Ćwiczenia/6. Ćwiczenia/Zadanie 5 (dwuwymiarowy problem plecakowy).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treść zadania"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sposób I\n",
    "### (O większej złożoności pamięciowej)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Omówienie algorytmu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem bardzo podobny do zwykłego problemu plecakowego wyłącznie z jednym czynnikiem ograniczającym. W bardzo analogiczny sposób możemy rozwiązać ten problem dla dowolnej liczby ograniczeń, które rosną liniowo (tzn. nie zależą od rozmieszczenia przedmiotów w plecaku - zakładamy, że kładziemy przedmioty jeden na drugim, a wysokość plecaka jest stała). Musimy jednakże, wykorzystując koncepcje, poznane w problemie plecakowym, uwzględnić wszystkie czynniki ograniczające, a więc wypełniać tak $ 3 $-wymiarową tablicę maksymalnych profitów dla każdej kolejnej komórki $ F[i][h][w] $ (gdzie $ i $ - indeks ostatniego przedmiotu, jaki w danym momencie możemy wziąć (wybieramy najbardziej opłacalne przedmioty spośród tych o indeksach $ 0, 1, 2, ..., i - 1, i $), $ h $ - maksymalna rozważana w danym momencie wysokość plecaka, $ w $ - maksymalna rozważana w danym momencie waga (bardziej prawidłowo: masa) plecaka, znalazła się w niej wartość nawiększego profitu, jaki uzyskamy przy opidanych wcześniej założeniach na $ i $, $ h $ oraz $ w $.\n",
    "<br><br>\n",
    "Ponownie dla pierwszego przedmiotu wypełniamy pierwszy poziom tablicy $ F $ wartościami jego profitu w tych komórkach, dla których zachodzi warunek $ H[0] \\le h $ oraz $ W[0] \\le w $ (innymi słowy, przedmiot mieści się w plecaku, uwzględniając wszystkie ograniczenia). <br>\n",
    "W analogiczny sposób jak dla problemu plecaka $ 1 $-wymiarowego, wypełniamy pozostałe komórki tablicy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Złożoność"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Obliczeniowa:\n",
    "$ O(n^3) $\n",
    "###### Pamięciowa:\n",
    "$ O(n^3) $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementacja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_items(H, W, MaxH, MaxW):\n",
    "    \"\"\"\n",
    "    This function filters items which have wrong values (e.g. negative)\n",
    "    or cannot fit in the knapsack even if they are used separately (that means\n",
    "    an item is to tall and too heavy to be put in a knapsack).\n",
    "    \"\"\"\n",
    "    # We assume that we got the same number of heights as weights and profits\n",
    "    # (number of items is valid)\n",
    "    n = len(H)\n",
    "    H_cp = []\n",
    "    W_cp = []\n",
    "    for i in range(n):\n",
    "        if 0 < H[i] <= MaxH or 0 < W[i] <= MaxW:\n",
    "            H_cp.append(H[i])\n",
    "            W_cp.append(W[i])\n",
    "    return H_cp, W_cp\n",
    "\n",
    "\n",
    "def create_arr(k: 'number of layers', i: 'number of rows', j: 'number of columns', val=0):\n",
    "    return [[[val] * j for _ in range(i)] for _ in range(k)]\n",
    "\n",
    "\n",
    "def knapsack2D(P: 'array of profits', \n",
    "               H: 'array of heights', \n",
    "               W: 'array of weights', \n",
    "               MaxH: 'max total height of items',\n",
    "               MaxW: 'max total weight of items'\n",
    "               ):\n",
    "    n = len(P)\n",
    "    H, W = filter_items(H, W, MaxH, MaxW)\n",
    "    # Create an array for caching values\n",
    "    F = create_arr(n, MaxH + 1, MaxW + 1)\n",
    "    \n",
    "    for h in range(H[0], MaxH + 1):\n",
    "        for w in range(W[0], MaxW + 1):\n",
    "            F[0][h][w] = P[0]\n",
    "            \n",
    "    for i in range(1, n):\n",
    "        for h in range(1, MaxH + 1):\n",
    "            for w in range(1, MaxW + 1):\n",
    "                F[i][h][w] = F[i - 1][h][w]\n",
    "                \n",
    "                if H[i] <= h and W[i] <= w:\n",
    "                    F[i][h][w] = max(F[i][h][w], F[i - 1][h - H[i]][w - W[i]] + P[i])\n",
    "                \n",
    "    print_F(F)\n",
    "    \n",
    "    return F[n - 1][MaxH][MaxW]\n",
    "    \n",
    "            \n",
    "def print_F(F):\n",
    "    for i, layer in enumerate(F):\n",
    "        print(f'\\nLayer for item #{i}:')\n",
