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Kilokem
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. feladat\n",
"\n",
"Hozzunk létre egy 3×3-as, csupa True logikai értéket tartalmazó, NumPy tömböt!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, True, True],\n",
" [ True, True, True],\n",
" [ True, True, True]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. megoldás\n",
"np.array([[True] * 3] * 3)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, True, True],\n",
" [ True, True, True],\n",
" [ True, True, True]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2. megoldás\n",
"np.ones((3, 3), dtype='bool')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. feladat\n",
"\n",
"Írjuk ki a páratlan elemeket az alábbi 2 dimenziós NumPy tömbben!"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3 5 7]\n"
]
}
],
"source": [
"b = np.array([[2, 3, 4], [5, 6, 7]])\n",
"print(b[b % 2 == 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. feladat\n",
"\n",
"Írjuk ki a 3-nál nagyobb elemeket az alábbi 2 dimenziós NumPy tömbben!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4 5 6 7]\n"
]
}
],
"source": [
"b = np.array([[2, 3, 4], [5, 6, 7]])\n",
"print(b[b > 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. feladat\n",
"\n",
"Írjuk ki az átlag feletti elemeket az alábbi NumPy tömbben!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10 8 9]\n"
]
}
],
"source": [
"b = np.array([1, 10, 2, 8, 3, 5, 9])\n",
"print(b[b > b.mean()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. feladat\n",
"\n",
"Készítsünk programot, amely egyenletes eloszlás szerint generál 1000 db térbeli pontot az egységkockában, majd meghatározza az 1. ponttól legtávolabb eső pontot!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.37454012, 0.95071431, 0.73199394],\n",
" [0.59865848, 0.15601864, 0.15599452],\n",
" [0.05808361, 0.86617615, 0.60111501],\n",
" ...,\n",
" [0.80000348, 0.55270708, 0.39655368],\n",
" [0.13171503, 0.86529576, 0.15727321],\n",
" [0.30978786, 0.29004553, 0.87141403]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pontok generálása\n",
"rs = np.random.RandomState(42)\n",
"P = rs.uniform(size=(1000, 3))\n",
"P"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.89633582 0.01300192 0.08550853]\n"
]
}
],
"source": [
"# legtávolabbi pont kiszámítása, 1. megoldás\n",
"\n",
"p = P[0]\n",
"dmax = 0\n",
"for pi in P:\n",
" act = ((p[0] - pi[0])**2 + (p[1] - pi[1])**2 +(p[2] - pi[2])**2)**0.5\n",
" if act > dmax:\n",
" dmax = act\n",
" pmax = pi\n",
"print(pmax)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.89633582, 0.01300192, 0.08550853])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# legtávolabbi pont kiszámítása, 2. megoldás (vektorizálással)\n",
"\n",
"# P: 1000 3\n",
"# p: - 3\n",
"\n",
"P[((P - p)**2).sum(axis=1).argmax()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6. feladat\n",
"\n",
"A [baseball.txt](baseball.txt) szövegfájl professzionális baseball játékosok testmagasságáról és testsúlyáról tartalmaz adatokat. Készítsünk programot, amely lineáris modellt ad a testsúly testmagasságból történő előrejelzésére! Részfeladatok:\n",
"- Határozzuk meg a minimális SSE (sum squared error) hibát adó modellparamétert!\n",
"- Adjunk becslést egy 180 cm magas játékos testsúlyára!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"linreg_1d.jpg\" width=\"600\"></img>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[188., 82.],\n",
" [188., 98.],\n",
" [183., 95.],\n",
" ...,\n",
" [190., 93.],\n",
" [190., 86.],\n",
" [185., 88.]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Adatok betöltése.\n",
"data = np.genfromtxt('baseball.txt', delimiter=',')\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# bemeneti vektor\n",
"x = data[:, 0]\n",
"\n",
"# célvektor\n",
"y = data[:, 1]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# átlag kivonása\n",
"xm = x.mean()\n",
"ym = y.mean()\n",
"x -= xm\n",
"y -= ym"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.92836399, 0.92836399, -4.07163601, ..., 2.92836399,\n",
" 2.92836399, -2.07163601])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-9.50338819, 6.49661181, 3.49661181, ..., 1.49661181,\n",
" -5.50338819, -3.50338819])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8561919786085497"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Optimális modellparaméter.\n",
"w = (x @ y) / (x @ x)\n",
"w"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"85.4487101609531"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Becslés egy 180 cm magas játékos testsúlyára.\n",
"(180 - xm) * w + ym"
]
}
],
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}