old_contents: from recommender_engine.recommender import make_recommendation
  from xlrd import open_workbook
  
  """
  	This sample used jester dataset @ http://goldberg.berkeley.edu/jester-data/
  """
  
  def make_preference_space(raw_data, start):
  	"""
  		create preference_space from raw data
  		the data structure is dictionary with keys are users and values are dictionaries of items and ratings
  	"""
  	preference_space = {}
  	for row in range(0,raw_data.nrows):	
  		preference_space[str('user ' + str(start + row))] = {}	
  		for col in range(1,raw_data.ncols):
  			if raw_data.cell_value(row,col) == 99: continue
  			preference_space[str('user ' + str(start + row))]['joke ' + str(col)] = float(raw_data.cell_value(row, col))		
  	
  	return preference_space
  
  
  def make_preference_space_Jester(file_path):
  	"""
  		The function takes in 3 jester dataset files and then merge the file
  	"""
  	
  	raw_data_1 = open_workbook(file_path + '/jester-data-1.xls').sheets()[0]
  	prefs_space_1 = make_preference_space(raw_data_1, 0)
  	raw_data_2 = open_workbook(file_path + '/jester-data-2.xls').sheets()[0]
  	prefs_space_2 = make_preference_space(raw_data_2, raw_data_1.nrows)
  	raw_data_3 = open_workbook(file_path + '/jester-data-3.xls').sheets()[0]
  	prefs_space_3 = make_preference_space(raw_data_3,raw_data_1.nrows + raw_data_2.nrows)
  
  	result = {}
  	for space in [prefs_space_1, prefs_space_2, prefs_space_3]:
  		result.update(space)
  	
  	return result
  	
  
  def jester():
  	"""
  		Running the application using Jester dataset:
  	"""
  
  	#Creating preference_space from Jester dataset
  	preference_space = make_preference_space_Jester('../Jester')
  	result = make_recommendation('user 36681', preference_space, 
  								recommender_approach = 'user_based', 
  								number_of_items_to_recommend = 5, 
  								similarity_measure = 'euclidean_distance')
  	print(result)
  
  
  if __name__=="__main__":
  	jester()
new_contents: from recommender_engine import make_recommendation
  from xlrd import open_workbook
  
  """
  	This sample used jester dataset @ http://goldberg.berkeley.edu/jester-data/
  """
  
  def make_preference_space(raw_data, start):
  	
  	""" create preference_space from raw data
  
  		Args:
  			raw_data
  			start
  
  		Returns:
  			dict: keys are users and values are dictionaries of items and ratings
  	"""
  	preference_space = {}
  	for row in range(0,raw_data.nrows):	
  		preference_space[str('user ' + str(start + row))] = {}	
  		for col in range(1,raw_data.ncols):
  			if raw_data.cell_value(row,col) == 99: continue
  			preference_space[str('user ' + str(start + row))]['joke ' + str(col)] = float(raw_data.cell_value(row, col))		
  	
  	return preference_space
  
  
  def make_preference_space_Jester(file_path):
  	"""
  		The function takes in 3 jester dataset files and then merge the file
  	"""
  	
  	raw_data_1 = open_workbook(file_path + '/jester-data-1.xls').sheets()[0]
  	prefs_space_1 = make_preference_space(raw_data_1, 0)
  	raw_data_2 = open_workbook(file_path + '/jester-data-2.xls').sheets()[0]
  	prefs_space_2 = make_preference_space(raw_data_2, raw_data_1.nrows)
  	raw_data_3 = open_workbook(file_path + '/jester-data-3.xls').sheets()[0]
  	prefs_space_3 = make_preference_space(raw_data_3,raw_data_1.nrows + raw_data_2.nrows)
  
  	result = {}
  	for space in [prefs_space_1, prefs_space_2, prefs_space_3]:
  		result.update(space)
  	
  	return result
  	
  
  def jester():
  	"""
  		Running the application using Jester dataset:
  	"""
  
  	#Creating preference_space from Jester dataset
  	preference_space = make_preference_space_Jester('../Jester')
  	result = make_recommendation('user 36681', preference_space, 
  								recommender_approach = 'user_based', 
  								number_of_items_to_recommend = 5, 
  								similarity_measure = 'euclidean_distance')
  	print(result)
  
  
  if __name__=="__main__":
  	jester()
subject: Update API