Merge pull request pushkar#15 from omscs-georgia-tech/master

Adds jython examples

jython/continuouspeaks.py

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+import sys
+import os
+import time
+
+import java.io.FileReader as FileReader
+import java.io.File as File
+import java.lang.String as String
+import java.lang.StringBuffer as StringBuffer
+import java.lang.Boolean as Boolean
+import java.util.Random as Random
+
+import dist.DiscreteDependencyTree as DiscreteDependencyTree
+import dist.DiscreteUniformDistribution as DiscreteUniformDistribution
+import dist.Distribution as Distribution
+import opt.DiscreteChangeOneNeighbor as DiscreteChangeOneNeighbor
+import opt.EvaluationFunction as EvaluationFunction
+import opt.GenericHillClimbingProblem as GenericHillClimbingProblem
+import opt.HillClimbingProblem as HillClimbingProblem
+import opt.NeighborFunction as NeighborFunction
+import opt.RandomizedHillClimbing as RandomizedHillClimbing
+import opt.SimulatedAnnealing as SimulatedAnnealing
+import opt.example.FourPeaksEvaluationFunction as FourPeaksEvaluationFunction
+import opt.ga.CrossoverFunction as CrossoverFunction
+import opt.ga.SingleCrossOver as SingleCrossOver
+import opt.ga.DiscreteChangeOneMutation as DiscreteChangeOneMutation
+import opt.ga.GenericGeneticAlgorithmProblem as GenericGeneticAlgorithmProblem
+import opt.ga.GeneticAlgorithmProblem as GeneticAlgorithmProblem
+import opt.ga.MutationFunction as MutationFunction
+import opt.ga.StandardGeneticAlgorithm as StandardGeneticAlgorithm
+import opt.ga.UniformCrossOver as UniformCrossOver
+import opt.prob.GenericProbabilisticOptimizationProblem as GenericProbabilisticOptimizationProblem
+import opt.prob.MIMIC as MIMIC
+import opt.prob.ProbabilisticOptimizationProblem as ProbabilisticOptimizationProblem
+import shared.FixedIterationTrainer as FixedIterationTrainer
+import opt.example.ContinuousPeaksEvaluationFunction as ContinuousPeaksEvaluationFunction
+from array import array
+
+
+
+"""
+Commandline parameter(s):
+   none
+"""
+
+N=60
+T=N/10
+fill = [2] * N
+ranges = array('i', fill)
+
+ef = ContinuousPeaksEvaluationFunction(T)
+odd = DiscreteUniformDistribution(ranges)
+nf = DiscreteChangeOneNeighbor(ranges)
+mf = DiscreteChangeOneMutation(ranges)
+cf = SingleCrossOver()
+df = DiscreteDependencyTree(.1, ranges)
+hcp = GenericHillClimbingProblem(ef, odd, nf)
+gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
+pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
+
+rhc = RandomizedHillClimbing(hcp)
+fit = FixedIterationTrainer(rhc, 200000)
+fit.train()
+print "RHC: " + str(ef.value(rhc.getOptimal()))
+
+sa = SimulatedAnnealing(1E11, .95, hcp)
+fit = FixedIterationTrainer(sa, 200000)
+fit.train()
+print "SA: " + str(ef.value(sa.getOptimal()))
+
+ga = StandardGeneticAlgorithm(200, 100, 10, gap)
+fit = FixedIterationTrainer(ga, 1000)
+fit.train()
+print "GA: " + str(ef.value(ga.getOptimal()))
+
+mimic = MIMIC(200, 20, pop)
+fit = FixedIterationTrainer(mimic, 1000)
+fit.train()
+print "MIMIC: " + str(ef.value(mimic.getOptimal()))

