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# directly running the DOE because existing surrogates can be explored with another workflow
from os import path
from os import sys
import importlib.util
import multiprocessing
import time
import re
from numpy import random as r
from numpy import *
import statistics
from functools import partial
import shutil
# Surrogate modelling
import itertools
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Test function representation
from rpy2 import robjects as robjs
from rpy2.robjects.packages import importr
from rpy2 import rinterface
# Test function characteristics
import statistics as st
from scipy import signal, misc, ndimage
def installFalcoo(mirror = 'https://utstat.toronto.edu/cran/'):
utils = importr('utils')
utils.install_packages('flacco', repos=mirror)
utils.install_packages('list', repos=mirror)
class counter:
#wraps a function, to keep a running count of how many
#times it's been called
def __init__(self, func):
self.func = func
self.count = 0
def __call__(self, *args, **kwargs):
self.count += 1
return self.func(*args, **kwargs)
def simulate(algName, algPath, funcname, funcpath, args, initpoint):
# loading the heuristic object into the namespace and memory
spec = importlib.util.spec_from_file_location(algName, algPath)
heuristic = importlib.util.module_from_spec(spec)
spec.loader.exec_module(heuristic)
# loading the test function object into the namespace and memory
testspec = importlib.util.spec_from_file_location(funcname, funcpath)
func = importlib.util.module_from_spec(testspec)
testspec.loader.exec_module(func)
# defining a countable test function
@counter
def testfunc(args):
return func.main(args)
# using a try statement to handle potential exceptions raised by child processes like the algorithm or test functions or the pooling algorithm
try:
#This timer calculates directly the CPU time of the process (Nanoseconds)
tic = time.process_time_ns()
# running the test by calling the heuritic script with the test function as argument
quality = heuristic.main(testfunc, initpoint, args)
toc = time.process_time_ns()
# ^^ The timer ends right above this; the CPU time is then calculated below by simple difference ^^
# CPU time in seconds
cpuTime = (toc - tic)*(10**-9)
numCalls = testfunc.count
converged = 1
except:
quality = NaN
cpuTime = NaN
numCalls = testfunc.count
converged = 0
return cpuTime, quality, numCalls, converged
def measure(heuristicpath, funcpath, args, connection):
'''
This function runs a set of optimization flows for each test function. it returns the mean and standard deviation of the performance results
'''
#defining the heuristic's name
heuristic_name = path.splitext(path.basename(heuristicpath))[0]
#defining the test function's name
funcname = path.splitext(path.basename(funcpath))[0]
# Seeding the random module for generating the initial point of the optimizer: Utilising random starting point for experimental validity
r.seed(int(time.time()))
# guetting the representation of the function
funcChars = representfunc(funcpath)
n = funcChars['dimmensions']
upper = funcChars['upper']
lower = funcChars['lower']
if upper is not list: upper = [upper for i in range(n)]
if lower is not list: lower = [lower for i in range(n)]
scale = list()
for i in range(n):
scale.append(upper[i] - lower[i])
# Defining random initial points to start testing the algorithms
initpoints = [[r.random() * scale[i] + lower[i] for i in range(n)] for run in range(30)] #update the inner as [r.random() * scale for i in range(testfuncDimmensions)]
# building the iterable arguments
partfunc = partial(simulate, heuristic_name, heuristicpath, funcname, funcpath, args)
n_proc = multiprocessing.cpu_count() # Guetting the number of cpus
with multiprocessing.Pool(processes = n_proc) as pool:
# running the simulations
newRun = pool.map(partfunc,initpoints)
cpuTime = array([resl[0] for resl in newRun])
quality = array([resl[1] for resl in newRun])
numCalls = array([resl[2] for resl in newRun])
converged = array([resl[3] for resl in newRun])
cpuTime = cpuTime[~(isnan(cpuTime))]
quality = quality[~(isnan(quality))]
numCalls = numCalls[~(isnan(numCalls))]
converged = converged[~(isnan(converged))]
results = dict()
results['cpuTime'] = array([statistics.fmean(cpuTime), statistics.stdev(cpuTime)])
results['quality'] = array([statistics.fmean(quality), statistics.stdev(quality)])
results['numCalls'] = array([statistics.fmean(numCalls), statistics.stdev(numCalls)])
results['convRate'] = array([statistics.fmean(converged), statistics.stdev(converged)])
connection.send((results,newRun))
def writerepresentation(funcpath, charas):
# Save a backup copy of the function file
shutil.copyfile(funcpath, funcpath + '.old')
# create a string format of the representation variables
representation = ''
for line in list(charas):
representation += '\n\t#_# ' + line + ': ' + repr(charas[line]).replace('\n','')