    "        print(*layer, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Kilka testów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Layer for item #0:\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "\n",
      "Layer for item #1:\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "\n",
      "Layer for item #2:\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 2, 2, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "\n",
      "Layer for item #3:\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4]\n",
      "[0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4]\n",
      "[0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4]\n",
      "[0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4]\n",
      "[0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 7]\n",
      "[0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 7]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 3, 3, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 13, 13, 13]\n",
      "\n",
      "Layer for item #4:\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 11, 11, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 10, 10, 11, 11, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "P = [4, 10, 2, 3, 8]\n",
    "W = [10, 4, 1, 2, 6]\n",
    "H = [3, 9, 12, 4, 2]\n",
    "\n",
    "MaxW = 12\n",
    "MaxH = 20\n",
    "\n",
    "print(knapsack2D(P, H, W, MaxH, MaxW))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Dodatkowo) Pobieranie rozwiązania\n",
    "##### (Rekurencyjnie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do funknkcji $ knapsack2D $ wprowadzamy jedynie drobną modyfikację, polegającą na tym, że funkcja ta zwraca dodatkowo tablicę $ F $."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_items(H, W, MaxH, MaxW):\n",
    "    \"\"\"\n",
    "    This function filters items which have wrong values (e.g. negative)\n",
    "    or cannot fit in the knapsack even if they are used separately (that means\n",
    "    an item is to tall and to heavy to be put in a knapsack).\n",
    "    \"\"\"\n",
    "    # We assume that we got the same number of heights as weights and profits\n",
    "    # (number of items is valid)\n",
    "    n = len(H)\n",
    "    H_cp = []\n",
    "    W_cp = []\n",
    "    for i in range(n):\n",
    "        if 0 < H[i] <= MaxH or 0 < W[i] <= MaxW:\n",
    "            H_cp.append(H[i])\n",
    "            W_cp.append(W[i])\n",
    "    return H_cp, W_cp\n",
    "\n",
    "\n",
    "def create_arr(k: 'number of layers', i: 'number of rows', j: 'number of columns', val=0):\n",
    "    return [[[val] * j for _ in range(i)] for _ in range(k)]\n",
    "\n",
    "\n",
    "def knapsack2D(P: 'array of profits', \n",
    "               H: 'array of heights', \n",
    "               W: 'array of weights', \n",
    "               MaxH: 'max total height of items',\n",
    "               MaxW: 'max total weight of items'\n",
    "               ):\n",
    "    n = len(P)\n",
    "    H, W = filter_items(H, W, MaxH, MaxW)\n",
    "    # Create an array for caching values\n",
    "    F = create_arr(n, MaxH + 1, MaxW + 1)\n",
    "    \n",
    "    for h in range(H[0], MaxH + 1):\n",
    "        for w in range(W[0], MaxW + 1):\n",
    "            F[0][h][w] = P[0]\n",
    "            \n",
    "    for i in range(1, n):\n",
    "        for h in range(1, MaxH + 1):\n",
    "            for w in range(1, MaxW + 1):\n",
    "                F[i][h][w] = F[i - 1][h][w]\n",
    "                \n",
    "                if H[i] <= h and W[i] <= w:\n",
    "                    F[i][h][w] = max(F[i][h][w], F[i - 1][h - H[i]][w - W[i]] + P[i])\n",
    "    \n",
    "    return F[n - 1][MaxH][MaxW], F\n",
    "\n",
    "\n",
    "def get_contents(F: '3D array returned by the knapsack function',\n",
    "                 P: 'array of profits', \n",
    "                 H: 'array of heights', \n",
    "                 W: 'array of weights',\n",
    "                 MaxH: 'max total height of items',\n",
    "                 MaxW: 'max total weight of items'\n",
    "                 ):\n",
    "    \n",
    "    def recur(i, h, w):\n",
    "        if i < 0: return []\n",
    "        if i == 0: return [0] if h >= H[0] and w >= W[0] else []\n",
    "        if h >= H[i] and w >= W[i] and F[i][h][w] == F[i - 1][h - H[i]][w - W[i]] + P[i]:\n",
    "            return recur(i - 1, h - H[i], w - W[i]) + [i]\n",
