目标函数的构建
\[
h_\theta(x) = \theta x + \theta_0 预测函数
\]
#目标函数(损失函数)
def cost(theta0,theta1,x,y):
J=0
m = len(x)
for i in range(m):
h = theta1*x[i] + theta0 #对应公式 h(x)值
J += (h-y[i])**2 #目标函数 J = (h(x) - y)**2
J /= (2*m)
return J
print(cost(0,1,pga.distance,pga.accuracy)) #1.599438422599817
theta0 = 100
theta1s = np.linspace(-3,2,100)
costs = []
for theta1 in theta1s:
costs.append(cost(theta0,theta1,pga.distance,pga.accuracy))
print(theta1s.shape) #(100,)
plt.plot(theta1s,costs)
plt.show()
