master-thesis/EvolutionaryAlgorithm/ea_tools.py

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#!/usr/bin/env python3
# coding: utf-8
# # Statistical Tools
import numpy as np
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import scipy.stats
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# * Helper function to calculate the wheel of fortune
def wheel_of_fortune(rank_i,n):
return rank_i / (0.5 * n * (n + 1))
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def wheel_of_fortune_weights(items:list, item_scores:list):
rank = scipy.stats.rankdata(item_scores)
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n = len(items)
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return wheel_of_fortune(rank, n)
def wheel_of_fortune_selection(items: list, item_scores:list, num_choices=1):
wheel_weights = wheel_of_fortune_weights(items, item_scores)
n = min(len(items), num_choices)
choice = np.random.choice(items, size=n, replace=False, p=wheel_weights)
if num_choices == 1:
return choice[0]
return choice
def combined_wheel_of_fortune_selection(items_list:list, item_scores_list:list, num_choices=1):
scores = {}
for i in range(len(items_list)):
items = items_list[i]
item_scores = item_scores_list[i]
w = wheel_of_fortune_weights(items, item_scores)
#print(items, item_scores)
#print(w)
for j, item in enumerate(items):
if item in scores:
scores[item] += w[j]
else:
scores[item] = w[j]
combined_items = []
combined_scores = []
for i,s in scores.items():
combined_items.append(i)
combined_scores.append(s)
combined_scores = np.array(combined_scores)
#print(combined_scores)
#print(np.sum(combined_scores))
combined_scores /= len(items_list)
#print(combined_scores)
#print(np.sum(combined_scores))
n = min(len(combined_items), num_choices)
return np.random.choice(combined_items, size=n, replace=False, p=combined_scores)
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