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Merge branch 'master' of https://git.wmi.amu.edu.pl/tdwojak/Python2017

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.gitignore vendored Normal file
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# wiki files
Python2017.wiki/*
# Jupyter Files
*/.ipynb_checkpoints/*
# Rope files
.ropeproject
*/.ropeproject
# Labs temp files
labs03/haslo2.txt

2
labs02/task10.py
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@ -13,7 +13,7 @@ def pokemon_speak(text):
def tests(f): def tests(f):
inputs = [['pokemon'], ['do not want'], 'POKEMON'] inputs = [['pokemon'], ['do not want'], ['POKEMON']]
outputs = ['PoKeMoN', 'Do nOt wAnT', 'POKEMON'] outputs = ['PoKeMoN', 'Do nOt wAnT', 'POKEMON']
for input, output in zip(inputs, outputs): for input, output in zip(inputs, outputs):

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labs02/test_task.py
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@ -18,5 +18,4 @@ def tests(f):
break break
return "TESTS PASSED" return "TESTS PASSED"
if __name__ == "__main__": print(tests(suma))
print(tests(suma))

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labs03/Podstawy 2.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Python: podstaw ciąg dalszy\n",
"\n",
"## Tomasz Dwojak\n",
"\n",
"### 2 grudnia 2017"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Co już znamy?\n",
" * podstawowe typy danych i operacje na nich\n",
" * Instrukcje sterujące: ``if``, ``for``\n",
" * pisanie funkcji\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"## Co na dziś?\n",
"\n",
" * ``tuple`` i ``set``,\n",
" * operacje na plikach,\n",
" * coś więcej o funkcjach\n",
" * korzystanie z bibliotek\n",
" * przegląd najważniejszych bibliotek"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"[1, 2, 3, 1, 2, 3]\n",
"123\n"
]
}
],
"source": [
"def dwojak(x): \n",
" x *= 2\n",
" return x\n",
" \n",
"l = [1, 2, 3]\n",
"s = \"123\"\n",
"\n",
"dwojak(l)\n",
"dwojak(s)\n",
"print(dwojak(1))\n",
"print(l)\n",
"print(s)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Mutable i Immutable\n",
"\n",
"### Mutable\n",
" * listy,\n",
" * słowniki,\n",
" * sety,\n",
" * własnoręcznie zdefiniowane klasy.\n",
" \n",
"### Immutable\n",
" * liczby: inty i floaty,\n",
" * napisy,\n",
" * tuple."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Nieoczywistości"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 1, 2, 3]\n",
"F: [1, 2, 3, 1, 2, 3]\n",
"[1, 2, 3]\n"
]
}
],
"source": [
"def dwojak1(x): x *= 2\n",
"def dwojak2(x): \n",
" x = x * 2\n",
" print(\"F:\", x)\n",
"\n",
"l = [1,2, 3]\n",
"dwojak1(l)\n",
"print(l)\n",
"\n",
"l = [1,2, 3]\n",
"dwojak2(l)\n",
"print(l)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3]\n",
"[1, 2, 3, 4]\n"
]
}
],
"source": [
"l = [1, 2, 3]\n",
"e = l[:]\n",
"e.append(4)\n",
"print(l)\n",
"print(e)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1], [1], [1]]\n"
]
}
],
"source": [
"e = []\n",
"f = [e for i in range(3)]\n",
"f[0].append(1)\n",
"print(f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## To może ``tuple``?\n",
" * stały rozmiar,\n",
" * immutable,\n",
" * mogą być kluczami w słownikach"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 'napis', [0])\n",
"3\n"
]
},
{
"ename": "TypeError",
"evalue": "unhashable type: 'list'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-2bd2fa17fbf5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
]
}
],
"source": [
"t = (1, \"napis\", [])\n",
"t[-1].append(0)\n",
"print(t)\n",
"print(len(t))\n",
"print({t: None})"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Funkcje cz. 2"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15\n",
"a == 1\n",
"b == (3, 4)\n"
]
}
],
"source": [
"def suma(*args):\n",
" return sum(args)\n",
"print(suma(1,2,3,4,5))\n",
"\n",
"def greet_me(z=None,**kwargs):\n",
" if kwargs is not None:\n",
" for key, value in kwargs.items():\n",
" print(\"%s == %s\" %(key,value))\n",
"greet_me(a=1, b=(3,4))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Generatory"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"97\n",
"a\n",
"98\n",
"b\n",
"99\n",
"c\n",
"100\n",
"d\n"
]
}
],
"source": [
"def alfaRange(x, y):\n",
" for i in range(ord(x), ord(y)):\n",
" print(i)\n",
" yield chr(i)\n",
"\n",
"for c in alfaRange('a', 'e'):\n",
" print(c)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Operacje na plikach"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W Paryżu najlepsze kasztany są na placu Pigalle\n",
"Zuzanna lubi je tylko jesienią.\n",
"\n",
">>\n"
]
}
],
"source": [
"plik = open(\"haslo.txt\", 'r')\n",
"for linia in plik.readlines():\n",
" print(linia.strip())\n",
"print(plik.read())\n",
"print(\">>\")\n",
"plik.close()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W Paryżu najlepsze kasztany są na placu Pigalle\n",
