{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### AITech — Uczenie maszynowe — laboratoria\n",
    "# 1a. Podstawowe narzędzia uczenia maszynowego"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elementy języka Python przydatne w uczeniu maszynowym"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listy składane (*List comprehension*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Przypuśćmy, że mamy dane zdanie i chcemy utworzyć listę, która będzie zawierać długości kolejnych wyrazów tego zdania. Możemy to zrobić w następujący sposób:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 4, 7, 3, 4, 1, 4, 3, 4, 1, 4, 7, 6, 4]\n"
     ]
    }
   ],
   "source": [
    "zdanie = 'tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł'\n",
    "wyrazy = zdanie.split()\n",
    "dlugosci_wyrazow = []\n",
    "for wyraz in wyrazy:\n",
    "    dlugosci_wyrazow.append(len(wyraz))\n",
    "    \n",
    "print(dlugosci_wyrazow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Możemy to też zrobić bardziej „pythonicznie”, przy użyciu list składanych:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 4, 7, 3, 4, 1, 4, 3, 4, 1, 4, 7, 6, 4]\n"
     ]
    }
   ],
   "source": [
    "zdanie = 'tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł'\n",
    "wyrazy = zdanie.split()\n",
    "dlugosci_wyrazow = [len(wyraz) for wyraz in wyrazy]\n",
    "\n",
    "print(dlugosci_wyrazow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jeżeli chcemy, żeby był sprawdzany dodatkowy warunek, np. chcemy pomijać wyraz „takt”, to wciąż możemy użyć list składanych:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 4, 7, 3, 1, 3, 1, 7, 6, 4]\n"
     ]
    }
   ],
   "source": [
    "zdanie = 'tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł'\n",
    "wyrazy = zdanie.split()\n",
    "\n",
    "# Ta konstrukcja:\n",
    "dlugosci_wyrazow = []\n",
    "for wyraz in wyrazy:\n",
    "    if wyraz != 'takt':\n",
    "        dlugosci_wyrazow.append(wyraz)\n",
    "        \n",
    "# ...jest równoważna tej jednolinijkowej:\n",
    "dlugosci_wyrazow = [len(wyraz) for wyraz in wyrazy if wyraz != 'takt']\n",
    "\n",
    "print(dlugosci_wyrazow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indeksowanie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wszystkie listy i krotki w Pythonie, w tym łańcuchy (które trakowane są jak krotki znaków), są indeksowane od 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "e\n"
     ]
    }
   ],
   "source": [
    "napis = 'abcde'\n",
    "print(napis[0])  # 'a'\n",
    "print(napis[4])  # 'e'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeksy możemy liczyć również „od końca”:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e\n",
      "d\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "napis = 'abcde'\n",
    "print(napis[-1])  # 'e' („ostatni”)\n",
    "print(napis[-2])  # 'd' („drugi od końca”)\n",
    "print(napis[-5])  # 'a' („piąty od końca”)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Łańcuchy możemy też „kroić na plasterki” (*slicing*):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bcd\n",
      "b\n",
      "cd\n",
      "bcd\n",
      "de\n",
      "abc\n",
      "abcde\n"
     ]
    }
   ],
   "source": [
    "napis = 'abcde'\n",
    "print(napis[1:4])  # 'bcd' („znaki od 1. włącznie do 4. wyłącznie”)\n",
    "print(napis[1:2])  # 'b' (to samo co `napis[1]`)\n",
    "print(napis[-3:-1])  # 'cd' (kroić można też stosując indeksowanie od końca)\n",
    "print(napis[1:-1])  # 'bcd' (możemy nawet mieszać te dwa sposoby indeksowania)\n",
    "print(napis[3:])  # 'de' (jeżeli koniec przedziału nie jest podany, to kroimy do samego końca łańcucha)\n",
    "print(napis[:3])  # 'abc' (jeżeli początek przedziału nie jest podany, to kroimy od początku łańcucha)\n",
    "print(napis[:])  # 'abcde' (kopia całego napisu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biblioteka _NumPy_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tablice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Głównym obiektem w NumPy jest **jednorodna**, **wielowymiarowa** tablica. Przykładem takiej tablicy jest macierz `x`.\n",
    "\n",
    "Macierz $x =\n",
    "    \\begin{pmatrix}\n",
    "    1 & 2 & 3  \\\\\n",
    "    4 & 5 & 6  \\\\\n",
    "    7 & 8 & 9\n",
    "    \\end{pmatrix}$\n",
