有人可以使用线性回归解释此代码进行面部检测吗

Question

我需要解释这段代码，有人可以让我了解火车中发生的事情并测试代码的某些部分

 #pre-processing
 X_ = io.imread_collection('dataset/ATnT/s[1-9]/[1-5].pgm')
 Y_ = io.imread_collection('dataset/ATnT/s[1-9]/1.pgm')
 Y_test = io.imread_collection('dataset/ATnT/s[1-9]/[6-9].pgm')
 train_size = 5
 test_size = 4

 X=[]
 for im in X_:
     im = resize(im, (10,5), mode='constant')
     X.append(im)

 Y=[]
 for im in Y_:
     im = resize(im, (10,5), mode='constant')
     Y.append(im)


test=[]
for im in Y_test:
    im = resize(im, (10,5), mode='constant')
    test.append(im)        



 X=np.array(X)/255
 Y=np.array(Y)/255
 test=np.array(test)/255

 # train model

 x_new = []
 hat_y_i = []
 for num, x in enumerate(X):    
    x_new.append(x.flatten('F'))

    if np.remainder(num + 1, train_size) == 0:

        hat_beta_i = np.dot(np.dot(np.linalg.pinv(np.dot(x.T, x)), x.T), Y[np.int(num / train_size)])
        hat_y_i.append(np.dot(x, hat_beta_i))
        x_new = []


 y_pred = []
 dist = []

#test Model

for num, y in enumerate(test):
 for h in hat_y_i:
    d =  round(np.linalg.norm(y - h),5) #round(np.linalg.norm(y - h), 2) 
    dist.append(d)

print('Actual Class : '+str(np.int(np.ceil((num + 1)/4)))+' / Predicted Class : '+str(np.argmin(dist) + 1) )

if np.int(num / test_size) == np.argmin(dist)
    y_pred.append(Y_test[num])

dist = []''


  # results
  total_img = len(test)
  correct = len(y_pred)
  print('Total images', total_img)
  print('Correctly Predicted:', correct)
  print('Accuracy :' + str(round(correct/total_img * 100, 2))+'%')