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    "# Miqyoslash\n",
    "\n",
    "Berilganlarni miqyoslash deganda biz ularning qiymatlarini yoki taqsimotini bir oraliqdan yoki taqsimotdan boshqa oraliqqa yoki taqsimotga ko‘chirishni tushinamiz. Masalan, biror mahsulotning vaznlari kilogrammlarda berilgan bo‘lsa, ularni grammlarga o‘tkazish yoki ushbu qiymatlarni boshqa biror oraliqqa ko‘chirishimiz zarur bo‘ladi. Chunki MO algoritmlari bularsiz yaxshi natijaga erishmaydi.\n",
    "\n",
    "## Normallashtirish\n",
    "\n",
    "Ingliz tilidagi *Normalization* so‘zni biz *Normallashtirish* deb tarjima qildik. Ushbu so‘zga yaqin so‘zni oldingi qismda vektorlar ustida amal sifatida kiritdik. Lekin, bu yerda biz ozroq boshqacharoq tushuncha haqida so‘z yuritmoqchimiz, ammo, buni ham umumiy holda vektorlar ustida amalga oshirildi deyish mumkin. Keling birinchi muammoni qo‘yaylikda, keyin ushbu muammoni tushinib uni yechishga kirishaylik. Faraz qilaylik, $\\mathbf{x}$ va $\\mathbf{y}$ ikkita vektor berilgan bo‘lsin, hamda shu vektorlarning o‘rtasidagi Evklid masofasini o‘lchaylik. Ushbu vektorlarning har biri 3 tadan elementga ega bo‘lib, ya’ni $\\mathbf{x}, \\mathbf{y} \\in \\mathbb{R}^3$, ular ikki odamning bo‘yining uzunligini metrlarda, yoshini yillarda va vaznini esa kilogrammlarda saqlasin. Masalan, $\\mathbf{x}=\\{1.8, 25, 88\\}$, $\\mathbf{y}=\\{1.6, 78, 67\\}$, ya’ni jadvalda ko‘radigan bo‘lsak:\n",
    "\n",
    "|#|Uzunligi|Yoshi|Vazni|\n",
    "|---|---|---|---|\n",
    "|1-odam, x|1.8|25|88|\n",
    "|2-odam, y|1.6|78|67|\n",
    "\n",
    "Shu yerda eng e’tibor qiladigan holat - biz hech qachon vektorga yoki matritsaga har bir qiymat qanday o‘lchov birligida(masalan, kilogramm, metr, yil) bo‘lishini saqlamaymiz, uning o‘rniga shunchaki shunday deb tushunamiz va qayerda zarur bo‘lsa, shunday joylarda oshkor ravishda foydalanuvchi uchun yozib qo‘yamiz. Endi keling, shu ikki vektor o‘rtasidagi Evklid masofasini topsak: $d(\\mathbf{x}, \\mathbf{y})=\\sqrt{(x_1-y_1)^2+(x_2-y_2)^2+(x_3-y_3)^2}$, $d(\\mathbf{x}, \\mathbf{y})=\\sqrt{(1.8-1.6)^2+(78-25)^2+(88-67)^2}$, $d(\\mathbf{x}, \\mathbf{y})=\\sqrt{(0.2)^2+(53)^2+(21)^2}$, $d(\\mathbf{x}, \\mathbf{y})=\\sqrt{(0.2)^2+(53)^2+(21)^2} \\approx 57$. Demak boshlang‘ich masofa taxminan 57 ga teng ekan. Navbatda, shu vektorlarni insoning bo‘yisiz tasvirlaylik, ya’ni $\\mathbf{x}=\\{25, 88\\}$, $\\mathbf{y}=\\{78, 67\\}$ va masofani qayta hisoblaylik. Bu holatda masofa yana shu taxminan 57 ga teng bo‘ladi. Bu esa bizga hozirgi holatda inson bo‘yining umuman ushbu masofaga ta'siri yo‘qligini anglatadi yoki ta’siri juda kichik bo‘lib, uni e’tiborga olish shart emas. Keyingi o‘rinda esa, ushbu muamoni yechish uchun inson bo‘yini