from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt

degree = 3   #多項式の次数
polynomial_features = PolynomialFeatures(degree = degree)
#8個のデータの読み込み
x=[[1.5],[2.0],[2.5],[3.0],[4.0],[5.0],[5.5],[6.0]]
y=[[1.5],[2.5],[3.1],[4.0],[4.0],[4.5],[4.9],[5.0]]

#回帰分析
x_poly = polynomial_features.fit_transform(x)
model = LinearRegression()
model.fit(x_poly, y)
y_poly_pred = model.predict(x_poly)

#相関式の切片と回帰係数の出力
print("model.intercept_",model.intercept_)
print("model.coef_",model.coef_)
print("R2値=", r2_score(y, y_poly_pred) )
#データのプロット
plt.scatter(x, y, color='r', s=50)

#回帰解析による予測のプロット
plt.plot(x, y_poly_pred, 'k')
#print(y_poly_pred)
plt.xlabel("x")
plt.ylabel("y")
plt.show()