"\n",
"Zuzanna lubi je tylko jesienią.\n",
"\n"
]
},
{
"ename": "ValueError",
"evalue": "I/O operation on closed file.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-47-f06513c1bbec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlinia\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mplik\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinia\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplik\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
]
}
],
"source": [
"with open(\"haslo.txt\", 'r') as plik:\n",
" for linia in plik.readlines():\n",
" print(linia)\n",
"print(plik.read())"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"with open(\"haslo2.txt\", 'w') as plik:\n",
" for word in ('corect', 'horse', 'battery', 'staple'):\n",
" plik.write(word)\n",
" plik.write('\\n')\n",
"with open(\"haslo2.txt\", 'w+') as plik:\n",
" plik.writelines([' '.join(('corect', 'horse', 'battery', 'staple'))])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Korzystanie z modułów"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Importowanie"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"posix\n",
"Nazwa uzytkownika: tomaszd\n"
]
}
],
"source": [
"import os\n",
"print(os.name)\n",
"\n",
"from os import getenv\n",
"print('Nazwa uzytkownika: {}'.format(getenv(\"USER\")))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({'o': 4, 'n': 4, 'a': 4, 'k': 3, 't': 3, 'y': 2, 'i': 2, 'c': 2, 'z': 2, 's': 1, 'p': 1, 'l': 1, 'ń': 1, 'w': 1, 'e': 1})\n"
]
},
{
"data": {
"text/plain": [
"array([[ 1., 3., 4., 5.]], dtype=float32)"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import *\n",
"print(Counter(\"konstantynopolitańczykowianeczka\"))\n",
"\n",
"import numpy as np\n",
"np.array([[1, 3, 4, 5]], dtype='float32')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Instalacja\n",
"\n",
" * lokalnie (per użytkownik) lub globalnie\n",
" * pyCharm lub linia komend, np. ``pip install --user flask`` lub ``python -m pip install --user flask``"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Wczytywanie z klawiatury"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What's your name?\n",
"Tomasz\n",
"Welcome home, Tomasz.\n"
]
}
],
"source": [
"name = input(\"What's your name?\\n\")\n",
"print(\"Welcome home, {}.\".format(name))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"liczby = eval(input(\"Podaj liczby\"))\n",
"print(sum(liczby))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Co ominąłem?\n",
"\n",
" * klasy (jutro)\n",
" * podział programu na pliki (jutro)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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# Laboratoria 3
## Zadania
**ćwiczenie 0**
Sklonuj repozytorium ``https://github.com/realpython/python-scripts``, które różne, przydatne skrypty. Przejrzyj je i zobacz na ile jesteś w stanie zrozumieć co i jak robią. Uruchom kilka z nich, np. ``27_send_sms.py``.
**ćwiczenie 1**
Każdy obiekt w Pythonie na wbudowaną funkcję ``id()``, która zwraca liczbę, która jest unikatowa i stała dla obiektu. Pozwala ona w prosty sposób sprawdzić, który obiekt jest *mutable*a, który *immutable*: jeżeli po wykonaniu operacji, zwracana liczba jest stała, to oznacza, że obiekt jest *mutable*. Sprawdź zachowanie funkcji na obiektach typy:
* lista,
* napis (string),
* liczba zmiennoprzecinkowa.
**ćwiczenie 2**
Napisz generator, który będzie zwracać ``n`` kolejnych liczb ciągu Fibonacciego (``F(0)=1, F(1)=1, FN=F(N-1) + F(N-2)``).
**ćwiczenie 3**
Strona ``https://api.fixer.io/latest`` udostępnia kursy różnych walut w stosunku do euro. Napisz skrypt, który:
* pobierze zawartość JSONa. Wykorzystaj bibliotekę ``requests`` (http://docs.python-requests.org/en/master/).
* korzystając z biblioteki ``json`` przekształć go do obiketu typu JSON.
* Wyświetl wartość kursu EUR do PLN.
**ćwiczenie 4**
Zainstaluj bibliotekę ``weather-api`` (https://pypi.python.org/pypi/weather-api). Korzystając z niej:
* Wypisz informacje o aktualnej pogodzie.
* Napisz funkcję, która zamieni stopnie ``F`` na ``C``.
* Korzystając z prognozy, znajdź dzień, w którym będzie najzimniej. Wypisz nazwę tygodnia (w języku polskim) i temperaturę w C.
**ćwiczenie 5**
Katalog scores zawiera 64 pliki tekstowe, które posiadają informacje o wysokości miary ``BLEU`` na różnych etapach trenowania modelu. Nazwa każdego pliku na postać ``model.iterXXXXXXX.npz.bleu``, gdzie ``XXXXXXX``, to liczba iteracji.Zawartość każdego pliku jest podobna i ma następującą formę: *BLEU = YY.YY, 44.4/18.5/9.3/5.0 (BP=1.000, ratio=1.072, hyp_len=45976, ref_len=42903)*, gdzie ``YY.YY`` to wartość miary ``BLEU``. Znajdź plik, który zawiera najwyższą wartość miary ``BLEU``.
* Wykorzystaj bibliotekę ``glob`` (https://docs.python.org/2/library/glob.html)
* Wyświetl tylko pełną nazwe pliku (wraz z ścieżką).

