ASPP 2024 material
This commit is contained in:
Pietro Berkes
commit
1f6bc07c51
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210
exercises/anagrams/.ipynb_checkpoints/anagrams-checkpoint.ipynb
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210
exercises/anagrams/.ipynb_checkpoints/anagrams-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "245d99ab",
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"metadata": {},
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"source": [
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"# Exercise: Find the anagrams for all words in a list"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1e46c4a",
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"metadata": {},
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"source": [
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"* You are given an English dictionary containing M words (“the dictionary”), and a separate list of N words (“the input”, saved in the file `words_to_search.txt`)\n",
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"* For each word in the input, find all the anagrams in the dictionary (e.g., for input 'acme' the anagrams are `['acme', 'came', 'mace']`)\n",
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"\n",
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"How to proceed?\n",
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"1. Write an algorithm to find all anagrams for one input word first\n",
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"2. What is the Big-O class of this algorithm when executed the full N-words input?\n",
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"3. Is there a way to pre-process the dictionary to improve the Big-O performance?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b9b9cecd",
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"metadata": {},
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"source": [
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"# 1. Load the system dictionary and the input words"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "24070a26",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the system dictionary\n",
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"with open('/usr/share/dict/words', 'r') as f:\n",
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" dict_words = [w.strip() for w in f.readlines()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "4002fcdd",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['A',\n",
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" 'a',\n",
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" 'aa',\n",
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" 'aal',\n",
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" 'aalii',\n",
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" '...',\n",
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" 'zythem',\n",
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" 'Zythia',\n",
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" 'zythum',\n",
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" 'Zyzomys',\n",
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" 'Zyzzogeton']"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Print the start and end of the dictionary\n",
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"dict_words[:5] + ['...'] + dict_words[-5:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "823537ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the input words\n",
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"with open('words_to_search.txt', 'r') as f:\n",
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" words = [w.strip() for w in f.readlines()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "4ccec6a3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['acer',\n",
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" 'acers',\n",
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" 'aces',\n",
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" 'aches',\n",
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" 'acme',\n",
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" '...',\n",
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" 'yap',\n",
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" 'yaw',\n",
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" 'yea',\n",
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" 'zendo',\n",
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" 'zoned']"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Print the start and end of the input list\n",
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"words[:5] + ['...'] + words[-5:]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "14d91685",
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"metadata": {},
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"source": [
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"# 2. Look for the anagrams of one input word, e.g. \"organ\"\n",
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"\n",
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"* There are several anagrams, including \"groan\" and \"argon\".\n",
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"\n",
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"* What is the Big-O performance oh your algorithm? In terms of M, the number of words in the dictionary, and K, the number of letters in a word"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "badf44c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"word = 'organ'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ef938d95",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "7cd1196c",
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"metadata": {},
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"source": [
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"The performance of this implementation is ... ."