representation+='\n\n\t#_# Represented: 1\n\n'
# Creating the new docstring to be inserted into the file
with open(funcpath, "r") as file:
content = file.read()
docstrs = re.findall("def main\(.*?\):.*?'''(.*?)'''.*?return\s+.*?", content, re.DOTALL)[0]
docstrs += representation
repl = "\\1"+docstrs+"\t\\2"
# Create the new content of the file to replace the old. Replacing the whole thing
pattrn = re.compile("(def main\(.*?\):.*?''').*?('''.*?return\s+.*?\n|$)", flags=re.DOTALL)
newContent = pattrn.sub(repl, content, count=1)
# Overwrite the test function file
with open(funcpath,"w") as file:
file.write(newContent)
def representfunc(funcpath, forced = False):
#defining the function name
funcname = path.splitext(path.basename(funcpath))[0]
# loading the function to be represented
spec = importlib.util.spec_from_file_location(funcname, funcpath)
funcmodule = importlib.util.module_from_spec(spec)
spec.loader.exec_module(funcmodule)
# Finding the function characteristics inside the docstring
if funcmodule.main.__doc__:
regex = re.compile("#_#\s?(\w+):(.+)?\n") # this regular expression matches the characteristics already specified in the docstring section of the function -- old exp: "#_#\s?(\w+):\s?([-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?)"
characs = re.findall(regex, funcmodule.main.__doc__)
results = {}
for charac in characs:
results[charac[0]] = eval(charac[1])
# Automatically generate the representation if the docstrings did not return anything
if not ('Represented' in results):
print("Warning, the Representation of the Test Function has not been specified\n===\n******Calculating the Characteristics******")
n = int(results['dimmensions'])
blocks = int(1+10/n)
if blocks< 3: blocks=3
# Importing FLACCO using rpy2
flacco = importr('flacco')
# creating the r functions
rlist = robjs.r['list']
rapply = robjs.r['apply']
rvector = robjs.r['c']
r_unlist = robjs.r['unlist']
rtestfunc = rinterface.rternalize(funcmodule.main)
# Verify if a list of limits has been specified for all dimensions or if all dimensions will use the same boundaries
if (type(results['lower']) is list):
lowerval = r_unlist(rvector(results['lower']))
upperval = r_unlist(rvector(results['upper']))
else:
lowerval = results['lower']
upperval = results['upper']
X = flacco.createInitialSample(n_obs = 500, dim = n, control = rlist(**{'init_sample.type' : 'lhs', 'init_sample.lower' : lowerval, 'init_sample.upper' : upperval}))
y = rapply(X, 1, rtestfunc)
testfuncobj = flacco.createFeatureObject(**{'X': X, 'y': y, 'fun': rtestfunc, 'lower': lowerval, 'upper': upperval, 'blocks': blocks, 'force': forced})
# these are the retained features. Note that some features are being excluded for being problematic and to avoid overcomplicating the neural network.... the feature sets are redundant and the most relevant ones have been retained
# the excluded feature sets are: 'bt', 'ela_level'
# feature sets that require special attention: 'cm_angle', 'cm_grad', 'limo', 'gcm' (large set with some nans),
featureset = ['cm_angle','cm_conv','cm_grad','ela_conv','ela_curv','ela_distr','ela_local','ela_meta','basic','disp','limo','nbc','pca','gcm','ic']
pyfeats = dict()
for feature in featureset:
rawfeats = flacco.calculateFeatureSet(testfuncobj, set=feature)
pyfeats[feature] = asarray(rawfeats)
writerepresentation(funcpath, pyfeats)
return results
def doe(heuristicpath, testfunctionpaths, args):
#defining the function's name
funcnames = [path.splitext(path.basename(funcpath))[0] for funcpath in testfunctionpaths]
#defining the heuristic's name
heuristic_name = path.splitext(path.basename(heuristicpath))[0]
# logic variables to deal with the processes
proc = []
connections = {}
# loading the test functions into the namespace and memory
for idx, funcpath in enumerate(testfunctionpaths):
funcname = funcnames[idx]
# Creating the connection objects for communication between the heuristic and this module
connections[funcname] = multiprocessing.Pipe(duplex=False)
proc.append(multiprocessing.Process(target=measure, name=funcname, args=(heuristicpath, funcpath, args, connections[funcname][1])))
# defining the response variables
responses = {}
failedfunctions = {}
processtiming = {}
# Starting the subprocesses for each testfunction
for idx,process in enumerate(proc):
process.start()
# Waiting for all the runs to be done
for process in proc: process.join()
for process in proc:
run = process.name
if process.exitcode == 0: responses[run] = connections[run][0].recv()
else:
responses[run] = "this run was not successful"
failedfunctions[run] = process.exitcode
connections[run][0].close()
connections[run][1].close()
# display output
print("\n\n||||| Responses: [mean,stdDev] |||||")
for process in proc: print(process.name + "____\n" + str(responses[process.name][0]) + "\n_________________")
#return the performance values
return responses
# %%

