python 写线性回归代码时遇到变量类型报错的问题
def train(self,alpha,num_iterations = 500):
cost_history = self.gradient_descent(alpha,num_iterations)##此处有问题
return self.theta,cost_history
def gradient_descent(self,alpha,num_iterations):
cost_history = []
for x in range(num_iterations):
self.gradient_step(alpha)
cost_history.append(self.cost_function(self.data,self.labels))
return cost_history
def gradient_step(self,alpha):
num_examples = data.shape[0]
prediction = LinearRegression.hypothesis(self.data,self.theta)
delta = prediction - self.labels
theta = self.theta
theta = theta - alpha*(1/num_examples)*(np.dot(delta.T,self.data)).T
self.theta = theta
def cost_function(self,data,labels):
self.m = len(labels)
delta = LinearRegression.hypothesis(data,self.theta) - labels
cost = (1/2)*np.dot(delta.T,delta)/self.m
return cost[0][0]
def hypothesis(data,theta):
predictions = np.dot(data,theta)
return predictions
x_train =rescombine
y_train = labels
num_iterations = 500
learning_rate = 0.01
linear_regression = LinearRegression(x_train, y_train)
(theta, cost_history) = LinearRegression.train(learning_rate, num_iterations)##此处有问题
print(theta, cost_history)
print('开始损失',cost_history[0])
print('结束损失',cost_history[-1])
发生异常: AttributeError
'float' object has no attribute 'gradient_descent'
File "C:\Users\Xpc\Desktop\LinearRegression\linear_regression.py", line 299, in train
cost_history = self.gradient_descent(alpha,num_iterations)
File "C:\Users\Xpc\Desktop\LinearRegression\linear_regression.py", line 340, in
(theta, cost_history) = LinearRegression.train(learning_rate, num_iterations)
我不知道是不是我写的程序有问题，还是数据的类型不对
你 self.gradient_descent(alpha,num_iterations) 中 self是float浮点数类型,不可能有gradient_descent方法吧
你调用train()方法的方式不对
要用LinearRegression类的实例对象 linear_regression 调用train()方法
不是用LinearRegression类本身调用train()方法,
(theta, cost_history) = LinearRegression.train(learning_rate, num_iterations)##此处有问题
改成
(theta, cost_history) = linear_regression.train(learning_rate, num_iterations)##此处有问题
44行(theta, cost_history) = LinearRegression.train(learning_rate, num_iterations)改成(theta, cost_history) = linear_regression.train(learning_rate, num_iterations)试试