mAPE python
def percentage_error(actual, predicted):
res = np.empty(actual.shape)
for j in range(actual.shape[0]):
if actual[j] != 0:
res[j] = (actual[j] - predicted[j]) / actual[j]
else:
res[j] = predicted[j] / np.mean(actual)
return res
def mean_absolute_percentage_error(y_true, y_pred):
return np.mean(np.abs(percentage_error(np.asarray(y_true), np.asarray(y_pred)))) * 100map function in python
# for the map function we give a function (action to act), a data action to act on that data.
# without map
def multiply_by2(li):
new_list = []
for number in li:
new_list.append(number*2)
return new_list
print(multiply_by2([1, 2, 3]))
# using map function
def multiply_by_2(item):
return item*2
print(list(map(multiply_by_2, [1, 2, 3])))
# function that returns usernames in all uppercase letters.
usernames = ["RN_Tejas", "RN_Tejasprogrammer", "Rajendra Naik", "yooo", "Brainnnni", "Ouuuuut"]
def no_lower(item):
return item.upper()
print(list(map(no_lower, usernames)))
# squares function
list_squares = list(range(1, 101))
print(list_squares)
def square(number):
return number**2
square_list = list(map(square, list_squares))
def square_root(number):
return number // number
print(list(map(square_root, square_list)))
def no_upper(string):
return string.lower()
print(list(map(no_upper, 'TEJAS')))função map python
lista = [1, 2, -3, 4, 5, -9]
def quadrado(n):
return n*n
map(quadrado, lista)
[1, 4, 9, 16, 25, 81]about map function in python
# Add two lists using map and lambda
numbers1 = [1, 2, 3]
numbers2 = [4, 5, 6]
result = map(lambda x, y: x + y, numbers1, numbers2,axis=1)
print(list(result))Copyright © 2021 Codeinu
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