线性回归数组行数不对应

问题遇到的现象

线性回归中遇到数组行数不对应的情况

问题相关代码，请勿粘贴截图

import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 change40.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400

z40=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z40.shape[0],30):
    z1=z40[i,:]
    z0.append(z1)
z0=np.array(z0)
x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)


list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label40=y_array
z40=np.vstack((x0,x1,x2,x3,x4,x5)).T





img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 50.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)


list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z50=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z50.shape[0],30):
    z1=z50[i,:]
    z0.append(z1)
z0=np.array(z0)

x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label50=y_array
z50=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T




 
img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 55.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z55=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z55.shape[0],30):
    z1=z55[i,:]
    z0.append(z1)
z0=np.array(z0)

x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label55=y_array
z55=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T





img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 -60.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)


list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z60=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z60.shape[0],30):
    z1=z60[i,:]
    z0.append(z1)
z0=np.array(z0)

x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label60=y_array
z60=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T





img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 65.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z65=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z65.shape[0],30):
    z1=z65[i,:]
    z0.append(z1)
z0=np.array(z0)
x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label65=y_array
z65=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T



img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 70.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z70=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z70.shape[0],30):
    z1=z70[i,:]
    z0.append(z1)
z0=np.array(z0)
x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label70=y_array
z70=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T


rescombine = np.vstack((z40,z50,z55,z60,z65,z70))
labels= np.hstack((label40,label50,label55,label60,label65,label70)).T
labels=labels.reshape(-1, 1)

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(rescombine,labels,test_size=0.25)

from sklearn.preprocessing import MinMaxScaler
mm = MinMaxScaler()
x_train = mm.fit_transform(x_train)
y_train = mm.fit_transform(y_train)
y_max = y_train.max(axis=0)
y_min = y_train.min(axis=0)

data=x_train
labels=y_train

class LinearRegression:
    def __init__(self,data,labels):
        
        self.data = data
        self.labels = labels
        num_features = len(data[1])
        self.theta = np.zeros((num_features,1))
        


    def train(self,alpha,num_iterations = 500):
       
        cost_history = self.gradient_descent(alpha,num_iterations)
        return self.theta,cost_history
        
    def gradient_descent(self,alpha,num_iterations):
        cost_history= []
        for _ 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   ##有问题...
        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) = linear_regression.train(learning_rate, num_iterations)



print (theta, cost_history)
print(len( cost_history))

运行结果及报错内容

发生异常: ValueError
operands could not be broadcast together with shapes (207,1) (155,1) 
  File "C:\Users\Xpc\Desktop\LinearRegression\linear_regression.py", line 380, in gradient_step
    delta = prediction - self.labels   ##有问题...
  File "C:\Users\Xpc\Desktop\LinearRegression\linear_regression.py", line 372, in gradient_descent
    self.gradient_step(alpha)
  File "C:\Users\Xpc\Desktop\LinearRegression\linear_regression.py", line 366, in train
    cost_history = self.gradient_descent(alpha,num_iterations)
  File "C:\Users\Xpc\Desktop\LinearRegression\linear_regression.py", line 408, in <module>
    (theta, cost_history) = linear_regression.train(learning_rate, num_iterations)

我的解答思路和尝试过的方法

我提取了一些数据

import numpy as np

import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 change40.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400

z40=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z40.shape[0],30):
    z1=z40[i,:]
    z0.append(z1)
z0=np.array(z0)
x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)


list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label40=y_array
z40=np.vstack((x0,x1,x2,x3,x4,x5)).T





img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 50.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)


list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z50=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z50.shape[0],30):
    z1=z50[i,:]
    z0.append(z1)
z0=np.array(z0)

x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label50=y_array
z50=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T




 
img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 55.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z55=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z55.shape[0],30):
    z1=z55[i,:]
    z0.append(z1)
z0=np.array(z0)

x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label55=y_array
z55=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T





img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 -60.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)


list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z60=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z60.shape[0],30):
    z1=z60[i,:]
    z0.append(z1)
z0=np.array(z0)

x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label60=y_array
z60=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T





img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 65.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z65=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z65.shape[0],30):
    z1=z65[i,:]
    z0.append(z1)
z0=np.array(z0)
x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label65=y_array
z65=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T



img = cv2.imread(r'C:\Users\Xpc\Desktop\weixin2222 - 70.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
low_hsv = np.array([0, 0, 221])
high_hsv = np.array([180, 30, 255])
mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)

list_y = []
list_x = []

for i in range(len(mask)):
    xmax = []
    for j in range(len(mask[i])):
        if mask[i][j] == 0:
            list_x.append(j)
            list_y.append(len(mask)-i)

