#
# µû¶óÇÏ¸ç ¹è¿ì´Â ÆÄÀ̽ã°ú µ¥ÀÌÅÍ°úÇÐ(»ý´ÉÃâÆÇ»ç 2020)
# LAB 14-1 Å°°¡ ºñ½ÁÇصµ ³²,³àÀÇ ¸ö¹«°Ô´Â ´Ù¸¦ °Í : ´ÙÂ÷¿ø ¼±Çüȸ±Í, 376ÂÊ
#
import numpy as np
from sklearn import linear_model
regr = linear_model.LinearRegression()
# ³²ÀÚ´Â 0, ¿©ÀÚ´Â 1À» ³Ö¾î Â÷¿øÀ» Ãß°¡ÇÏ¿´À½
# ÀԷµ¥ÀÌÅ͸¦ 2Â÷¿øÀ¸·Î ¸¸µé¾î¾ß ÇÔ
X = [[164, 1],[167, 1],[165, 0],[170, 0],[179, 0],[163, 1],[159, 0],[166, 1]]
y = [43, 48, 47, 66, 67, 50, 52, 44] # y °ªÀº 1Â÷¿ø µ¥ÀÌÅÍ
regr.fit(X, y) # ÇнÀ
print('°è¼ö :', regr.coef_ )
print('ÀýÆí :', regr.intercept_)
print('Á¡¼ö :', regr.score(X, y))
print('ÀºÁö¿Í µ¿¹ÎÀÌÀÇ ÃßÁ¤ ¸ö¹«°Ô :', regr.predict([[166, 1], [166, 0]]))