m0_72041809 2022-07-08 10:29 采纳率: 50%
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已结题

线性回归类无法正常计算

问题遇到的现象和发生背景

运行空值，python做线性回归问题

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,len(z40)):
    if z40[i][1]!=z40[i-1][1]:
        z0.append(z40[i,:])
z0=np.array(z0)
z1=[]
for i in range(0,len(z0)):
    if z0[i][1]!=z0[i-1][1]:
        z1.append(z0[i,:])
z1=np.array(z1)
x_array=z1[:,0]
y_array=z1[:,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,len(z40)):
    if z40[i][1]!=z40[i-1][1]:
        z0.append(z40[i,:])
z0=np.array(z0)
z1=[]
for i in range(0,len(z0)):
    if z0[i][1]!=z0[i-1][1]:
        z1.append(z0[i,:])
z1=np.array(z1)
x_array=z1[:,0]
y_array=z1[:,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,len(z40)):
    if z40[i][1]!=z40[i-1][1]:
        z0.append(z40[i,:])
z0=np.array(z0)
z1=[]
for i in range(0,len(z0)):
    if z0[i][1]!=z0[i-1][1]:
        z1.append(z0[i,:])
z1=np.array(z1)
x_array=z1[:,0]
y_array=z1[:,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,len(z40)):
    if z40[i][1]!=z40[i-1][1]:
        z0.append(z40[i,:])
z0=np.array(z0)
z1=[]
for i in range(0,len(z0)):
    if z0[i][1]!=z0[i-1][1]:
        z1.append(z0[i,:])
z1=np.array(z1)
x_array=z1[:,0]
y_array=z1[:,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,len(z40)):
    if z40[i][1]!=z40[i-1][1]:
        z0.append(z40[i,:])
z0=np.array(z0)
z1=[]
for i in range(0,len(z0)):
    if z0[i][1]!=z0[i-1][1]:
        z1.append(z0[i,:])
z1=np.array(z1)
x_array=z1[:,0]
y_array=z1[:,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,len(z40)):
    if z40[i][1]!=z40[i-1][1]:
        z0.append(z40[i,:])
z0=np.array(z0)
z1=[]
for i in range(0,len(z0)):
    if z0[i][1]!=z0[i-1][1]:
        z1.append(z0[i,:])
z1=np.array(z1)
x_array=z1[:,0]
y_array=z1[:,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
data=rescombine


class LinearRegression:
    def __init__(self,data,labels):
        
        self.data = data
        self.labels = labels
        num_features = len(data[0])
        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
        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
[[nan nan nan ... nan nan nan]
[nan nan nan ... nan nan nan]
[nan nan nan ... nan nan nan]
[nan nan nan ... nan nan nan]
[nan nan nan ... nan nan nan]
[nan nan nan ... nan nan nan]] [6118556.131405536, 4018487273561706.0, 2.639223964113227e+24, 1.733364487322568e+33, 1.138422690444368e+42, 7.476824589388318e+50, 4.910557950901458e+59, 3.2251096840475907e+68, 2.118156954491935e+77, 1.3911430380351452e+86, 9.136617322760469e+94, 6.000660882469051e+103, 3.941057149968831e+112, 2.588370141811722e+121, 1.6999651961594744e+130, 1.1164870207205908e+139, 7.332757577941671e+147, 4.815938985314657e+156, 3.162966737103545e+165, 2.0773432991011666e+174, 1.3643378324845315e+183, 8.960568635689615e+191, 5.885037295248817e+200, 3.865118986815737e+209, 2.538496195139561e+218, 1.6672094584200277e+227, 1.0949740179115684e+236, 7.191466518176075e+244, 4.723143182948494e+253, 3.1020211900104493e+262, 2.0373160606295184e+271, 1.3380491223804328e+280, 8.787912138432926e+288, 5.771641598286526e+297, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]

我的解答思路和尝试过的方法
我想要达到的结果

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  • 写回答

1条回答 默认 最新

  • 艾鹤 2022-07-08 15:31
    关注

    怎么这么多nan， 这些填0或者删除吧，别影响训练。

    评论

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  • 系统已结题 7月16日
  • 赞助了问题酬金10元 7月9日
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