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W Paryżu najlepsze kasztany są na placu Pigalle
Zuzanna lubi je tylko jesienią.

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BLEU = 2.02, 17.1/3.6/1.0/0.3 (BP=1.000, ratio=1.872, hyp_len=80326, ref_len=42903)

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BLEU = 13.99, 44.4/18.5/9.3/5.0 (BP=1.000, ratio=1.072, hyp_len=45976, ref_len=42903)

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BLEU = 14.35, 44.9/19.0/9.6/5.2 (BP=1.000, ratio=1.087, hyp_len=46657, ref_len=42903)

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BLEU = 15.75, 47.1/20.5/10.7/6.0 (BP=1.000, ratio=1.030, hyp_len=44211, ref_len=42903)

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BLEU = 15.96, 47.7/20.8/10.9/6.0 (BP=1.000, ratio=1.029, hyp_len=44160, ref_len=42903)

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BLEU = 15.42, 46.8/20.3/10.5/5.7 (BP=1.000, ratio=1.043, hyp_len=44729, ref_len=42903)

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BLEU = 15.84, 47.3/20.7/10.8/5.9 (BP=1.000, ratio=1.034, hyp_len=44374, ref_len=42903)

1
labs03/scores/model.iter160000.npz.bleu Normal file
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BLEU = 15.99, 47.7/20.8/10.9/6.0 (BP=1.000, ratio=1.031, hyp_len=44233, ref_len=42903)

1
labs03/scores/model.iter170000.npz.bleu Normal file
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BLEU = 16.15, 47.9/21.0/11.0/6.1 (BP=1.000, ratio=1.027, hyp_len=44065, ref_len=42903)