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import math as m
import numpy as np
from numpy import random as r
import time
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import copy as cp
#def func(Sc):
# x1 = Sc[0]
# x2 = Sc[1]
# return m.sqrt(x1**2+x2**2)
def tweak(St,p,sigma,high,low):
for i in range(len(St)):
if p > r.random():
while True:
n = r.normal(loc=0, scale=sigma)
if (high > St[i]+n) and (low < St[i]+n):
St[i]+=n
break
return St
def Quality(Sc,objective,func):
func_output = func(Sc)
if type(func_output) == list:
error = [func_output[i]-objective[i] for i in range(len(func_output))]
else:
error = func_output - objective
print("Error is: "+str(error))
return 1/abs(error)
def main(func, S, args):
r.seed(int(time.time()))
route = list()
#Parsing arguments
y = args["objs"]
t = args["t"]
p = args["p"]
high = 20
low = -20
Best = list()
Best[:] = cp.deepcopy(S)
sigma = 0.1
route.append(Best[:])
while True:
print('\n\n\n')
R = tweak(cp.deepcopy(S),p,sigma,high, low)
print(R)
print(S)
Qr = Quality(R,y,func)
Qs = Quality(S,y,func)
try:
P = m.e**((Qr-Qs)/t)
except:
pass
print('QUALITY_R///{}'.format(Qr))
print('QUALITY_S///{}'.format(Qs))
print('fraction is:{}'.format(P))
if (Qr > Qs) or (r.random() < P):
print('NEW_S')
S[:] = R[:]
if t > 0.01:
t-= t/10
print('t = {}'.format(t))
if (Quality(S,y,func) > Quality(Best,y,func)):
print('new Best****:{}'.format(Best))
Best[:] = S[:]
route.append(Best[:])
print(route)
if t < 0 or Quality(Best,y,func) > 50:
break
#print('the Best Quality obtained was:{}'.format(Quality(Best,y)))
print("Final Quality is: {}".format(Quality(Best,y,func)))
print("final Temperature is: {}".format(t))
return Quality(Best,y,func)

