p1/izpiti/2020/03_resitev.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "0a1bbda9",
   "metadata": {},
   "source": [
    "Če želiš (ni pa ti treba), si napiši funkciji:\n",
    "\n",
    "- `preblizu(x1, y1, x2, y2)`, ki vrne `True`, če je razdalja med podanima koordinatama manjša od 1.5;\n",
    "- `koordinate(ime, osebe)`, ki vrne koordinate osebe s podanim imenom.\n",
    "\n",
    "Vse funkcije, ki jih sprogramiraš, se lahko kličejo med seboj – kjer ti to pomaga.\n",
    "\n",
    "### Rešitev\n",
    "\n",
    "Ubogajmo nasvet, napišimo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5222b40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preblizu(x1, y1, x2, y2):\n",
    "    return (x1 - x2) ** 2 + (y1 - y2) ** 2 < 1.5 ** 2\n",
    "\n",
    "def koordinate(ime, osebe):\n",
    "    for ime1, x, y in osebe:\n",
    "        if ime == ime1:\n",
    "            return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d73fd7db",
   "metadata": {},
   "source": [
    "Mimogrede opazimo, da funkcija `koordinate` vrne `None`, če oseba ne obstaja. Lepo bi bilo, če bi bil ta rezultat kar ekspliciten, torej, če dodamo `return None`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87430b08",
   "metadata": {},
   "source": [
    "## 1. Preblizu stoječi\n",
    "\n",
    "Razpored oseb na določeni prireditvi, je podan s seznamom trojk, ki vsebujejo ime in koordinati (v metrih); glej primer. Napiši funkcijo `krsitelji(osebe)`, ki prejme takšen seznam in vrne množico imen vseh oseb, ki so manj kot 1,5 m oddaljene od najbližje osebe. V primeru na sliki so to vsi razen Dani in Klare.\n",
    "\n",
    "```text\n",
    "osebe = [(\"Ana\", 2, 4.5),\n",
    "         (\"Berta\", 1, 3),\n",
    "         (\"Cilka\", 1, 4),\n",
    "         (\"Dani\", -1, 2),\n",
    "         (\"Ema\", 1, 1),\n",
    "         (\"Fanči\", 2, 0.5),\n",
    "         (\"Greta\", -1, -1.5),\n",
    "         (\"Helga\", 0, -1),\n",
    "         (\"Iva\", 2, 0),\n",
    "         (\"Jana\", 0, 0),\n",
    "         (\"Klara\", 5, 1)]\n",
    "```"
   ]
  },
  {
   "attachments": {},
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\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d918912c",
   "metadata": {},
   "source": [
    "### Rešitev\n",
    "\n",
    "Najpreprostejša rešitev: pogledamo vse pare. Če potrebujemo pare, bo to vedno pomenilo dvojno zanko, v tem primeru prek seznama oseb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60ba0bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def krsitelji(osebe):\n",
    "    imena = set()\n",
    "    for ime, x1, y1 in osebe:\n",
    "        for drugi, x2, y2 in osebe:\n",
    "            if ime != drugi and preblizu(x1, y1, x2, y2):\n",
    "                imena.add(ime)\n",
    "                break\n",
    "    return imena"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c091c51e",
   "metadata": {},
   "source": [
    "Rešitev je preprosta, vseeno pa zasluži nekaj komentarjev.\n",
    "\n",
    "- Ne smemo pozabiti `ime1 != ime2`. Če ga izpustimo, bo seznam na koncu vseboval vse osebe, saj je vsak preblizu sebe.\n",
    "- Dodali smo samo `ime`. Lahko bi napisali še `imena.add(drugi)`, vendar bo oseba z imenom `drugi` tako ali tako dodana kasneje (ko bosta `ime` in `drugi` v ravno zamenjanih vlogah).\n",
    "- `break` je nepotreben; rezultat bi bil enak brez njega, le funkcija bi bila malenkost počasnejša. Je pa lušten zato, ker se zaradi njega funkcija lepo bere: zunanja zanka gre prek oseb in notranja preveri, ali obstaja kak drugi, ki stoji tej osebi preblizu. Če ga najde, doda osebo med kršitelje in prekine zanko.\n",
    "\n",
    "Krajša oblika funkcije skrči notranjo zanko v `any`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18ed1207",
   "metadata": {},
   "outputs": [],
   "source": [
    "def krsitelji(osebe):\n",
    "    imena = set()\n",
    "    for ime, x1, y1 in osebe:\n",
    "        if any(ime != druga and preblizu(x1, y1, x2, y2) for druga, x2, y2 in osebe):\n",
    "            imena.add(ime)\n",
    "    return imena"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e2e1a32",
   "metadata": {},
   "source": [
