In python:
age = float(input("How old are you? "))
weight = float(input("How much do you weigh? "))
heart_rate = float(input("What's your heart rate? "))
time = float(input("What's the time? "))
print("The calories burned for men is {}, and the calories burned for women is {}.".format(
((age * 0.2017) - (weight * 0.09036) + (heart_rate * 0.6309) - 55.0969) * (time / 4.184),
((age * 0.074) - (weight * 0.05741) + (heart_rate * 0.4472) - 20.4022) * (time / 4.184)))
This is the program.
When you enter 49 155 148 60, the output is:
The calories burned for men is 489.77724665391963, and the calories burned for women is 580.939531548757.
Round to whatever you desire.
Answer: True
Explanation:
Subset sum problem and Knapsack problem can be solved using dynamic programming.
In case of Knapsack problem there is a set of weights associative with objects and a set of profits associated with each object and a total capacity of knapsack let say C. With the help of dynamic programming we try to include object's weight such that total profit is maximized without fragmenting any weight of objects and without exceeding the capacity of knapsack, it is also called as 0/1 knapsack problem.
Similar to knapsack problem, in subset sum problem there is set of items and a set of weights associated with the items and a capacity let say C, task is to choose the subset of items such that total sum of weights associated with items of subset is maximized without exceeding the total capacity.
On the basis of above statements we can say that subset sum problem is generalization of knapsack problem.
The compiler translates each source code instruction into the appropriate machine language instruction, an
