def covariance(x, y):
'''
공분산(covariance)
cov = sum((xi - x_bar)(yi - y_bar)) / (n - 1)
:param x: 원소가 n개인 (1차원) 리스트
:param y: 원소가 n개인 (1차원) 리스트
:return: 공분산
'''
n = len(x)
x_bar = mean(x)
y_bar = mean(y)
x_deviations = [x_i - x_bar for x_i in x]
y_deviations = [y_i - y_bar for y_i in y]
# x_total = 0
# y_total = 0
# for x_i in x:
# x_total += (x_i - mean(x))
# for y_i in y:
# y_total += (y_i - mean(y))
#
# return ((x_total) * (y_total))/(n-1)
xy_total = 0
for x_i, y_i in zip(x, y):
xy_total += (x_i - mean(x)) * (y_i - mean(y))
return xy_total / (n - 1)
sum_of_deviations = dot(x, y)
def covariance(x, y):
"""
공분산(covariance)
Cov = sum((xi - x_bar)(yi - y_bar)) / (n - 1)
:param x: 원소 n개인 (1차원) 리스트
:param y: 원소 n개인 (1차원) 리스트
:return: 공분산
"""
x_bar = mean(x) # x의 평균
y_bar = mean(y) # y의 평균
x_deviations = [x_i - x_bar for x_i in x] # 편차들의 리스트
y_deviations = [y_i - y_bar for y_i in y]
sum_of_deviations = dot(x_deviations, y_deviations)
# sum_of_deviations = 0
# for xd, yd in zip(x, y):
# sum_of_deviations += (xd - x_bar) * (yd - y_bar)
return sum_of_deviations / (len(x) - 1)
def covariance(x, y):
"""
공분산
Cov = sum((x_i - x_bar)(y_i - y_bar)) / (n-1)
:param x: 원소가 n개인 1차원 리스트
:param y: 원소가 n개인 1차원 리스트
:return: 공분산
"""
# if len(x) != len(y):
# raise ValueError('x와 y의 길이는 같아야 합니다!')
# de_x = de_mean(x)
# de_y = de_mean(y)
# result = []
# for i, j in zip(de_x, de_y):
# result.append(i*j)
# return sum(result) / (len(x) - 1)
# if len(x) != len(y):
# raise ValueError('x와 y의 길이는 같아야 합니다!')
# de_x = de_mean(x)
# de_y = de_mean(y)
# sum = 0
# for i, j in zip(de_x, de_y):
# sum += (i*j)
# return sum / (len(x) - 1)
# x_bar = mean(x)
# y_bar = mean(y)
# x_dev = [x_i - x_bar for x_i in x]
# y_dev = [y_i - y_bar for y_i in y]
# sum_of_sq = 0
# for xd, yd in zip(x_dev, y_dev):
# sum_of_sq += xd * yd
# return sum_of_sq / (len(x) - 1)
x_bar = mean(x)
y_bar = mean(y)
x_dev = [x_i - x_bar for x_i in x]
y_dev = [y_i - y_bar for y_i in y]
sum_of_dev = dot(x_dev, y_dev)
return sum_of_dev / (len(x) - 1)
def variance(x):
"""
(x1 - mean)**2 + (x2 - mean)***2 + ... + (xn - mean)**2 / n-1
:param x: 원소가 n개인 1차원 리스트
:return: 분산
"""
# x_mean = mean(x)
# sum = 0
# for i in x:
# sum += (i - x_mean)**2
# return sum / (len(x)-1)
# demean = de_mean(x)
# sum = 0
# for i in demean:
# sum += i**2
# return sum / (len(x)-1)
demean = de_mean(x)
return dot(demean, demean) / (len(x) - 1)
def variance(x):
"""
(x1 - mean) ** 2 + (x2 - mean) ** 2 + ... + (xn - mean) ** 2 / (n - 1)
:param x: 원소 n개인 (1차원) 리스트
:return: x의 분산 값
"""
# sum = 0
# for i in x:
# sum += (i - mean(x)) ** 2
# v = sum / (len(x)-1)
n = len(x) # 원소 개수
# x_bar = mean(x) # 평균
# return sum([(x_i - x_bar) ** 2 for x_i in x]) / (n-1)
# 다른방법
# deviations = de_mean(x) # 편차들의 리스트
# return sum([d ** 2 for d in deviations]) / (n-1)
# dot()함수를 import해서 하는 방법
print('dot 함수 사용')
deviations = de_mean(x)
return dot(deviations, deviations) / (n - 1)
def variance(x):
print('dot 사용')
n = len(x)
deviations = de_mean(x)
return dot(deviations, deviations) / (n - 1)
def variance2(x):
n = len(x)
deviations = de_mean(x)
return dot(deviations, deviations) / (n - 1)