    "        return recur(i - 1, h, w)\n",
    "    \n",
    "    return recur(len(P) - 1, MaxH, MaxW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Kilka testów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max profit: 18\n",
      "Contents indices: [1, 4]\n",
      "Heights sum: 18\n",
      "Weights sum: 12\n"
     ]
    }
   ],
   "source": [
    "P = [4, 10, 2, 3, 8]\n",
    "W = [10, 6, 1, 2, 6]\n",
    "H = [3, 9, 12, 4, 9]\n",
    "\n",
    "MaxW = 12\n",
    "MaxH = 20\n",
    "\n",
    "profit, F = knapsack2D(P, H, W, MaxH, MaxW)\n",
    "contents = get_contents(F, P, H, W, MaxH, MaxW)\n",
    "print('Max profit:', profit)\n",
    "print('Contents indices:', contents)\n",
    "print('Heights sum:', sum(H[i] for i in contents))\n",
    "print('Weights sum:', sum(W[i] for i in contents))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Dodatkowo) Pobieranie rozwiązania\n",
    "##### (Iteracyjnie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_items(H, W, MaxH, MaxW):\n",
    "    \"\"\"\n",
    "    This function filters items which have wrong values (e.g. negative)\n",
    "    or cannot fit in the knapsack even if they are used separately (that means\n",
    "    an item is to tall and to heavy to be put in a knapsack).\n",
    "    \"\"\"\n",
    "    # We assume that we got the same number of heights as weights and profits\n",
    "    # (number of items is valid)\n",
    "    n = len(H)\n",
    "    H_cp = []\n",
    "    W_cp = []\n",
    "    for i in range(n):\n",
    "        if 0 < H[i] <= MaxH or 0 < W[i] <= MaxW:\n",
    "            H_cp.append(H[i])\n",
    "            W_cp.append(W[i])\n",
    "    return H_cp, W_cp\n",
    "\n",
    "\n",
    "def create_arr(k: 'number of layers', i: 'number of rows', j: 'number of columns', val=0):\n",
    "    return [[[val] * j for _ in range(i)] for _ in range(k)]\n",
    "\n",
    "\n",
    "def knapsack2D(P: 'array of profits', \n",
    "               H: 'array of heights', \n",
    "               W: 'array of weights', \n",
    "               MaxH: 'max total height of items',\n",
    "               MaxW: 'max total weight of items'\n",
    "               ):\n",
    "    n = len(P)\n",
    "    H, W = filter_items(H, W, MaxH, MaxW)\n",
    "    # Create an array for caching values\n",
    "    F = create_arr(n, MaxH + 1, MaxW + 1)\n",
    "    \n",
    "    for h in range(H[0], MaxH + 1):\n",
    "        for w in range(W[0], MaxW + 1):\n",
    "            F[0][h][w] = P[0]\n",
    "            \n",
    "    for i in range(1, n):\n",
    "        for h in range(1, MaxH + 1):\n",
    "            for w in range(1, MaxW + 1):\n",
    "                F[i][h][w] = F[i - 1][h][w]\n",
    "                \n",
    "                if H[i] <= h and W[i] <= w:\n",
    "                    F[i][h][w] = max(F[i][h][w], F[i - 1][h - H[i]][w - W[i]] + P[i])\n",
    "    \n",
    "    return F[n - 1][MaxH][MaxW], F\n",
    "\n",
    "\n",
    "def get_contents(F: '3D array returned by the knapsack function',\n",
    "                 P: 'array of profits', \n",
    "                 H: 'array of heights', \n",
    "                 W: 'array of weights',\n",
    "                 ):\n",
    "    n = len(F)\n",
    "    h = len(F[0]) - 1\n",
    "    w = len(F[0][0]) - 1\n",
    "    contents = []\n",
    "    \n",
    "    for i in range(n - 1, 0, -1):\n",
    "        # If we have taken an item from the 'i'th layer, a profit stored\n",
    "        # in this layer will be different than a profit in a layer before\n",
    "        if F[i][h][w] != F[i - 1][h][w]:\n",
    "            contents.append(i)\n",
    "            w -= W[i]\n",
    "            h -= H[i]\n",
    "    # As we will never check the first row in a loop above, we have\n",
    "    # to asses whether the item from the first layer was taken separately\n",
    "    # We decide to take the first element only if the remaining weight\n",
    "    # and the remaining height that can be used are no lower than \n",
    "    # a weight of this element and a height of this element\n",
    "    if w >= W[0] and h >= H[0]:\n",
    "        contents.append(0)\n",
    "        \n",
    "    contents.reverse()\n",
    "    return contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Kilka testów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max profit: 18\n",
