def correlation(x, y): if(len(x) == len(y)): n = len(x) sum_x = float(Addition.sumList(x)) sum_y = float(Addition.sumList(y)) sum_x_sq = sum(map(lambda x: pow(x, 2), x)) sum_y_sq = sum(map(lambda x: pow(x, 2), y)) psum = sum(imap(lambda x, y: x * y, x, y)) num = psum - (sum_x * sum_y / n) den = pow((sum_x_sq - pow(sum_x, 2) / n) * (sum_y_sq - pow(sum_y, 2) / n), 0.5) if den == 0: return 0 return num / den else: return -1
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 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 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 Mean_Calculator(data): num = len(data) count = Addition.sumList(data) return Division.divide(count, num)
def mean(data): num = len(data) total = Addition.sumList(data) return Division.divide(total, num)