1
labs03/scores/model.iter180000.npz.bleu Normal file
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BLEU = 15.86, 47.1/20.8/10.8/6.0 (BP=1.000, ratio=1.053, hyp_len=45191, ref_len=42903)

1
labs03/scores/model.iter190000.npz.bleu Normal file
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BLEU = 16.07, 47.7/21.0/11.0/6.0 (BP=1.000, ratio=1.044, hyp_len=44795, ref_len=42903)

1
labs03/scores/model.iter20000.npz.bleu Normal file
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BLEU = 5.87, 31.1/8.9/3.3/1.3 (BP=1.000, ratio=1.155, hyp_len=49533, ref_len=42903)

1
labs03/scores/model.iter200000.npz.bleu Normal file
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BLEU = 16.82, 48.9/21.7/11.5/6.6 (BP=0.998, ratio=0.998, hyp_len=42837, ref_len=42903)

1
labs03/scores/model.iter210000.npz.bleu Normal file
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BLEU = 16.70, 48.7/21.7/11.5/6.4 (BP=1.000, ratio=1.017, hyp_len=43622, ref_len=42903)

1
labs03/scores/model.iter220000.npz.bleu Normal file
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BLEU = 16.85, 48.9/21.9/11.7/6.5 (BP=1.000, ratio=1.020, hyp_len=43777, ref_len=42903)

1
labs03/scores/model.iter230000.npz.bleu Normal file
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BLEU = 16.59, 48.3/21.6/11.4/6.3 (BP=1.000, ratio=1.029, hyp_len=44127, ref_len=42903)

1
labs03/scores/model.iter240000.npz.bleu Normal file
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BLEU = 16.94, 48.6/22.0/11.7/6.6 (BP=1.000, ratio=1.038, hyp_len=44517, ref_len=42903)

1
labs03/scores/model.iter250000.npz.bleu Normal file
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BLEU = 17.52, 49.7/22.5/12.2/6.9 (BP=1.000, ratio=1.003, hyp_len=43053, ref_len=42903)

1
labs03/scores/model.iter260000.npz.bleu Normal file
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BLEU = 16.82, 48.6/21.9/11.6/6.5 (BP=1.000, ratio=1.037, hyp_len=44475, ref_len=42903)

1
labs03/scores/model.iter270000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.16, 49.2/22.3/11.9/6.6 (BP=1.000, ratio=1.025, hyp_len=43965, ref_len=42903)

1
labs03/scores/model.iter280000.npz.bleu Normal file
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BLEU = 16.92, 49.4/22.1/11.7/6.4 (BP=1.000, ratio=1.013, hyp_len=43453, ref_len=42903)

1
labs03/scores/model.iter290000.npz.bleu Normal file
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BLEU = 17.20, 49.2/22.1/11.9/6.8 (BP=1.000, ratio=1.016, hyp_len=43578, ref_len=42903)

1
labs03/scores/model.iter30000.npz.bleu Normal file
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BLEU = 8.17, 33.9/11.6/5.0/2.3 (BP=1.000, ratio=1.207, hyp_len=51768, ref_len=42903)

1
labs03/scores/model.iter300000.npz.bleu Normal file
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BLEU = 17.31, 49.2/22.2/12.0/6.8 (BP=1.000, ratio=1.017, hyp_len=43642, ref_len=42903)

1
labs03/scores/model.iter310000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.57, 49.5/22.5/12.3/7.0 (BP=1.000, ratio=1.000, hyp_len=42900, ref_len=42903)

1
labs03/scores/model.iter320000.npz.bleu Normal file
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BLEU = 17.68, 49.6/22.6/12.3/7.1 (BP=1.000, ratio=1.013, hyp_len=43465, ref_len=42903)

1
labs03/scores/model.iter330000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.02, 48.3/21.9/11.8/6.7 (BP=1.000, ratio=1.044, hyp_len=44801, ref_len=42903)

1
labs03/scores/model.iter340000.npz.bleu Normal file
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BLEU = 17.51, 49.7/22.7/12.2/6.9 (BP=1.000, ratio=1.011, hyp_len=43368, ref_len=42903)

