{ "nbformat": 4, "nbformat_minor": 2, "metadata": { "language_info": { "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 } }, "orig_nbformat": 2, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "npconvert_exporter": "python", "pygments_lexer": "ipython3", "version": 3 }, "cells": [ { "cell_type": "markdown", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Statistical Tools" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "* Helper function to calculate the wheel of fortune" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def wheel_of_fortune(rank_i,n):\n", " return rank_i / (0.5 * n * (n + 1))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def wheel_of_fortune_selection(items: list, item_scores:list):\n", " ordering = np.argsort(item_scores)\n", " ordering = ordering + 1\n", "\n", " wheel_weights = wheel_of_fortune(ordering, len(ordering))\n", "\n", " return np.random.choice(items, p=wheel_weights)\n" ] } ] }