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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}

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def main(args):
'''
#_# dimmensions: 6
#_# upper: 4
#_# lower: -1
#_# minimum: 0
#_# cm_angle: array([[4.38674589e+00], [1.19556006e+00], [4.47966360e+00], [1.19983352e+00], [5.60286032e+00], [1.07792176e+01], [2.25826784e-03], [2.51639450e-02], [0.00000000e+00], [2.62000000e-01]])
#_# cm_conv: array([[0.33635988], [0.16095749], [0.76392901], [0.23607099], [0. ], [0.57 ]])
#_# cm_grad: array([[0.74319842], [0.11137735], [0. ], [0.095 ]])
#_# ela_conv: array([[ 9.80000000e-01], [ 0.00000000e+00], [-2.06944119e+18], [ 2.06944119e+18], [ 1.00000000e+03], [ 1.12000000e-01]])
#_# ela_curv: array([[4.79755856e+00], [1.34573105e+03], [5.12834662e+19], [3.70342074e+07], [3.86444487e+12], [8.88644003e+21], [5.30415946e+20], [0.00000000e+00], [3.81373465e+00], [4.90886432e+02], [5.97651830e+14], [1.97651206e+05], [9.10648718e+08], [7.52108388e+16], [5.45247010e+15], [1.94000000e-01], [4.54265125e+00], [3.29125394e+03], [9.25189949e+46], [2.18081469e+07], [9.89777937e+09], [2.74236176e+49], [1.35682556e+48], [5.00000000e-02], [1.07656000e+05], [1.04290000e+01]])
#_# ela_distr: array([[1.33769544e+01], [1.94701124e+02], [1.80000000e+01], [0.00000000e+00], [2.90000000e-02]])
#_# ela_local: array([[2.70000000e+02], [9.00000000e-01], [2.35864648e-04], [4.89568303e-02], [8.33333333e-02], [3.40768278e-03], [3.33333333e-03], [1.17000000e+02], [2.08000000e+02], [2.95273333e+02], [2.73000000e+02], [3.77000000e+02], [7.28000000e+02], [1.02839577e+02], [8.88520000e+04], [7.16600000e+00]])
#_# ela_meta: array([[ 2.12758964e-02], [-6.21915065e+18], [ 1.35144338e+16], [ 2.24256868e+18], [ 1.65938782e+02], [ 6.15453221e-02], [ 5.94927163e-02], [ 6.24028558e+00], [ 2.43822411e-01], [ 0.00000000e+00], [ 1.50000000e-02]])
#_# basic: array([[ 6.00000000e+00], [ 5.00000000e+02], [-1.00000000e+00], [-1.00000000e+00], [ 4.00000000e+00], [ 4.00000000e+00], [ 2.88961528e+00], [ 4.10691709e+20], [ 3.00000000e+00], [ 3.00000000e+00], [ 7.29000000e+02], [ 3.96000000e+02], [ 1.00000000e+00], [ 0.00000000e+00], [ 0.00000000e+00]])
#_# disp: array([[ 0.54940333], [ 0.63595834], [ 0.77089952], [ 0.89491857], [ 0.53989005], [ 0.63417083], [ 0.7646051 ], [ 0.88560301], [-2.18734742], [-1.76718039], [-1.1121306 ], [-0.5101005 ], [-2.24631371], [-1.78602329], [-1.14922704], [-0.55850027], [ 0. ], [ 0.01 ]])
#_# limo: array([[ nan], [ nan], [ nan], [ nan], [ nan], [ nan], [ nan], [ nan], [ nan], [ nan], [ nan], [ nan], [0. ], [0.033]])
#_# nbc: array([[ 0.47974042], [ 0.96061989], [ 0.34786143], [ 0.0693798 ], [-0.0889751 ], [ 0. ], [ 0.04 ]])
#_# pca: array([[1. ], [1. ], [0.14285714], [1. ], [0.18943524], [0.18942493], [1. ], [0.17000728], [0. ], [0.003 ]])
#_# gcm: array([[1.00000000e+00], [1.37174211e-03], [5.41838134e-01], [0.00000000e+00], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [ nan], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [ nan], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [ nan], [5.43209877e-01], [5.43209877e-01], [1.37174211e-03], [0.00000000e+00], [7.93900000e+00], [1.00000000e+00], [1.37174211e-03], [5.41838134e-01], [0.00000000e+00], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [ nan], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [ nan], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [5.43209877e-01], [ nan], [5.43209877e-01], [5.43209877e-01], [1.37174211e-03], [0.00000000e+00], [7.53700000e+00], [2.00000000e+00], [2.74348422e-03], [9.97256516e-01], [1.00000000e+00], [5.00000000e-01], [5.00000000e-01], [5.00000000e-01], [5.00000000e-01], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [0.00000000e+00], [4.88340192e-01], [5.00000000e-01], [5.00000000e-01], [5.11659808e-01], [1.64894585e-02], [1.00000000e+00], [1.00000000e+00], [2.74348422e-03], [0.00000000e+00], [1.21150000e+01]])
#_# ic: array([[ 0.57808329], [ nan], [68.69928016], [10.73573574], [ 0.53413655], [ 0. ], [ 0.281 ]])
#_# Represented: 1
'''
result = 0
for i,x in enumerate(args[0:-1]):
result += (x**2)**(args[i+1]**2+1) + (args[i+1]**2)**(x**2 + 1)
return result

