def choose_category(self, trans_time=None, num_purchases=None):
category_weights = self.customer_state.item_category_weights(
trans_time)
if num_purchases != 0:
category_weights.append(("stop", 0.1))
weight_sum = 0.0
for category, weight in category_weights:
weight_sum += weight
category_probabilities = []
for category, weight in category_weights:
category_probabilities.append((category, weight / weight_sum))
sampler = RouletteWheelSampler(category_probabilities)
return sampler.sample()
def __init__(self, stores=None, zipcode_objs=None, avg_distance=None):
lambd = 1.0 / avg_distance
self.stores = stores
self.zipcode_objs = zipcode_objs
zipcode_weights = dict()
weight_sum = 0.0
for zipcode in zipcode_objs.iterkeys():
dist, nearest_store = self._closest_store(zipcode)
weight = lambd * np.exp(-lambd * dist)
weight_sum += weight
zipcode_weights[zipcode] = weight
zipcode_probs = []
for zipcode in zipcode_objs.iterkeys():
zipcode_probs.append(
(zipcode, zipcode_weights[zipcode] / weight_sum))
self.sampler = RouletteWheelSampler(zipcode_probs)
def __init__(self, zipcode_objs, income_scaling_factor=None):
pop_probs = dict()
income_probs = dict()
pop_sum = 0.0
max_income = 0.0
min_income = 100000.0
for obj in zipcode_objs.itervalues():
pop_sum += obj.population
max_income = max(max_income, obj.median_household_income)
min_income = min(min_income, obj.median_household_income)
income_k = np.log(income_scaling_factor) / (max_income - min_income)
income_normalization_factor = 0.0
income_weights = dict()
for obj in zipcode_objs.itervalues():
w = np.exp(income_k * (obj.median_household_income - min_income))
income_normalization_factor += w
income_weights[obj.zipcode] = w
income_probs = dict()
for obj in zipcode_objs.itervalues():
income_probs[obj.zipcode] = income_weights[
obj.zipcode] / income_normalization_factor
prob_probs = dict()
for obj in zipcode_objs.itervalues():
pop_probs[obj.zipcode] = obj.population / pop_sum
normalization_factor = 0.0
for z in income_probs.iterkeys():
normalization_factor += income_probs[z] * pop_probs[z]
zipcode_probs = []
for z in income_probs.iterkeys():
zipcode_probs.append(
(z, income_probs[z] * pop_probs[z] / normalization_factor))
self.sampler = RouletteWheelSampler(zipcode_probs)
def __init__(self, first_names, last_names):
normalized_first_names = self.normalize(first_names)
normalized_last_names = self.normalize(last_names)
self.first_name_sampler = RouletteWheelSampler(normalized_first_names)
self.last_name_sampler = RouletteWheelSampler(normalized_last_names)