rain-sprinkler-wet

package cc.factorie.example

import cc.factorie.directed.{DirectedFactorWithStatistics1, DirectedFactorWithStatistics3, DirectedModel}
import cc.factorie.infer.Maximize
import cc.factorie.model.{Factor1, ItemizedModel, TupleFactorWithStatistics3}
import cc.factorie.variable.{BooleanValue, BooleanVariable, MaximizeDiscrete, RefVariable}

import scala.util.Random

/**
 * Created by ali on 1/13/15.
 */
object SimpleVDirected {
  def main(args: Array[String]) {
    implicit val model = DirectedModel()
    implicit val random = new Random(0)

    val rain = new BooleanVariable()
    val sprinkler = new BooleanVariable()
    val wet = new BooleanVariable()

    val f1 = new DirectedFactorWithStatistics1(rain) {
      val probability = 0.7
      override def pr(v1: BooleanVariable#Value): Double = {
        if (v1.booleanValue)
          probability
        else
          1 - probability
      }
      override def sampledValue(implicit random: Random): BooleanVariable#Value = {
        BooleanValue(random.nextDouble() < probability)
      }
    }
    val f2 = new DirectedFactorWithStatistics1(sprinkler) {
      val probability = 0.3
      override def pr(v1: BooleanVariable#Value): Double = {
        if (v1.booleanValue)
          probability
        else
          1 - probability
      }
      override def sampledValue(implicit random: Random): BooleanVariable#Value = {
        BooleanValue(random.nextDouble() < probability)
      }
    }

    val f3 = new DirectedFactorWithStatistics3(wet, rain, sprinkler) {
      val cpt = Array(
        // (sprinkler=f,sprinkler=t)
        Array(0.1, 0.7),   // no rain
        Array(0.8, 0.9)    // rain
      )
      override def pr(v1: BooleanVariable#Value, rainVal: BooleanVariable#Value, sprinklerVal: BooleanVariable#Value): Double = {
        val probability = cpt(rainVal.intValue)(sprinklerVal.intValue)
        if (v1.booleanValue) probability else 1 - probability
      }
      override def sampledValue(p1: BooleanVariable#Value, p2: BooleanVariable#Value)(implicit random: Random): BooleanVariable#Value = {
        val probability = cpt(p1.intValue)(p2.intValue)
        BooleanValue(random.nextDouble() < probability)
      }
    }

    // add factors to model
    model ++= Seq(f1, f2, f3)

    // observed variables
    wet := true

    val t = new RefVariable[BooleanVariable]()
    t := rain

    Maximize(t.value)
    // When add more observation, the result without maximize in equal to maximize result
    println("Max " + t.value + " " + t.value.proportions(model))
  }
}

object SimpleVFactor {
  def main(args: Array[String]) {
    implicit val model = new ItemizedModel()
    implicit val random = new Random(0)

    val rain = new BooleanVariable()
    val sprinkler = new BooleanVariable()
    val wet = new BooleanVariable()

    val f1 = new Factor1(rain) {
      val probability = 0.7
      override def score(v1: BooleanVariable#Value): Double = {
        if (v1.booleanValue)
          probability
        else
          1 - probability
      }

      override type StatisticsType = this.type
    }

    val f2 = new Factor1(rain) {
      val probability = 0.3
      override def score(v1: BooleanVariable#Value): Double = {
        if (v1.booleanValue)
          probability
        else
          1 - probability
      }

      override type StatisticsType = this.type
    }

    val f3 = new TupleFactorWithStatistics3(wet, rain, sprinkler) {
      val cpt = Array(
        // (sprinkler=f,sprinkler=t)
        Array(0.1, 0.7),   // no rain
        Array(0.8, 0.9)    // rain
      )
      override def score(v1: BooleanVariable#Value, v2: BooleanVariable#Value, v3: BooleanVariable#Value): Double = {
        val probability = cpt(v2.intValue)(v3.intValue)
        if (v1.booleanValue)
          math.log(probability)
        else
          math.log(1 - probability)
      }
    }

    model ++= Seq(f1, f2, f3)
    wet := true

    val t = new RefVariable[BooleanVariable]()
    t := rain

    MaximizeDiscrete(rain, model)
    println("Max " + t.value + " " + t.value.proportions(model))
  }
}