{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import linear_sum_assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, array([0, 1, 2, 3]), array([3, 2, 0, 1]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost = np.array([\n",
    "    [2, 5, 1, 1],\n",
    "    [6, 8, 4, 6],\n",
    "    [3, 7, 3, 2],\n",
    "    [0, 0, 0, 0]\n",
    "])\n",
    "row_ind, col_ind = linear_sum_assignment(cost)\n",
    "cost[row_ind, col_ind].sum(), row_ind, col_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15.0, array([0, 1, 2]), array([0, 3, 1]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost = np.array([\n",
    "   [8.0, 5.0, 9.0, 9.0],\n",
    "    [4.0, 2.0, 6.0, 4.0],\n",
    "    [7.0, 3.0, 7.0, 8.0],\n",
    "])\n",
    "\n",
    "row_ind, col_ind = linear_sum_assignment(cost)\n",
    "cost[row_ind, col_ind].sum(), row_ind, col_ind"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
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  "language_info": {
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   "file_extension": ".py",
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 "nbformat": 4,
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