def test_listPickListSeed(self):
temp = ListPick.listPickListSeed(5, 2, self.test)
temp2 = ListPick.listPickListSeed(5, 2, self.test)
x = None
if temp == temp2:
x = True
self.assertEqual(True, x)
def test_listPickSeed(self):
result = ListPick.listPickSeed(3, self.test)
result2 = ListPick.listPickSeed(3, self.test)
x = None
if result in self.test and type(result) == int:
if result == result2:
x = True
self.assertEqual(True, x)
def test_listPickList(self):
temp = ListPick.listPickList(2, self.test)
x = None
if len(temp) == 2:
for item in temp:
if item in self.test and type(item) == int:
x = True
self.assertEqual(True, x)
def systematicSamp(seed, nums, data):
return ListPick.listPickListSeed(seed, nums, data)
def populationPorportion(seeds, nums, data):
p = ListPick.listPickListSeed(seeds, nums, data)
pp = len(p) / len(data)
return pp
def test_listPick(self):
result = ListPick.listPick(self.test)
x = None
if result in self.test and type(result) == int:
x = True
self.assertEqual(True, x)
def samplecorrelation(seed, sample_size, data1, data2):
dataA = ListPick.listPickListSeed(seed, sample_size, data1)
dataB = ListPick.listPickListSeed(seed, sample_size, data2)
return Covariance.covariance(dataA, dataB) / (Stddev.stddev(dataA) * Stddev.stddev(dataB))
def sampCorrelation(seed, samp_size, data1, data2):
dataset1 = ListPick.listPickListSeed(seed, samp_size, data1)
dataset2 = ListPick.listPickListSeed(seed, samp_size, data2)
return Covariance.covariance(dataset1, dataset2) / (StdDeviation.stdDeviation(dataset1) * StdDeviation.stdDeviation(dataset2))
def simpRandSamp(seed,nums,data):
random.seed(seed)
return ListPick.listPickListSeed(seed,nums,data)
def z_score(seed, data):
num = ListPick.listPickSeed(seed, data)
mean = Mean.mean(data)
return (num - mean) / StdDeviation.stdDeviation(data)