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| # Copyright 2023 by Jan Philip Wahle, https://jpwahle.com/ | |
| # All rights reserved. | |
| import numpy as np | |
| def calculate_gini_simpson(dictionary): | |
| """ | |
| Function to Calculate Gini Simpson's Diversity Index | |
| """ | |
| total = sum(dictionary.values()) | |
| sum_squares = sum((n / total) ** 2 for n in dictionary.values()) | |
| return 1 - sum_squares | |
| def calculate_gini(frequencies): | |
| """ | |
| Function to Calculate Gini's Diversity Index | |
| """ | |
| frequencies = np.array(frequencies) | |
| if len(frequencies) == 0 or np.mean(frequencies) == 0: | |
| return None | |
| total = sum( | |
| np.sum(np.abs(xi - frequencies[i:])) | |
| for i, xi in enumerate(frequencies[:-1], 1) | |
| ) | |
| return total / (len(frequencies) ** 2 * np.mean(frequencies)) | |