prediction_functions.py
def MAE(c,d,feature=int(0),scaling=False):
    """    
          Computing the mean absolute error for two lists of lists c and d
 
    """        
    a = [item for sublist in c for item in sublist]
    b= [item for sublist in d for item in sublist]  
    
    if not scaling:
        a=scaling_list(a1,feature=feature)
        b=scaling_list(b1,feature=feature)
  
    if len(a)!=len(b):
        print("something is wrong.")
    else:
        
        sd=np.array([np.abs(a[i]-b[i]) for i in range(len(a))])
        
        return len(np.where(sd<1)[0])/len(sd), np.mean(sd)
    
          Computing the mean absolute error for two lists of lists c and d
 
    
Computing the mean absolute error for two lists of lists c and d
Computing the mean absolute error for two lists of lists c and d
def MAE(c,d,feature=int(0),scaling=False):
    a = [item for sublist in c for item in sublist]
    b= [item for sublist in d for item in sublist]  
    if not scaling:
        a=scaling_list(a1,feature=feature)
        b=scaling_list(b1,feature=feature)
    if len(a)!=len(b):
        print("something is wrong.")
    else:
        sd=np.array([np.abs(a[i]-b[i]) for i in range(len(a))])
        return len(np.where(sd<1)[0])/len(sd), np.mean(sd)
def MAE ( c , d , feature = int ( 0 ) , scaling = False ) : a = [ item for sublist in c for item in sublist ] b = [ item for sublist in d for item in sublist ] if not scaling : a = scaling_list ( a1 , feature = feature ) b = scaling_list ( b1 , feature = feature ) if len ( a ) != len ( b ) : print ( "something is wrong." ) else : sd = np . array ( [ np . abs ( a [ i ] - b [ i ] ) for i in range ( len ( a ) ) ] ) return len ( np . where ( sd < 1 ) [ 0 ] ) / len ( sd ) , np . mean ( sd )
Computing the mean absolute error for two lists of lists c and d
Computing the mean absolute error for two lists of lists c and d
"""    
          Computing the mean absolute error for two lists of lists c and d
 
    """
c
d
feature
scaling