metrlarda emas balki santimetrlarda o‘lchaylik, unda vektorlarimiz quyidagi ko‘rinishga ega bo‘ladi: $\\mathbf{x}=\\{180, 25, 88\\}$, $\\mathbf{y}=\\{160, 78, 67\\}$. Bu holda esa masofa taxminan 60 ga teng bo‘ladi. Hozir bizda savol tug‘ildi qaysi masofani olishimiz kerak, 57 yoki 60? Yoki biz quyidagi xulosaga kelishimiz mumkinmi Evklid masofasi yoki shu kabi masofalar turli qiymatlar chiqaradimi (unday emas)! Bu kabi savollar yoki muammolar yana topiladi. Shuning uchun ham masofani topishda avval qiymatlarni biror me'yorga, ya’ni normaga keltirish zarur bo‘ladi aks holda algoritm yaxshi tanlangan bo‘lsada, natija yomonligicha qoladi.\n",
    "\n",
    "Normallashtirishning bir qancha usullari mavjud hamda ular bir biridan ma’lum bir kichik farqlarga ega. Agar, eslasangiz, biz statistika bo‘limida o‘zgaruvchi degan tushunchani kiritgan edik. Endi, bu yerda normallashtirishni bitta o‘zgaruvchining o‘zida qolganlariga bog‘lamasdan amalga oshirishimiz zarur bo‘ladi. Yodga solish uchun, bu yerda o‘zgaruvchi deb yoshni, vazni va uzunlikni alohida qaraymiz. Birinchi, eng sodda usul sifatida biz o‘zgaruvchini o‘zining joiz eng katta qiymatiga bo‘lish orqali amalga oshiramiz. Biz yuqoridagi namuna uchun inson yoshining taxminan chegarasini bilamiz, masalan, 120 yosh eng katta yosh bo‘lishi mumkin. Xuddi shunday, eng uzun odam 2.3 metr va eng og‘ir odam 200 kg deylik. Shunda yuroqidagi ikkita vektorning normallashgan ko‘rinishini quyidagicha hisoblaymiz: $\\mathbf{x}=\\{\\frac{1.8}{2.3}, \\frac{25}{120}, \\frac{88}{200} \\}=\\{ 0.78, 0.21, 0.44\\}$ va $\\mathbf{y}=\\{\\frac{1.6}{2.3}, \\frac{78}{120}, \\frac{67}{200}\\}=\\{0.7, 0.65, 0.34\\}$. Bu yerda har bir o‘zgaruvchini o‘zing mos eng katta qiymatiga bo‘lish orqali, biz har bir o‘zgaruvchining qiymatini 0 va 1 oralig‘ida qayta tasvirlaymiz. Shundagina, har bir o‘zgaruvchining masofaga ta'siri bir xil bo‘ladi. Aks holda yuqoridagi turli xillik bizda paydo bo‘ladi.\n",
    "\n",
    "> **Ta'rif**. Normallashtirish deb har bir o‘zgaruvchini ma’lum bir oraliqqa o‘tkazishga aytiladi.\n",
    "\n",
    "Yuqoridagi namunada esa biz qiymatlarni $[0, 1]$ intervalga o‘tkazdik. Bu umumiy ta'rif esa bizga qiymatlarni ixtiyoriy oraliqa o‘tkazish imkonini beradi. Lekin, odatda *Normallashtirishni* $[0, 1]$ yoki $[-1, 1]$ oraliqqa o‘tkazish ma’qul hisoblanadi, ayniqsa MOda. Chunki, agar biz bir nechta o‘zgaruvchilar bilan biror natija olmoqchi bo‘lsak, u holda bu oraliqlar bizga eng yaxshi turg‘unlikni beradi.\n",
    "\n",
    "> **Eslatma**. Normallashtirish jarayoni odatda har bir MO modelini ishlab chiqishda bo‘ladi hamda uni biz *berilganlarga dastlabki ishlov berish* deymiz, bu atama Ingliz tilida esa *data preprocessing* deb nomlanadi.\n",
    "\n",