    "To nas vodi v še krajšo obliko, kjer preprosto naberemo vse v izpeljano množico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37d8b2f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def krsitelji(osebe):\n",
    "    return {ime for ime, x1, y1 in osebe\n",
    "            if any(ime != druga and preblizu(x1, y1, x2, y2) for druga, x2, y2 in osebe)}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fec824c1",
   "metadata": {},
   "source": [
    "## 2. Kazni\n",
    "\n",
    "Vsaki osebi izrečemo toliko kazni, kolikor osebam stoji preblizu. Emi, recimo, 3, ker stoji preblizu Fanči, Ive in Jane.\n",
    "\n",
    "Napiši funkcijo `kazni(osebe)`, ki vrne slovar, katerega ključi so imena kaznovanih oseb, vrednosti pa število izrečenih kazni.\n",
    "\n",
    "### Rešitev\n",
    "\n",
    "Funkcija je praktično enaka prejšnji, le da ne iščemo, temveč štejemo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c17008c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kazni(osebe):\n",
    "    izrecene = {}\n",
    "    for ime, x1, y1 in osebe:\n",
    "        kazen = 0\n",
    "        for druga, x2, y2 in osebe:\n",
    "            if ime != druga and preblizu(x1, y1, x2, y2):\n",
    "                kazen += 1\n",
    "        if kazen:\n",
    "            izrecene[ime] = kazen\n",
    "    return izrecene"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eacec0",
   "metadata": {},
   "source": [
    "Z malo zvitosti se lahko znebimo pogoje `ime != druga`: pustimo, da bo vsak kaznovan zaradi sebe, a kazni začnemo šteti pri `-1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c05c603",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kazni(osebe):\n",
    "    izrecene = {}\n",
    "    for ime, x1, y1 in osebe:\n",
    "        kazen = -1\n",
    "        for druga, x2, y2 in osebe:\n",
    "            if preblizu(x1, y1, x2, y2):\n",
    "                kazen += 1\n",
    "        if kazen:\n",
    "            izrecene[ime] = kazen\n",
    "    return izrecene"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c549f4e7",
   "metadata": {},
   "source": [
    "Če se spomnimo, da je `True` isto kot `1` in `False` isto kot `0`, lahko nadomestimo notranjo zanko s seštevanjem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a98feb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kazni(osebe):\n",
    "    izrecene = {}\n",
    "    for ime, x1, y1 in osebe:\n",
    "        kazen = sum(preblizu(x1, y1, x2, y2) for _, x2, y2 in osebe) - 1\n",
    "        if kazen:\n",
    "            izrecene[ime] = kazen\n",
    "    return izrecene"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90f57aa",
   "metadata": {},
   "source": [
    "Se da tudi to skrčiti v eno vrstico. Da. Uporabiti je potrebno \"walrus operator\", ki ga pri Programiranju 1 (vsaj v doslejšnjih letih) nismo omenjali. Šlo bi tudi brez njega, vendar je z njim preprosteje. Tako."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d502f1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kazni(osebe):\n",
    "    return {ime: kazen for ime, x1, y1 in osebe\n",
    "            if (kazen := sum(preblizu(x1, y1, x2, y2) for _, x2, y2 in osebe) - 1) > 0}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e740715",
   "metadata": {},
   "source": [
    "Preveriti moramo, ali je vsota kazni večja od 0 in potem to kazen uporabiti kot vrednost. Da kazni ne bi računali dvakrat, ali pa delali gnezdenih generatorjev, izračunano vsoto shranimo v `kazen`. Walrus, `:=`, je potrebno uporabiti, ker `=` ne bi deloval, saj v Pythonu prirejanje ni izraz. Kdor ne razume, nič hudega.\n",
    "\n",
    "Če drugo nalogo rešimo prej kot prvo, lahko prvo funkcijo sprogramiramo kar s preprostim klicem druge: kršitelji so vsi, ki so kaznovani."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5416aadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def krsitelji(osebe):\n",
    "    return set(kazni(osebe))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7bc53b79",
   "metadata": {},
   "source": [
    "## 3. Severni veter\n",
    "\n",
    "Na prireditvi se je pojavila nova različica virusa, omega, ki te v trenutku okuži in naredi kužnega. Ker pa je pihal severni veter, vsaka oseba okuži le vse osebe, ki stojijo južno od nje na razdalji manjši od 1,5 metra. Če je Ema bolna, zaradi nje zbolijo Fanči, Iva, Jana, Greta in Helga. Če pa je bolna Jana, zaradi nje zbolita samo Greta in Helga, ne pa tudi Ema.\n",
