目录
常规求解：
矩阵求解
sklean算法求解
# 二元一次方程 # x + y = 14 # 2x - y = 10
常规求解：
x = np.array([[1,1],[2,-1]])
print(x)
# [[ 1 1]
# [ 2 -1]]
y = np.array([14, 10])
w = np.linalg.solve(x, y)
print('正常求救：')
print(w)
# [8. 6.]矩阵求解
print(x.T)
# y = w0.x1 + w1 .x2
# w = (xt.x)-1 . (xt.y)
A = x.T.dot(x)
print(A)
# 逆矩阵
B = np.linalg.inv(A)
print("B is :")
print(B)
re = B.dot(x.T).dot(y)
print('矩阵求救:')
print(re)
sklean算法求解
# fit_intercept 不计算截距
model = LinearRegression(fit_intercept=False)
f = model.fit(x,y)
print("sklean算法求救：")
# coef 斜率
print(model.coef_)
import numpy as np
from sklearn.linear_model import LinearRegression
# pip install -U scikit-learn
# 正规方程
# 二元一次方程
# x + y = 14
# 2x - y = 10
if __name__ == '__main__':
x = np.array([[1,1],[2,-1]])
print(x)
# [[ 1 1]
# [ 2 -1]]
y = np.array([14, 10])
w = np.linalg.solve(x, y)
print('正常求救：')
print(w)
# [8. 6.]
print(x.T)
# y = w0.x1 + w1 .x2
# w = (xt.x)-1 . (xt.y)
A = x.T.dot(x)
print(A)
# 逆矩阵
B = np.linalg.inv(A)
print("B is :")
print(B)
re = B.dot(x.T).dot(y)
print('矩阵求救:')
print(re)
# sklean算法
# fit_intercept 不计算截距
model = LinearRegression(fit_intercept=False)
f = model.fit(x,y)
print("sklean算法求救：")
# coef 斜率
print(model.coef_)
参考地址：
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