mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

erikerlandson

  def kMedoids[T :ClassTag, U >: T :ClassTag](
      data: RDD[T],
      k: Int,
      metric: (U,U) => Double,
      sampleSize: Int = 10000,
      maxIterations: Int = 10,
      resampleInterval: Int = 3
    ): (Seq[T], Double) = {

    val n = data.count

Enchufa2

#' @param h5File HDF5 file path
#' @param dataset data frame path in the HDF5 file
#' @examples
#' df <- loadhdf5data("/path/to/file.hdf5", "/path/to/dataset")
#' 
loadhdf5data <- function(h5File, dataset) {
  require(h5) # available on CRAN
  
  f <- h5file(h5File)
  nblocks <- h5attr(f[dataset], "nblocks")