    "można zapisać jako:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Najczęsciej używane metody tablic typu `array`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 15, 18])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 5., 8.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do tworzenia sekwencji liczbowych jako obiekty typu `array` należy wykorzystać funkcję `arange`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5. , 5.5, 6. , 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5, 10, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kształt tablicy można zmienić za pomocą metody `reshape`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(1, 13)\n",
    "print(x)\n",
    "y = x.reshape(3, 4)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funkcją podobną do `arange` jest `linspace`, która wypełnia wektor określoną liczbą elementów z przedziału o równych automatycznie obliczonych odstępach (w `arange` należy podać rozmiar kroku):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.   1.25 2.5  3.75 5.  ]\n"
     ]
    }
   ],
   "source": [
    "x = np.linspace(0, 5, 5)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dodatkowe informacje o funkcjach NumPy uzyskuje się za pomocą polecenia `help(nazwa_funkcji)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function shape in module numpy:\n",
      "\n",
      "shape(a)\n",
      "    Return the shape of an array.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    a : array_like\n",
      "        Input array.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    shape : tuple of ints\n",
      "        The elements of the shape tuple give the lengths of the\n",
      "        corresponding array dimensions.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    alen\n",
      "    ndarray.shape : Equivalent array method.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> np.shape(np.eye(3))\n",
      "    (3, 3)\n",
      "    >>> np.shape([[1, 2]])\n",
      "    (1, 2)\n",
      "    >>> np.shape([0])\n",
      "    (1,)\n",
      "    >>> np.shape(0)\n",
      "    ()\n",
      "    \n",
      "    >>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])\n",
      "    >>> np.shape(a)\n",
      "    (2,)\n",
      "    >>> a.shape\n",
      "    (2,)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tablice mogą składać się z danych różnych typów (ale tylko jednego typu danych równocześnie, stąd jednorodność)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3] - typ:  int32\n",
      "[0.1 0.2 0.3] - typ:  float64\n",
      "[1. 2. 3.] - typ:  float64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2, 3])\n",
    "print(x, \"- typ: \", x.dtype)\n",
    "\n",
    "x = np.array([0.1, 0.2, 0.3])\n",
    "print(x, \"- typ: \", x.dtype)\n",
    "\n",
    "x = np.array([1, 2, 3], dtype='float64')\n",
    "print(x, \"- typ: \", x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tworzenie tablic składających się z samych zer lub jedynek umożliwiają funkcje `zeros` oraz `ones`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros([3,4])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.ones([3,4])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podstawowe operacje arytmetyczne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operatory arytmetyczne na tablicach w NumPy działają **element po elemencie**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2. 3. 4.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([3, 4, 5])\n",
    "b = np.ones(3)\n",
    "print(a - b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Za mnożenie macierzy odpowiadają funkcje `dot` i `matmul` (**nie** operator `*`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1, 2], [3, 4]])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[1, 2], [3, 4]])\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4],\n",
       "       [ 9, 16]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b  # mnożenie element po elemencie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 10],\n",
       "       [15, 22]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(a,b)  # mnożenie macierzowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 10],\n",
       "       [15, 22]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matmul(a,b)  # mnożenie macierzowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Przykłady innych operacji dodawania i mnożenia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 5.],\n",
       "       [5., 5.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((2, 2), dtype='float')\n",
    "a += 5\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25., 25.],\n",