    "> **Eslatma**. Odatda, biror matematik modelga kiruvchi qiymatlar juda kichik miqdorda o‘zgardanda, masalaning natijasi o‘zgarmasa yoki juda kichik(arzimas darajada) o‘zgarsa, bu modelni turg‘un deb ataymiz. Yuqorida esa biz turg‘unlik deb shunga o‘xshash narsani aytik, lekin yuqoridagi turg‘unlik ko‘proq kiruvchi qiymatlarning qanday holatda ekanligiga bog‘liq hisoblanadi. \n",
    "\n",
    "Biz yuqorida eng katta qiymatni tabiyatan mavjud tushunchalar orqali hosil qildik, ammo, bu narsani hamma o‘zgaruvchilar uchun qo‘llay olmaymiz. Shuning uchun ham, odatda, bunday eng katta qiymatlarni berilganlarning o‘zidan topish qulayroq, chunki turli mutaxasislar turlicha og‘irlik chegarasida faoliyat yuritishi mumkin. Masalan, bolalar shifokoriga 20 kg ham yetarli bo‘lishi mumkin, ya’ni eng og‘ir bola 20 kg bo‘lishi mumkin. Yuqorida biz faqat 2 ta obyekt(vektor, odam) oldik, shuning uchun ham, har bir o‘zgaruvchi uchun eng katta qiymat quyidagicha bo‘ladi: 1.8 metr uzunlik uchun, 78 yil yosh uchun va 88 kg esa vazn uchun bo‘ladi. Buni hisoblash ko‘rib turganimizdek juda sodda, ya’ni har bir o‘zgaruvchining qiymatlarining eng kattasini olish yetarlidir.\n",
    "\n",
    "> **Eslatma**. Biz yuqorida ikkita vektordagi mavjud qiymatlarni, masalan inson yoshlarini olib, uni statistikaga oid atama bilan *o‘zgaruvchi* deb yozdik, bu narsa MOda shunchaki *alomat* deyiladi. Ushbu atama boshqa sohalarda ham turlicha nomlanadi, masalan, tibiyotda *ko‘rsatgich* deb ham nomlanishi mumkin. Shuning uchun ham atamalardagi umumiylikni imkon qadar bilish zarur bo‘ladi, ya’ni biror manba o‘qiganimizda atama o‘zgarsa ham mazmunni tushunishimiz zarur bo‘ladi.\n",
    "\n",
    "Keling endi, yuqoridagi o‘rgangan usulga asosan biror alamotni normallashtiraylik. Buning uchun birinchi biz alomat sifatida biror $\\mathbf{x}$ vektorni qaraylik, unda ushbu vektorning normallashgan ko‘rinishini quyidagicha ifodalash mumkin: $\\mathbf{x}^{*}=\\frac{\\mathbf{x}}{max(\\mathbf{x})}$, bu yerda $max()$ funksiyasi berilgan vektorning eng katta qiymatini topadi. Siz o‘quvchiga bir narsa adashtiradigan tuyilishi mumkin, ya’ni yuqorida biz vektor deb bir insoning 3 ta ko‘rsatgichini(vazni, yoshi, bo‘yining uzunligini) olgan edik. Lekin yuqoridagi oxirgi $\\mathbf{x}$ vektor bu bitta odamning bir nechta ko‘rsatgichlari emas, balki berilgan hamma odamning bitta ko‘rsatgichini o‘zida saqlaydi. Masalan, 100 ta odamning yoshini. Agar shu odamlarning yoshi eng kattasi 70 yoshda bo‘lsa, hamma odamning yoshini shu songa bo‘lib chiqamiz.\n",
    "\n",
