# Optimize SchwefelA function with differential evolution
# Collect cost function and plot progress
# Run the example with mpirun/mpiexec to use parallel processing

from pyopus.optimizer.de import DifferentialEvolution
from pyopus.problems import glbc
from pyopus.optimizer.base import Reporter, CostCollector, RandomDelay
from numpy import array, zeros, arange
from numpy.random import seed
from pyopus.parallel.cooperative import cOS
from pyopus.parallel.mpi import MPI
# Import after MPI is imported so that only the master process fires up the GUI thread
from pyopus.plotter import interface as pyopl

if __name__=='__main__':
	cOS.setVM(MPI())
	
	ndim=30
	popSize=50
	
	prob=glbc.SchwefelA(n=ndim)
	slowProb=RandomDelay(prob, [0.001, 0.010])
	
	opt=DifferentialEvolution(
		slowProb, prob.xl, prob.xh, debug=0, 
		maxGen=150, populationSize=popSize, w=0.5, pc=0.3, seed=None
	)
	cc=CostCollector()
	opt.installPlugin(cc)
	opt.installPlugin(Reporter(onIterStep=1000))
	opt.reset()
	opt.run()
	cc.finalize()
	
	f1=pyopl.figure()
	pyopl.lock(True)
	if pyopl.alive(f1):
		ax=f1.add_subplot(1,1,1)
		ax.semilogy(arange(len(cc.fval))/popSize, cc.fval)
		ax.set_xlabel('generations')
		ax.set_ylabel('f')
		ax.set_title('Progress of differential evolution')
		ax.grid()
		pyopl.draw(f1)
	pyopl.lock(False)
	
	print("x=%s f=%e" % (str(opt.x), opt.f))
	
	pyopl.join()
	
	cOS.finalize()