text: import numpy as np
  import Queue
  from mf.mf_func import MFOptFunction
  from utils.general_utils import map_to_cube
  import sys
  from examples.synthetic_functions import *
  from mf.mf_func import get_noisy_mfof_from_mfof
  from letters.letters_classifier import *
  import time
  
  from MFTree.POO import *
  
  import synthetic_functions
  
  
  NUM_EXP = 5
  EXP_NAME = 'Branin'
  #EXP_NAME = 'CurrinExp'
  #EXP_NAME = 'Hartmann3'
  #EXP_NAME = 'Hartmann6'
  #EXP_NAME = 'Borehole'
  
  
  def run_one_experiment(mfobject,nu,rho,times,sigma,C,t0,filname):
  	R = []
  	T = []
  	for t in times:
  		budget = t*mfobject.opt_fidel_cost
  		t1 = time.time()
  		MP = POO(mfobject=mfobject, nu_max=nu, rho_max=rho, total_budget=budget, sigma=sigma, C=C, mult=0.5, tol = 1e-3, Randomize = False, Auto = False, unit_cost=t0 )
  		MP.run_all_MFHOO()
  		X, E = MP.get_point()
  		t2 = time.time()	
  
  		R = R + [E]
  		T = T + [MP.cost]
  		#print 'Total HOO Queries: ' + str(MP.t) 
  		print str(MP.cost) + ' : ' + str(E)
  
  	np.save(filename,R)
  	return np.array(R),np.array(T)
  
  
  if __name__ == '__main__':
  
  
  	if EXP_NAME == 'Hartmann3':
  		mfof = synthetic_functions.get_mf_hartmann_as_mfof(1, 3)
  		noise_var = 0.01
  		sigma = np.sqrt(noise_var)
  
  	elif EXP_NAME == 'Hartmann6':
  		mfof = synthetic_functions.get_mf_hartmann_as_mfof(1, 6)
  		max_capital = 200 * mfof.opt_fidel_cost
  		noise_var = 0.05
  		sigma = np.sqrt(noise_var)
  	elif EXP_NAME == 'CurrinExp':
  		mfof = synthetic_functions.get_mf_currin_exp_as_mfof()
  		max_capital = 200 * mfof.opt_fidel_cost
  		noise_var = 0.5
  		sigma = np.sqrt(noise_var)
  	elif EXP_NAME == 'Branin':
  		mfof = synthetic_functions.get_mf_branin_as_mfof(1)
  		max_capital = 200 * mfof.opt_fidel_cost
  		noise_var = 0.05
  		sigma = np.sqrt(noise_var)
  	elif EXP_NAME == 'Borehole':
  		mfof = synthetic_functions.get_mf_borehole_as_mfof()
  		max_capital = 200 * mfof.opt_fidel_cost
  		noise_var = 5
  		sigma = np.sqrt(noise_var)
  
  
  	times = [10,20,50,75,100,150,175, 200]
  	mfobject = get_noisy_mfof_from_mfof(mfof, noise_var)
  	nu = 1.0
  	rho = 0.95
  	C = 0.02
  	t0 = mfobject.opt_fidel_cost
  
  	NT = str(time.time())
  	print 'Running Experiment 1: '
  	filename = 'POO_' + EXP_NAME + '_' + NT + '_' + '1.npy'
  	R,T = run_one_experiment(mfobject,nu,rho,times,sigma,C,t0,filename)
  	result = R
  
  	for i in range(1,NUM_EXP):
  		print 'Running Experiment' + str(i+1) + ': '
  		filename = 'POO_' + EXP_NAME + '_' + NT + '_' + str(i+1) + '.npy'
  		R,T = run_one_experiment(mfobject,nu,rho,times,sigma,C,t0,filename)
  		result = np.vstack([result,R])
  
  
  	mu = np.mean(result,axis = 0)
  	std = np.std(result,axis = 0)
  	result = mfobject.opt_val - mu
  	filename = './examples/results/POO_' + EXP_NAME + '_' + NT + '_' + '.csv'
  	dfdic = {}
  	dfdic['Capital'] = np.array(times)
  	dfdic['Value'] = result
  	dfdic['Std'] = std
  	df = pd.DataFrame(dfdic)
  	df.to_csv(filename) 
  
  
  
  
  