1
labs03/scores/model.iter350000.npz.bleu Normal file
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BLEU = 17.68, 50.3/22.9/12.4/7.0 (BP=0.995, ratio=0.995, hyp_len=42702, ref_len=42903)

1
labs03/scores/model.iter360000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.43, 49.0/22.4/12.2/6.9 (BP=1.000, ratio=1.040, hyp_len=44629, ref_len=42903)

1
labs03/scores/model.iter370000.npz.bleu Normal file
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BLEU = 17.16, 49.2/22.2/11.9/6.7 (BP=1.000, ratio=1.028, hyp_len=44085, ref_len=42903)

1
labs03/scores/model.iter380000.npz.bleu Normal file
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BLEU = 17.04, 49.1/22.2/11.8/6.6 (BP=1.000, ratio=1.030, hyp_len=44200, ref_len=42903)

1
labs03/scores/model.iter390000.npz.bleu Normal file
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BLEU = 17.77, 49.8/22.8/12.4/7.1 (BP=1.000, ratio=1.008, hyp_len=43258, ref_len=42903)

1
labs03/scores/model.iter40000.npz.bleu Normal file
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BLEU = 8.55, 32.5/11.9/5.3/2.6 (BP=1.000, ratio=1.341, hyp_len=57542, ref_len=42903)

1
labs03/scores/model.iter400000.npz.bleu Normal file
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BLEU = 17.96, 50.0/23.0/12.6/7.2 (BP=1.000, ratio=1.002, hyp_len=43009, ref_len=42903)

1
labs03/scores/model.iter410000.npz.bleu Normal file
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BLEU = 17.66, 49.6/22.6/12.3/7.0 (BP=1.000, ratio=1.019, hyp_len=43697, ref_len=42903)

1
labs03/scores/model.iter420000.npz.bleu Normal file
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BLEU = 17.93, 49.8/23.1/12.6/7.2 (BP=1.000, ratio=1.021, hyp_len=43824, ref_len=42903)

1
labs03/scores/model.iter430000.npz.bleu Normal file
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BLEU = 16.55, 47.0/21.3/11.5/6.5 (BP=1.000, ratio=1.071, hyp_len=45947, ref_len=42903)

1
labs03/scores/model.iter440000.npz.bleu Normal file
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BLEU = 17.59, 49.8/22.7/12.2/6.9 (BP=1.000, ratio=1.009, hyp_len=43301, ref_len=42903)

1
labs03/scores/model.iter450000.npz.bleu Normal file
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BLEU = 17.41, 49.2/22.5/12.1/6.9 (BP=1.000, ratio=1.023, hyp_len=43894, ref_len=42903)

1
labs03/scores/model.iter460000.npz.bleu Normal file
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BLEU = 17.52, 49.9/22.7/12.2/6.8 (BP=1.000, ratio=1.005, hyp_len=43127, ref_len=42903)

1
labs03/scores/model.iter470000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.30, 49.1/22.3/12.0/6.8 (BP=1.000, ratio=1.024, hyp_len=43917, ref_len=42903)

1
labs03/scores/model.iter480000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.21, 48.8/22.1/12.0/6.8 (BP=1.000, ratio=1.036, hyp_len=44454, ref_len=42903)

1
labs03/scores/model.iter490000.npz.bleu Normal file
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BLEU = 17.79, 50.0/22.9/12.4/7.1 (BP=1.000, ratio=1.000, hyp_len=42891, ref_len=42903)

1
labs03/scores/model.iter50000.npz.bleu Normal file
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BLEU = 11.03, 39.5/15.1/7.1/3.5 (BP=1.000, ratio=1.116, hyp_len=47860, ref_len=42903)

1
labs03/scores/model.iter500000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.25, 49.4/22.3/12.0/6.7 (BP=1.000, ratio=1.014, hyp_len=43517, ref_len=42903)

1
labs03/scores/model.iter510000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.78, 50.0/22.8/12.4/7.1 (BP=1.000, ratio=1.000, hyp_len=42890, ref_len=42903)