plt.plot(list_x, list_y, 'o', color='r')
plt.show()

x_array=np.array(list_x)
x_array=x_array/400
y_array=np.array(list_y)
y_array=y_array*0.2/400
z70=np.stack((x_array,y_array),axis=0).T
z0=[]
for i in range(0,z70.shape[0],30):
    z1=z70[i,:]
    z0.append(z1)
z0=np.array(z0)
x_array=z0[:,0]
y_array=z0[:,1]
print(x_array)
print(y_array)

list_x1=[]
for i in range(0,len(x_array)):
    list_x1.append(40)
x1_array=np.array(list_x1)

list_x0=[]
for i in range(0,len(x_array)):
    list_x0.append(1)
x0=np.array(list_x0)
x1=x_array
x2=x1_array
x3=x1*x2
x4=x1*x1
x5=x2*x2
label70=y_array
z70=np.stack((x0,x1,x2,x3,x4,x5),axis=0).T


rescombine = np.vstack((z40,z50,z55,z60,z65,z70))
labels= np.hstack((label40,label50,label55,label60,label65,label70)).T
labels=labels.reshape(-1, 1)

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(rescombine,labels,test_size=0.25)

from sklearn.preprocessing import MinMaxScaler
mm = MinMaxScaler()
x_train = mm.fit_transform(x_train)
y_train = mm.fit_transform(y_train)
y_max = y_train.max(axis=0)
y_min = y_train.min(axis=0)

data=x_train
labels=y_train

num_features = len(data[1])
theta = np.zeros((num_features,1))
predictions = np.dot(data,theta)
print(len(data[1]))#列数
print(len(data))#行数
print(len(theta[1]))
print(len(theta))
print(len(predictions [1]))
print(len(predictions))
print(len(labels))
print(len(labels[1]))

运行结果显示prediction并没有出错

6
155
1
6
1
155
155
1

我想要达到的结果 找出原程序里的错误原因

我没太看明白你取得那6个特征，x0、2、5不都是常量吗x1和x3是一次函数，x4是二次函数，这个是否用3个特征就可以了，你最后想要的效果是要一个二次函数去拟合你原本图片中的那条线吗？
我用你其中一张图片研究了一下,目前的效果是这样的：

import numpy as np
import matplotlib.pyplot as plt
import cv2

class LinearRegression:
    def __init__(self,data,labels):
        self.data = data
        self.labels = labels
        self.features = np.zeros((1,self.data.shape[1]))
    
    def train(self,learning_rate, num_iterations):
        for i in range(num_iterations):
            self.step_gradient(learning_rate)
            # loss = self.loss_fuction()
            # print(f'第{i}轮loss={loss}, features={self.features}')
        return self.features
    
    def step_gradient(self, learning_rate):
        N = float(len(self.labels))
        err_current = np.sum(self.features*self.data,axis=1) - self.labels
        features_gradient = np.array([sum([x**i*err for x,err in zip(self.data[:,i],err_current)])*(2/N) for i in range(self.features.shape[1])])
        self.features = self.features - (learning_rate* features_gradient)
    
    def loss_fuction(self):
        totalError = sum([(y-(np.sum(self.features*x,axis=1)))**2 for x,y in zip(self.data,self.labels)])
        return totalError / float(len(self.labels))
    
    def draw(self,num_iterations):
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.rcParams['axes.unicode_minus'] = False
        x = self.data[:,1]
        sl = [s[0] for s in sorted(enumerate(x), key=lambda a:a[1])]
        y = [sum(x*self.features[0]) for x in self.data]
        sort_y = [y[i] for i in sl]
        sort_labels = [self.labels[i] for i in sl]
        x.sort()
        plt.scatter(x,sort_labels,label='source')
        plt.plot(x, sort_y,color='r',label='predict')
        plt.xlabel('X')
        plt.ylabel('Y')
        plt.title(str(num_iterations)+'轮')
        plt.legend()
        plt.show()
 
if __name__ == '__main__':
    img = cv2.imread('line.jpg')
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    low_hsv = np.array([0, 0, 221])
    high_hsv = np.array([180, 30, 255])
    mask = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)
    list_y = []
    list_x = []
    
    for i in range(0,len(mask),5):
        xmax = []
        for j in range(0,len(mask[i]),5):
            if mask[i][j] == 0:
                list_x.append(j)
                list_y.append(i)
    list_x = [x/100 for x in list_x]
    list_y = [y/100 for y in list_y]
    list_x = [[1,x,x**2] for x in list_x]
    learning_rate = 0.001
    num_iterations = 1000
    lr = LinearRegression(np.array(list_x),np.array(list_y))
    lr.train(learning_rate,num_iterations)
    lr.draw(num_iterations)