Answer:
def find_max(num_1, num_2):
max_val = 0.0
if (num_1 > num_2): # if num1 is greater than num2,
max_val = num_1 # then num1 is the maxVal.
else: # Otherwise,
max_val = num_2 # num2 is the maxVal
return max_val
max_sum = 0.0
num_a = float(input())
num_b = float(input())
num_y = float(input())
num_z = float(input())
max_sum = find_max(num_a, num_b) + find_max(num_y, num_z)
print('max_sum is:', max_sum)
Explanation:
I added the missing part. Also, you forgot the put parentheses. I highlighted all.
To find the max_sum, you need to call the find_max twice and sum the result of these. In the first call, use the parameters num_a and num_b (This will give you greater among them). In the second call, use the parameters num_y and num_z (This will again give you greater among them)
Answer:
Option D
Explanation:
Artificial intelligence is a technology where the information gathered in the past is processed for the future actions.
Here, based on the preferences of customer in the past and purchase history, AI can suggest the customer new products and services
Hence, option D is correct
Answer:
Big Oh notation is used to asymptotically bound the growth of running time above and below the constant factor.
Big Oh notation is used to describe time complexity, execution time of an algorithm.
Big Oh describes the worst case to describe time complexity.
For the equation; T(N) = 10000*N + 0.00001*N^3.
To calculate first of all discard all th constants.
And therefore; worst case is the O(N^3).
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