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PackageCode/MDAF/TestFunctions/Bukin2.py Normal file
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def main(args):
'''
#_# dimmensions: 2
#_# upper: [-5, 3]
#_# lower: [-15, -3]
#_# minimum: [-10,0]
#_# cm_angle: array([[3.49881571e-01], [1.07838645e-01], [3.22906733e-01], [1.09086923e-01], [1.36740093e+02], [3.72333248e+01], [6.24743683e-02], [1.45683932e-02], [0.00000000e+00], [7.70000000e-02]])
#_# cm_conv: array([[0.08928571], [0.04166667], [0.51785714], [0.48214286], [0. ], [0.038 ]])
#_# cm_grad: array([[0.80986036], [0.11715403], [0. ], [0.05 ]])
#_# ela_conv: array([[0.00000000e+00], [0.00000000e+00], [2.74360332e+00], [2.74360332e+00], [1.00000000e+03], [1.39000000e-01]])
#_# ela_curv: array([[1.00520667e+02], [1.01157352e+02], [1.02102548e+02], [1.01875092e+02], [1.03042277e+02], [1.04371521e+02], [1.09894163e+00], [0.00000000e+00], [3.34559633e+00], [4.02357156e+00], [5.48926800e+00], [5.13982675e+00], [6.55405463e+00], [9.78678891e+00], [1.70558030e+00], [0.00000000e+00], [3.29353234e+00], [1.48351404e+04], [2.19236653e+31], [4.03941866e+04], [3.14916313e+07], [4.17710288e+33], [2.95558281e+32], [0.00000000e+00], [8.40000000e+03], [1.06400000e+00]])
#_# ela_distr: array([[ 0.01739786], [-1.01700978], [ 1. ], [ 0. ], [ 0.02 ]])
#_# ela_local: array([[1.000e+00], [1.000e-02], [1.000e+00], [ nan], [1.000e+00], [0.000e+00], [1.000e+00], [1.000e+01], [1.000e+01], [1.000e+01], [1.000e+01], [1.000e+01], [1.000e+01], [0.000e+00], [1.001e+03], [9.300e-02]])
#_# ela_meta: array([[9.98338849e-01], [1.91750766e+02], [1.99998995e+01], [1.00059864e+02], [5.00301834e+00], [9.98335620e-01], [1.00000000e+00], [3.55077954e+14], [1.00000000e+00], [0.00000000e+00], [9.00000000e-03]])
#_# basic: array([[ 2. ], [ 500. ], [ -15. ], [ -3. ], [ -5. ], [ 3. ], [-388.17734244], [ 369.94308917], [ 10. ], [ 10. ], [ 100. ], [ 100. ], [ 1. ], [ 0. ], [ 0. ]])
#_# disp: array([[ 0.26139886], [ 0.36358526], [ 0.5516879 ], [ 0.80572442], [ 0.25023966], [ 0.32900436], [ 0.49402763], [ 0.72135232], [-3.13648576], [-2.70254902], [-1.90376706], [-0.82499549], [-3.02850003], [-2.7103465 ], [-2.04376951], [-1.125539 ], [ 0. ], [ 0.01 ]])
#_# limo: array([[ 1.02002699e+02], [ 9.98507972e-01], [ 1.02155253e+02], [ 1.13240181e+00], [-3.88783841e-02], [-9.92102532e-01], [ 5.48006296e+00], [ 1.74733189e+00], [ 3.39374032e+01], [ 5.00221530e+00], [ 2.94406289e+00], [ 3.22874553e-02], [ 0.00000000e+00], [ 9.50000000e-02]])
#_# nbc: array([[ 0.5341454 ], [ 0.87561488], [ 0.47062778], [ 0.15902654], [-0.19513986], [ 0. ], [ 0.027 ]])
#_# pca: array([[1. ], [1. ], [0.33333333], [0.66666667], [0.73549361], [0.51109865], [0.99976032], [0.66217335], [0. ], [0.002 ]])
#_# gcm: array([[1. ], [0.01 ], [0.99 ], [0. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [0.01 ], [0. ], [0.085], [1. ], [0.01 ], [0.99 ], [0. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [0.01 ], [0. ], [0.084], [1. ], [0.01 ], [0.99 ], [0. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [1. ], [1. ], [1. ], [ nan], [1. ], [1. ], [0.01 ], [0. ], [0.088]])
#_# ic: array([[ 0.67118887], [ 2.02702703], [70.28244264], [ 1.90690691], [ 0.23293173], [ 0. ], [ 0.204 ]])
#_# Represented: 1
'''
return 100*(args[1]-0.01*args[0]**2+1)+0.01*(args[0]+10)**2