    "\n",
    "Napiši funkcijo `okuzeni(ime, osebe)`, ki prejme ime okužene in seznam oseb; vrne množico imen oseb, ki se okužijo.\n",
    "\n",
    "### Rešitev\n",
    "\n",
    "Tole je naloga iz rekurzije. V bistvu gre za nalogo, v kateri je bilo potrebno dobiti vsa imena članov neke rodbine, le da nimamo seznama otrok, temveč je \"otrok\" vsak, ki stoji južno in je premalo oddaljen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9de77f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def okuzeni(ime, osebe):\n",
    "    imena = {ime}\n",
    "    x1, y1 = koordinate(ime, osebe)\n",
    "    for drugi, x2, y2 in osebe:\n",
    "        if y1 > y2 and preblizu(x1, y1, x2, y2):\n",
    "            imena |= okuzeni(drugi, osebe)\n",
    "    return imena"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1462031",
   "metadata": {},
   "source": [
    "Smo pozabili `drugo != ime`? Nismo. Vendar ni potreben, saj imamo `y1 > y2`. Če gre za eno in isto osebo, pač ne stoji severno od sebe."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cb74c3a6",
   "metadata": {},
   "source": [
    "## 4. Kihanje\n",
    "\n",
    "Če oseba kihne, s prireditve takoj odstranijo njo in vse, ki so manj kot 1,5 m oddaljene od nje. Če torej kihne Jana, takoj odstranijo tudi Helgo in Emo. Napiši funkcijo `kihanje(imena, osebe)`, ki prejme seznam imen oseb, ki so kihale (naštete so v vrstnem redu kihanja!) in seznam oseb na prireditvi. Vrne naj množico imen oseb, ki so po tem še na prizorišču.\n",
    "\n",
    "Pazi na tole: če najprej kihne Jana in nato Helga, ostane Greta na prizorišču, saj je Helga kihnila, ko je bila že odstranjena!\n",
    "\n",
    "### Rešitev\n",
    "\n",
    "Na vsak izpit se potrudim dati nalogo, ki vsebuje kakšno malo zamotano zanko. Tokrat je to ta naloga. Takšne naloge je možno rešiti na kup načinov. Eden je tale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef7f8f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kihanje(imena, osebe):\n",
    "    osebe = osebe.copy()\n",
    "    for ime in imena:\n",
    "        koord = koordinate(ime, osebe)\n",
    "        if koord is None:\n",
    "            continue\n",
    "        x1, y1 = koord\n",
    "        i = 0\n",
    "        while i < len(osebe):\n",
    "            _, x2, y2 = osebe[i]\n",
    "            if preblizu(x1, y1, x2, y2):\n",
    "                del osebe[i]\n",
    "            else:\n",
    "                i += 1\n",
    "    return {ime for ime, x, y in osebe}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd79cc8",
   "metadata": {},
   "source": [
    "Najprej skopiramo seznam oseb, ker bomo iz njega brisali. Funkcija nima pravice spreminjati vrednosti svojih argumentov!\n",
    "\n",
    "Potem gremo čez seznam vseh, ki so kihnili. S funkcijo `koordinate` pridobimo njegove koordinate. Če so `None`, je bila oseba že odstranjena in s `continue` preskočimo ostanek telesa zanke. Lahko bi pisali tudi `if koord is not None:` in ves ostanek zanke bi bil znotraj tega `if`. To mi je manj všeč iz dveh razlogov. Prvi je, da imamo potem en nivo zamikov več in to nikoli ni praktično. Drugi je, da se funkcija tako lepše bere: če osebe ni več, potem ni potrebno narediti ničesar.\n",
    "\n",
    "V notranji zanki bomo brisali osebe iz seznama. Ker z zanko `for` ne moremo prek seznama, iz katerega brišemo elemente, uporabimo `while`. Če oseba stoji preblizu, jo pobrišemo (in naslednji element se premakne na trenutno, `i`-to mesto), sicer stopimo na naslednji element (povečamo `i`). V tej zanki pobrišemo tudi kihajočega, saj \"stoji preblizu sebe\".\n",
    "\n",
    "Na koncu vrnemo množico imen, ki so ostala v seznamu osebe. To bi lahko naredili tudi z običajno zanko, ne z izpeljano množico. Vendar je tako hitreje.\n",
    "\n",
    "Alternativna rešitev je, da ne brišemo iz seznama `osebe`, temveč sestavljamo nov seznam, takole:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38cfa14f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kihanje(imena, osebe):\n",
    "    for ime in imena:\n",
    "        koord = koordinate(ime, osebe)\n",
    "        if koord is None:\n",
    "            continue\n",
    "        x1, y1 = koord\n",
