example_translated_function.py

import cppyy
import numpy as np

# 'translation' of the Python function below done by ChatGPT
cppyy.cppdef("""
#include <iostream>
#include <vector>
#include <algorithm>
#include <numeric>

double gini_cpp(const std::vector<double>& x, const std::vector<double>& w) {
// The rest of the code requires numpy arrays.
std::vector<double> sorted_x = x;
std::sort(sorted_x.begin(), sorted_x.end());
std::vector<double> sorted_w = w;
std::sort(sorted_w.begin(), sorted_w.end());
// Force float dtype to avoid overflows
std::vector<double> cumw(sorted_w.size());
std::partial_sum(sorted_w.begin(), sorted_w.end(), cumw.begin());
std::vector<double> cumxw(sorted_x.size());
std::transform(sorted_x.begin(), sorted_x.end(), sorted_w.begin(), cumxw.begin(), std::multiplies<double>());
std::partial_sum(cumxw.begin(), cumxw.end(), cumxw.begin());
return (std::inner_product(cumxw.begin() + 1, cumxw.end(), cumw.begin(), 0.0) -
std::inner_product(cumxw.begin(), cumxw.end() - 1, cumw.begin() + 1, 0.0)) /
(cumxw.back() * cumw.back());
}
""")

from cppyy.gbl import gini_cpp

# found the function here: https://stackoverflow.com/questions/48999542/more-efficient-weighted-gini-coefficient-in-python
def gini_py(x, w=None):
    # The rest of the code requires numpy arrays.
    x = np.asarray(x)
    if w is not None:
        w = np.asarray(w)
        sorted_indices = np.argsort(x)
        sorted_x = x[sorted_indices]
        sorted_w = w[sorted_indices]
        # Force float dtype to avoid overflows
        cumw = np.cumsum(sorted_w, dtype=float)
        cumxw = np.cumsum(sorted_x * sorted_w, dtype=float)
        return (np.sum(cumxw[1:] * cumw[:-1] - cumxw[:-1] * cumw[1:]) / 
                (cumxw[-1] * cumw[-1]))
    else:
        sorted_x = np.sort(x)
        n = len(x)
        cumx = np.cumsum(sorted_x, dtype=float)
        # The above formula, with all weights equal to 1 simplifies to:
        return (n + 1 - 2 * np.sum(cumx) / cumx[-1]) / n


print(gini_cpp([6,5,8,7], [0.1,0.1,0.2,0.2]))

print(gini_py([6,5,8,7], [0.1,0.1,0.2,0.2]))