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PackageCode/MDAF/TestFunctions/Bukin4.py Normal file
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def main(args):
'''
#_# dimmensions: 2
#_# upper: [-5, 3]
#_# lower: [-15, -3]
#_# minimum: [-10,0]
#_# cm_angle: array([[3.46336319e-01], [1.11596448e-01], [3.32688632e-01], [1.10567852e-01], [1.26709762e+02], [3.96040507e+01], [1.47616482e-01], [9.06041269e-02], [0.00000000e+00], [7.90000000e-02]])
#_# cm_conv: array([[0.23809524], [0.19642857], [0.67857143], [0.32142857], [0. ], [0.033 ]])
#_# cm_grad: array([[0.81854428], [0.1192788 ], [0. ], [0.052 ]])
#_# ela_conv: array([[ 1.00000000e+00], [ 0.00000000e+00], [-1.06441804e+02], [ 1.06441804e+02], [ 1.00000000e+03], [ 1.04000000e-01]])
#_# ela_curv: array([[3.20384285e+00], [1.59352986e+02], [3.11716056e+02], [3.16753438e+02], [4.75852875e+02], [5.98482830e+02], [1.80339798e+02], [0.00000000e+00], [3.20382725e+02], [1.59352986e+04], [3.11716055e+04], [3.16753436e+04], [4.75852869e+04], [5.98482828e+04], [1.80339798e+04], [0.00000000e+00], [5.31778742e+06], [4.94191117e+07], [1.07037935e+36], [3.96591773e+08], [6.61335123e+12], [1.60389805e+38], [1.16318553e+37], [0.00000000e+00], [8.40000000e+03], [9.98000000e-01]])
#_# ela_distr: array([[ 0.63700187], [-0.86671884], [ 2. ], [ 0. ], [ 0.024 ]])
#_# ela_local: array([[9.00000000e+01], [9.00000000e-01], [1.00000000e+00], [1.64410646e-01], [1.00000000e-01], [1.01123596e-02], [1.00000000e-02], [4.50000000e+01], [8.00000000e+01], [8.93000000e+01], [9.00000000e+01], [1.00000000e+02], [1.35000000e+02], [1.65758130e+01], [9.02000000e+03], [7.07000000e-01]])
#_# ela_meta: array([[-6.03426790e-03], [ 3.06569525e+02], [ 2.05064239e-02], [ 6.61146864e-01], [ 3.22409635e+01], [-7.94339775e-03], [ 1.00000000e+00], [ 5.33390671e+04], [ 1.00000000e+00], [ 0.00000000e+00], [ 1.40000000e-02]])
#_# basic: array([[ 2.00000000e+00], [ 5.00000000e+02], [-1.50000000e+01], [-3.00000000e+00], [-5.00000000e+00], [ 3.00000000e+00], [ 8.36446791e-03], [ 8.95463288e+02], [ 1.00000000e+01], [ 1.00000000e+01], [ 1.00000000e+02], [ 1.00000000e+02], [ 1.00000000e+00], [ 0.00000000e+00], [ 1.00000000e-03]])
#_# disp: array([[ 0.90387646], [ 0.87562005], [ 0.80237804], [ 0.81384686], [ 0.90019544], [ 0.85330973], [ 0.74450389], [ 0.74867028], [-0.40830656], [-0.52833206], [-0.83944411], [-0.79072771], [-0.40329912], [-0.59275905], [-1.03243135], [-1.01559546], [ 0. ], [ 0.011 ]])
#_# limo: array([[4.17880614e-01], [4.99806181e-03], [2.99599766e+02], [1.70588576e+02], [6.54778826e-02], [5.20933692e-03], [9.01491863e+02], [3.18988622e+03], [9.03063909e+01], [3.88297785e+01], [1.74942209e+02], [5.15283205e-01], [0.00000000e+00], [1.12000000e-01]])
#_# nbc: array([[ 0.19285636], [ 0.84080448], [ 0.11842373], [ 0.17016598], [-0.30584099], [ 0. ], [ 0.034 ]])
#_# pca: array([[1. ], [1. ], [0.33333333], [1. ], [0.73538694], [0.50398941], [0.99984257], [0.33688393], [0. ], [0.003 ]])
#_# gcm: array([[5. ], [0.05 ], [0.95 ], [0.93 ], [0.16376097], [0.2 ], [0.18658697], [0.26119064], [0.03940216], [0.01 ], [0.014 ], [0.01 ], [0.02 ], [0.00547723], [0.07 ], [0.08 ], [0.2 ], [0.21 ], [0.32 ], [0.10024969], [1. ], [0.21551545], [0.01 ], [0. ], [0.091 ], [4. ], [0.04 ], [0.96 ], [0.9 ], [0.10082007], [0.25 ], [0.19083529], [0.51750934], [0.18364899], [0.01 ], [0.025 ], [0.015 ], [0.06 ], [0.02380476], [0.1 ], [0.05 ], [0.25 ], [0.18 ], [0.59 ], [0.23537205], [1. ], [0.17734396], [0.01 ], [0. ], [0.094 ], [4. ], [0.04 ], [0.96 ], [0.88 ], [0.15058257], [0.25 ], [0.22415964], [0.40109815], [0.10875386], [0.01 ], [0.03 ], [0.025 ], [0.06 ], [0.0244949 ], [0.12 ], [0.14 ], [0.25 ], [0.22 ], [0.42 ], [0.13241349], [1. ], [0.40109815], [0.01 ], [0. ], [0.095 ]])
#_# ic: array([[ 0.56692136], [ 2.74774775], [116.69898186], [ 2.36736737], [ 0.27710843], [ 0. ], [ 0.228 ]])
#_# Represented: 1
'''
return 100*args[1]**2+0.01*abs(args[0]+10)

