#
# µû¶óÇÏ¸ç ¹è¿ì´Â ÆÄÀ̽ã°ú µ¥ÀÌÅÍ°úÇÐ(»ý´ÉÃâÆÇ»ç 2020)
# 14.7 ¼±Çü ȸ±Í ÇнÀ°á°ú¸¦ È®ÀÎÇÏ°í ¿¹ÃøÇϱâ, 374ÂÊ
#
#14-6ÀÇ ÄÚµå
import numpy as np
from sklearn import linear_model # scikit-learn ¸ðµâÀ» °¡Á®¿Â´Ù
regr = linear_model.LinearRegression()
X = [[164], [179], [162], [170]] # ´ÙÁßȸ±Í¿¡µµ »ç¿ëÇϵµ·Ï ÇÔ
y = [53, 63, 55, 59] # y = f(X)ÀÇ °á°ú
regr.fit(X, y)
########################################
coef = regr.coef_ # Á÷¼±ÀÇ ±â¿ï±â
intercept = regr.intercept_ # Á÷¼±ÀÇ ÀýÆí
score = regr.score(X, y) # ÇнÀµÈ Á÷¼±ÀÌ µ¥ÀÌÅ͸¦ ¾ó¸¶³ª Àß µû¸£³ª
print("y =", coef, "* X + ", intercept)
print("The score of this line for the data: ", score)
####################################
input_data = [ [180], [185] ]
result = regr.predict(input_data)
print(result)