       "       [25., 25.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a *= 5\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50., 50.],\n",
       "       [50., 50.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sklejanie tablic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = np.array([4, 5, 6])\n",
    "c = np.array([7, 8, 9])\n",
    "np.hstack([a, b, c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack([a, b, c])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typowe funkcje matematyczne:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.14159265, 4.44288294, 5.44139809, 6.28318531])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(1, 5)\n",
    "np.sqrt(x) * np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(np.e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "x.max() - x.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indeksy i zakresy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tablice jednowymiarowe zachowują sie podobnie do zwykłych list pythonowych."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:10:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tablice wielowymiarowe mają po jednym indeksie na wymiar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(12).reshape(3, 4)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6, 7])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:3, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Warunki"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Warunki pozwalają na selekcję elementów tablicy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 3, 3, 3])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3])\n",
    "a[a > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5], dtype=int64),)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(a < 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(a < 3)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(a > 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pętle i wypisywanie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "[4 5 6 7]\n",
      "[ 8  9 10 11]\n"
     ]
    }
   ],
   "source": [
    "for row in x:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for element in x.flat:\n",
    "    print(element) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Liczby losowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 7, 3, 5])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.7907838 , -0.65971486,  0.0375355 ,  2.00045956,  0.32631216])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(0, 1, 5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.50130054, 1.20710594, 0.45451505, 0.70098876, 0.90371663])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(0, 2, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Macierze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy jest pakietem wykorzystywanym do obliczeń w dziedzinie algebry liniowej, co jeszcze szczególnie przydatne w uczeniu maszynowym. \n",
    "\n",
    "Wektor o wymiarach $1 \\times N$ \n",
    "$$\n",
    "    x =\n",
    "    \\begin{pmatrix}\n",
    "    x_{1}  \\\\\n",
    "    x_{2}  \\\\\n",
    "    \\vdots \\\\\n",
    "    x_{N}\n",
    "    \\end{pmatrix} \n",
    "$$\n",
    "\n",
    "i jego transpozycję $x^\\top = (x_{1}, x_{2},\\ldots,x_{N})$ można wyrazić w Pythonie w następujący sposób:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1, 2, 3]]).T\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xt = x.T\n",
    "xt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Macierz kolumnowa** w NumPy.\n",
    "$$X =\n",
    "    \\begin{pmatrix}\n",
    "    3  \\\\\n",
    "    4  \\\\\n",
    "    5  \\\\\n",
    "    6  \n",
    "    \\end{pmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [4],\n",
       "       [5],\n",
       "       [6]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[3,4,5,6]]).T\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Macierz wierszowa** w NumPy.\n",
    "$$ X =\n",
    "    \\begin{pmatrix}\n",
    "    3 & 4 & 5 & 6\n",
    "    \\end{pmatrix}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5, 6]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[3,4,5,6]])\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oprócz obiektów typu `array` istnieje wyspecjalizowany obiekt `matrix`, dla którego operacje `*` (mnożenie) oraz `**-1` (odwracanie) są określone w sposób właściwy dla macierzy (w przeciwieństwie do operacji elementowych dla obiektów `array`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1,2,3,4,5,6,7,8,9]).reshape(3,3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 6 3]\n",