    "Hozirgacha o‘rgangan normallashtirish usuli odatda *maksimal* normallashtirish deyiladi hamda ushbu usul qiymatlarni har doim ham $[0, 1]$ oraliqqa o‘tkazmaydi, balki $(0, 1]$ o‘tkazadi. Shuning uchun ham ko‘pincha biz quyidagi normallashtirish usulidan foydalanamiz: $\\mathbf{x}^{*}=\\frac{\\mathbf{x} - min(\\mathbf{x})}{max(\\mathbf{x})-min(\\mathbf{x})}$, bu yerda $max()$ va $min()$ funksiyalari berilgan vektorning eng katta va eng kichik qiymatlarini qaytaradi. Agar $\\mathbf{x}$ vektorning eng kichik qiymati nolga teng bo‘lsa, u holda oldingi usul bilan bu usul bir xil bo‘ladi, ya’ni $[0, 1]$ oraliqqa o‘tkazadi.\n",
    "\n",
    "> **Eslatma**. Agar biz oraliqni belgilashda $[a, b]$ ko‘rinishdagi qavslardan foydalansak, u holda oraliqning ikki tomonidagi qiymatlar ham kiradi deb hisoblaymiz va buni odatda to‘liq oraliq yoki interval deb ataymiz. Agar shuni o‘zgartirsak, ya’ni $(a, b]$ ushbu holda oraliq $a$ sonidan boshlanib $b$ sonida tugaydi, lekin $a$ soni hisoblanmaydi va $b$ soni oraliqqa kiradi. Masalan, biror son $(10, 100]$ oraliqda deyilsa, u holda bu son $11, 12, \\dots, 100$ bo‘lishi mumkin. Bunday oraliqlarni biz *yarim-ochiq* oraliq deb ataymiz. Agar $(a, b)$ bo‘lsa, u holda buni *ochiq* oraliq deymiz.\n",
    "\n",
    "Ushbu qismda biz ikki xil normallashtirish usullari bilan tanishdik. Hozir esa umumiy nazariy qism va atamalar bilan tanishib ularni mulohaza qilamiz. Birinchidan, statistika sohasida va MOda umumiy ikkita tushuncha bor: normallashtirish (buni me’yorlashtirish deb tarjima qilish mumkin) va standartlashtirish (Ingliz tilida *standardization*, O‘zbek tilida balki *qoliplash* deyish mumkindir). Normallashtirish deb biror qiymatni qat’iy belgilangan oraliqqa o‘tkazish tushinilsa, standartlashtirish deb esa, biz o‘zgaruvchining o‘rtacha qiymatini nolga va farqlanishini esa birga teng qilishga aytamiz. Ikkinchidan, mashinaga hech qachon o‘lchov birliklarini yozmaymiz hamda mashina uni qabul qilmaydi. Uning o‘rniga esa biz o‘zimiz bu haqida qayg‘uramiz, ya’ni har doim mashinga biror kiruvchi qiymatni berayotganimizda uni normallashtirib yoki standartlashtirib olamiz. Uchunchidan, agar bizning umumiy modelimizda shu kabi normallashtirish jarayoni mavjud bo‘lsa, u holda bu modelni o‘lchovga (yoki umumiy holda kiruvchi qiymatga) nisbatan *o‘zgarmas* deymiz. Ushbu *o‘zgarmas* atamasi Ingliz tilida *invariant* deb nomlanadi. Hamda bu normallashtirish orqali deyarli hamma modellar kiruvchi qiymatga nisbatan *o‘zgarmas* bo‘ladi, aks holda natijalar kutilganidek bo‘lmaydi. Nihoyat, quyida biz odam yoshi yillarda hamda uning uzunligi metrlarda berilgan holda, normallashtirish ikki o‘zgaruvchiga qanday ta’sir qilishini ko‘rishimiz mumkin. Buning uchun birinchi ushbu o‘zgaruvchilarni tasodifiy ravishda tanlab olamiz, hamda keyin ularning boshlang‘ich ko‘rinishi va normallashgan ko‘rinishini nuqtalar sifatida rasmda tasvirlaymiz qilamiz."