1
labs03/scores/model.iter520000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.68, 49.8/22.7/12.4/7.0 (BP=1.000, ratio=1.003, hyp_len=43021, ref_len=42903)

1
labs03/scores/model.iter530000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.31, 48.9/22.3/12.1/6.8 (BP=1.000, ratio=1.032, hyp_len=44262, ref_len=42903)

1
labs03/scores/model.iter540000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.92, 49.8/22.9/12.5/7.2 (BP=1.000, ratio=1.015, hyp_len=43562, ref_len=42903)

1
labs03/scores/model.iter550000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.84, 50.0/22.9/12.5/7.1 (BP=1.000, ratio=1.011, hyp_len=43389, ref_len=42903)

1
labs03/scores/model.iter560000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.75, 49.8/22.8/12.4/7.1 (BP=1.000, ratio=1.014, hyp_len=43494, ref_len=42903)

1
labs03/scores/model.iter570000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.94, 50.0/23.1/12.6/7.1 (BP=1.000, ratio=1.013, hyp_len=43442, ref_len=42903)

1
labs03/scores/model.iter580000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 18.12, 50.3/23.1/12.7/7.3 (BP=1.000, ratio=1.004, hyp_len=43077, ref_len=42903)

1
labs03/scores/model.iter590000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.29, 48.8/22.3/12.1/6.8 (BP=1.000, ratio=1.042, hyp_len=44688, ref_len=42903)

1
labs03/scores/model.iter60000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 11.92, 40.2/16.1/7.8/4.0 (BP=1.000, ratio=1.144, hyp_len=49071, ref_len=42903)

1
labs03/scores/model.iter600000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.46, 49.5/22.5/12.2/6.8 (BP=1.000, ratio=1.026, hyp_len=44035, ref_len=42903)

1
labs03/scores/model.iter610000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 18.19, 50.3/23.2/12.7/7.4 (BP=1.000, ratio=1.007, hyp_len=43221, ref_len=42903)

1
labs03/scores/model.iter620000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 17.84, 50.1/22.9/12.5/7.1 (BP=1.000, ratio=1.016, hyp_len=43604, ref_len=42903)

1
labs03/scores/model.iter630000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 18.30, 50.8/23.4/12.9/7.5 (BP=0.994, ratio=0.994, hyp_len=42632, ref_len=42903)

1
labs03/scores/model.iter640000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 0, 0/0/0/0 (BP=0, ratio=0, hyp_len=0, ref_len=0)

1
labs03/scores/model.iter70000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 12.77, 42.4/17.1/8.4/4.4 (BP=1.000, ratio=1.096, hyp_len=47008, ref_len=42903)

1
labs03/scores/model.iter80000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 14.43, 46.7/19.4/9.7/5.2 (BP=0.988, ratio=0.988, hyp_len=42376, ref_len=42903)

1
labs03/scores/model.iter90000.npz.bleu Normal file
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@ -0,0 +1 @@
BLEU = 13.85, 44.1/18.5/9.2/4.9 (BP=1.000, ratio=1.092, hyp_len=46859, ref_len=42903)