      " [8 7 1]\n",
      " [3 0 3]]\n"
     ]
    }
   ],
   "source": [
    "y = np.array([4,6,3,8,7,1,3,0,3]).reshape(3,3)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.matrix(x)\n",
    "Y = np.matrix(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4 12  9]\n",
      " [32 35  6]\n",
      " [21  0 27]]\n"
     ]
    }
   ],
   "source": [
    "print(x * y)  # Tablice np.array mnożone są element po elemencie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 29  20  14]\n",
      " [ 74  59  35]\n",
      " [119  98  56]]\n"
     ]
    }
   ],
   "source": [
    "print(X * Y)  # Macierze np.matrix mnożone są macierzowo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 29  20  14]\n",
      " [ 74  59  35]\n",
      " [119  98  56]]\n"
     ]
    }
   ],
   "source": [
    "print(np.matmul(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wyznacznik macierzy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.000000000000014"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[3,-9],[2,5]])\n",
    "np.linalg.det(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Macierz odwrotna**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4, -2],\n",
       "       [ 5,  5]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[-4,-2],[5,5]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.5, -0.2],\n",
       "       [ 0.5,  0.4]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invA = np.linalg.inv(A)\n",
    "invA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(np.dot(A, invA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(ponieważ $AA^{-1} = A^{-1}A = I$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wartości i wektory własne**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 0, 3]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.diag((1, 2, 3))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3.]\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "w, v = np.linalg.eig(a)\n",
    "print(w)  # wartości własne\n",
    "print(v)  # wektory własne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biblioteka PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biblioteka PyTorch została stworzona z myślą o uczeniu maszynowym. Oprócz wykonywania rozmaitych działań matematycznych takich jak te, które można wykonywać w bibliotece NumPy, dostarcza metod przydatnych w uczeniu maszynowym, z których chyba najbardziej charakterystyczną jest automatyczne różniczkowanie (moduł `autograd`).\n",
    "\n",
    "Ale o tym później."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instalacja\n",
    "\n",
    "    pip install torch torchvision\n",
    "\n",
    "lub\n",
    "\n",
    "    conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podstawowym typem danych dla pakietu `pytorch` jest tensor (`torch.tensor`). Tensor to uogólnienie macierzy na dowolną liczbę wymiarów. Można powiedzieć, że macierze są dwuwymiarowymi tensorami."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1, 2, 3],\n",
      "        [4, 5, 6],\n",
      "        [7, 8, 9]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "x = torch.tensor([[1,2,3],[4,5,6],[7,8,9]])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operacje na tensorach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Działania na tensorach w bibliotece PyTorch wykonuje się bardzo podobnie do działań na miacierzach w bibliotece NumPy. Czasami nazwy metod się trochę różnią."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 3])\n",
      "torch.Size([3, 3])\n"
     ]
    }
   ],
   "source": [
    "# Wymiary (rozmiar) tensora\n",
    "\n",
    "print(x.shape)\n",
    "print(x.size())  # Można użyć `size()` zamiast `shape`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1, 2, 3]) - type: torch.int64\n",
      "tensor([0.1000, 0.2000, 0.3000]) - type: torch.float32\n",
      "tensor([1., 2., 3.], dtype=torch.float64) - type: torch.float64\n"
     ]
    }
   ],
   "source": [
    "# Typy elementów\n",
    "\n",
    "x = torch.tensor([1, 2, 3])\n",
    "print(x, \"- type:\", x.dtype)\n",
    "\n",
    "x = torch.tensor([0.1, 0.2, 0.3])\n",
    "print(x, \"- type:\", x.dtype)\n",
    "\n",
    "x = torch.tensor([1, 2, 3], dtype=torch.float64)  # Uwaga: inaczej niż w NumPy\n",
    "print(x, \"- type:\", x.dtype)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.zeros([3,4])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1., 1., 1.],\n",
      "        [1., 1., 1., 1.],\n",
