| |
| import random |
| import numpy as np |
|
|
| def _custom_pmf(population, weights, size): |
| |
| if len(size) == 2: |
| ans = np.zeros(shape=size) |
| for i in range(size[0]): |
| ans[i,:] = random.choices( |
| population=population, |
| weights=weights, |
| k=size[1] |
| ) |
| else: |
| ans = np.array(random.choices( |
| population=population, |
| weights=weights, |
| k=size[0] |
| )) |
| return ans |
|
|
| def _custom_item_specific(distributions, size): |
| ans = np.zeros(size) |
|
|
| for i, distr in enumerate(distributions): |
| if distr['name'] == 'normal': |
| ans[i,:] = np.random.normal( |
| distr['mu'], |
| distr['sigma'], |
| size=ans[i,:].shape |
| ).astype(int) |
| elif distr['name'] == 'discrete_uniform': |
| ans[i,:] = np.random.randint( |
| low=distr['low'], |
| high=distr['high'], |
| size=ans[i,:].shape |
| ) |
| elif distr['name'] =='binomial': |
| ans[i,:] = np.random.binomial( |
| distr['n'], |
| distr['p'], |
| size=ans[i,:].shape |
| ) |
| elif distr['name'] == 'probability_mass_function': |
| ans[i,:] = _custom_pmf( |
| population=distr['demand_distribution']['vals'], |
| weights=distr['demand_distribution']['probs'], |
| size=ans[i,:].shape |
| ) |
| return ans |
|
|
|
|
| class StochasticDemandModel(): |
| def __init__(self, settings): |
| self.settings = settings |
| |
| distr_dict = { |
| 'normal': lambda x: np.random.normal( |
| self.settings['demand_distribution']['mu'], |
| self.settings['demand_distribution']['sigma'], |
| size=x |
| ), |
| 'discrete_uniform': lambda x: np.random.randint( |
| low=self.settings['demand_distribution']['low'], |
| high=self.settings['demand_distribution']['high'], |
| size=x |
| ), |
| 'binomial': lambda x: np.random.binomial( |
| self.settings['demand_distribution']['n'], |
| self.settings['demand_distribution']['p'], |
| size=x |
| ), |
| 'probability_mass_function': lambda x: _custom_pmf( |
| population=self.settings['demand_distribution']['vals'], |
| weights=self.settings['demand_distribution']['probs'], |
| size=x |
| ), |
| 'item_specific_uniform': lambda x: _custom_item_specific( |
| distributions=self.settings['demand_distribution']['distributions'], |
| size=x |
| ) |
| } |
| if self.settings['demand_distribution']['name'] == 'probability_mass_function': |
| if sum(self.settings['demand_distribution']['probs']) != 1: |
| raise ValueError('sum of prob different than one') |
| self.name_distribution = self.settings['demand_distribution']['name'] |
| self.generate = distr_dict[self.settings['demand_distribution']['name']] |
| self.n_items = self.settings['n_items'] |
|
|
| def fit(self, data): |
| pass |
|
|
| def generate_scenario(self, history=None, n_time_steps=1): |
| |
| |
| |
| |
| return self.generate( (self.n_items, n_time_steps) ) |
|
|
