def deviation_Calculator(data):
newdata = []
data.sort()
n = len(data)
mean = Mean.Mean_Calculator(data)
for i in data:
newdata.append(Exponentiation.power((i - mean),2))
sum = Addition.sumList(newdata)
return nthRoot.rooting(2,Division.divide(sum,n))
def meanDev(data):
newlist = []
meanOfData = Mean.mean(data)
for i in data:
newlist.append(abs(i - meanOfData))
total = Addition.sumList(newlist)
return Division.divide(total, len(data))
def stardardDev(data):
n = len(data)
mn = Mean.mean(data)
newlist = []
for i in data:
newlist.append(Exponentiation.power(i - mn, 2))
total = Addition.sumList(newlist)
return nthroot.root(2, Division.divide(total, n))
def divide(self, a, b):
self.Result = Division.divide(a, b)
return self.Result
def Quotient(self, a, b):
self.Result = Division.quotient(a, b)
return self.Result
def mean(data):
num_values = len(data)
total = 0
for num in data:
total = Addition.sum(total, num)
return Division.divide(total, num_values)
def Mean_Calculator(data):
num = len(data)
count = Addition.sumList(data)
return Division.divide(count, num)
def test_MO_div(self):
self.assertEqual(2, Division.divide(2, 1))
def zscore(sd, data):
X = PickSeed.pickSeed(sd, data)
meanData = Mean.mean(data)
sd = StandardDeviation.standardDeviation(data)
z = Division.divide(X - meanData, sd)
return z
def test_MathOperations_Division(self):
self.assertEqual(2, Division.quotient(4, 2))
def mean(data):
num = len(data)
total = Addition.sumList(data)
return Division.divide(total, num)