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import org.apache.spark.mllib.classification.LogisticRegressionModel
import org.apache.spark.mllib.linalg.{Vectors, Vector => MLVector}
import org.apache.spark.mllib.regression.LabeledPoint


// Object which will provides list of scores

class Prediction(val model: LogisticRegressionModel, val offers: Vector[(String,MLVector)]) extends Serializable{
/*
  THE PROBLEM WITH CHILL LIBRARY

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import tensorflow as tf

# a = tf.constant([3,2], name="a", dtype=tf.float32)
# b = tf.constant([3,2], shape=[2,1], name="b")
# c = tf.constant([[3.0], [2.0]], name="c")


a = tf.constant([3], name="a")
b = tf.constant([5], name="b")
c = tf.add(a,b,name="c")

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import zio.{DefaultRuntime, RIO, UIO, ZIO}

import scala.collection.mutable
import scala.util.Random

object MapSort {

  val numValues    = 10
  val ceilingValue = 100000

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package ziomlscoring

import zio._
import ziomlscoring.SizeMyShirt.moduleExperimentFramework.ExperimentFramework
import ziomlscoring.SizeMyShirt.moduleInferenceLogger.InferenceLogger
import ziomlscoring.SizeMyShirt.moduleTShirtSizer.TShirtSizer
import ziomlscoring.SizeMyShirt.sizeMyShirtMicroService

object SizeMyShirt {
  case class UserId(value: Long) extends AnyVal