the suboprocesses are running and the algorithm is functionning to optimize the function it is given

SourceCode/AlgorithmAnalyser.py

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+# directly running the DOE because existing surrogates can be explored with another workflow
+
+from numpy import random as r
+import time
+import importlib.util
+import multiprocessing
+
+
+# initialise the logic helpers
+r.seed(int(time.time()))
+
+heuristicpath = "/home/remi/Documents/MDAF-GitLAB/SourceCode/SampleAlgorithms/SimmulatedAnnealing.py"
+heuristic_name = "SimmulatedAnnealing"
+testfunctionpaths = ["/home/remi/Documents/MDAF-GitLAB/SourceCode/TestFunctions/Bukin2.py"]
+funcnames = ["Bukin2"]
+objs = 0
+args = {"high": 200, "low": -200, "t": 0.01, "p": 0.8}
+scale = 62
+
+
+def doe(heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale):
+    spec = importlib.util.spec_from_file_location(heuristic_name, heuristicpath)
+    heuristic = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(heuristic)
+    proc = list()
+    #heuristic.MyClass()
+    for idx, funcpath in enumerate(testfunctionpaths):
+        testspec = importlib.util.spec_from_file_location(funcnames[idx], funcpath)
+        func = importlib.util.module_from_spec(testspec)
+        testspec.loader.exec_module(func)
+        #func.MyClass()
+        initpoint = [r.random() * scale, r.random() * scale]
+        proc.append(multiprocessing.Process(target=heuristic.main, name=funcnames[idx], args=(func, objs, initpoint, args)))
+        best = proc[idx].run()
+        print("started :" + str(initpoint) + "\nEnded  :" + str(best))
+
+# simulatedAnnealing(S = [9.00,4.00],y = 0,high = 10,low = -8,t =0.01,p = 0.8)
+# proc = subprocess.call(["python", heuristic, "arg-15", "arg2", "argN"])
+
+doe (heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale)

SourceCode/Analyse_folders_and_description.ipynb

@@ -0,0 +1,70 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "abstract-broad",
+   "metadata": {},
+   "source": [
+    "# Program to read through folders in a file-path and spit out description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "similar-algeria",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_algorithm():\n",
+    "    import os # To enable you walk through the OS directory\n",
+    "    import re # To enable pattern recognition\n",
+    "    \n",
+    "    \n",
+    "    # Take file-path and extract names of files in file-path as list data type\n",
+    "    check_folder = input(\"Please enter the file-path of the folder: \\n\")\n",
+    "    found_items = os.listdir(check_folder)\n",
+    "    \n",
+    "    \n",
+    "    # Initialize what the pattern to be searched for should look like\n",
+    "    pattern = \"\\W\\W\\W\"\n",
+    "    \n",
+    "    # Look into the individual txt files and extraxt the description \n",
+    "    for items in range(0, len(found_items)):\n",
+    "        print(f\"\\n({items + 1}): I found the file named: {found_items[items]}\")\n",
+    "        inside_file = check_folder + '\\\\' + found_items[items]\n",
+    "        \n",
+    "        with open(inside_file, mode ='r', encoding = 'utf-8') as myfile:\n",
+    "            print(\"Inside the file, I found this description: \\n \")\n",
+    "            contents = myfile.readlines()\n",
+    "            \n",
+    "            for indexing in range(0, len(contents)):\n",
+    "                if re.search(pattern, contents[indexing]):\n",
+    "                    print(contents[indexing+1])\n",
+    "                    break\n",
+    "                    \n",
+    "            print(\"\\n\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

SourceCode/SampleAlgorithms/SimmulatedAnnealing.py

@@ -0,0 +1,81 @@
+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.main(Sc)
+    if type(func_output) == list:
+        error = [func_output[i]-objective[i] for i in range(len(func_output))]
+    else:
+        error = func_output - objective
+    return 1/abs(error)
+
+def main(func,obj,S,args):
+    r.seed(int(time.time()))
+    route = list()
+    #Parsing arguments
+    y = obj
+    high = args["high"]
+    low = args["low"]
+    t = args["t"]
+    p = args["p"]
+    
+    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) > 200:
+            break
+    #print('the Best Quality obtained was:{}'.format(Quality(Best,y)))
+    return Best
+
+
+
+

SourceCode/TestFunctions/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

SourceCode/TestFunctions/Bukin2.py

@@ -0,0 +1,2 @@
+def main(args):
+    return 100*(args[1]-0.01*args[0]**2+1)+0.01*(args[0]+10)**2

SourceCode/TestFunctions/Untitled.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}