标签：cti print 多次 near ict 参数 linear rom dataset
"一元多项式回归": 自变量只有一个 ；"多元多项式回归": 自变量有多个。
一元n次多项式：$\hat{y}=w_{0}+w_{1} x_{1}+ w_{2} x^{2}+\cdots+w_{n} x^{n}$
多元多次多项式（二元二次多项式为例）：$\hat{y}=w_{0}+w_{1} x_{1}+ w_{2} x^{2}+w_{3}x_{1}^{2}+w_{4}x_{2}^{2}+w_{5} x_{1}x_{2}$
$\hat{y}=w_{0}+\sum_{i=1}^{n}w_{i} x_{i}$
from sklearn.linear_model import LinearRegression datasets_X = [] # 形如[[1,2,3],[4,5,6],[7,8,9]] datasets_Y = [] # 形如[6,15,24] fr = open(‘train.dat‘, ‘r‘) lines = fr.readlines() for line in lines: items = line.strip().split(‘\t‘) datasets_X.append([float(ele) for ele in items[:-1]]) datasets_Y.append(float(items[-1])) model = LinearRegression() model.fit(datasets_X, datasets_Y) # 加载测试数据,这儿直接用的训练数据 X_test = datasets_X y_test = datasets_Y predictions = model.predict(X_test) for i, prediction in enumerate(predictions): print(‘Predict_value: %s, True_value: %s‘ % (prediction, y_test[i])) print(‘R2-squared: %.2f‘ % model.score(X_test, y_test)) print(model.coef_) # 参数 print(model.intercept_) # 偏置bias exit()
model.coef_就是一个参数列表，对应$w_{1}$,$w_{2}$,$\cdots$,$w_{n}$,model.intercept_就是偏置$w_{0}$
以三元三次多项式为例
一共有19项特征加一个偏置常数项
$\textbf{f}=[x_{1}、 x_{2}、 x_{3}、 x_{1}x_{1}、 x_{2}x_{2}、 x_{3}x_{3}、 x_{1}x_{2}、 x_{2}x_{3}、 x_{1}x_{3}、 x_{1}x_{1}x_{2}、 x_{1}x_{1}x_{3}、 x_{2}x_{2}x_{1}、 x_{2}x_{2}x_{3}、 x_{3}x_{3}x_{1}、 x_{3}x_{3}x_{2}、 x_{1}x_{1}x_{1}、 x_{2}x_{2}x_{2}、 x_{3}x_{3}x_{3}、 x_{1}x_{2}x_{3}]$
$\hat{y}=w_{0}+\sum_{i=1}^{19}w_{i} f_{i}$
from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures datasets_X = [[1,2,3],[4,5,6],[7,8,9]] datasets_Y = [6,15,24] poly_feat = PolynomialFeatures(degree=3) datasets_X_ploy = poly_feat.fit_transform(datasets_X) model = LinearRegression() model.fit(datasets_X_ploy, datasets_Y) print(model.coef_) # 参数 print(model.intercept_) # 偏置bias # model.coef_ = [-5.86336535e-16 4.23053648e-03 4.23053648e-03 4.23053648e-03 # 9.72135088e-03 1.39518874e-02 1.81824238e-02 1.81824238e-02 # 2.24129603e-02 2.66434968e-02 -4.83347121e-02 -3.86133612e-02 # -2.88920103e-02 -2.46614738e-02 -1.07095864e-02 7.47283740e-03 # -6.47904996e-03 1.17033739e-02 3.41163342e-02 6.07598310e-02] # model.intercept_ = 3.400113258456912
标签：cti print 多次 near ict 参数 linear rom dataset
原文地址：https://www.cnblogs.com/sunupo/p/12833508.html
