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testing_tools.py
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import random as rnd
import time
import math
import itertools
from math import inf
from statistics import stdev, mean, median
import matplotlib.pyplot as plt
class Test_Run:
""" Simulation of a single function with a sing set of parameters performed multiple times"""
def __init__(self, function_to_test, number_of_tests, states_mode, *args, init_state = None, repetitive = 0):
"""Test the class function_to_test number_of_tests times using the argument_list provided.
The states mode determines what initial state is used.
init_state defines the initial state for mode 3
repetive provides the period at which energy samples should be taken, 0 = dont take any samples.
0 = Use all 0s
1 = Use all 1s
2 = Use random
3 = Use the one from argument_list
return [min, max, avg, median, std]"""
self.array_size = len(args[1])
self.fe =[]
self.rst =[]
self.m =[]
self.r = []
for i in range(number_of_tests):
states_for_testing = self.restore_states(states_mode)
inst = function_to_test(states_for_testing, *args)
if not repetitive != 0:
inst.run_simulation()
else:
inst.run_simulation_period(repetitive)
fe, rst, m, r = inst.items_to_return()
self.fe.append(fe)
self.rst.append(rst)
self.m.append(m)
self.r.append(r)
del inst
self.name = function_to_test.__name__
self.number_of_tests = number_of_tests
self.min_energy, self.min_energy_indeces = self.get_min(self.fe)
self.min_energy_count = len(self.min_energy_indeces)
self.max_energy, self.max_energy_indeces = self.get_max(self.fe)
self.average_energy = mean(self.fe)
self.median_energy = median(self.fe)
self.stddev_energy = self.get_stddev(self.fe)
self.average_time = mean(self.rst)
self.stddev_time = self.get_stddev(self.rst)
def get_min(self, l):
min_energy = inf
min_energy_indeces = []
for i, value in enumerate(l):
if min_energy > value:
min_energy = value
min_energy_indeces = [i]
elif min_energy == value:
min_energy_indeces.append(i)
return min_energy, min_energy_indeces
def get_max(self, l):
max_energy = -inf
max_energy_indeces = []
for i, value in enumerate(l):
if max_energy < value:
max_energy = value
max_energy_indeces = [i]
elif max_energy == value:
max_energy_indeces.append(i)
return max_energy, max_energy_indeces
def get_stddev(self, l):
if len(l) == 1:
return 0
else:
return stdev(l)
def restore_states(self, states_mode):
if states_mode == 0:
return [0 for i in range(self.array_size)]
elif states_mode == 1:
return [1 for i in range(self.array_size)]
elif states_mode == 2:
return [rnd.randint(0,1) for i in range(self.array_size)]
elif states_mode == 3:
return self.init_state[:]
else:
return Exception("Invalid states_mode")
def graph_runs(self):
""" Work in progress"""
if self.number_of_tests > 9:
ans = input(str("Are you sure you want to print " + str( self.number_of_tests) + " runs? 1 = yes"))
if int(ans) != 1:
return
window_size = math.ceil(math.sqrt(self.number_of_tests))
pre = str(window_size) + str(window_size)
fig = plt.figure()
for i, item in enumerate(self.r, 1):
ax1 = fig.add_subplot(int(pre + str(i)))
plt.plot(item[1],item[0])
plt.ylabel('System Energy')
plt.xlabel("Step number")
plt.show()
def print_test_results(self):
print ("Number of Tests: ", self.number_of_tests)
print ("Lowest energy: ", self.min_energy, " was achieved ", len(self.min_energy_indeces))
print ("Highest energy: ", self.max_energy)
print ("Average energy: ", self.average_energy)
print ("Median energy: ", self.median_energy)
print ("Standard deviation: ", self.stddev_energy)
print ("Average time: ", self.average_time)
print ("Standard Deviation for time: ", self.stddev_time)
print ()
return
def save_test_results(self, file_name = "results.txt"):
f = open(file_name, 'w')
string1 = "Number of Tests: " + str(self.number_of_tests) + '\n'
string2 = "Lowest energy: " + str(self.min_energy) + " was achieved "+ str(len(self.min_energy_indeces)) + " times. \n"
string3 = "Highest energy: " + str(self.max_energy) + '\n'
string4 = "Average energy: " + str(self.average_energy) + '\n'
string5 = "Median energy: " + str(self.median_energy) + '\n'
string6 = "Standard deviation: " + str(self.stddev_energy) + '\n'
string7 = "Average time: " + str(self.average_time) + '\n'
string8 = "Standard Deviation for time: " + str(self.stddev_time) + '\n'
list_for_file = [string1, string2, string3, string4, string5, string6, string7, string8]
f.writelines(list_for_file)
f.close()
class Test_Runs:
def __init__(self, function_to_test, number_of_tests,states_mode,weights,bias, arguments, init_state = None):
size = len(arguments)
args = []
for item in arguments:
a = [item[0]]
b = item[0]
step = item[2]
while b < item[1]:
b += step
a.append(b)
args.append(a)
iterator = itertools.product(*args)
self.instances = []
self.parameters = []
while True:
try:
v = iterator.__next__()
print (v)
self.parameters.append(v)
self.instances.append(Test_Run(function_to_test, number_of_tests, states_mode, weights, bias, *v, init_state = init_state))
except Exception:
break
return
def searchmode_lowest(self):
self.list_of_min = []
for item in self.instances:
self.list_of_min.append(item.min_energy)
self.list_of_min_indeces = [i for i in range(len(self.instances))]
self.list_of_min, self.list_of_min_indeces = zip(*sorted(zip(self.list_of_min, self.list_of_min_indeces)))
print("Minimum energy achieved was: ", self.list_of_min[0], " by: ", self.list_of_min_indeces[0])
def searchmode_average(self):
self.list_of_average= []
for item in self.instances:
self.list_of_average.append(item.average_energy)
self.list_of_average_indeces = [i for i in range(len(self.instances))]
self.list_of_average, self.list_of_min_average = zip(*sorted(zip(self.list_of_average, self.list_of_average_indeces)))
print("Minimum averagen energy achieved was: ", self.list_of_average[0], " by: ", self.list_of_average_indeces[0])
def searchmode_median(self):
self.list_of_median = []
for item in self.instances:
self.list_of_median.append(item.median_energy)
self.list_of_median_indeces = [i for i in range(len(self.instances))]
self.list_of_median, self.list_of_median_indeces = zip(*sorted(zip(self.list_of_median, self.list_of_median_indeces)))
print("Minimum energy achieved was: ", self.list_of_median[0], " by: ", self.list_of_median_indeces[0])