jython/countones.py

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+import sys
+import os
+import time
+
+import java.io.FileReader as FileReader
+import java.io.File as File
+import java.lang.String as String
+import java.lang.StringBuffer as StringBuffer
+import java.lang.Boolean as Boolean
+import java.util.Random as Random
+
+import dist.DiscreteDependencyTree as DiscreteDependencyTree
+import dist.DiscreteUniformDistribution as DiscreteUniformDistribution
+import dist.Distribution as Distribution
+import opt.DiscreteChangeOneNeighbor as DiscreteChangeOneNeighbor
+import opt.EvaluationFunction as EvaluationFunction
+import opt.GenericHillClimbingProblem as GenericHillClimbingProblem
+import opt.HillClimbingProblem as HillClimbingProblem
+import opt.NeighborFunction as NeighborFunction
+import opt.RandomizedHillClimbing as RandomizedHillClimbing
+import opt.SimulatedAnnealing as SimulatedAnnealing
+import opt.example.FourPeaksEvaluationFunction as FourPeaksEvaluationFunction
+import opt.ga.CrossoverFunction as CrossoverFunction
+import opt.ga.SingleCrossOver as SingleCrossOver
+import opt.ga.DiscreteChangeOneMutation as DiscreteChangeOneMutation
+import opt.ga.GenericGeneticAlgorithmProblem as GenericGeneticAlgorithmProblem
+import opt.ga.GeneticAlgorithmProblem as GeneticAlgorithmProblem
+import opt.ga.MutationFunction as MutationFunction
+import opt.ga.StandardGeneticAlgorithm as StandardGeneticAlgorithm
+import opt.ga.UniformCrossOver as UniformCrossOver
+import opt.prob.GenericProbabilisticOptimizationProblem as GenericProbabilisticOptimizationProblem
+import opt.prob.MIMIC as MIMIC
+import opt.prob.ProbabilisticOptimizationProblem as ProbabilisticOptimizationProblem
+import shared.FixedIterationTrainer as FixedIterationTrainer
+import opt.example.CountOnesEvaluationFunction as CountOnesEvaluationFunction
+from array import array
+
+
+
+
+"""
+Commandline parameter(s):
+   none
+"""
+
+N=80
+fill = [2] * N
+ranges = array('i', fill)
+
+ef = CountOnesEvaluationFunction()
+odd = DiscreteUniformDistribution(ranges)
+nf = DiscreteChangeOneNeighbor(ranges)
+mf = DiscreteChangeOneMutation(ranges)
+cf = SingleCrossOver()
+df = DiscreteDependencyTree(.1, ranges)
+hcp = GenericHillClimbingProblem(ef, odd, nf)
+gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
+pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
+
+rhc = RandomizedHillClimbing(hcp)
+fit = FixedIterationTrainer(rhc, 200)
+fit.train()
+print "RHC: " + str(ef.value(rhc.getOptimal()))
+
+sa = SimulatedAnnealing(100, .95, hcp)
+fit = FixedIterationTrainer(sa, 200)
+fit.train()
+print "SA: " + str(ef.value(sa.getOptimal()))
+
+ga = StandardGeneticAlgorithm(20, 20, 0, gap)
+fit = FixedIterationTrainer(ga, 300)
+fit.train()
+print "GA: " + str(ef.value(ga.getOptimal()))
+
+mimic = MIMIC(50, 10, pop)
+fit = FixedIterationTrainer(mimic, 100)
+fit.train()
+print "MIMIC: " + str(ef.value(mimic.getOptimal()))

