def test_average_string(self):
stringCheck = False
try:
main.average(["1, 2, -3, 4, -5, -6, 7, 8"])
except:
stringCheck = True
self.assertEqual(True, stringCheck)
def test_average_empty(self):
NaN = False
try:
main.average([])
NaN = False
except:
NaN = True
self.assertEqual(False, NaN)
def __init__(self, adults):
ft = [x.fitness for x in adults]
sd = 2*main.standard_deviation(ft)
if sd == 0:
self.h = [1]*len(adults)
else:
avg = main.average(ft)
self.h = []
for a in adults:
self.h.append((1+(a.fitness-avg)/sd))
s = sum(self.h,0.0)
self.h[0] /= s
for i in range(1, len(self.h)):
self.h[i] /= s
self.h[i] += self.h[i-1]
self.h = zip(self.h, adults)
def test_average_float(self):
self.assertEqual(-0.333, main.average([1, 1 / 3, -5, -6, 8]))
def test_average_complex(self):
self.assertEqual(
1.625 + 0.625j,
main.average([1, 2 + 8j, 2 - 3j, 4, -5, -6, 7 + 0j, 8]))
def test_average_negs(self):
self.assertEqual(1.0, main.average([1, 2, -3, 4, -5, -6, 7, 8]))
def test_average_int(self):
self.assertEqual(4.5, main.average([1, 2, 3, 4, 5, 6, 7, 8]))
import main
import matplotlib.pyplot as plt
import numpy as np
import math
from scipy.stats import norm
dataSet = main.dataSet
average = main.average(main.dataSet) # mean
stDev = main.stdev # sigma
calculated = [] # list that will get appended with (dataset[i] - stDev)
accepted = []
rejected = []
confidenceLevel = 99.7
for i in range (0, len(dataSet)):
calcMeanDev = float(dataSet[i] - stDev)
calculated.append(calcMeanDev)
print(calcMeanDev)
for i in range (0, len(calculated)):
if calculated[i] >= (-3 * stDev) and calculated[i] <= (3 * stDev):
accepted.append(calculated[i])
else:
rejected.append(calculated[i])
print(f'The rejected values were {rejected}')
def marginOfError(stDev, confidenceLevel, calculated):
standardError = (stDev / (math.sqrt(len(calculated))))
errorMargin = standardError * confidenceLevel
def test_avg(self):
self.assertEqual(main.average([1, 2, 3]), 2)
self.assertEqual(main.average([1, 1, 1]), 1)