{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c5107489",
   "metadata": {},
   "source": [
    "# Deb’s Feasibility Rule Ranking Distribution (`distr_Deb`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d12250a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "from pyxla import distr_Deb\n",
    "from pyxla.util import load_data\n",
    "from pyxla.sampling import HilbertCurveSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4168f6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:The Hilbert curve with dimension 1 is just a number line. You are sampling around points on a number line.\n",
      "WARNING:root:The Hilbert curve with dimension 1 is just a number line. You are sampling around points on a number line.\n"
     ]
    }
   ],
   "source": [
    "sphere_sample = {\n",
    "    \"name\": \"Sphere\",\n",
    "    \"X\": HilbertCurveSampler(sample_size=100, dim=1, l_bound=-5, u_bound=5, seed=42),\n",
    "    \"F\": lambda x: x**2,\n",
    "    \"V\": [lambda x: x**2 - 2, lambda x: 8 * math.sin(20 * x)]\n",
    "}\n",
    "\n",
    "deceptive_sample = {\n",
    "    \"name\": \"Deceptive\",\n",
    "    \"X\": HilbertCurveSampler(sample_size=100, dim=1, l_bound=0, u_bound=6),\n",
    "    \"F\": lambda x: (0.4 * x - 2) ** 2 if x > 0.625 else (10 * x - 4) ** 2 - 2,\n",
    "    \"V\": lambda x: ((0.4 * x - 2) ** 2 if x > 0.625 else (10 * x - 4) ** 2 - 2) - 1\n",
    "}\n",
    "\n",
    "load_data(sphere_sample)\n",
    "load_data(deceptive_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9f466fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'Deb_min': 1,\n",
       "  'Deb_max': 100,\n",
       "  'Deb_mean': 50.5,\n",
       "  'Deb_med': 50.5,\n",
       "  'Deb_q1': 25.25,\n",
       "  'Deb_q3': 75.75,\n",
       "  'Deb_sd': 29.011491975882016,\n",
       "  'Deb_skew': 0.0,\n",
       "  'Deb_kurt': -1.2002400240024003},\n",
       " <seaborn.axisgrid.FacetGrid at 0x7f8c02a41f40>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distr_Deb(sphere_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed3ef4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'Deb_min': 1,\n",
       "  'Deb_max': 100,\n",
       "  'Deb_mean': 50.5,\n",
       "  'Deb_med': 50.5,\n",
       "  'Deb_q1': 25.25,\n",
       "  'Deb_q3': 75.75,\n",
       "  'Deb_sd': 29.011491975882016,\n",
       "  'Deb_skew': 0.0,\n",
       "  'Deb_kurt': -1.2002400240024003},\n",
       " <seaborn.axisgrid.FacetGrid at 0x7f8c02a42000>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distr_Deb(deceptive_sample)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}