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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kutubxonalar\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 200 yoshlarni va vaznlarni tasodifiy hosil qilamiz\n",
    "# yosh [1, 120) intervalda\n",
    "ages = np.random.randint(1, 120, size=200)\n",
    "# vazn [1, 2.3] intervalda\n",
    "weights = 1 + 1.3 * np.random.rand(200)\n",
    "\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "fig.set_size_inches(10, 10)\n",
    "# birinchi normallashmagan qiymatlarni\n",
    "# nuqtalar sifatida chiqaramiz\n",
    "axes[0].scatter(ages, weights)\n",
    "axes[0].set_aspect('equal', adjustable='box')\n",
    "axes[0].set_xlabel(\"Yosh\")\n",
    "axes[0].set_ylabel(\"Vazn\")\n",
    "axes[0].grid(True)\n",
    "axes[0].set_title(\"Normallashga va normallashmagan qiymatlarning solishtirmasi\")\n",
    "\n",
    "ages = (ages - ages.min()) / (ages.max() - ages.min())\n",
    "weights = (weights - weights.min()) / (weights.max() - weights.min())\n",
    "\n",
    "axes[1].scatter(ages, weights)\n",
    "axes[1].set_aspect('equal', adjustable='box')\n",
    "axes[1].set_xlabel(\"Yosh\")\n",
    "axes[1].set_ylabel(\"Vazn\")\n",
    "axes[1].grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8097f79b",
   "metadata": {},
   "source": [
    "E’tibor bering, birinchi holda yoshning qiymati kattaligindan vazning ahamiyati deyarli qolmagan. Lekin normallashtirilgandan so‘ng esa, bu narsa pastki rasmda mavjud emas. Ushbu rasmlar bizga tasviriy jihatdan normallashtirishni qanday tushunishimiz kerakligini ko‘rsatadi."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeefa5d9",
   "metadata": {},
   "source": [
    "## Standartlash\n",
    "\n",
    "Oldingi mavzuda sizlar bilan normallashtirish haqida so‘z yuritdik. Standartlashtirish normallashtirishning bir ko‘rinishi bo‘lib, asosan taqsimotning xususiyatlarini o‘zgartirishga hissa qo‘shadi. Biz haligacha taqsimotning umumiy ta’rifini rasmiy ravishda bermaganimiz uchun bu yerda shunchaki qanday qilib standartlashtirish mumkinligini eng ko‘p qo‘llaniladigan usullardan biri bo‘lgan, Z-baho(Ingliz tilida Z-score)ni qanday qo‘lashni sodda qilib keltiramiz: $\\mathbf{x}^{*}=\\frac{\\mathbf{x} - mean(\\mathbf{x})}{std(\\mathbf{x})}$, bu yerda $mean()$ va $std()$ funksiyasilari mos ravishda berilgan vektorning o‘rtacha va standart og‘ishini qaytaradi. Quyida esa yuqoridagi tasviriy dasturning natijalarini ushbu standartlashtirishda ko‘rishimiz mumkin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2bd82cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages = np.random.randint(1, 120, size=200)\n",
    "weights = 1 + 1.3 * np.random.rand(200)\n",
    "\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "fig.set_size_inches(10, 10)\n",
    "# birinchi normallashmagan qiymatlarni\n",
    "# nuqtalar sifatida chiqaramiz\n",
    "axes[0].scatter(ages, weights)\n",
    "axes[0].set_aspect('equal', adjustable='box')\n",
    "axes[0].set_xlabel(\"Yosh\")\n",
    "axes[0].set_ylabel(\"Vazn\")\n",
    "axes[0].grid(True)\n",
    "axes[0].set_title(\"Normallashga va normallashmagan qiymatlarning solishtirmasi\")\n",
    "\n",
    "ages = (ages - ages.mean()) / ages.std()\n",
    "weights = (weights - weights.mean()) / weights.std()\n",
    "\n",
    "axes[1].scatter(ages, weights)\n",
    "axes[1].set_aspect('equal', adjustable='box')\n",
    "axes[1].set_xlabel(\"Yosh\")\n",
    "axes[1].set_ylabel(\"Vazn\")\n",
    "axes[1].grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47408528",
   "metadata": {},
   "source": [
    "Normallashtirishda biz shunchaki qiymatlarini $[0, 1]$ intervalga ko‘chirgan bo‘lsak, bu yerda esa standartlashtirilgandan so‘ng qiymatlarning o‘rtachasi taxminan nolga teng bo‘lishi kerak hamda farqlanish esa birga. Ushbu ko‘rinishga keltirishdan asosiy maqsad, berilgan o‘zgaruvchini standart normal taqsimot sifatida tasvirlashdir. Ushbu taqsimotlar haqida keyingi statistika darslarimizda to‘liq ma’lumot beramiz."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai-intro",
   "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": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}