jython/fourpeaks.py

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+import sys
+import os
+import time
+
+import java.io.FileReader as FileReader
+import java.io.File as File
+import java.lang.String as String
+import java.lang.StringBuffer as StringBuffer
+import java.lang.Boolean as Boolean
+import java.util.Random as Random
+
+import dist.DiscreteDependencyTree as DiscreteDependencyTree
+import dist.DiscreteUniformDistribution as DiscreteUniformDistribution
+import dist.Distribution as Distribution
+import opt.DiscreteChangeOneNeighbor as DiscreteChangeOneNeighbor
+import opt.EvaluationFunction as EvaluationFunction
+import opt.GenericHillClimbingProblem as GenericHillClimbingProblem
+import opt.HillClimbingProblem as HillClimbingProblem
+import opt.NeighborFunction as NeighborFunction
+import opt.RandomizedHillClimbing as RandomizedHillClimbing
+import opt.SimulatedAnnealing as SimulatedAnnealing
+import opt.example.FourPeaksEvaluationFunction as FourPeaksEvaluationFunction
+import opt.ga.CrossoverFunction as CrossoverFunction
+import opt.ga.SingleCrossOver as SingleCrossOver
+import opt.ga.DiscreteChangeOneMutation as DiscreteChangeOneMutation
+import opt.ga.GenericGeneticAlgorithmProblem as GenericGeneticAlgorithmProblem
+import opt.ga.GeneticAlgorithmProblem as GeneticAlgorithmProblem
+import opt.ga.MutationFunction as MutationFunction
+import opt.ga.StandardGeneticAlgorithm as StandardGeneticAlgorithm
+import opt.ga.UniformCrossOver as UniformCrossOver
+import opt.prob.GenericProbabilisticOptimizationProblem as GenericProbabilisticOptimizationProblem
+import opt.prob.MIMIC as MIMIC
+import opt.prob.ProbabilisticOptimizationProblem as ProbabilisticOptimizationProblem
+import shared.FixedIterationTrainer as FixedIterationTrainer
+
+from array import array
+
+
+
+"""
+Commandline parameter(s):
+   none
+"""
+
+N=200
+T=N/5
+fill = [2] * N
+ranges = array('i', fill)
+
+ef = FourPeaksEvaluationFunction(T)
+odd = DiscreteUniformDistribution(ranges)
+nf = DiscreteChangeOneNeighbor(ranges)
+mf = DiscreteChangeOneMutation(ranges)
+cf = SingleCrossOver()
+df = DiscreteDependencyTree(.1, ranges)
+hcp = GenericHillClimbingProblem(ef, odd, nf)
+gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
+pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
+
+rhc = RandomizedHillClimbing(hcp)
+fit = FixedIterationTrainer(rhc, 200000)
+fit.train()
+print "RHC: " + str(ef.value(rhc.getOptimal()))
+
+sa = SimulatedAnnealing(1E11, .95, hcp)
+fit = FixedIterationTrainer(sa, 200000)
+fit.train()
+print "SA: " + str(ef.value(sa.getOptimal()))
+
+ga = StandardGeneticAlgorithm(200, 100, 10, gap)
+fit = FixedIterationTrainer(ga, 1000)
+fit.train()
+print "GA: " + str(ef.value(ga.getOptimal()))
+
+mimic = MIMIC(200, 20, pop)
+fit = FixedIterationTrainer(mimic, 1000)
+fit.train()
+print "MIMIC: " + str(ef.value(mimic.getOptimal()))