    "        preostali = []\n",
    "        for ime2, x2, y2 in osebe:\n",
    "            if not preblizu(x1, y1, x2, y2):\n",
    "                preostali.append((ime2, x2, y2))\n",
    "        osebe = preostali\n",
    "    return {ime for ime, x, y in osebe}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6fcf9a",
   "metadata": {},
   "source": [
    "To je krajše. Porabi več pomnilnika, a manj časa. Brisanje iz seznama vzame veliko časa, dodajanje na konec seznama pa je hitro. Zakaj tako, boste bolj razumeli v drugem letniku.\n",
    "\n",
    "Tretja (izmed mnogih) možnosti je, da vzdržujemo seznam prisotnih oseb. Za vsakega, ki kiha, preverimo, ali je še prisoten. V notranji zanki gremo prek oseb in če je oseba prisotna ter stoji preblizu, jo odstranimo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd981b37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kihanje(imena, osebe):\n",
    "    prisotne = {ime for ime, x, y in osebe}\n",
    "    for ime in imena:\n",
    "        if ime not in prisotne:\n",
    "            continue\n",
    "        x1, y1 = koordinate(ime, osebe)\n",
    "        for ime2, x2, y2 in osebe:\n",
    "            if ime2 in prisotne and preblizu(x1, y1, x2, y2):\n",
    "                prisotne.remove(ime2)\n",
    "    return prisotne"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fcd4c4",
   "metadata": {},
   "source": [
    "Če namesto `remove` uporabimo `discard`, ni potrebno preverjati, ali je oseba še vedno prisotna. \n",
    "\n",
    "```python\n",
    "            if preblizu(x1, y1, x2, y2):\n",
    "                prisotne.discard(ime2)\n",
    "```\n",
    "\n",
    "Metoda `remove` javi napako, če poskusimo odstraniti element, ki ga ni; `discard` pa v tem primeru ne naredi ničesar."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4b9f2af8",
   "metadata": {},
   "source": [
    "## 5. Prireditev\n",
    "\n",
    "Sprogramiraj razred `Prireditev` z naslednjimi metodami.\n",
    "\n",
    "- konstruktor kot argument prejme minimalno predpisano razdaljo (in, seveda, inicializira objekt)\n",
    "- `prihod(ime, x, y)` prejme ime in želeni koordinati oseb. Če na predpisani razdalji (tisti, ki jo je prejel konstruktor) od teh koordinat ni nobene druge osebe, sprejme to osebo na te koordinate. V nasprotnem primeru to osebo zavrne. Metoda naj ne vrača ničesar.\n",
    "- `udelezenci()` vrne množico imen vseh oseb, ki so bile sprejete na prireditev.\n",
    "\n",
    "### Rešitev\n",
    "\n",
    "Kakšne podatke mora shranjevati razred? Minimalno zahtevano razdaljo in seznam ime in koordinat oseb na prireditvi. To bi lahko izgledalo tako:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "582869d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Prireditev:\n",
    "    def __init__(self, min_razdalja):\n",
    "        self.min_razdalja = min_razdalja\n",
    "        self.osebe = []\n",
    "\n",
    "    def prihod(self, ime, x, y):\n",
    "        if all((x - x2) ** 2 + (y - y2) ** 2 >= self.min_razdalja ** 2\n",
    "               for _, x2, y2 in self.osebe):\n",
    "            self.osebe.append((ime, x, y))\n",
    "\n",
    "    def udelezenci(self):\n",
    "        return {ime for ime, x, y in self.osebe}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03c6088",
   "metadata": {},
   "source": [
    "Ker nas v resnici ne zanima, kje je kdo, temveč le, katere koordinate so zasedene, lahko koordinate in imena shranjujemo tudi v ločenih atributih. To malenkost poenostavi `prihod` in čisto poenostavi `udelezenci`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "91c79d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Prireditev:\n",
    "    def __init__(self, min_razdalja):\n",
    "        self.min_razdalja = min_razdalja\n",
    "        self.koordinate = []\n",
    "        self.osebe = set()\n",
    "\n",
    "    def prihod(self, ime, x, y):\n",
    "        if all((x - x2) ** 2 + (y - y2) ** 2 >= self.min_razdalja ** 2\n",
    "               for x2, y2 in self.koordinate):\n",
    "            self.osebe.add(ime)\n",
    "            self.koordinate.append((x, y))\n",
    "\n",
    "    def udelezenci(self):\n",
    "        return self.osebe"
   ]
  }
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