import numpy as np
from sklearn import preprocessing as pp

print('normalization function imported')

#normalize data in respect with keys in dictionary
def normalize_data(data):
    # get keys from original data
    gestures = list(data)
    
    # create empty dictionary to store normalized data with gestures
    gdata = {}

    # get max/min of x/y across samples and frames
    for gesture in gestures:
        data_gesture = np.asarray(data[gesture])
        max_x = np.max(data_gesture[:,:,:,:,0])
        min_x = np.min(data_gesture[:,:,:,:,0])
        max_y = np.max(data_gesture[:,:,:,:,1])
        min_y = np.min(data_gesture[:,:,:,:,1])
        data_gesture[:,:,:,:,0]=(data_gesture[:,:,:,:,0]-min_x)/(max_x - min_x)
        data_gesture[:,:,:,:,1]=(data_gesture[:,:,:,:,1]-min_y)/(max_y - min_y)

        #store normalized data into dictionary
        gdata[gesture] = data_gesture
    data = gdata
    return data
    return print('data normalized')

import numpy as np
from sklearn import preprocessing as pp

print('normalization function imported')

#normalize data in respect with keys in dictionary
def normalize_data(data):
    # get keys from original data
    gestures = list(data)
    
    # create empty dictionary to store normalized data with gestures
    gdata = {}

    # get max/min of x/y across samples and frames
    for gesture in gestures:
        data_gesture = np.asarray(data[gesture])
        max_x = np.max(data_gesture[...,0])
        min_x = np.min(data_gesture[...,0])
        max_y = np.max(data_gesture[...,1])
        min_y = np.min(data_gesture[...,1])
        data_gesture[...,0]=(data_gesture[...,0]-min_x)/(max_x - min_x)
        data_gesture[...,1]=(data_gesture[...,1]-min_y)/(max_y - min_y)

        #store normalized data into dictionary
        gdata[gesture] = data_gesture
    data = gdata
    return data
    return print('data normalized')

Replace integer indices with ellipsis
Replace integer indices with ellipsis

  import numpy as np
  from sklearn import preprocessing as pp
  
  print('normalization function imported')
  
  #normalize data in respect with keys in dictionary
  def normalize_data(data):
      # get keys from original data
      gestures = list(data)
      
      # create empty dictionary to store normalized data with gestures
      gdata = {}
  
      # get max/min of x/y across samples and frames
      for gesture in gestures:
          data_gesture = np.asarray(data[gesture])
-         max_x = np.max(data_gesture[:,:,:,:,0])
+         max_x = np.max(data_gesture[...,0])
-         min_x = np.min(data_gesture[:,:,:,:,0])
+         min_x = np.min(data_gesture[...,0])
-         max_y = np.max(data_gesture[:,:,:,:,1])
+         max_y = np.max(data_gesture[...,1])
-         min_y = np.min(data_gesture[:,:,:,:,1])
+         min_y = np.min(data_gesture[...,1])
-         data_gesture[:,:,:,:,0]=(data_gesture[:,:,:,:,0]-min_x)/(max_x - min_x)
+         data_gesture[...,0]=(data_gesture[...,0]-min_x)/(max_x - min_x)
-         data_gesture[:,:,:,:,1]=(data_gesture[:,:,:,:,1]-min_y)/(max_y - min_y)
+         data_gesture[...,1]=(data_gesture[...,1]-min_y)/(max_y - min_y)
  
          #store normalized data into dictionary
          gdata[gesture] = data_gesture
      data = gdata
      return data
      return print('data normalized')
  
Replace integer indices with ellipsis
## Code Before:
import numpy as np
from sklearn import preprocessing as pp

print('normalization function imported')

#normalize data in respect with keys in dictionary
def normalize_data(data):
    # get keys from original data
    gestures = list(data)
    
    # create empty dictionary to store normalized data with gestures
    gdata = {}

    # get max/min of x/y across samples and frames
    for gesture in gestures:
        data_gesture = np.asarray(data[gesture])
        max_x = np.max(data_gesture[:,:,:,:,0])
        min_x = np.min(data_gesture[:,:,:,:,0])
        max_y = np.max(data_gesture[:,:,:,:,1])
        min_y = np.min(data_gesture[:,:,:,:,1])
        data_gesture[:,:,:,:,0]=(data_gesture[:,:,:,:,0]-min_x)/(max_x - min_x)
        data_gesture[:,:,:,:,1]=(data_gesture[:,:,:,:,1]-min_y)/(max_y - min_y)

        #store normalized data into dictionary
        gdata[gesture] = data_gesture
    data = gdata
    return data
    return print('data normalized')

## Instruction:
Replace integer indices with ellipsis
## Code After:
import numpy as np
from sklearn import preprocessing as pp

print('normalization function imported')

#normalize data in respect with keys in dictionary
def normalize_data(data):
    # get keys from original data
    gestures = list(data)
    
    # create empty dictionary to store normalized data with gestures
    gdata = {}

    # get max/min of x/y across samples and frames
    for gesture in gestures:
        data_gesture = np.asarray(data[gesture])
        max_x = np.max(data_gesture[...,0])
        min_x = np.min(data_gesture[...,0])
        max_y = np.max(data_gesture[...,1])
        min_y = np.min(data_gesture[...,1])
        data_gesture[...,0]=(data_gesture[...,0]-min_x)/(max_x - min_x)
        data_gesture[...,1]=(data_gesture[...,1]-min_y)/(max_y - min_y)

        #store normalized data into dictionary
        gdata[gesture] = data_gesture
    data = gdata
    return data
    return print('data normalized')