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]
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},
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{
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"cell_type": "markdown",
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"id": "115c3219",
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"metadata": {},
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"source": [
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"# 3. Look for the anagrams of the words in the input list\n",
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"\n",
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"* How does the Big-O performance of your one-word implementation scale to an input list of M words?\n",
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"* Is there a way to pre-process the dictionary words in a data structure that is better suited for this task?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "03ce3e28",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a2fc5ec4",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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File diff suppressed because it is too large
Load diff
210
exercises/anagrams/anagrams.ipynb
Normal file
210
exercises/anagrams/anagrams.ipynb
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@ -0,0 +1,210 @@
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{
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||||
"cells": [
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{
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"cell_type": "markdown",
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"id": "245d99ab",
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||||
"metadata": {},
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"source": [
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"# Exercise: Find the anagrams for all words in a list"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1e46c4a",
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"metadata": {},
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"source": [
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"* You are given an English dictionary containing M words (“the dictionary”), and a separate list of N words (“the input”, saved in the file `words_to_search.txt`)\n",
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"* For each word in the input, find all the anagrams in the dictionary (e.g., for input 'acme' the anagrams are `['acme', 'came', 'mace']`)\n",
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"\n",
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"How to proceed?\n",
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"1. Write an algorithm to find all anagrams for one input word first\n",
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"2. What is the Big-O class of this algorithm when executed the full N-words input?\n",
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"3. Is there a way to pre-process the dictionary to improve the Big-O performance?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b9b9cecd",
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||||
"metadata": {},
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||||
"source": [
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"# 1. Load the system dictionary and the input words"
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]
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},
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{
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"cell_type": "code",
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||||
"execution_count": 7,
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||||
"id": "24070a26",
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||||
"metadata": {},
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||||
"outputs": [],
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"source": [
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"# Load the system dictionary\n",
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"with open('/usr/share/dict/words', 'r') as f:\n",
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" dict_words = [w.strip() for w in f.readlines()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "4002fcdd",
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||||
"metadata": {
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||||
"scrolled": true
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||||
},
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||||
"outputs": [
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||||
{
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||||
"data": {
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||||
"text/plain": [
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"['A',\n",
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" 'a',\n",
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" 'aa',\n",
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" 'aal',\n",
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" 'aalii',\n",
|
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" '...',\n",
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" 'zythem',\n",
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" 'Zythia',\n",
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" 'zythum',\n",
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" 'Zyzomys',\n",
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" 'Zyzzogeton']"
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]
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},
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"execution_count": 8,
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||||
"metadata": {},
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||||
"output_type": "execute_result"
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}
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],
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"source": [
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"# Print the start and end of the dictionary\n",
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"dict_words[:5] + ['...'] + dict_words[-5:]"
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": 13,
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"id": "823537ef",
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||||
"metadata": {},
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||||
"outputs": [],
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"source": [
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"# Load the input words\n",
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"with open('words_to_search.txt', 'r') as f:\n",
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" words = [w.strip() for w in f.readlines()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "4ccec6a3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['acer',\n",
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" 'acers',\n",
|
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" 'aces',\n",
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" 'aches',\n",
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" 'acme',\n",
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" '...',\n",
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" 'yap',\n",
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" 'yaw',\n",
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" 'yea',\n",
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" 'zendo',\n",
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" 'zoned']"
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]
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},
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"execution_count": 14,
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"metadata": {},
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||||
"output_type": "execute_result"
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}
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],
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"source": [
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"# Print the start and end of the input list\n",
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"words[:5] + ['...'] + words[-5:]"
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]
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},
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{
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||||
"cell_type": "markdown",
|
||||
"id": "14d91685",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"# 2. Look for the anagrams of one input word, e.g. \"organ\"\n",
|
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"\n",
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||||
"* There are several anagrams, including \"groan\" and \"argon\".\n",
|
||||
"\n",
|
||||
"* What is the Big-O performance oh your algorithm? In terms of M, the number of words in the dictionary, and K, the number of letters in a word"
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": 15,
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"id": "badf44c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"word = 'organ'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||||
"id": "ef938d95",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"id": "7cd1196c",
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||||
"metadata": {},
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||||
"source": [
|
||||
"The performance of this implementation is ... ."
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
|
||||
"id": "115c3219",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"# 3. Look for the anagrams of the words in the input list\n",
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||||
"\n",
|
||||
"* How does the Big-O performance of your one-word implementation scale to an input list of M words?\n",
|
||||
"* Is there a way to pre-process the dictionary words in a data structure that is better suited for this task?"
|
||||
]
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||||
},
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{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "03ce3e28",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "a2fc5ec4",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
}
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||||
],
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||||
"metadata": {
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||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
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||||
"name": "python3"
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||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
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||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
}
|
||||
},
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||||
"nbformat": 4,
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||||
"nbformat_minor": 5
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||||
}
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1451
exercises/anagrams/anagrams_solution.ipynb
Normal file
1451
exercises/anagrams/anagrams_solution.ipynb
Normal file
File diff suppressed because it is too large
Load diff
1410
exercises/anagrams/words_to_search.txt
Normal file
1410
exercises/anagrams/words_to_search.txt
Normal file
File diff suppressed because it is too large
Load diff
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