // number of random number streams available
    protected val pmf      = new VectorD (x.dim )                        // pmf for choosing centroids

    //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
    /** Initialize the centroids according to the k-means++ technique.
     */
    override def initCentroids (): Boolean =
        val ranI = new Randi (0, x.dim-1, stream)                        // uniform random integer generator
        cent(0)  = x(ranI.igen)                                          // pick first centroid uniformly at random
        for c <- 1 until k do                                            // pick remaining centroids
            val ranD = update_pmf (c)                                    // update distance derived pmf
            cent(c)  = x(ranD.igen)                                      // pick next centroid according to pmf
        end for
        debug ("initCentroids", s"cent = $cent")
        true
    end initCentroids

    //:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
    /** Update the probability mass function (pmf) used for picking the next centroid.
     *  The farther 'x_i' is from any existing centroid, the higher its probability. 
     *  Return the corresponding distance-derived random variate generator.
     *  @param c  the current centroid index
     */
    def update_pmf (c: Int): Discrete =
        for i <- x.indices do pmf(i) = distance (x(i), cent, c).min      // shortest distances to any centroid
        pmf /= pmf.sum                                                   // divide by sum to get probabilities
        Discrete (pmf, stream = (stream + c) % NSTREAMS)                 // distance-derived random variate generator
    end update_pmf

end KMeansClustererPP

import Clusterer.test

//:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
/** The `kMeansClustererTestPP` object is used to test the `KMeansClustererPP` class.
 *  > runMain scalation.modeling.clustering.kMeansClustererPPTest
 */
@main def kMeansClustererPPTest (): Unit =

    import Clusterer.x

    val k   = 3
    val opt = 3.0

    println (s"x = $x")
    println (s"k = $k")
    println ("-" * 60)

    val tf = Array (true, false)
    for fl0 <- tf; fl1 <- tf do
        val fls = Array (fl0, fl1)
        test (x, fls, new KMeansClustererPP (x, k, fls), opt)
    end for

    new Plot (x(?, 0), x(?, 1), null, "x0 vs x1")

end kMeansClustererPPTest


//:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
/** The `kMeansClustererPPTest` object is used to test the `KMeansClustererPP` class.
 *  > runMain scalation.modeling.clustering.kMeansClustererPPTest2
 */
@main def kMeansClustererPPTest2 (): Unit =

    //                         x0    x1
    val x = MatrixD ((8, 2),  1.0,  1.0,
                              1.0,  3.0,
                              5.0, 18.0,
                              5.0, 20.0,
                              9.0, 10.0,
                              9.0, 12.0,
                             15.0, 30.0,
                             15.0, 32.0)

    val k   = 4
    val opt = 8.0

    println (s"x = $x")
    println (s"k = $k")
    println ("-" * 60)

    val tf = Array (true, false)
    for fl0 <- tf; fl1 <- tf do
        val fls = Array (fl0, fl1)
        test (x, fls, new KMeansClustererPP (x, k, fls), opt)
    end for

    new Plot (x(?, 0), x(?, 1), null, "x0 vs x1")

end kMeansClustererPPTest2


//:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
/** The `kMeansClustererPPTest3` object is used to test the `KMeansClustererPP` class.
 *  > runMain scalation.modeling.clustering.kMeansClustererPPTest3
 */
@main def kMeansClustererPPTest3 (): Unit =

    import scalation.random.{Bernoulli, Normal}

    val coin  = Bernoulli ()
    val dist1 = Normal (2.0, 1.0)
    val dist2 = Normal (8.0, 1.0)
    val x     = new MatrixD (50, 2)
    val k     = 4
    val opt   = 76.0

    for i <- x.indices do x(i) = VectorD (if coin.gen == 0 then dist1.gen else dist2.gen,
                                          if coin.gen == 0 then dist1.gen else dist2.gen)

    println (s"x = $x")
    println (s"k = $k")
    println ("-" * 60)

    val tf = Array (true, false)
    for fl0 <- tf; fl1 <- tf do
        val fls = Array (fl0, fl1)
        test (x, fls, new KMeansClustererPP (x, k, fls), opt)
    end for

    new Plot (x(?, 0), x(?, 1))

end kMeansClustererPPTest3