      "Contents indices: [1, 4]\n",
      "Heights sum: 18\n",
      "Weights sum: 12\n"
     ]
    }
   ],
   "source": [
    "P = [4, 10, 2, 3, 8]\n",
    "W = [10, 6, 1, 2, 6]\n",
    "H = [3, 9, 12, 4, 9]\n",
    "\n",
    "MaxW = 12\n",
    "MaxH = 20\n",
    "\n",
    "profit, F = knapsack2D(P, H, W, MaxH, MaxW)\n",
    "contents = get_contents(F, P, H, W)\n",
    "print('Max profit:', profit)\n",
    "print('Contents indices:', contents)\n",
    "print('Heights sum:', sum(H[i] for i in contents))\n",
    "print('Weights sum:', sum(W[i] for i in contents))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sposób #2\n",
    "### (O mniejszej złożoności pamięciowej)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Omówienie algorytmu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Idea identyczna jak wyżej, ale tym razem korzystamy z $ 2 $-wymiarowej tablicy na wartości. Podobne podejście zostało przedstawione m.in. <a href=\"../../../Pozostałe zadania/Zadania BIT Algo/9. Zajęcia/Zadanie 6. (Pakowanie bagaży - zwykła algorytm na sumę podzbioru z użyciem plecakowego).ipynb\">TUTAJ</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Złożoność"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Obliczeniowa:\n",
    "$ O(n^3) $\n",
    "###### Pamięciowa:\n",
    "$ O(n^2) $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementacja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_items(H, W, MaxH, MaxW):\n",
    "    \"\"\"\n",
    "    This function filters items which have wrong values (e.g. negative)\n",
    "    or cannot fit in the knapsack even if they are used separately (that means\n",
    "    an item is to tall and too heavy to be put in a knapsack).\n",
    "    \"\"\"\n",
    "    # We assume that we got the same number of heights as weights and profits\n",
    "    # (number of items is valid)\n",
    "    n = len(H)\n",
    "    H_cp = []\n",
    "    W_cp = []\n",
    "    for i in range(n):\n",
    "        if 0 < H[i] <= MaxH or 0 < W[i] <= MaxW:\n",
    "            H_cp.append(H[i])\n",
    "            W_cp.append(W[i])\n",
    "    return H_cp, W_cp\n",
    "\n",
    "\n",
    "def knapsack2D(P: 'array of profits', \n",
    "               H: 'array of heights', \n",
    "               W: 'array of weights', \n",
    "               MaxH: 'max total height of items',\n",
    "               MaxW: 'max total weight of items'\n",
    "               ):\n",
    "    n = len(P)\n",
    "    H, W = filter_items(H, W, MaxH, MaxW)\n",
    "    # Create an array for caching values\n",
    "    F = [[0] * (MaxW + 1) for _ in range(MaxH + 1)]\n",
    "    \n",
    "    for h in range(H[0], MaxH + 1):\n",
    "        for w in range(W[0], MaxW + 1):\n",
    "            F[h][w] = P[0]\n",
    "            \n",
    "    for i in range(1, n):\n",
    "        for h in range(MaxH, -1, -1):\n",
    "            for w in range(MaxW, -1, -1):\n",
    "                if H[i] <= h and W[i] <= w:\n",
    "                    F[h][w] = max(F[h][w], F[h - H[i]][w - W[i]] + P[i])\n",
    "                \n",
    "    print(*F, sep='\\n')\n",
    "    \n",
    "    return F[MaxH][MaxW]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Kilka testów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 3, 3, 8, 8, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 11, 11, 11, 11, 11]\n",
      "[0, 0, 3, 3, 10, 10, 10, 10, 11, 11, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 10, 10, 11, 11, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 18, 18, 18]\n",
      "[0, 2, 3, 3, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "[0, 2, 3, 5, 10, 10, 13, 13, 13, 13, 18, 18, 21]\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "P = [4, 10, 2, 3, 8]\n",
    "W = [10, 4, 1, 2, 6]\n",
    "H = [3, 9, 12, 4, 2]\n",
    "\n",
    "MaxW = 12\n",
    "MaxH = 20\n",
    "\n",
    "print(knapsack2D(P, H, W, MaxH, MaxW))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### UWAGA\n",
    "W przypadku pracy na macierzy $ 2 $-wymiarowej, odtwarzanie rozwiązania jest niemożliwe (chyba, że byśmy utworzyli macierz $ 3 $-wymiarową do zapamiętywania, z jakiego zestawu poprzednio skorzystaliśmy), ponieważ tracimy całą informację o rozwiązaniu dla $ i - 1 $ (dla poprzednio rozważanego zestawu przedmiotów z pominięciem obecnie ostatniego)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}