jython/knapsack.py

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+import sys
+import os
+import time
+
+import java.io.FileReader as FileReader
+import java.io.File as File
+import java.lang.String as String
+import java.lang.StringBuffer as StringBuffer
+import java.lang.Boolean as Boolean
+import java.util.Random as Random
+
+import dist.DiscreteDependencyTree as DiscreteDependencyTree
+import dist.DiscreteUniformDistribution as DiscreteUniformDistribution
+import dist.Distribution as Distribution
+import opt.DiscreteChangeOneNeighbor as DiscreteChangeOneNeighbor
+import opt.EvaluationFunction as EvaluationFunction
+import opt.GenericHillClimbingProblem as GenericHillClimbingProblem
+import opt.HillClimbingProblem as HillClimbingProblem
+import opt.NeighborFunction as NeighborFunction
+import opt.RandomizedHillClimbing as RandomizedHillClimbing
+import opt.SimulatedAnnealing as SimulatedAnnealing
+import opt.example.FourPeaksEvaluationFunction as FourPeaksEvaluationFunction
+import opt.ga.CrossoverFunction as CrossoverFunction
+import opt.ga.SingleCrossOver as SingleCrossOver
+import opt.ga.DiscreteChangeOneMutation as DiscreteChangeOneMutation
+import opt.ga.GenericGeneticAlgorithmProblem as GenericGeneticAlgorithmProblem
+import opt.ga.GeneticAlgorithmProblem as GeneticAlgorithmProblem
+import opt.ga.MutationFunction as MutationFunction
+import opt.ga.StandardGeneticAlgorithm as StandardGeneticAlgorithm
+import opt.ga.UniformCrossOver as UniformCrossOver
+import opt.prob.GenericProbabilisticOptimizationProblem as GenericProbabilisticOptimizationProblem
+import opt.prob.MIMIC as MIMIC
+import opt.prob.ProbabilisticOptimizationProblem as ProbabilisticOptimizationProblem
+import shared.FixedIterationTrainer as FixedIterationTrainer
+import opt.example.KnapsackEvaluationFunction as KnapsackEvaluationFunction
+from array import array
+
+
+
+
+"""
+Commandline parameter(s):
+    none
+"""
+
+# Random number generator */
+random = Random()
+# The number of items
+NUM_ITEMS = 40
+# The number of copies each
+COPIES_EACH = 4
+# The maximum weight for a single element
+MAX_WEIGHT = 50
+# The maximum volume for a single element
+MAX_VOLUME = 50
+# The volume of the knapsack 
+KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4
+
+# create copies
+fill = [COPIES_EACH] * NUM_ITEMS
+copies = array('i', fill)
+
+# create weights and volumes
+fill = [0] * NUM_ITEMS
+weights = array('d', fill)
+volumes = array('d', fill)
+for i in range(0, NUM_ITEMS):
+    weights[i] = random.nextDouble() * MAX_WEIGHT
+    volumes[i] = random.nextDouble() * MAX_VOLUME
+
+
+# create range
+fill = [COPIES_EACH + 1] * NUM_ITEMS
+ranges = array('i', fill)
+
+ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies)
+odd = DiscreteUniformDistribution(ranges)
+nf = DiscreteChangeOneNeighbor(ranges)
+mf = DiscreteChangeOneMutation(ranges)
+cf = UniformCrossOver()
+df = DiscreteDependencyTree(.1, ranges)
+hcp = GenericHillClimbingProblem(ef, odd, nf)
+gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
+pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
+
+rhc = RandomizedHillClimbing(hcp)
+fit = FixedIterationTrainer(rhc, 200000)
+fit.train()
+print "RHC: " + str(ef.value(rhc.getOptimal()))
+
+sa = SimulatedAnnealing(100, .95, hcp)
+fit = FixedIterationTrainer(sa, 200000)
+fit.train()
+print "SA: " + str(ef.value(sa.getOptimal()))
+
+ga = StandardGeneticAlgorithm(200, 150, 25, gap)
+fit = FixedIterationTrainer(ga, 1000)
+fit.train()
+print "GA: " + str(ef.value(ga.getOptimal()))
+
+mimic = MIMIC(200, 100, pop)
+fit = FixedIterationTrainer(mimic, 1000)
+fit.train()
+print "MIMIC: " + str(ef.value(mimic.getOptimal()))

jython/run.sh

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+#!/bin/bash
+# edit the classpath to to the location of your ABAGAIL jar file
+#
+export CLASSPATH=./lib/ABAGAIL.jar:$CLASSPATH
+mkdir -p data/plot logs image
+
+# four peaks
+echo "four peaks"
+jython fourpeaks.py
+
+# count ones
+echo "count ones"
+jython countones.py
+
+# continuous peaks
+echo "continuous peaks"
+jython continuouspeaks.py
+
+# knapsack
+echo "Running knapsack"
+jython knapsack.py
+