662
labs04/Klasy.ipynb Normal file
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@ -0,0 +1,662 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Wprowadzenie do Pythona: Klasy\n",
"\n",
"## Tomasz Dwojak\n",
"\n",
"### 3 grudnia 2017"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Plan na dziś:\n",
" * klasy,\n",
" * wyjątki."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Python jest językiem obiektowym \n",
" * Wszystko jest obiektem: liczby, napisy, None, funkcje, moduły (biblioteki)..."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"Python da się lubić !\n"
]
}
],
"source": [
"print((2017).imag)\n",
"print(' '.join(['Python', 'da', 'się', 'lubić', '!']))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Konstrukcja"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class '__main__.NajprostszaKlasa'>\n"
]
}
],
"source": [
"class NajprostszaKlasa:\n",
" pass\n",
"\n",
"nasza_klasa = NajprostszaKlasa() # Uwaga na nawiasy na końcu!\n",
"print(type(nasza_klasa))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## (Pseudo) Konstruktor \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n"
]
}
],
"source": [
"class Punkt:\n",
" def __init__(self, x, y):\n",
" self.x = x\n",
" self.y = y\n",
"\n",
"punkt = Punkt(2, 3)\n",
"print(punkt.x)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class Figura:\n",
" def __init__(self, vertexes):\n",
" self.vertexes = vertexes\n",
" \n",
" def liczba_wierzcholkow(self):\n",
" return len(self.vertexes)\n",
" \n",
" def dodaj_wierzcholek(self, x):\n",
" self.vertexes.append(x)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class Prostokat(Figura):\n",
" def __init__(self, vertexes):\n",
" super().__init__(vertexes)\n",
" \n",
" def czy_jestem_kwadratem(self):\n",
" pass\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class Figura:\n",
" def __init__(self, vertexes):\n",
" self.vertexes = vertexes\n",
" \n",
" def liczba_wierzcholkow(self):\n",
" return len(self.vertexes)\n",
" \n",
" def __len__(self):\n",
" return self.liczba_wierzcholkow()\n",
" \n",
"len(Figura([Punkt(2,3), Punkt(3,4), Punkt(0, 0)]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Dobre praktyki: komentarze"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"class Punkt(object):\n",
" \"\"\"Klasa reprezentująca punkt w 2D.\"\"\"\n",
" def __init__(self, x, y):\n",
" \"\"\"opis argumentów x i y.\"\"\"\n",
" self.x = x\n",
" self.y = y"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on class Punkt in module __main__:\n",
"\n",
"class Punkt(builtins.object)\n",
" | Klasa reprezentująca punkt w 2D.\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, x, y)\n",
" | opis argumentów x i y.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors defined here:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
"\n"
]
}
],
"source": [
"help(Punkt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Enkapsulacja: publiczne czy prywatne?\n",
" * Prywatne zaczynają się od dwóch podkreśleń: ``__``, np. ``def __policz(self)``\n",
" * chronione tylko w konwencji, zaczynają się od '\\_', np. ``def _parse(self)``\n",
" * publiczne jest wszystko co nie zaczyna się od '\\_'."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Parser' object has no attribute '__parse'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-80ee186598d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Parser' object has no attribute '__parse'"
]
}
],
"source": [
"class Parser(object):\n",
" def __parse(self): pass\n",
" def _get(self): pass\n",
"parser = Parser()\n",
"parser._get()\n",
"parser.__parse()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Iteratory"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<__main__.Punkt object at 0x7f728015b358>\n",
"<__main__.Punkt object at 0x7f728015b4a8>\n",
"<__main__.Punkt object at 0x7f728015b438>\n"
]
}
],
"source": [
"class Figura:\n",
" def __init__(self, vertexes):\n",
" self.vertexes = vertexes \n",
" \n",
" def __iter__(self):\n",
" self.index = -1\n",
" return self\n",
" \n",
" def __next__(self):\n",
" self.index += 1\n",
" if self.index == len(self.vertexes):\n",
" raise StopIteration\n",
" return self.vertexes[self.index]\n",
" \n",
"for v in Figura([Punkt(2,3), Punkt(3,4), Punkt(0, 0)]):\n",
" print(v)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Atrybuty i metody statyczne"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"0\n"
]
}
],
"source": [
"class Klasa:\n",
" atrybut = 0\n",
"\n",
"klasa = Klasa()\n",
"print(Klasa.atrybut)\n",
"print(klasa.atrybut)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Jestem statyczna!\n"
]
}
],
"source": [
"class Klasa:\n",
" def __init__(self):\n",
" self.t = 0\n",
" def metoda():\n",