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PackageCode/MDAF/TestFunctions/Bukin6.py Normal file
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from math import sqrt, fabs
def main(args):
'''
#_# dimmensions: 2
#_# upper: [-5, 3]
#_# lower: [-15, -3]
#_# minimum: [-10,1]
'''
return 100*sqrt(fabs(args[1]-0.01*args[0]**2))+0.01*fabs(args[0]+10)

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PackageCode/MDAF/TestFunctions/Keane.py Normal file
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#Import math library
import math
def main(args):
'''
#_# dimmensions: 2
'''
for x in args:
if(x<0 | x>10): return 0
return (math.sin(args[0]-args[1])**2*math.sin(args[0]+args[1])**2)/(math.sqrt(args[0]**2+args[1]**2))

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PackageCode/MDAF/TestFunctions/Leon.py Normal file
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#Import math library
def main(args):
'''
#_# dimmensions: 2
'''
for x in args:
if x < -1.2 or x > 1.2:
return 0
return (100*(args[1]-args[0])**2)+(1-args[0])**2

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PackageCode/MDAF/TestFunctions/Matyas.py Normal file
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def main(args):
'''
>>> main([0,1])
0.26
#_# dimmensions: 2
'''
for x in args:
if x < -10 or x > 10:
return 0
return (0.26*(args[0]**2+args[1]**2))-(0.48*args[0]*args[1])
if __name__ == "__main__":
import doctest
doctest.testmod()

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PackageCode/MDAF/TestFunctions/McCormick.py Normal file
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import math
def main(args):
'''
>>>main([-0.547, -1.547])
0
#_# dimmensions: 2
'''
for args[0] in args:
if args[0] < -1.5 or args[0] > 4:
return 0
if args[1] < -3 or args[1] > 3:
return 0
return math.sin(args[0]+args[1])+(args[0]-args[1])**2-(3*args[0]/2)+(5*args[1]/2)+1

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PackageCode/MDAF/TestFunctions/Miele_Cantrell.py Normal file
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import math
def main(args):
'''
>>>main([0, 1, 1, 1])
0
#_# dimmensions: 4
'''
for x in args:
if x < -1 or x > 1:
return 0
return (math.exp(-args[0])-args[1])**4+(100*(args[1]-args[2])**6)+(math.tan(args[2]-args[3]))**4+args[0]**8

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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}

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# MDAF
THe desc will go here

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[build-system]
requires = ["setuptools>=42", "wheel"]
build-backend = "setuptools.build_meta"

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PackageCode/setup.cfg Normal file
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[metadata]
name = MDAF
version = 0.1
description =A Framework for the Analysis and Benchmarking of Metaheuristics
url = https://git.rehounou.ca/remi/MDAF
author = Remi Ehounou
author_email = remi.ehounou@outlook.com
license = MIT
long_description = file: README.md
long_description_content_type = text/markdown
classifiers =
Programming Language :: Python :: 3
License :: OSI Approved :: MIT License
Operating System :: OS Independent
[options]
package_dir =
= .
include_package_data = True
packages = find:
python_requires = >=3.6
install_requires =
numpy
importlib
rpy2 == 3.4.4