      "        [1., 1., 1., 1.]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.ones([3,4])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.9863, 0.9173, 0.5301, 0.4279],\n",
      "        [0.7708, 0.4671, 0.2965, 0.0578],\n",
      "        [0.6684, 0.4432, 0.9817, 0.1521]])\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand([3,4])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Wiersz 0:\n",
      "0.9863188862800598\n",
      "0.917273998260498\n",
      "0.53009432554245\n",
      "0.42788761854171753\n",
      "\n",
      "Wiersz 1:\n",
      "0.7708230018615723\n",
      "0.46713775396347046\n",
      "0.2964947819709778\n",
      "0.057803571224212646\n",
      "\n",
      "Wiersz 2:\n",
      "0.6684107780456543\n",
      "0.4432327151298523\n",
      "0.9817106127738953\n",
      "0.15205740928649902\n"
     ]
    }
   ],
   "source": [
    "# Iterowanie po elementach tensora\n",
    "\n",
    "for i, row in enumerate(x):\n",
    "    print(f\"\\nWiersz {i}:\")\n",
    "    for element in row:\n",
    "        print(element.item())  # `item()` zamienia jednoelementowy (bezwymiarowy) tensor na liczbę"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1572, 0.4592, 0.7481, 0.6673],\n",
      "        [0.1138, 0.9820, 0.4452, 0.5775],\n",
      "        [0.7510, 0.3174, 0.6937, 0.8904]])\n",
      "tensor([[0.0661, 0.6596, 0.7498, 0.5254],\n",
      "        [0.3271, 0.8968, 0.3188, 0.9255],\n",
      "        [0.2099, 0.5828, 0.4611, 0.6856]])\n",
      "tensor([[0.0738, 0.3776],\n",
      "        [0.2646, 0.5449],\n",
      "        [0.6779, 0.0567],\n",
      "        [0.0348, 0.3072]])\n"
     ]
    }
   ],
   "source": [
    "# Przykładowe macierze\n",
    "\n",
    "A = torch.rand([3, 4])\n",
    "print(A)\n",
    "\n",
    "B = torch.rand([3, 4])\n",
    "print(B)\n",
    "\n",
    "C = torch.rand([4, 2])\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2232, 1.1188, 1.4978, 1.1928],\n",
      "        [0.4409, 1.8788, 0.7640, 1.5029],\n",
      "        [0.9609, 0.9002, 1.1548, 1.5760]])\n",
      "tensor([[ 0.0911, -0.2005, -0.0017,  0.1419],\n",
      "        [-0.2133,  0.0853,  0.1264, -0.3480],\n",
      "        [ 0.5410, -0.2654,  0.2326,  0.2048]])\n",
      "tensor([[0.0104, 0.3029, 0.5609, 0.3506],\n",
      "        [0.0372, 0.8807, 0.1419, 0.5344],\n",
      "        [0.1577, 0.1850, 0.3199, 0.6105]])\n",
      "tensor([[2.3777, 0.6961, 0.9977, 1.2701],\n",
      "        [0.3478, 1.0951, 1.3966, 0.6240],\n",
      "        [3.5770, 0.5446, 1.5044, 1.2988]])\n"
     ]
    }
   ],
   "source": [
    "# Działania \"element po elemencie\"\n",
    "\n",
    "print(A + B)\n",
    "print(A - B)\n",
    "print(A * B)\n",
    "print(A / B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.6635, 0.5570],\n",
      "        [0.5902, 0.7807],\n",
      "        [0.6406, 0.7694]])\n"
     ]
    }
   ],
   "source": [
    "# Mnożenie macierzowe\n",
    "\n",
    "print(torch.matmul(A, C))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Konwersja między PyTorch i NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1572, 0.4592, 0.7481, 0.6673],\n",
      "        [0.1138, 0.9820, 0.4452, 0.5775],\n",
      "        [0.7510, 0.3174, 0.6937, 0.8904]])\n",
      "[[0.15715027 0.45915365 0.7480644  0.66733134]\n",
      " [0.11377418 0.98203135 0.4451999  0.5774748 ]\n",
      " [0.7509776  0.3174067  0.69367564 0.8904279 ]]\n"
     ]
    }
   ],
   "source": [
    "# Konwersja z PyTorch do NumPy\n",
    "\n",
    "print(A)\n",
    "\n",
    "A_numpy = A.numpy()\n",
    "print(A_numpy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.84580006 0.49270934 0.67969751 0.27546956 0.10600392]\n",
      " [0.84610871 0.11680263 0.3535065  0.83725955 0.07995571]\n",
      " [0.4586334  0.64818257 0.53201793 0.77786372 0.8584107 ]]\n",
      "tensor([[0.8458, 0.4927, 0.6797, 0.2755, 0.1060],\n",
      "        [0.8461, 0.1168, 0.3535, 0.8373, 0.0800],\n",
      "        [0.4586, 0.6482, 0.5320, 0.7779, 0.8584]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Konwersja z numpy do PyTorch\n",
    "\n",
    "X = np.random.rand(3, 5)\n",
    "print(X)\n",
    "\n",
    "X_pytorch = torch.from_numpy(X)\n",
    "print(X_pytorch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Przydatne materiały\n",
    "\n",
    " * NumPy - dokumentacja: https://numpy.org/doc/stable\n",
    " * PyTorch - dokumentacja: https://pytorch.org/docs/stable"
   ]
  }
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