" print(\"Jestem statyczna!\")\n",
"\n",
"Klasa.metoda()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Wyjątki"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'nieistniejący_plik.txt'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-41928d542bef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nieistniejący_plik.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplik\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplik\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'nieistniejący_plik.txt'"
]
}
],
"source": [
"with open(\"nieistniejący_plik.txt\") as plik:\n",
" content = plik.read()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"try:\n",
" with open(\"nieistniejący_plik.txt\") as plik:\n",
" content = plik.read()\n",
"except FileNotFoundError:\n",
" contenct = \"\""
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning [Errno 2] No such file or directory: 'nieistniejący_plik.txt'\n"
]
}
],
"source": [
"try:\n",
" with open(\"nieistniejący_plik.txt\") as plik:\n",
" content = plik.read()\n",
"except FileNotFoundError as e:\n",
" print(\"Warning {}\".format(e))\n",
" contenct = \"\""
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning [Errno 2] No such file or directory: 'nieistniejący_plik.txt'\n"
]
}
],
"source": [
"try:\n",
" with open(\"nieistniejący_plik.txt\") as plik:\n",
" content = plik.read()\n",
"except Exception as e:\n",
" print(\"Warning {}\".format(e))\n",
" contenct = \"\""
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"try:\n",
" with open(\"nieistniejący_plik.txt\") as plik:\n",
" content = plik.read()\n",
"except:\n",
" contenct = \"\""
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class Figura:\n",
" def __init__(self, vertexes):\n",
" if len(vertexes) == 0:\n",
" raise Exception(\"Empty list of vertexes\")\n",
" self.vertexes = vertexes"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"class MyError(Exception):\n",
" def __init__(self, text):\n",
" self.text = text\n",
" def __str__(self):\n",
" return self.text"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"ename": "MyError",
"evalue": "Coś poszło nie tak!",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mMyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-36-4fb306b42ebc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Coś poszło nie tak!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mMyError\u001b[0m: Coś poszło nie tak!"
]
}
],
"source": [
"raise MyError(\"Coś poszło nie tak!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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**ćwiczenie 1**
Napisz funckję ``is_numeric``, która sprawdzi, czy każdy element z przekazanej listy jest typu int lub float. Wykorzystaj funcję ``isinstance()`` (https://docs.python.org/2/library/functions.html#isinstance).
**ćwiczenie 2**
Napisz prostą hierarchię klas:
* Klasa bazowa ``Employee``, która będzie zawierać informacje o imieniu i nazwisku pracownika. Ponadto każdy pracownik otrzyma numer ``id``, który będzie unikatowy. Wykorzystaj do tego atrybut statyczny. Napisz metodę ``get_id``, która zwraca identyfikator pracownika.
* Klasy pochodna: ``Recruiter``, która ma dodatkową mtodę ``recruit``, która jako parament przyjmuje obiekt ``Employee`` i zapisuje jego ``id`` w liście ``self.recruited``.
* Klasa pochodna ``Programmer``. Klasa ``Programmer`` ma przyjąć w konstruktorze podstawowe informacje (imię i nazwisko) oraz obiekt rekturera. Ponadto stwórz atrybut ``recruiter``, który będzie przechowywać ``id`` rekrutera.
**ćwiczenie 3 (zadanie domowe) **
Stwórz klasę ``Point``, która będzie reprezentować punkt w przestrzeni wielowymiarowej:
* Konstruktor ma przyjąc tylko 1 parametr: listę współrzednych. Wykorzystaj funkcję z pierwszego zadania, żeby sprawdzić, czy lista zawiera wyłącznie liczby.
* Napisz metodę add, która dida dwa punkty po współrzędnych i zwróci obiekt typu ``Punkt``. Zaimplementuj własny wyjątek ``DimensionError``, który zostaje wyrzucony, jeżeli dodawany punkt ma inny wymiar.
* Napisz metodę ``to\_string``, która zwróci łancuch znakowy, który w czytelny sposób przedstawi punkt.
* Napisz metodę __len__, która zwróci liczbę współrzędnych punktu. Zobacz, czy możesz teraz wywołać funkcję len na obiekcie typy punkt.
* Napisz metodę __str__, która bedzie działać dokładnie tak samo jak metoda ``to_string``. Wyświetl obiekt typy Point korzystając z funkcji print.

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#!/usr/bin/env python2
# -*- coding: utf-8 -*-

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#!/usr/bin/env python2
# -*- coding: utf-8 -*-

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#!/usr/bin/env python2
# -*- coding: utf-8 -*-