print("--------------SAVE_BEST_ESTIMATOR----------------- \n")
print("Best Model Features Saved {}".format(data['Best_F']))
print("Best Model Object Saved: \n {} ".format(data_2['Best_M']))
print("Theta-0 Saved Best Model: {} ".format(data_2['Best_M'].intercept_))
print("")
print("")
#TEST CLASS TRAINER - are_equals
l1 = [1, 1, 1]
l2 = [1, 1, 1]
l3 = [1, 2, 2]
l4 = [0, 1, 2]
l5 = [0, 2, 2]
print("--------------ARE_EQUALS----------------- \n")
print("Are Equals l1 vs l2: {}".format(tr.are_equals(l1, l2))) #EXPECTED 2
print("Are Equals l1 vs l3: {}".format(tr.are_equals(l1, l3))) #EXPECTED 1
print("Are Equals l1 vs l4: {}".format(tr.are_equals(l1, l4))) #EXPECTED 0
print("Are Equals l4 vs l5: {}".format(tr.are_equals(l4, l5))) #EXPECTED 1
print("")
print("")
#TEST CLASS TRAINER - take_degree
print("--------------TAKE_DEGREE----------------- \n")
print("Degree l5: {}".format(tr.take_degree(l5))) #EXPECTED 2
print("Degree l1: {}".format(tr.take_degree(l1))) #EXPECTED 1
print("")
print("")
