mandar2812

import scala.util.Random

/**
 * @author mandar2812
 */
object MedianRecursive {
  
    def median(list: Stream[Double]): Double = {
    val random: (Int) => Int = Random.nextInt

mandar2812

object Hermite {
  def hermite(n: Int, x: Double): Double = {
    def hermiteHelper(k: Int, x: Double, a: Double, b: Double): Double =
      k match {
        case 0 => a
        case 1 => b
        case _ => hermiteHelper(k-1, x, b, x*b - (k-1)*a)
      }
    hermiteHelper(n, x, 1, x)
  }

mandar2812

import breeze.linalg.DenseVector

object Stats {
  /**
   * Get the mean and variance of a data set
   * which is a [[List]] of [[DenseVector]].
   *
   * @param data The data set.
   *
   * @return A [[Tuple2]] containing the mean

mandar2812

import breeze.linalg.DenseVector
import io.github.mandar2812.dynaml.DynaMLPipe._
import io.github.mandar2812.dynaml.evaluation.RegressionMetrics
import io.github.mandar2812.dynaml.kernels._
import io.github.mandar2812.dynaml.modelpipe.GPRegressionPipe
import io.github.mandar2812.dynaml.models.gp.AbstractGPRegressionModel
import io.github.mandar2812.dynaml.pipes.{BifurcationPipe, DataPipe, StreamDataPipe}
import io.github.mandar2812.dynaml.utils.GaussianScaler

/**

mandar2812

import breeze.linalg.{DenseMatrix, DenseVector}
import com.quantifind.charts.Highcharts._
import io.github.mandar2812.dynaml.evaluation.RegressionMetrics
import io.github.mandar2812.dynaml.kernels.{CovarianceFunction, LocalSVMKernel}
import io.github.mandar2812.dynaml.models.svm.DLSSVM
import io.github.mandar2812.dynaml.optimization.{GPMLOptimizer, GridSearch}
import io.github.mandar2812.dynaml.pipes.{StreamDataPipe, DynaMLPipe, DataPipe}
import org.apache.log4j.Logger

/**

mandar2812

import breeze.linalg.{DenseMatrix, DenseVector}
import com.quantifind.charts.Highcharts._
import io.github.mandar2812.dynaml.evaluation.RegressionMetrics
import io.github.mandar2812.dynaml.kernels.CovarianceFunction
import io.github.mandar2812.dynaml.models.gp.GPNarModel
import io.github.mandar2812.dynaml.optimization.{GPMLOptimizer, GridSearch}
import io.github.mandar2812.dynaml.pipes.{DynaMLPipe, DataPipe}
import org.apache.log4j.Logger

/**

mandar2812

import breeze.linalg.{DenseMatrix, DenseVector}
import com.quantifind.charts.Highcharts._
import io.github.mandar2812.dynaml.evaluation.RegressionMetrics
import io.github.mandar2812.dynaml.kernels.CovarianceFunction
import io.github.mandar2812.dynaml.models.gp.GPNarXModel
import io.github.mandar2812.dynaml.optimization.{GPMLOptimizer, GridSearch}
import io.github.mandar2812.dynaml.pipes.{StreamDataPipe, DynaMLPipe, DataPipe}
import org.apache.log4j.Logger

/**

mandar2812

import breeze.linalg.{DenseMatrix, DenseVector}
import io.github.mandar2812.dynaml.evaluation.RegressionMetrics
import io.github.mandar2812.dynaml.kernels._
import io.github.mandar2812.dynaml.models.gp.GPRegression
import io.github.mandar2812.dynaml.models.neuralnets.{FeedForwardNetwork, FFNeuralGraph}
import io.github.mandar2812.dynaml.pipes.{StreamDataPipe, DataPipe}
import io.github.mandar2812.dynaml.utils

/**
  * Created by mandar on 11/1/16.

mandar2812

import breeze.linalg.DenseVector
import io.github.mandar2812.dynaml.evaluation.BinaryClassificationMetrics
import io.github.mandar2812.dynaml.models.neuralnets.{FFNeuralGraph, FeedForwardNetwork}
import io.github.mandar2812.dynaml.pipes.{DataPipe, DynaMLPipe, StreamDataPipe}

/**
  * Created by mandar on 11/1/16.
  */
object TestNNWineQuality {
  def apply (hidden: Int = 2, nCounts:List[Int] = List(), acts:List[String],

mandar2812

import breeze.linalg.DenseVector
import io.github.mandar2812.dynaml.evaluation.BinaryClassificationMetrics
import io.github.mandar2812.dynaml.models.lm.LogisticGLM
import io.github.mandar2812.dynaml.pipes.{DataPipe, DynaMLPipe, StreamDataPipe}

object TestLogisticWineQuality {
  def apply(training: Int = 100, test: Int = 1000,
            columns: List[Int] = List(11, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
            stepSize: Double = 0.01, maxIt: Int = 30, mini: Double = 1.0,
            regularization: Double = 0.5, wineType: String = "red",