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val osAovCount = data.filter { x => interestedBrowsers.contains(x.osFamily)}
  .filter (_.categoryId == 128)
  .map { x => x.osFamily }
  .countByValue
  .toSeq

draw(column(osAovCount.toList))

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//CODE of MIC

import data.VarPairData
import mine.core.MineParameters
import analysis.Analysis
import analysis.results.BriefResult
import scala.util.Random 
 
//Code a discrete value by randomly changing the order of the "codes
def encode(col: Array[String]): Array[Double] = {

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//CODE

import org.ddahl.rscala._
import ru.retailrocket.ispark._
 
def connect() = RClient("R", false)
@transient
val r = connect()
R.plot(r, "barplot(cs, names.arg=names, col = 'red' )", Map("cs"->os.Aov.map(_._2), "names" -> osAov.map(_._1)) )

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//CODE of Highcharts

import com.quantifind.charts.Highcharts._
import ru.retailrocket.ispark.wisp._

draw(column(osAov.toList))

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//CODE:
val interestedBrowsers = List("Android", "OS X", "iOS", "Linux", "Windows")
val osAov = dataAov.filter(x => interestedBrowsers.contains(x.osFamily)) //we leave only the desired OS
    .filter(_.categoryId == 128) // filter categories
    .map(x => (x.osFamily, (x.aov, 1.0))) // need to calculate average purchase amount
    .reduceByKey((x, y) => (x._1 + y._1, x._2 + y._2))
    .map{ case(osFamily, (revenue, orders)) => (osFamily, revenue/orders) }
    .collect()
 
//OUT

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//CODE:
//The most popular category.
dataAov.map { x => x.categoryId } // select the categoryId field
    .countByValue()  // calculate how often each categoryId appears
    .toSeq
    .sortBy( - _._2) // sort by frequency in descending order
    .take(10) //take the top 10 records

//OUT:
//format: (categoryId, count)

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//CODE:

import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.tribbloid.ispark.display.dsl._
import scala.util.Try

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._

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//CODE:

import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.tribbloid.ispark.display.dsl._
import scala.util.Try

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._

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import org.nd4j.linalg.api.ndarray.INDArray
import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastAddOp
import org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution
import org.nd4j.linalg.factory.Nd4j

import scala.util.Random

object Xavier {
  def init(shape: Array[Int]): INDArray = {
    var n = shape.dropRight(1).product

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package org.deeplearning4j.learn

import java.util.Arrays

import org.nd4j.api.linalg.DSL._
import org.nd4j.linalg.api.ndarray.INDArray
import org.nd4j.linalg.factory.Nd4j
import org.nd4j.linalg.indexing.{NDArrayIndex, BooleanIndexing}
import org.nd4j.linalg.indexing.conditions.Conditions
import org.nd4j.linalg.indexing.functions.Value