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
# Python program to demonstrate working
# of map.
# Return double of n
def addition(n):
return n + n
# We double all numbers using map()
numbers = (1, 2, 3, 4)
result = map(addition, numbers)
print(list(result))map python
# The map() function applies a given function
# to each item of an iterable (list, tuple etc.) and returns an iterator
numbers = [2, 4, 6, 8, 10]
# returns square of a number
def square(number):
return number * number
# apply square() function to each item of the numbers list
squared_numbers_iterator = map(square, numbers)
# converting to list
squared_numbers = list(squared_numbers_iterator)
print(squared_numbers)
# Output: [4, 16, 36, 64, 100]Python Map function
# Let's define general python function
>>> def doubleOrNothing(num):
... return num * 2
# now use Map on it.
>>> map(doubleOrNothing, [1,2,3,4,5])
<map object at 0x7ff5b2bc7d00>
# apply built-in list method on Map Object
>>> list(map(doubleOrNothing, [1,2,3,4,5])
[2, 4, 6, 8, 10]
# using lambda function
>>> list(map(lambda x: x*2, [1,2,3,4,5]))
[2, 4, 6, 8, 10]Copyright © 2021 Codeinu
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