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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
For more detailed information about these algorithms and their purpose, see: | |
https://tommyodland.com/articles/2022/sampling-a-discrete-distributing-with-a-markov-chain | |
""" | |
import numbers | |
import itertools |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
Shapley value (Python implementation) | |
------------------------------------- | |
@author: tommyod (https://github.com/tommyod) | |
A coalition of players cooperates, and obtains an overall gain from cooperation. | |
Since some players may contribute more to the coalition than others, | |
or may possess different bargaining power, what final distribution of |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
The linear-time Wagelmans algorithm for the dynamic lot size problem | |
-------------------------------------------------------------------- | |
@author: tommyod (https://github.com/tommyod) | |
An implementation of the Wagelmans O(n) algorithm for the dynamic lot | |
size problem, also called the single-item uncapacitated lot-sizing problem. |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
The Wagner-Whitin algorithm for the dynamic lot size problem | |
-------------------------------------------------------------- | |
@author: tommyod (https://github.com/tommyod) | |
An implementation of the Wagner-Whitin O(n^2) algorithm for the dynamic lot | |
size problem, also called the single-item uncapacitated lot-sizing problem. | |
Although the algorithm is O(n^2) in theory, it scales linearily with input |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
Created on Sun Jul 5 09:28:57 2020 | |
@author: tommyod (https://github.com/tommyod) | |
""" | |
import numbers |
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# -*- coding: utf-8 -*- | |
#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
MEAL PLANNER - Automatic planning of cheap, varied, nutritional meals | |
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Summary | |
------- | |
This Python module uses optimization (Linear Programming) and stochastic methods to |